diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml index 98996a29..7d3f149f 100644 --- a/.github/workflows/docker-build.yml +++ b/.github/workflows/docker-build.yml @@ -1,4 +1,4 @@ -name: Docker Env Image (cuda-12-3) +name: Docker Env Image (cuda-12-1) on: push: @@ -21,7 +21,7 @@ env: jobs: build_env: - name: Docker Env Image (cuda-12-3) + name: Docker Env Image (cuda-12-1) runs-on: ubuntu-latest permissions: @@ -103,110 +103,110 @@ jobs: # Build and push Docker image with Buildx (don't push on PR) # https://github.com/docker/build-push-action - - name: Build and push Docker image (env-cuda-12-3) + - name: Build and push Docker image (env-cuda-12-1) id: build-and-push uses: docker/build-push-action@v4 with: - context: "{{defaultContext}}:docker/env-cuda-12-3" + context: "{{defaultContext}}:docker/env-cuda-12-1" push: ${{ github.event_name != 'pull_request' }} # Don't push on PR - tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_LC }}:env-cuda-12-3 + tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_LC }}:env-cuda-12-1 # tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} - cache-from: type=gha,src=docker/env-cuda-12-3 + cache-from: type=gha,src=docker/env-cuda-12-1 cache-to: type=gha,mode=max - # build_runner: - # name: Docker Env Image (github-worker-12-3) + build_runner: + name: Docker Env Image (github-worker-12-1) - # needs: build_env - # runs-on: ubuntu-latest - # permissions: - # contents: read - # packages: write - # # This is used to complete the identity challenge - # # with sigstore/fulcio when running outside of PRs. - # id-token: write - - # steps: - # # Get and log the free space - # - name: Get system free space (Before reclaim) - # run: | - # echo "Free space:" - # df -h + needs: build_env + runs-on: ubuntu-latest + permissions: + contents: read + packages: write + # This is used to complete the identity challenge + # with sigstore/fulcio when running outside of PRs. + id-token: write + + steps: + # Get and log the free space + - name: Get system free space (Before reclaim) + run: | + echo "Free space:" + df -h - # # Due to the docker image being > available space on the runner - # # we need to do some optimization, to create more space. - # # https://github.com/marketplace/actions/disk-space-reclaimer - # # https://stackoverflow.com/questions/76294509/github-actions-docker-service-container-25gb-cannot-be-loaded - # - name: Maximize build space - # uses: insightsengineering/disk-space-reclaimer@v1 - # with: - # # this might remove tools that are actually needed, - # # if set to "true" but frees about 6 GB - # tools-cache: true - - # # all of these default to true, but feel free to set to - # # "false" if necessary for your workflow - # android: true - # dotnet: true - # haskell: true - # large-packages: true - # swap-storage: true - # docker-images: true + # Due to the docker image being > available space on the runner + # we need to do some optimization, to create more space. + # https://github.com/marketplace/actions/disk-space-reclaimer + # https://stackoverflow.com/questions/76294509/github-actions-docker-service-container-25gb-cannot-be-loaded + - name: Maximize build space + uses: insightsengineering/disk-space-reclaimer@v1 + with: + # this might remove tools that are actually needed, + # if set to "true" but frees about 6 GB + tools-cache: true + + # all of these default to true, but feel free to set to + # "false" if necessary for your workflow + android: true + dotnet: true + haskell: true + large-packages: true + swap-storage: true + docker-images: true - # # Get and log the free space - # - name: Get system free space (After reclaim) - # run: | - # echo "Free space:" - # df -h - - # - name: Checkout repository - # uses: actions/checkout@v3 - - # # Install the cosign tool except on PR - # # https://github.com/sigstore/cosign-installer - # - name: Install cosign - # if: github.event_name != 'pull_request' - # uses: sigstore/cosign-installer@v3.3.0 - # with: - # cosign-release: 'v3.3.0' - - # # Workaround: https://github.com/docker/build-push-action/issues/461 - # - name: Setup Docker buildx - # uses: docker/setup-buildx-action@v2 - - # # Login against a Docker registry except on PR - # # https://github.com/docker/login-action - # - name: Log into registry ${{ env.REGISTRY }} - # if: github.event_name != 'pull_request' - # uses: docker/login-action@28218f9b04b4f3f62068d7b6ce6ca5b26e35336c - # with: - # registry: ${{ env.REGISTRY }} - # username: ${{ github.actor }} - # password: ${{ secrets.GITHUB_TOKEN }} - - # # Extract metadata (tags, labels) for Docker - # # https://github.com/docker/metadata-action - # - name: Extract Docker metadata - # id: meta - # uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 - # with: - # images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} + # Get and log the free space + - name: Get system free space (After reclaim) + run: | + echo "Free space:" + df -h + + - name: Checkout repository + uses: actions/checkout@v3 + + # Install the cosign tool except on PR + # https://github.com/sigstore/cosign-installer + - name: Install cosign + if: github.event_name != 'pull_request' + uses: sigstore/cosign-installer@v3.3.0 + # with: + # cosign-release: 'v2.2.0' + + # Workaround: https://github.com/docker/build-push-action/issues/461 + - name: Setup Docker buildx + uses: docker/setup-buildx-action@v2 + + # Login against a Docker registry except on PR + # https://github.com/docker/login-action + - name: Log into registry ${{ env.REGISTRY }} + if: github.event_name != 'pull_request' + uses: docker/login-action@28218f9b04b4f3f62068d7b6ce6ca5b26e35336c + with: + registry: ${{ env.REGISTRY }} + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + # Extract metadata (tags, labels) for Docker + # https://github.com/docker/metadata-action + - name: Extract Docker metadata + id: meta + uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 + with: + images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} - # - name: downcase IMAGE_NAME - # run: | - # echo "IMAGE_NAME_LC=${IMAGE_NAME,,}" >>${GITHUB_ENV} + - name: downcase IMAGE_NAME + run: | + echo "IMAGE_NAME_LC=${IMAGE_NAME,,}" >>${GITHUB_ENV} - # # Build and push Docker image with Buildx (don't push on PR) - # # https://github.com/docker/build-push-action - # - name: Build and push Docker image (github-worker-cuda-12-3) - # id: build-and-push - # uses: docker/build-push-action@v4 - # with: - # context: "{{defaultContext}}:docker/github-worker-cuda-12-3" - # push: ${{ github.event_name != 'pull_request' }} # Don't push on PR - # tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_LC }}:github-worker-cuda-12-3 - # # tags: ${{ steps.meta.outputs.tags }} - # labels: ${{ steps.meta.outputs.labels }} - # cache-from: type=gha,src=docker/github-worker-cuda-12-3 - # cache-to: type=gha,mode=max + # Build and push Docker image with Buildx (don't push on PR) + # https://github.com/docker/build-push-action + - name: Build and push Docker image (github-worker-cuda-12-1) + id: build-and-push + uses: docker/build-push-action@v4 + with: + context: "{{defaultContext}}:docker/github-worker-cuda-12-1" + push: ${{ github.event_name != 'pull_request' }} # Don't push on PR + tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_LC }}:github-worker-cuda-12-1 + # tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha,src=docker/github-worker-cuda-12-1 + cache-to: type=gha,mode=max diff --git a/RWKV-v5/config-example.yaml b/RWKV-v5/config-example.yaml index 0b2a7140..4817f598 100644 --- a/RWKV-v5/config-example.yaml +++ b/RWKV-v5/config-example.yaml @@ -373,6 +373,10 @@ data: # source: "teven/enwiki_00k" # Hugging face dataset # source: text # Text mode, used with source_data_dir + # Dataset split to use from HF dataset + # --- + # source_dataset_split: train + # Additional source dataset params, used to grab subsets of the dataset # --- # source_dataset_params: @@ -419,6 +423,7 @@ data: # Custom text column to use, useful for dataset with alternative training columns labels # This is checked before multi column merging, default is null (disabled) + # If set this takes priority # eg: 'code' # --- # custom_text_key: 'code' diff --git a/RWKV-v5/datapack-example.yaml b/RWKV-v5/datapack-example.yaml index be5d6086..95374b3a 100644 --- a/RWKV-v5/datapack-example.yaml +++ b/RWKV-v5/datapack-example.yaml @@ -35,15 +35,20 @@ datapack: # Mixing mode to use, this is used to alternate between datasets # - # - batch : Meaning one dataset worth per batch, partial batches are discarded - # - sample : Dataset is mixed on a per sample level - mixing_mode: "batch" + # - concat : Keep It Simple Silly, lets just concat the datasets together + # - shuffle : Dataset is mixed on a per sample level + # + # (@TODO: Advance operations) + # - batch : Meaning one dataset worth per batch, partial batches are discarded + mixing_mode: "shuffle" # # Mixing distribution to use # # - weighted : Dataset batches/mixture is distrbuted randomly, but weighted by dataset size # # - uniform : Dataset batches/mixture is distrbuted randomly, but with uniform probability # distribution: "weighted" + # (@TODO: Advance operations) + # # Mixed batch percentage # # % of batches which will contain a mixture of records from multiple datasets diff --git a/RWKV-v5/lightning_trainer.py b/RWKV-v5/lightning_trainer.py index f569a853..8b96e553 100644 --- a/RWKV-v5/lightning_trainer.py +++ b/RWKV-v5/lightning_trainer.py @@ -23,7 +23,7 @@ # Get the JIT / torch compile option flags default specific for lightning training mode # This enables torch compile by default RWKV_JIT_ON = os.getenv("RWKV_JIT_ON", "1").lower() in ("1", "true", "yes") -RWKV_TORCH_COMPILE = os.getenv("RWKV_TORCH_COMPILE", f"1").lower() in ("1", "true", "yes") +RWKV_TORCH_COMPILE = os.getenv("RWKV_TORCH_COMPILE", f"0").lower() in ("1", "true", "yes") # Disable CUDA for RWKV RWKV_NO_CUDA = os.getenv("RWKV_NO_CUDA", "0").lower() in ("1", "true", "yes") diff --git a/RWKV-v5/src/data.py b/RWKV-v5/src/data.py index 4e999cd2..56073660 100644 --- a/RWKV-v5/src/data.py +++ b/RWKV-v5/src/data.py @@ -41,6 +41,8 @@ def prepare_data_static( source_data_dir: str = None, # Additional dataset params source_dataset_params: dict = None, + # Source dataset split to use + source_dataset_split: str = "train", # Test split of source data, if it was not already done test_split: float = 0.01, test_split_shuffle: bool = False, @@ -288,16 +290,19 @@ def gen(): # Log the whole load_dataset_params # print("load_dataset_params: " + str(load_dataset_params)) + # The split to use + source_dataset_split = kargs["source_dataset_split"] + # Load the dataset) src_dataset = load_dataset(**load_dataset_params) # If for some reason the dataset is a "test" only split, and missing a "train" split, we remap it as a "train" split - if "train" not in src_dataset.keys(): - if "test" in src_dataset.keys(): - src_dataset["train"] = src_dataset["test"] - del src_dataset["test"] - else: - raise ValueError('Dataset must have a "train" split') + if source_dataset_split not in src_dataset.keys(): + raise ValueError('Dataset missing split: ' + source_dataset_split) + + if source_dataset_split != "train": + src_dataset["train"] = src_dataset[source_dataset_split] + del src_dataset[source_dataset_split] # If an int value is used, it is interprated as document count # If a floating value (<1.0) is used, it is interprated as a percentage of the dataset @@ -935,9 +940,11 @@ def merge_into_existing_samples(i): src_dataset['train'] = src_dataset['train'].map(pack_dataset_in_sequence, batched=True, batch_size=min(packing_min_ctx_len*2*3*5, processing_max_batch_size), num_proc=num_cpus) - else: - # Remove the sample_length column, as it is no longer needed - src_dataset['train'] = src_dataset['train'].remove_columns(["sample_length"]) + + # ===================================================== + + # Remove the sample_length column, as it is no longer needed / causes problems down the line + src_dataset['train'] = src_dataset['train'].remove_columns(["sample_length"]) # If an int value is used, it is interprated as document count # If a floating value (<1.0) is used, it is interprated as a percentage of the dataset @@ -976,20 +983,21 @@ def reverse_dataset(x, idx): # # Convert to iterable datasets (does not support saving to disk???) # src_dataset["train"] = src_dataset["train"].to_iterable_dataset() # src_dataset["test"] = src_dataset["test"].to_iterable_dataset() + + # # @TODO: Fix dataset_index / name labels + # # Dataset labeling, for custom wandb graphing + # if kargs["dataset_name"] is not None or kargs["dataset_index"] >= 0: + # # Lets label every sample with the dataset name or index + # def label_dataset(x): + # if kargs["dataset_name"] is not None: + # x["dataset_name"] = kargs["dataset_name"] + # if kargs["dataset_index"] >= 0: + # x["dataset_index"] = kargs["dataset_index"] + # return x - # Dataset labeling, for custom wandb graphing - if kargs["dataset_name"] is not None or kargs["dataset_index"] >= 0: - # Lets label every sample with the dataset name or index - def label_dataset(x): - if kargs["dataset_name"] is not None: - x["dataset_name"] = kargs["dataset_name"] - if kargs["dataset_index"] >= 0: - x["dataset_index"] = kargs["dataset_index"] - return x - - # Apply the label function - src_dataset["train"] = src_dataset["train"].map(label_dataset, num_proc=num_cpus) - src_dataset["test"] = src_dataset["test"].map(label_dataset, num_proc=num_cpus) + # # Apply the label function + # src_dataset["train"] = src_dataset["train"].map(label_dataset, num_proc=num_cpus) + # src_dataset["test"] = src_dataset["test"].map(label_dataset, num_proc=num_cpus) # Save the dataset to disk (if enabled) # For the skip datapath saving string @@ -1060,11 +1068,11 @@ def dataloader_collator_fn(records): out_index = 0 out_name = None - # Add dataset_index if its set - if "dataset_index" in records: - out_index = records[0]["dataset_index"] - if "dataset_name" in records: - out_name = records[0]["dataset_name"] + # # Add dataset_index if its set + # if "dataset_index" in records: + # out_index = records[0]["dataset_index"] + # if "dataset_name" in records: + # out_name = records[0]["dataset_name"] # Loop through the records and copy the values to the output arrays @@ -1074,9 +1082,9 @@ def dataloader_collator_fn(records): out_attention_mask[i][:len(records[i]["attention_mask"])] = records[i]["attention_mask"] out_data_ctx_len[i] = len(records[i]["input_ids"]) - if i > 0 and out_index > 0 and out_index != records[i]["dataset_index"]: - out_index = -1 - out_name = "mixed" + # if i > 0 and out_index > 0 and out_index != records[i]["dataset_index"]: + # out_index = -1 + # out_name = "mixed" # Build & return the output object out = { @@ -1084,8 +1092,8 @@ def dataloader_collator_fn(records): 'token_type_ids': out_token_type_ids, 'attention_mask': out_attention_mask, 'data_ctx_len': out_data_ctx_len, - 'dataset_index': out_index, - 'dataset_name': out_name + # 'dataset_index': out_index, + # 'dataset_name': out_name } return out @@ -1096,9 +1104,14 @@ def prepare_datapack_static(**kargs): datapack_config = kargs["datapack"] default_config = kargs["default"] dataset_config_arr = kargs["dataset"] + + # packing_batchsize + packing_batchsize = 64 + if "packing_batchsize" in datapack_config: + packing_batchsize = datapack_config["packing_batchsize"] # Join the various default settings - defaultVals = { "packing_batchsize": datapack_config["batchsize"], "dataset_weight": 1.0 } + defaultVals = { "packing_batchsize": packing_batchsize, "dataset_weight": 1.0 } defaultVals = { **defaultVals, **default_config } # Prepare the array of all the datasets to be merged @@ -1149,163 +1162,183 @@ def reset_dataset_arr_item(i): datasets_train_used_count[i] = 0 # Loop through the dataset config + # And prepare each dataset seperately for i in range(len(dataset_config_arr)): - print(">> Preparing dataset - index: ", i) + print(">> Preparing dataset - index: ", i, " - name: ", dataset_config_arr[i]["name"]) reset_dataset_arr_item(i) - # Compute the total dataset lengths - total_train_count = sum(datasets_train_count) + # The final dataset to build together + final_dataset = None + final_trainset = None + final_testset = None + + # Packing Mode + mixing_mode = datapack_config["mixing_mode"] + print(">> Dataset Mixing mode: ", mixing_mode) + + if mixing_mode == "concat" or mixing_mode == "shuffle": + # --------------------------------- + # Simple concatenation mode + # --------------------------------- + train_dataset = [] + test_dataset = [] + + # Loop through the datasets and build the final dataset + for i in range(len(datasets_arr)): + train_dataset.append(datasets_arr[i]["train"]) + test_dataset.append(datasets_arr[i]["test"]) + + # Build the final training dataset + final_trainset = concatenate_datasets(train_dataset) + if mixing_mode == "shuffle": + final_trainset = final_trainset.shuffle(seed=101) + + # And the final test set + final_testset = concatenate_datasets(test_dataset) + else: + # --------------------------------- + # Complicated batch mixing mode + # --------------------------------- - # Get the datapack batchsize - datapack_batchsize = datapack_config["batchsize"] - mixed_batch_percentage = datapack_config["mixed_batch_percentage"] - full_batch_percentage = 1.0 - mixed_batch_percentage + raise ValueError("BATCH mixing mode not yet supported") - # Override batch percentage if needed - if datapack_config["mixing_mode"] != "batch": - mixed_batch_percentage = 1.0 - full_batch_percentage = 0.0 + # Compute the total dataset lengths + total_train_count = sum(datasets_train_count) - # Compute the number of batches for each dataset - datasets_train_batches = [] - datasets_test_batches = [] + # Get the datapack batchsize + datapack_batchsize = datapack_config["batchsize"] + mixed_batch_percentage = datapack_config["mixed_batch_percentage"] + full_batch_percentage = 1.0 - mixed_batch_percentage + + # Override batch percentage if needed + if datapack_config["mixing_mode"] == "shuffle": + mixed_batch_percentage = 1.0 + full_batch_percentage = 0.0 - # Compute the number of batches for each dataset - for i in range(len(dataset_config_arr)): - dataset_weight = 1.0 - if "dataset_weight" in datasets_config_merged_arr[i]: - dataset_weight = datasets_config_merged_arr[i]["dataset_weight"] - - # Throw if dataset_weight != 1.0 (not yet supported) - if dataset_weight != 1.0: - raise ValueError("dataset_weight != 1.0 is not yet supported") - - # Initialize empty values, if not set - if i >= len(datasets_train_batches): - datasets_train_batches.append(0) - datasets_test_batches.append(0) - # Compute the number of batches for each dataset - datasets_train_batches[i] = math.floor( datasets_train_count[i] * dataset_weight * full_batch_percentage / datapack_batchsize ) - datasets_test_batches[i] = math.floor( datasets_test_count[i] / datapack_batchsize ) - - # Compute the total number of batches for training - total_train_batches = math.floor( total_train_count / datapack_batchsize ) - total_full_batches = sum( datasets_train_batches ) - - print(">> Total approx train batches ( full | random ) :", total_train_batches, " ( ", total_full_batches, " | ", total_train_batches - total_full_batches, " )") - - # --- - # The full dataset will contain the following columns - # input_ids, token_type_ids, attention_mask, sample_length, dataset_index, dataset_name - # --- - - # Int type for the dataset (based on index:0 dataset) - # Note: these are hugging face "Value(dtype=x)", and not the dtype itself - dataset_input_id_type = datasets_arr[0]["train"].features["input_ids"].feature - dataset_token_type_id_type = datasets_arr[0]["train"].features["token_type_ids"].feature - dataset_attention_mask_type = datasets_arr[0]["train"].features["attention_mask"].feature - - # Setup the dataset features - final_dataset_features = Features({ - 'input_ids': Sequence(dataset_input_id_type), - 'token_type_ids': Sequence(dataset_token_type_id_type), - 'attention_mask': Sequence(dataset_attention_mask_type), - 'dataset_index': Value(dtype="int16"), - 'dataset_name': Value(dtype="string"), - }) - - # Build the full train / test split dataset slices - train_fullset_arr = [] - train_randomset_arr = [] - test_fullset_arr = [] - - # For each dataset, build the full and random sets - for i in range(len(datasets_arr)): - if datasets_train_batches[i] > 0: - train_fullset_arr.append( datasets_arr[i]["train"].select(numpy.arange(0, datasets_train_batches[i]*datapack_batchsize)) ) - train_randomset_arr.append( datasets_arr[i]["train"].select(numpy.arange(datasets_train_batches[i]*datapack_batchsize, datasets_train_count[i])) ) + datasets_train_batches = [] + datasets_test_batches = [] + + # Compute the number of batches for each dataset + for i in range(len(dataset_config_arr)): + dataset_weight = 1.0 + if "dataset_weight" in datasets_config_merged_arr[i]: + dataset_weight = datasets_config_merged_arr[i]["dataset_weight"] + + # Throw if dataset_weight != 1.0 (not yet supported) + if dataset_weight != 1.0: + raise ValueError("dataset_weight != 1.0 is not yet supported") + + # Initialize empty values, if not set + if i >= len(datasets_train_batches): + datasets_train_batches.append(0) + datasets_test_batches.append(0) + + # Compute the number of batches for each dataset + datasets_train_batches[i] = math.floor( datasets_train_count[i] * dataset_weight * full_batch_percentage / datapack_batchsize ) + datasets_test_batches[i] = math.floor( datasets_test_count[i] / datapack_batchsize ) + + # Compute the total number of batches for training + total_train_batches = math.floor( total_train_count / datapack_batchsize ) + total_full_batches = sum( datasets_train_batches ) + + print(">> Total approx train batches ( full | random ) :", total_train_batches, " ( ", total_full_batches, " | ", total_train_batches - total_full_batches, " )") + + # --- + # The full dataset will contain the following columns + # input_ids, token_type_ids, attention_mask, sample_length, dataset_index, dataset_name + # --- + + # Build the full train / test split dataset slices + train_fullset_arr = [] + train_randomset_arr = [] + test_fullset_arr = [] + + # For each dataset, build the full and random sets + for i in range(len(datasets_arr)): + if datasets_train_batches[i] > 0: + train_fullset_arr.append( datasets_arr[i]["train"].select(numpy.arange(0, datasets_train_batches[i]*datapack_batchsize)) ) + train_randomset_arr.append( datasets_arr[i]["train"].select(numpy.arange(datasets_train_batches[i]*datapack_batchsize, datasets_train_count[i])) ) + else: + train_randomset_arr.append( datasets_arr[i]["train"] ) + test_fullset_arr.append( datasets_arr[i]["test"] ) + + # Concat the full dataset + train_fullset = concatenate_datasets(train_fullset_arr) + train_randomset = concatenate_datasets(train_randomset_arr) + test_fullset = concatenate_datasets(test_fullset_arr) + + # Shuffle the train random sets, and merge it + train_randomset_len = len(train_randomset) + train_randomset = train_randomset.shuffle(seed=101) + train_randomset_chunks = math.floor( train_randomset_len / datapack_batchsize ) + + if train_randomset_chunks > 0: + train_fullset = concatenate_datasets([train_fullset,train_randomset.select(numpy.arange(0, train_randomset_chunks*datapack_batchsize))]) + train_last_randomset_chunk = train_randomset.select(numpy.arange(train_randomset_chunks*datapack_batchsize, train_randomset_len)) else: - train_randomset_arr.append( datasets_arr[i]["train"] ) - test_fullset_arr.append( datasets_arr[i]["test"] ) - - # Concat the full dataset - train_fullset = concatenate_datasets(train_fullset_arr) - train_randomset = concatenate_datasets(train_randomset_arr) - test_fullset = concatenate_datasets(test_fullset_arr) - - # Shuffle the train random sets, and merge it - train_randomset_len = len(train_randomset) - train_randomset = train_randomset.shuffle(seed=101) - train_randomset_chunks = math.floor( train_randomset_len / datapack_batchsize ) - - if train_randomset_chunks > 0: - train_fullset = concatenate_datasets([train_fullset,train_randomset.select(numpy.arange(0, train_randomset_chunks*datapack_batchsize))]) - train_last_randomset_chunk = train_randomset.select(numpy.arange(train_randomset_chunks*datapack_batchsize, train_randomset_len)) - else: - train_last_randomset_chunk = train_randomset - - # Get the total fullset chunks - train_fullset_chunks = math.floor( len(train_fullset) / datapack_batchsize ) + train_last_randomset_chunk = train_randomset + + # Get the total fullset chunks + train_fullset_chunks = math.floor( len(train_fullset) / datapack_batchsize ) - # Lets prepare an array of the various dataset indexes - dataset_shuffle_index_arr = list( numpy.arange(0, train_fullset_chunks) ) - random.Random(101).shuffle(dataset_shuffle_index_arr) - - # Label shuffle index - def label_shuffle_index(x, idx): - x["shuffle_index"] = idx - return x - - # Add a shuffled index to the fullset - train_fullset = train_fullset.map( - label_shuffle_index, - with_indices=True, - batched=True, - batch_size=datapack_batchsize, - num_proc=num_cpus, - ) + # Lets prepare an array of the various dataset indexes + dataset_shuffle_index_arr = list( numpy.arange(0, train_fullset_chunks) ) + random.Random(101).shuffle(dataset_shuffle_index_arr) + + # Label shuffle index + def label_shuffle_index(x, idx): + x["shuffle_index"] = idx + return x + + # Add a shuffled index to the fullset + train_fullset = train_fullset.map( + label_shuffle_index, + with_indices=True, + batched=True, + batch_size=datapack_batchsize, + num_proc=num_cpus, + ) + + # Sort the fullset by the shuffle index + train_fullset = train_fullset.sort("shuffle_index") + + # Remove the shuffle index + train_fullset = train_fullset.remove_columns(["shuffle_index"]) + + # Add the last randomset chunk to the fullset + final_trainset = concatenate_datasets([train_fullset, train_last_randomset_chunk]) + final_testset = test_fullset + + # --------------------------------- + # Final dataset merger + # --------------------------------- - # Sort the fullset by the shuffle index - train_fullset = train_fullset.sort("shuffle_index") - - # Remove the shuffle index - train_fullset = train_fullset.remove_columns(["shuffle_index"]) - - # Add the last randomset chunk to the fullset - train_fullset = concatenate_datasets([train_fullset, train_last_randomset_chunk]) - - # Build the full train / test split dataset - final_dataset = Dataset.from_dict({key: [None,None] for key in final_dataset_features}, features=final_dataset_features) - final_dataset = final_dataset.train_test_split(1,1) - - # Lets override the full dataset - final_dataset["train"] = train_fullset - final_dataset["test"] = test_fullset - - # # Full dataset chunks slices - # fullset_chunks_slices = [] - # for i in range(len(datasets_arr)): - # fullset_chunks_slices.append( [] ) - - # # Lets iterate each row of the training datset - # # and log the various attribute sizes - # print(">> [START] DEBUGGING LOGS") - # for i in range(len(final_dataset["train"])): - # # Get the row - # row = final_dataset["train"][i] - # - # # Get size map - # log_map = { - # "index": i, - # "input_ids": len(row["input_ids"]), - # "token_type_ids": len(row["token_type_ids"]), - # "attention_mask": len(row["attention_mask"]), - # "dataset_index": row["dataset_index"], - # "dataset_name": row["dataset_name"], - # } - # print(log_map) - # print(">> [ENDIN] DEBUGGING LOGS") + # Build the final_dataset + if final_dataset is None: + + # Int type for the dataset (based on index:0 dataset) + # Note: these are hugging face "Value(dtype=x)", and not the dtype itself + dataset_input_id_type = datasets_arr[0]["train"].features["input_ids"].feature + dataset_token_type_id_type = datasets_arr[0]["train"].features["token_type_ids"].feature + dataset_attention_mask_type = datasets_arr[0]["train"].features["attention_mask"].feature + + # Setup the dataset features + final_dataset_features = Features({ + 'input_ids': Sequence(dataset_input_id_type), + 'token_type_ids': Sequence(dataset_token_type_id_type), + 'attention_mask': Sequence(dataset_attention_mask_type), + # 'dataset_index': Value(dtype="int16"), + # 'dataset_name': Value(dtype="string"), + }) + + # Build the full train / test split dataset + final_dataset = Dataset.from_dict({key: [None,None] for key in final_dataset_features}, features=final_dataset_features) + final_dataset = final_dataset.train_test_split(1,1) + + # Lets override the full dataset + final_dataset["train"] = final_trainset + final_dataset["test"] = final_testset # Log the saving process print(">> Saving dataset to data_path : ", datapack_config["data_path"]) @@ -1329,8 +1362,8 @@ def label_shuffle_index(x, idx): # Log the finished dataset sizes final_train_len = len(final_dataset["train"]) final_test_len = len(final_dataset["test"]) - print(">> Final dataset count ( train ) :", "{:,}".format(final_train_len)) - print(">> Final dataset count ( test ) :", "{:,}".format(final_test_len)) + print(">> Final dataset count ( train ) :", "{:,}".format(final_train_len), " samples/chunks/packs") + print(">> Final dataset count ( test ) :", "{:,}".format(final_test_len), " samples") print(">> -----------------------------------") # Compute the total dataset token count @@ -1376,6 +1409,9 @@ def __init__( # Note: As of Jan 2023, these options seems very buggy, YMMV data_path_storage_options:dict = None, + # Skip database setup checks if datapath exists, ignored if using preload_datapath.py + skip_datapath_setup: bool = False, + # load_dataset(path) param source: str = None, # load_dataset(data_dir) param @@ -1499,9 +1535,6 @@ def __init__( # System tweaks # ---------------------------- - # Skip database setup checks if datapath exists, ignored if using preload_datapath.py - skip_datapath_setup: bool = False, - # Batch size scanning range, used for deciding the max number of documents # to process simultaneously at a time. This is used to prevent OOM errors # while rearranging the dataset, etc. Used for both packing / sorting operations @@ -1516,6 +1549,9 @@ def __init__( # Pin the preloaded documents into GPU memory in advance # very small overhead, slight speed bump, disable if your deperate for vram dataloader_pin_memory: bool = True, + + # Swap the train / test split, used for debugging purposes + dataloader_swap_train_test_split: bool = False, ): # Capture the init parameters self._init_locals = locals() @@ -1529,6 +1565,7 @@ def __init__( self.dataloader_pin_memory = dataloader_pin_memory self.dataloader_shuffle_training = dataloader_shuffle_training self.sort_by_length = sort_by_length + self.dataloader_swap_train_test_split = dataloader_swap_train_test_split self._loaded_dataset = None @@ -1555,12 +1592,19 @@ def setup(self, stage): # Return the train dataloader def train_dataloader(self): self._internal_setup() - dataset = self._loaded_dataset['train']; + + if self.dataloader_swap_train_test_split == False: + dataset = self._loaded_dataset['train']; + else: + dataset = self._loaded_dataset['test']; + sampler = DistributedSampler( dataset, shuffle=self.dataloader_shuffle_training and not self.sort_by_length, num_replicas=self.trainer.world_size, rank=self.trainer.global_rank, + ## This is required due to multi node alignment errors + drop_last=True ) microbatch_size = 1 @@ -1586,12 +1630,18 @@ def train_dataloader(self): # Return the validation dataloader def val_dataloader(self): self._internal_setup() - dataset = self._loaded_dataset['test']; + if self.dataloader_swap_train_test_split == False: + dataset = self._loaded_dataset['test']; + else: + dataset = self._loaded_dataset['train']; + sampler = DistributedSampler( dataset, shuffle=False, num_replicas=self.trainer.world_size, rank=self.trainer.global_rank, + ## This is required due to multi node alignment errors + drop_last=True ) microbatch_size = 1 diff --git a/RWKV-v5/src/model.py b/RWKV-v5/src/model.py index db34ff72..0b5f9408 100644 --- a/RWKV-v5/src/model.py +++ b/RWKV-v5/src/model.py @@ -812,14 +812,14 @@ def compute_loss(self, batch, batch_idx, is_training_run: bool = False, is_valid assert isinstance(seq, torch.Tensor) and seq.ndim == 2 ori_seq_mask = batch['attention_mask'] - # Get the dataset index - dataset_index = 0 - dataset_name = "dataset_0" - if "dataset_index" in batch: - dataset_index = batch["dataset_index"] - dataset_name = f"dataset_{dataset_index}" - if "dataset_name" in batch and dataset_name is not None: - dataset_name = batch["dataset_name"] + # # Get the dataset index + # dataset_index = 0 + # dataset_name = "dataset_0" + # if "dataset_index" in batch: + # dataset_index = batch["dataset_index"] + # dataset_name = f"dataset_{dataset_index}" + # if "dataset_name" in batch and dataset_name is not None: + # dataset_name = batch["dataset_name"] # Check if attent mask is set, if not initialize it if ori_seq_mask is None or ori_seq_mask.ndim != 2: @@ -913,21 +913,24 @@ def compute_loss(self, batch, batch_idx, is_training_run: bool = False, is_valid # If total_mask_sum, we skip, as there is no tokens of value to learn from anyway total_mask_sum = torch.sum(seq_mask) - # Do a quick return, if there is no tokens of value to learn from due to full masking - if num_devices > 1 and total_mask_sum == 0: - return 0 + avg_mask_sum = ( total_mask_sum / B ) + + # # Do a quick return, if there is no tokens of value to learn from due to full masking + # # DO NOT DO THIS : This causes multi node / multi GPU to go out of sync + # if num_devices <= 1 and total_mask_sum == 0: + # return 0 # Checkpoint steps def checkpointed_step(idx, targets, mask, last_shift_states, last_wkv_states): - # Skip if there is no tokens of value to learn from - if idx.shape[1] == 0: - # Prepare dummy loss - train_loss = torch.tensor(0, dtype=self.emb.weight.dtype).requires_grad_() - sample_loss = train_loss.clone().detach().requires_grad_(False) + # # Skip if there is no tokens of value to learn from + # if idx.shape[1] == 0: + # # Prepare dummy loss + # train_loss = torch.tensor(0, dtype=self.emb.weight.dtype).requires_grad_() + # sample_loss = train_loss.clone().detach().requires_grad_(False) - # Return the checkpoint values - return sample_loss, train_loss, last_shift_states, last_wkv_states, 0 + # # Return the checkpoint values + # return sample_loss, train_loss, last_shift_states, last_wkv_states, 0 # Get the logits, and the new states logits, new_shift_states, new_wkv_states = self( @@ -947,7 +950,7 @@ def checkpointed_step(idx, targets, mask, last_shift_states, # to encourage the logits to be close to 0 # factor_divisor is typically the total token count - L2Wrap_factor = 1e-4 / total_mask_sum + L2Wrap_factor = 1e-4 / avg_mask_sum # Submask count submask_count = torch.sum(submask) @@ -983,7 +986,7 @@ def checkpointed_step(idx, targets, mask, last_shift_states, train_token_count = torch.sum(train_mask) # Adjust the factor accordingly - L2Wrap_factor = L2Wrap_factor * (submask_count / train_token_count) + # L2Wrap_factor = L2Wrap_factor * (submask_count / train_token_count) else: train_loss = torch.sum(token_loss * submask) / total_mask_sum @@ -1254,20 +1257,20 @@ def checkpointed_step(idx, targets, mask, last_shift_states, # Log the line values wandb.log({ # The original loss and ctx_len (averaged by batch size) - 'train/ctx_len': ctx_len, + 'train/data_ctxlen': ctx_len, 'train/data_loss': sampling_loss, + # "train/dataset_index": dataset_index, # The selective training tokens, and loss - 'train/tokens': tokens, - 'train/loss': training_loss, - "train/dataset_index": dataset_index, + 'train/learn_tokens': tokens, + 'train/learn_loss': training_loss, - # Dataset based tracking - f'dataset/train/{dataset_index}.loss': training_loss, - f'dataset/train/{dataset_index}.data_loss': sampling_loss, - f'dataset/train/{dataset_index}.tokens': tokens, - f'dataset/train/{dataset_index}.ctx_len': ctx_len, - f'dataset/train/{dataset_index}.name': dataset_name, + # # Dataset based tracking (not working) + # f'dataset/train/{dataset_index}.loss': training_loss, + # f'dataset/train/{dataset_index}.data_loss': sampling_loss, + # f'dataset/train/{dataset_index}.tokens': tokens, + # f'dataset/train/{dataset_index}.ctx_len': ctx_len, + # f'dataset/train/{dataset_index}.name': dataset_name, # Perf tracking f'perf/kTokens_per_sec.gpu.{global_rank}': self._counting_tokens / max(time.time() - self._counting_time_start, 1), @@ -1286,19 +1289,19 @@ def checkpointed_step(idx, targets, mask, last_shift_states, # Log the line values wandb.log({ # The original loss and ctx_len (averaged by batch size) - 'validation/ctx_len': T, + 'validation/data_ctxlen': T, 'validation/data_loss': sampling_loss, + # "validation/dataset_index": dataset_index, # The selective training tokens, and loss - 'validation/tokens': training_tokens, - 'validation/loss': training_loss, - "validation/dataset_index": dataset_index, - - # Dataset based tracking - f'dataset/validation/{dataset_index}.loss': training_loss, - f'dataset/validation/{dataset_index}.data_loss': sampling_loss, - f'dataset/validation/{dataset_index}.ctx_len': T, - f'dataset/validation/{dataset_index}.name': dataset_name, + 'validation/learn_tokens': training_tokens, + 'validation/learn_loss': training_loss, + + # # Dataset based tracking (not working) + # f'dataset/validation/{dataset_index}.loss': training_loss, + # f'dataset/validation/{dataset_index}.data_loss': sampling_loss, + # f'dataset/validation/{dataset_index}.ctx_len': T, + # f'dataset/validation/{dataset_index}.name': dataset_name, # Step and trainer tracking 'global_rank': global_rank, diff --git a/RWKV-v5/src/module/CoreDependencies.py b/RWKV-v5/src/module/CoreDependencies.py index f36b24e4..1e463a3d 100644 --- a/RWKV-v5/src/module/CoreDependencies.py +++ b/RWKV-v5/src/module/CoreDependencies.py @@ -54,16 +54,16 @@ def is_torch_version_above(required_version): if 'RWKV_JIT_ON' not in globals(): RWKV_JIT_ON = os.getenv("RWKV_JIT_ON", "1").lower() in ("1", "true", "yes") if 'RWKV_TORCH_COMPILE' not in globals(): - RWKV_TORCH_COMPILE = os.getenv("RWKV_TORCH_COMPILE", f"1").lower() in ("1", "true", "yes") + RWKV_TORCH_COMPILE = os.getenv("RWKV_TORCH_COMPILE", f"0").lower() in ("1", "true", "yes") # The RWKV_NO_CUDA global global RWKV_NO_CUDA if 'RWKV_NO_CUDA' not in globals(): - RWKV_NO_CUDA = os.getenv("RWKV_NO_CUDA", f"0").lower() in ("1", "true", "yes") + RWKV_NO_CUDA = os.getenv("RWKV_NO_CUDA", f"1").lower() in ("1", "true", "yes") # Enforce no cuda, if there is no cuda if torch.cuda is None or torch.cuda.is_available() == False or torch.cuda.device_count() <= 0: - print(f"[RWKV.model] No CUDA device found, setting RWKV_NO_CUDA=True") + print(f"[RWKV.model] No CUDA device found, enforcing RWKV_NO_CUDA=True") RWKV_NO_CUDA = True # Disable torch compile if its not atleast v2.1.0 diff --git a/docker/env-cuda-12-3/Dockerfile b/docker/env-cuda-12-1/Dockerfile similarity index 95% rename from docker/env-cuda-12-3/Dockerfile rename to docker/env-cuda-12-1/Dockerfile index 9efd337a..d6ac39e5 100644 --- a/docker/env-cuda-12-3/Dockerfile +++ b/docker/env-cuda-12-1/Dockerfile @@ -1,6 +1,6 @@ # We start from the CUDA base image # todo: consider other base images? -FROM nvidia/12.3.1-devel-ubuntu20.04 +FROM nvidia/cuda:12.1.1-devel-ubuntu22.04 # Install ninja, and several common stuff RUN apt-get update && \ diff --git a/docker/github-worker-cuda-11-8/entrypoint.sh b/docker/github-worker-cuda-11-8/entrypoint.sh index 662d4515..6231935b 100644 --- a/docker/github-worker-cuda-11-8/entrypoint.sh +++ b/docker/github-worker-cuda-11-8/entrypoint.sh @@ -27,7 +27,7 @@ else --token "${RUNNER_TOKEN}" \ --name "${RUNNER_NAME}" \ --replace \ - --labels "nolane,${CUDA_VER},${RUNNER_LABELS}" + --labels "nolane,any-gpu,gpu-count-any,${CUDA_VER},${RUNNER_LABELS}" # Run it in background, and get the PID ./run.sh & @@ -41,7 +41,7 @@ else --token "${RUNNER_TOKEN}" \ --name "${RUNNER_NAME}-lane1" \ --replace \ - --labels "lane1,${CUDA_VER},${RUNNER_LABELS}" + --labels "lane1,any-gpu,gpu-count-any,${CUDA_VER},${RUNNER_LABELS}" # Run it in background, and get the PID ./run.sh & @@ -55,7 +55,7 @@ else --token "${RUNNER_TOKEN}" \ --name "${RUNNER_NAME}-lane2" \ --replace \ - --labels "lane2,${CUDA_VER},${RUNNER_LABELS}" + --labels "lane2,any-gpu,gpu-count-any,${CUDA_VER},${RUNNER_LABELS}" # Run it in background, and get the PID ./run.sh & diff --git a/docker/github-worker-cuda-12-1/Dockerfile b/docker/github-worker-cuda-12-1/Dockerfile new file mode 100644 index 00000000..32871a53 --- /dev/null +++ b/docker/github-worker-cuda-12-1/Dockerfile @@ -0,0 +1,41 @@ +# Temporary, until the rwkv package is public +FROM ghcr.io/rwkv/rwkv-infctx-trainer:env-cuda-12-1 +# FROM ghcr.io/rwkv/rwkv-infctx-trainer:env-cuda-12-1 + +# Install the github runner +RUN cd / && mkdir actions-runner && cd actions-runner && \ + curl -o actions-runner-linux-x64-2.312.0.tar.gz -L \ + https://github.com/actions/runner/releases/download/v2.312.0/actions-runner-linux-x64-2.312.0.tar.gz && \ + tar xzf ./actions-runner-linux-x64-2.312.0.tar.gz && \ + rm ./actions-runner-linux-x64-2.312.0.tar.gz + +# Clone the runner, for lane2 track +RUN cd / && cp -r /actions-runner /actions-runner-lane2 + +# Install dependencies +RUN cd /actions-runner && ./bin/installdependencies.sh && \ + cd /actions-runner-lane2 && ./bin/installdependencies.sh + +# Copy the entrypoint script, and set it up +COPY entrypoint.sh /entrypoint.sh +RUN chmod +x /entrypoint.sh +ENTRYPOINT ["/entrypoint.sh"] + +# Configure default env variables +ENV RUNNER_LABELS="" +ENV RUNNER_NAME="" +ENV RUNNER_TOKEN="" +ENV RUNNER_REPO_URL="https://github.com/RWKV" + +# Runner with lane2 track +# --- +# this helps setup dual runs on the same machine +# to help ensure better utilization of GPUs. +# +# In general DS2/3_offload should be used. +# +# Tags should be adjusted to be half their original spec +# to account for the fact that we are running two runners +# +# This is only useful for high GPU, and high ram count machines +ENV RUNNER_LANE2="false" diff --git a/docker/github-worker-cuda-12-1/entrypoint.sh b/docker/github-worker-cuda-12-1/entrypoint.sh new file mode 100644 index 00000000..bd3db193 --- /dev/null +++ b/docker/github-worker-cuda-12-1/entrypoint.sh @@ -0,0 +1,110 @@ +#!/bin/bash + +export RUNNER_ALLOW_RUNASROOT="1" +cd /actions-runner + +# CUDA version for label +CUDA_VER="cuda-12-1" + +# Check if nvidia-smi is available +SMI_CALL=$(nvidia-smi) +if [ $? -ne 0 ]; then + echo "# [ERROR] nvidia-smi is not available, shutting down (after long delay)" + echo "# ---" + echo "$SMI_CALL" + echo "# ---" + + # Sleep for 1 hour, a failure on start is a sign of a bigger problem + echo "# Performing a long sleep, to stagger container flow ..." + sleep 3600 + echo "# Exiting now" + exit 1 +fi + +# Check the URL, token, and name of the runner from the container ENV vars +# and if they are not set, provide default values +if [[ -z "${RUNNER_NAME}" ]]; then + export RUNNER_NAME=$(hostname) +fi +if [[ -z "${RUNNER_TOKEN}" ]]; then + echo "# [WARNING] RUNNER_TOKEN is missing, skipping github runner setup" +else + echo "# [INFO] lane1 starting up ... " + + # If lane2 runner is enabled, start it + # this is enabled with RUNNER_LANE2=true + if [ "$RUNNER_LANE2" != true ]; then + + # Configure unattended + ./config.sh \ + --unattended \ + --url "${RUNNER_REPO_URL}" \ + --token "${RUNNER_TOKEN}" \ + --name "${RUNNER_NAME}" \ + --replace \ + --labels "nolane,${CUDA_VER},${RUNNER_LABELS}" + + # Run it in background, and get the PID + ./run.sh & + + echo "# [INFO] lane2 runner is disabled" + else + # Configure unattended + ./config.sh \ + --unattended \ + --url "${RUNNER_REPO_URL}" \ + --token "${RUNNER_TOKEN}" \ + --name "${RUNNER_NAME}-lane1" \ + --replace \ + --labels "lane1,${CUDA_VER},${RUNNER_LABELS}" + + # Run it in background, and get the PID + ./run.sh & + + echo "# [INFO] lane2 starting up ... " + + cd /actions-runner-lane2 + ./config.sh \ + --unattended \ + --url "${RUNNER_REPO_URL}" \ + --token "${RUNNER_TOKEN}" \ + --name "${RUNNER_NAME}-lane2" \ + --replace \ + --labels "lane2,${CUDA_VER},${RUNNER_LABELS}" + + # Run it in background, and get the PID + ./run.sh & + fi +fi + +# Follow up on any forwarded command args +if [[ $# -gt 0 ]]; then + cd /root + exec "$@" & +fi + +# Wait for everything to exit +# wait $RUNNER_PID +while true; do + # Check if any background process is still running + # Break if there is no background process + if [ -z "$(jobs -p)" ]; then + echo "# [INFO] All runners have exited, shutting down" + break + fi + + # Call nvidia-smi, check if any error orccured + SMI_CALL=$(nvidia-smi) + + # If nvidia-smi failed, exit + if [ $? -ne 0 ]; then + echo "# [ERROR] nvidia-smi failed, shutting down now!" + echo "# ---" + echo "$SMI_CALL" + echo "# ---" + break + fi + + # Performs a small sleep wait + sleep 10 +done \ No newline at end of file diff --git a/notebook/finetune-example/Eagle-x-ALMA-prompt-completion.yaml b/notebook/finetune-example/Eagle-x-ALMA-prompt-completion.yaml index 9776a73c..9ab2d712 100644 --- a/notebook/finetune-example/Eagle-x-ALMA-prompt-completion.yaml +++ b/notebook/finetune-example/Eagle-x-ALMA-prompt-completion.yaml @@ -74,8 +74,8 @@ model: load_model: ../model/L6-D512-neox-init.pth # Starting and ending learning rate - lr_init: 5e-5 - lr_final: 5e-5 + lr_init: 3e-5 + lr_final: 3e-5 # Training context length, note that the dataset can be # larger then the context size, in which the trainer diff --git a/notebook/finetune-example/Eagle-x-capybara-chat.yaml b/notebook/finetune-example/Eagle-x-capybara-chat.yaml index 1b944640..cd2d34d2 100644 --- a/notebook/finetune-example/Eagle-x-capybara-chat.yaml +++ b/notebook/finetune-example/Eagle-x-capybara-chat.yaml @@ -79,8 +79,8 @@ model: load_model: ../model/L6-D512-neox-init.pth # Starting and ending learning rate - lr_init: 5e-5 - lr_final: 5e-5 + lr_init: 3e-5 + lr_final: 3e-5 # Training context length, note that the dataset can be # larger then the context size, in which the trainer diff --git a/notebook/finetune-example/Eagle-x-openhermes1-instruct.yaml b/notebook/finetune-example/Eagle-x-openhermes1-instruct.yaml index e5910fd6..cb4eecac 100644 --- a/notebook/finetune-example/Eagle-x-openhermes1-instruct.yaml +++ b/notebook/finetune-example/Eagle-x-openhermes1-instruct.yaml @@ -74,8 +74,8 @@ model: load_model: ../model/L6-D512-neox-init.pth # Starting and ending learning rate - lr_init: 5e-5 - lr_final: 5e-5 + lr_init: 3e-5 + lr_final: 3e-5 # Training context length, note that the dataset can be # larger then the context size, in which the trainer diff --git a/notebook/finetune-example/Eagle-x-textbooks.yaml b/notebook/finetune-example/Eagle-x-textbooks.yaml index 2a361db3..9e2ecbeb 100644 --- a/notebook/finetune-example/Eagle-x-textbooks.yaml +++ b/notebook/finetune-example/Eagle-x-textbooks.yaml @@ -79,8 +79,8 @@ model: load_model: ../model/L6-D512-neox-init.pth # Starting and ending learning rate - lr_init: 5e-5 - lr_final: 5e-5 + lr_init: 3e-5 + lr_final: 3e-5 # Training context length, note that the dataset can be # larger then the context size, in which the trainer diff --git a/notebook/finetune-example/Eagle-x-zMultipack-build.yaml b/notebook/finetune-example/Eagle-x-zMultipack-build.yaml index d2028e3f..2a9f07b4 100644 --- a/notebook/finetune-example/Eagle-x-zMultipack-build.yaml +++ b/notebook/finetune-example/Eagle-x-zMultipack-build.yaml @@ -25,35 +25,14 @@ datapack: # secret: # endpoint_url: - # Batch size to use to alternate between datasets - # This should be a multiple of the GPU and node count - # - # Uses, `8 * (3 * 4 * 5 * 6 * 7) = 20160` for default, as it should align across - # a large number of batch size combinations. This helps reduce the amount of - # misaligned batches, and thus reduce the amount of wasted training time - batchsize: 512 - # Mixing mode to use, this is used to alternate between datasets # - # - batch : Meaning one dataset worth per batch, partial batches are discarded - # - sample : Dataset is mixed on a per sample level - mixing_mode: "batch" - - # # Mixing distribution to use - # # - weighted : Dataset batches/mixture is distrbuted randomly, but weighted by dataset size - # # - uniform : Dataset batches/mixture is distrbuted randomly, but with uniform probability - # distribution: "weighted" - - # Mixed batch percentage - # - # % of batches which will contain a mixture of records from multiple datasets - # instad of limiting each batch to a single dataset + # - concat : Keep It Simple Silly, lets just concat the datasets together + # - shuffle : Dataset is mixed on a per sample level # - # Use 0, to disable mixed batches, sampled mixing_mode is the equavalent of mixed batch 1.0 - # - # NOTE: This is a guideline percentage, and is not guaranteed to be exact - # if a partial batch is built, it may get converted to a mixed batch - mixed_batch_percentage: 0.5 + # (@TODO: Advance operations) + # - batch : Meaning one dataset worth per batch, partial batches are discarded + mixing_mode: "shuffle" # # Default settings used across all datasets in the datapack @@ -115,7 +94,7 @@ default: # If given an int value, the number of data sample is used. # # Due to the limitaitons in the trainer process, there is always a minimum of 1 test sample - test_split: 8 # Intentionally set to a low sample for test, cause the real eval is humans + test_split: 0.01 # Intentionally set to a low sample for test, cause the real eval is humans test_split_shuffle: true # Tokenizer to use, use either the inbuilt 'neox', or 'world' tokenizer @@ -265,10 +244,14 @@ dataset: - # Text book is all you need # https://huggingface.co/datasets/TanvirOnHF/muse_textbooks - source: "TanvirOnHF/muse_textbooks" + source: "teven/enwiki_100k" # Optional, provide a name for the dataset - name: "muse_textbooks" + name: "enwiki_100k" + + # Minimum / Maximum token size of the dataset to use + min_token_size: 1024 + max_token_size: -1 # Various over write settings # --- @@ -277,6 +260,29 @@ dataset: packing_enable: False max_token_size: -1 + - # SuperWiki (Multi-lingual) + # https://huggingface.co/datasets/RyokoExtra/SuperWIKI-Cleaned + source: "RyokoExtra/SuperWIKI-Cleaned" + + # Optional, provide a name for the dataset + name: "super_wiki" + + # Various over write settings + # --- + text_rechunk_size: 32768 + text_rechunk_force: true + packing_enable: False + max_token_size: -1 + + source_dataset_split: lang25 + + # Custom text column to use, useful for dataset with alternative training columns labels + # This is checked before multi column merging, default is null (disabled) + # If set this takes priority + # eg: 'code' + # --- + custom_text_key: 'text' + # All other settings found in default can be overriden here # --- # ... @@ -297,8 +303,7 @@ dataset: # https://huggingface.co/datasets/kristaller486/ALMA-prompt-completion source: "kristaller486/ALMA-prompt-completion" name: "ALMA-prompt-completion" - - # Prompt completion, notiong else + # Prompt completion, nothing else else - # Instruct, input, output format # https://huggingface.co/datasets/teknium/openhermes @@ -380,6 +385,26 @@ dataset: conversation_input_key_mask: {'input': false, 'output': true} conversation_sender_suffix: {'input': "", 'output': ""} + ###################################################### + # Note: We found the ML generated textbooks + # too low in perplexity that it hurts the model + # so we are using the original enwiki_100k & superwiki + ###################################################### + # - # Text book is all you need + # # https://huggingface.co/datasets/TanvirOnHF/muse_textbooks + # source: "TanvirOnHF/muse_textbooks" + + # # Optional, provide a name for the dataset + # name: "muse_textbooks" + + # # Various over write settings + # # --- + # text_rechunk_size: 32768 + # text_rechunk_force: true + # packing_enable: False + # max_token_size: -1 + ###################################################### + diff --git a/notebook/finetune-example/Eagle-x-zMultipack.ipynb b/notebook/finetune-example/Eagle-x-zMultipack.ipynb index 011e9e23..40b4b937 100644 --- a/notebook/finetune-example/Eagle-x-zMultipack.ipynb +++ b/notebook/finetune-example/Eagle-x-zMultipack.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -183,85 +183,60 @@ "output_type": "stream", "text": [ ">> Starting datapack build process for: /workspace/picocreator/RWKV-infctx-trainer/notebook/finetune-example/./Eagle-x-zMultipack-build.yaml\n", - ">> Preparing dataset - index: 0\n", - "Resolving data files: 100%|███████████████| 1267/1267 [00:00<00:00, 2769.84it/s]\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Saving the dataset (16/16 shards): 100%|█| 38611/38611 [00:16<00:00, 2306.08 exa\n", - "Saving the dataset (1/1 shards): 100%|████| 8/8 [00:00<00:00, 469.88 examples/s]\n", - ">> Preparing dataset - index: 1\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Saving the dataset (1/1 shards): 100%|█| 160/160 [00:00<00:00, 4198.48 examples/\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1214.20 examples/s]\n", - ">> Preparing dataset - index: 2\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Saving the dataset (1/1 shards): 100%|█| 411/411 [00:00<00:00, 1797.83 examples/\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1132.14 examples/s]\n", - ">> Preparing dataset - index: 3\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Saving the dataset (20/20 shards): 100%|█| 48977/48977 [00:19<00:00, 2502.48 exa\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1037.10 examples/s]\n", - ">> Preparing dataset - index: 4\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Saving the dataset (1/1 shards): 100%|█| 2394/2394 [00:00<00:00, 2401.14 example\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1026.57 examples/s]\n", - ">> Preparing dataset - index: 5\n", - "Downloading data: 100%|████████████████████| 1.94G/1.94G [01:21<00:00, 23.8MB/s]\n", - "Setting num_proc from 160 back to 1 for the train split to disable multiprocessing as it only contains one shard.\n", - "Generating train split: 1001551 examples [00:19, 52186.70 examples/s]\n", - "Map (num_proc=160): 100%|███| 1001551/1001551 [00:13<00:00, 72873.61 examples/s]\n", - "Filter (num_proc=160): 100%|█| 1001551/1001551 [00:04<00:00, 200670.79 examples/\n", - "Map (num_proc=160): 100%|██| 1001543/1001543 [00:07<00:00, 133141.86 examples/s]\n", - "Map (num_proc=160): 100%|██| 1001543/1001543 [00:09<00:00, 103921.25 examples/s]\n", - "Map (num_proc=160): 100%|████████| 11780/11780 [00:04<00:00, 2502.38 examples/s]\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Map (num_proc=8): 100%|████████████████████| 8/8 [00:00<00:00, 27.16 examples/s]\n", - "Saving the dataset (5/5 shards): 100%|█| 11780/11780 [00:05<00:00, 2347.79 examp\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1008.91 examples/s]\n", - ">> Preparing dataset - index: 6\n", - "Map (num_proc=160): 100%|███████| 16006/16006 [00:01<00:00, 12755.01 examples/s]\n", - "Filter (num_proc=160): 100%|████| 16006/16006 [00:00<00:00, 23321.48 examples/s]\n", - "Map (num_proc=160): 100%|███████| 15998/15998 [00:01<00:00, 11351.40 examples/s]\n", - "Map (num_proc=160): 100%|███████| 15998/15998 [00:01<00:00, 12205.94 examples/s]\n", - "Map (num_proc=160): 100%|█████████████| 548/548 [00:00<00:00, 849.48 examples/s]\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Map (num_proc=8): 100%|████████████████████| 8/8 [00:00<00:00, 31.16 examples/s]\n", - "Saving the dataset (1/1 shards): 100%|█| 548/548 [00:00<00:00, 1767.44 examples/\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1013.06 examples/s]\n", - ">> Preparing dataset - index: 7\n", - "Map (num_proc=160): 100%|██████████| 3857/3857 [00:00<00:00, 4524.76 examples/s]\n", - "Filter (num_proc=160): 100%|███████| 3857/3857 [00:00<00:00, 6157.25 examples/s]\n", - "Map (num_proc=160): 100%|██████████| 3849/3849 [00:01<00:00, 2682.75 examples/s]\n", - "Map (num_proc=160): 100%|██████████| 3849/3849 [00:01<00:00, 3181.50 examples/s]\n", - "Map (num_proc=160): 100%|█████████████| 160/160 [00:00<00:00, 245.60 examples/s]\n", - "num_proc must be <= 8. Reducing num_proc to 8 for dataset of size 8.\n", - "Map (num_proc=8): 100%|████████████████████| 8/8 [00:00<00:00, 30.53 examples/s]\n", - "Saving the dataset (1/1 shards): 100%|█| 160/160 [00:00<00:00, 1721.67 examples/\n", - "Saving the dataset (1/1 shards): 100%|███| 8/8 [00:00<00:00, 1106.86 examples/s]\n", - ">> Total approx train batches ( full | random ) : 201 ( 97 | 104 )\n", - "Map (num_proc=160): 100%|██████| 102912/102912 [00:47<00:00, 2171.63 examples/s]\n", + ">> Preparing dataset - index: 0 name: enwiki_100k\n", + "Saving the dataset (4/4 shards): 100%|█| 9921/9921 [00:03<00:00, 2611.43 example\n", + "Saving the dataset (1/1 shards): 100%|█| 101/101 [00:00<00:00, 1169.23 examples/\n", + ">> Preparing dataset - index: 1 name: super_wiki\n", + "Saving the dataset (9/9 shards): 100%|█| 21437/21437 [00:09<00:00, 2359.10 examp\n", + "Saving the dataset (1/1 shards): 100%|█| 217/217 [00:00<00:00, 1340.51 examples/\n", + ">> Preparing dataset - index: 2 name: MedText\n", + "Saving the dataset (1/1 shards): 100%|█| 160/160 [00:00<00:00, 4117.79 examples/\n", + "Saving the dataset (1/1 shards): 100%|██| 15/15 [00:00<00:00, 655.74 examples/s]\n", + ">> Preparing dataset - index: 3 name: ALMA-prompt-completion\n", + "Saving the dataset (1/1 shards): 100%|█| 376/376 [00:00<00:00, 1606.80 examples/\n", + "Saving the dataset (1/1 shards): 100%|█| 1175/1175 [00:00<00:00, 33642.85 exampl\n", + ">> Preparing dataset - index: 4 name: OpenOrca\n", + "Saving the dataset (20/20 shards): 100%|█| 48493/48493 [00:19<00:00, 2438.84 exa\n", + "Saving the dataset (1/1 shards): 100%|█| 42340/42340 [00:00<00:00, 63813.68 exam\n", + ">> Preparing dataset - index: 5 name: openhermes-1-instruct\n", + "Saving the dataset (1/1 shards): 100%|█| 2381/2381 [00:00<00:00, 2534.65 example\n", + "Saving the dataset (1/1 shards): 100%|█| 2429/2429 [00:00<00:00, 5320.17 example\n", + ">> Preparing dataset - index: 6 name: openhermes-2-convo\n", + "Saving the dataset (5/5 shards): 100%|█| 11659/11659 [00:05<00:00, 2242.87 examp\n", + "Saving the dataset (1/1 shards): 100%|█| 10016/10016 [00:00<00:00, 52668.39 exam\n", + ">> Preparing dataset - index: 7 name: Capybara-chat\n", + "Saving the dataset (1/1 shards): 100%|█| 546/546 [00:00<00:00, 1743.15 examples/\n", + "Saving the dataset (1/1 shards): 100%|█| 161/161 [00:00<00:00, 5264.01 examples/\n", + ">> Preparing dataset - index: 8 name: Pure-Dove\n", + "Map (num_proc=160): 100%|██████████| 3857/3857 [00:00<00:00, 3869.18 examples/s]\n", + "Filter (num_proc=160): 100%|███████| 3857/3857 [00:00<00:00, 4566.25 examples/s]\n", + "Map (num_proc=160): 100%|██████████| 3818/3818 [00:02<00:00, 1495.35 examples/s]\n", + "Map (num_proc=160): 100%|██████████| 3818/3818 [00:01<00:00, 2402.94 examples/s]\n", + "Saving the dataset (1/1 shards): 100%|█| 160/160 [00:00<00:00, 1797.18 examples/\n", + "Saving the dataset (1/1 shards): 100%|█| 39/39 [00:00<00:00, 1402.74 examples/s]\n", + ">> Dataset Mixing mode: shuffle\n", ">> Saving dataset to data_path : ../datapath/world/Eagle-x-multipack/\n", - "Saving the dataset (41/41 shards): 100%|█| 103041/103041 [00:50<00:00, 2042.55 e\n", - "Saving the dataset (1/1 shards): 100%|█| 64/64 [00:00<00:00, 2299.45 examples/s]\n", + "Saving the dataset (38/38 shards): 100%|█| 95133/95133 [00:43<00:00, 2202.38 exa\n", + "Saving the dataset (1/1 shards): 100%|█| 56493/56493 [00:00<00:00, 67770.86 exam\n", ">> Dataset saved to data_path\n", ">> -----------------------------------\n", ">> Performing dataset counting\n", ">> -----------------------------------\n", - ">> Final dataset count ( train ) : 103,041\n", - ">> Final dataset count ( test ) : 64\n", + ">> Final dataset count ( train ) : 95,133\n", + ">> Final dataset count ( test ) : 56,493\n", ">> -----------------------------------\n", - "Map (num_proc=160): 100%|██████| 103041/103041 [01:09<00:00, 1474.72 examples/s]\n", - "num_proc must be <= 64. Reducing num_proc to 64 for dataset of size 64.\n", - "Map (num_proc=64): 100%|████████████████| 64/64 [00:00<00:00, 190.67 examples/s]\n", + "Map (num_proc=160): 100%|████████| 95133/95133 [00:45<00:00, 2111.56 examples/s]\n", + "Map (num_proc=160): 100%|████████| 56493/56493 [00:17<00:00, 3260.12 examples/s]\n", ">> -----------------------------------\n", ">> Final 'train' dataset token count ...\n", - ">> - Total tokens : 3,354,691,235\n", - ">> - Valid tokens : 2,073,274,062\n", - ">> - Hidden tokens : 1,281,417,173\n", + ">> - Total tokens : 3,096,170,994\n", + ">> - Valid tokens : 1,827,596,154\n", + ">> - Hidden tokens : 1,268,574,840\n", ">> -----------------------------------\n", ">> Final 'test' dataset token count ...\n", - ">> - Total tokens : 281,239\n", - ">> - Valid tokens : 276,273\n", - ">> - Hidden tokens : 4,966\n", + ">> - Total tokens : 31,237,792\n", + ">> - Valid tokens : 18,390,493\n", + ">> - Hidden tokens : 12,847,299\n", ">> -----------------------------------\n" ] } @@ -275,26 +250,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Start the training run!" + "## Do the initial validation run (for reference)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-02-02 18:49:43,863] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/finetune-example/./Eagle-x-zMultipack.yaml', '--model.load_model=../model/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=../checkpoint/Eagle-x-zMultipack/', '--trainer.logger.init_args.name=RWKV-v5-Finetune - Eagle-x-zMultipack (tctxlen=4096, deepspeed_stage_2)', '--trainer.logger.init_args.project=RWKV-v5-Finetune', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=512', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/finetune-example/./Eagle-x-zMultipack.yaml', '--model.load_model=../model/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=../checkpoint/Eagle-x-zMultipack/', '--trainer.logger.init_args.name=RWKV-v5-Finetune - Eagle-x-zMultipack (tctxlen=4096, deepspeed_stage_2)', '--trainer.logger.init_args.project=RWKV-v5-Finetune', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=512', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 190722569\n", - "Seed set to 190722569\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "[2024-02-04 10:19:42,000] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['validate', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/finetune-example/./Eagle-x-zMultipack.yaml', '--model.load_model=../model/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=../checkpoint/Eagle-x-zMultipack/', '--trainer.logger.init_args.name=RWKV-v5-Finetune - Eagle-x-zMultipack (tctxlen=4096, deepspeed_stage_2)', '--trainer.logger.init_args.project=RWKV-v5-Finetune', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=512', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['validate', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/finetune-example/./Eagle-x-zMultipack.yaml', '--model.load_model=../model/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=../checkpoint/Eagle-x-zMultipack/', '--trainer.logger.init_args.name=RWKV-v5-Finetune - Eagle-x-zMultipack (tctxlen=4096, deepspeed_stage_2)', '--trainer.logger.init_args.project=RWKV-v5-Finetune', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=512', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/usr/local/lib/python3.10/dist-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 3680227481\n", + "Seed set to 3680227481\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", @@ -309,186 +294,191 @@ " - accumulate_grad_batches: 8\n", " - effective_batch_size: 512\n", "\n", - "[rank: 0] Seed set to 190722569\n", + "[rank: 0] Seed set to 3680227481\n", "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", - "[2024-02-02 18:50:23,466] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-02-02 18:50:23,504] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-02-02 18:50:23,557] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-02-02 18:50:23,558] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-02-02 18:50:23,686] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-02-02 18:50:23,690] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-02-02 18:50:23,691] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[RWKV.model][WARNING] - torch.compile is enabled, but this has been observed to perform worse, or even crash in some setup. Ensure to test if you actually measure speedups over JIT before using for large training runs'\n", - "[RWKV.model] Running RWKV infctx using 'torch-compile' with torch '2.1.2'\n", - "[rank: 3] Seed set to 190722569\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "[2024-02-04 10:20:28,520] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 10:20:28,531] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 10:20:28,532] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 10:20:28,559] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 10:20:28,582] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 10:20:28,587] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 10:20:28,587] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[rank: 6] Seed set to 3680227481\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "[rank: 1] Seed set to 190722569\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "[rank: 4] Seed set to 3680227481\n", + "[rank: 7] Seed set to 3680227481\n", + "[rank: 3] Seed set to 3680227481\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "[rank: 7] Seed set to 190722569\n", - "[rank: 5] Seed set to 190722569\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "[rank: 2] Seed set to 190722569\n", - "[rank: 6] Seed set to 190722569\n", - "[rank: 4] Seed set to 190722569\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "[rank: 1] Seed set to 3680227481\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "[rank: 2] Seed set to 3680227481\n", + "[rank: 5] Seed set to 3680227481\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", " return self.fget.__get__(instance, owner)()\n", - "[rank: 2] Seed set to 190722569\n", - "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", - "[rank: 3] Seed set to 190722569\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 1] Seed set to 3680227481\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 3680227481\n", "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", - "[rank: 6] Seed set to 190722569\n", - "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", - "[rank: 4] Seed set to 190722569\n", - "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", - "[rank: 5] Seed set to 190722569\n", + "[rank: 5] Seed set to 3680227481\n", "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", - "[rank: 7] Seed set to 190722569\n", + "[rank: 2] Seed set to 3680227481\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 4] Seed set to 3680227481\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 6] Seed set to 3680227481\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 7] Seed set to 3680227481\n", "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", - "[rank: 1] Seed set to 190722569\n", - "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240202_185125-po507nn3\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_102130-n9pwg2hp\u001b[0m\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mRWKV-v5-Finetune - Eagle-x-zMultipack (tctxlen=4096, deepspeed_stage_2)\u001b[0m\n", "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-v5-Finetune\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-v5-Finetune/runs/po507nn3\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-v5-Finetune/runs/n9pwg2hp\u001b[0m\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "\n", "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "#\n", - "# RWKV lighting_trainer.py important notes \n", - "# https://github.com/RWKV/RWKV-infctx-trainer \n", - "#\n", - "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", - "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", - "# - When resuming from checkpoint, the estimated time is inaccurate\n", - "#\n", - "\n", - "[RWKV.model] Configuring optimizer with\n", - " - lr_init: 5.000e-05 (5e-05)\n", - " - lr_final: 4.000e-05 (4e-05)\n", - "\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Detected CUDA files, patching ldflags\n", - "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", - "Building extension module fused_adam...\n", - "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", - "ninja: no work to do.\n", - "Loading extension module fused_adam...\n", - "Time to load fused_adam op: 0.049744367599487305 seconds\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "Time to load fused_adam op: 0.10116791725158691 seconds\n", - "Time to load fused_adam op: 0.10123014450073242 seconds\n", - "Loading extension module fused_adam...\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Time to load fused_adam op: 0.10133147239685059 secondsLoading extension module fused_adam...\n", - "\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Loading `train_dataloader` to estimate number of stepping batches.\n", - "Time to load fused_adam op: 0.10153722763061523 seconds\n", - "Loading extension module fused_adam...\n", - "Time to load fused_adam op: 0.10162758827209473 seconds\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Time to load fused_adam op: 0.10161066055297852 seconds\n", - "Loading extension module fused_adam...\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Time to load fused_adam op: 0.1011042594909668 seconds\n", - "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------\n", - "0 | emb | Embedding | 268 M \n", - "1 | blocks | ModuleList | 7.0 B \n", - "2 | ln_out | LayerNorm | 8.2 K \n", - "3 | head | Linear | 268 M \n", - "--------------------------------------\n", - "7.5 B Trainable params\n", - "0 Non-trainable params\n", - "7.5 B Total params\n", - "30,072.177Total estimated model params size (MB)\n", - "Epoch 0: 0%| | 0/1611 [00:00 The label does get long + +``` +docker run -it --gpus all \ + -e RUNNER_LABELS='nvidia-gpu,gpu-vram-16,gpu-vram-24,gpu-vram-eq-24,gpu-count-8,gpu-4090,gpu-8x4090,any-gpu,gpu-count-any' \ + -e RUNNER_NAME='your-runner-name-here' \ + -e RUNNER_TOKEN='your-runner-token-here' \ + -e RUNNER_REPO_URL='https://github.com/RWKV' \ + --name RWKV-GH-GPU-Runner \ + ghcr.io/rwkv/rwkv-infctx-trainer:github-worker-cuda-12-1 +``` + +Consult the RWKV group runner manager : @picocreator +If you like to donate your GPU compute to the pool +The runner name, is meant to make it easy to identify the GPU source, and isolate issues if needed. diff --git a/notebook/major-runs/Eagle-2T-calibration/1e-5-cuda-bs1.ipynb b/notebook/major-runs/Eagle-2T-calibration/1e-5-cuda-bs1.ipynb new file mode 100644 index 00000000..ad638ee5 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/1e-5-cuda-bs1.ipynb @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"1e-5-with-cuda-bs1\"\n", + "LEARNING_RATE=\"1e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 02:10:51,741] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/1e-5-with-cuda-bs1/', '--trainer.logger.init_args.name=C2-7B2T - 1e-5-with-cuda-bs1 (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=1', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/1e-5-with-cuda-bs1/', '--trainer.logger.init_args.name=C2-7B2T - 1e-5-with-cuda-bs1 (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=1', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 316461279\n", + "Seed set to 316461279\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 1\n", + " - accumulate_grad_batches: 128\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 02:11:36,418] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:36,451] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:36,468] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:36,499] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:36,511] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:36,593] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:36,596] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 1] Seed set to 316461279\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 316461279\n", + "[rank: 4] Seed set to 316461279\n", + "[rank: 3] Seed set to 316461279\n", + "[rank: 2] Seed set to 316461279\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 316461279\n", + "[rank: 7] Seed set to 316461279\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 5] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 2] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 3] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 4] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 7] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 1] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 6] Seed set to 316461279\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_021240-8kqgtwis\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 1e-5-with-cuda-bs1 (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/8kqgtwis\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 1.000e-05 (1e-05)\n", + " - lr_final: 1.000e-05 (1e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.050655364990234375 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10103893280029297 seconds\n", + "Time to load fused_adam op: 0.10112667083740234 seconds\n", + "Time to load fused_adam op: 0.10122942924499512 secondsLoading extension module fused_adam...\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10148215293884277 seconds/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + "Time to load fused_adam op: 0.10121464729309082 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10165143013000488 seconds\n", + "Time to load fused_adam op: 0.10171651840209961 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 2%| | 2039/106218 [2:40:14<136:26:58, 0.21it/s, v_num=twis, train/lo^C\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=1 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/1e-5-cuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/1e-5-cuda.ipynb new file mode 100644 index 00000000..cee4a56e --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/1e-5-cuda.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"1e-5-with-cuda\"\n", + "LEARNING_RATE=\"1e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 02:10:43,211] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/1e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 1e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/1e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 1e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 3021536099\n", + "Seed set to 3021536099\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 02:11:32,135] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:32,174] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:32,200] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:32,217] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:32,220] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:32,221] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:32,222] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 3] Seed set to 3021536099\n", + "[rank: 5] Seed set to 3021536099\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 3021536099\n", + "[rank: 1] Seed set to 3021536099\n", + "[rank: 7] Seed set to 3021536099\n", + "[rank: 2] Seed set to 3021536099\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 4] Seed set to 3021536099\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 1] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 2] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 4] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 7] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 5] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 6] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 3] Seed set to 3021536099\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_021233-vc67gs6i\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 1e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/vc67gs6i\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 1.000e-05 (1e-05)\n", + " - lr_final: 1.000e-05 (1e-05)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.04787254333496094 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...Loading extension module fused_adam...\n", + "\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10111594200134277 seconds\n", + "Time to load fused_adam op: 0.10112905502319336 seconds\n", + "Loading extension module fused_adam...Loading extension module fused_adam...\n", + "\n", + "Time to load fused_adam op: 0.1010894775390625 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.1014394760131836 secondsTime to load fused_adam op: 0.10131096839904785 seconds\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.1012270450592041 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10168576240539551 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 3%| | 400/13278 [2:01:22<65:07:29, 0.05it/s, v_num=gs6i, train/loss=/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "Epoch 0: 3%| | 416/13278 [2:08:56<66:26:38, 0.05it/s, v_num=gs6i, train/loss=`Trainer.fit` stopped: `max_steps=26` reached.\n", + "Epoch 0: 3%| | 416/13278 [2:08:56<66:26:38, 0.05it/s, v_num=gs6i, train/loss=\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: | 0.040 MB of 0.040 MB uploaded (0.002 MB deduped)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 █▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss ▇▆▄█▅▅▆▆▃▃▂▇▅▅▄▇▄▆▃▂▅▇▇▂▆▅▄▅▅▂▄▅▇▃▆█▆▅▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss ▇▆▄█▅▅▆▆▃▃▂▇▅▅▄▇▄▆▃▂▅▇▇▂▆▅▄▅▅▂▄▅▇▃▆█▆▅▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx 415\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 7.05118\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 54525.952\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep 3320\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen 16384.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss 0.80469\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss 0.80469\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens 16383.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 25\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate 1e-05\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mC2-7B2T - 1e-5-with-cuda (deepspeed_stage_2)\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/vc67gs6i\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzNjg5NDk3OA==/version_details/v1\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240204_021233-vc67gs6i/logs\u001b[0m\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/1e-6-cuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/1e-6-cuda.ipynb new file mode 100644 index 00000000..8f9b4c0e --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/1e-6-cuda.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"1e-6-with-cuda\"\n", + "LEARNING_RATE=\"1e-6\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1e-6 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 00:48:24,156] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-6', '--model.lr_final=1e-6', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/1e-6-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 1e-6-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-6', '--model.lr_final=1e-6', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/1e-6-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 1e-6-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 2424149494\n", + "Seed set to 2424149494\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 00:49:09,839] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:09,903] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:09,946] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:10,013] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:10,040] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:10,056] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:10,058] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 2424149494\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 2424149494\n", + "[rank: 5] Seed set to 2424149494\n", + "[rank: 7] Seed set to 2424149494\n", + "[rank: 4] Seed set to 2424149494\n", + "[rank: 6] Seed set to 2424149494\n", + "[rank: 1] Seed set to 2424149494\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 4] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 7] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 1] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 5] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 6] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 2] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 3] Seed set to 2424149494\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_005012-sx9lpy1u\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 1e-6-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/sx9lpy1u\u001b[0m\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 1.000e-06 (1e-06)\n", + " - lr_final: 1.000e-06 (1e-06)\n", + "\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.04851174354553223 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10104203224182129 seconds\n", + "Time to load fused_adam op: 0.10108327865600586 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10114192962646484 seconds\n", + "Time to load fused_adam op: 0.10115528106689453 secondsTime to load fused_adam op: 0.10108017921447754 seconds\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10137057304382324 secondsLoading extension module fused_adam...\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.1016380786895752 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 2%| | 257/13278 [1:18:00<65:52:28, 0.05it/s, v_num=py1u, train/loss=^C\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/3e-5-cuda-bs1.ipynb b/notebook/major-runs/Eagle-2T-calibration/3e-5-cuda-bs1.ipynb new file mode 100644 index 00000000..40435236 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/3e-5-cuda-bs1.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"3e-5-with-cuda-bs1\"\n", + "LEARNING_RATE=\"3e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 02:10:55,117] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/3e-5-with-cuda-bs1/', '--trainer.logger.init_args.name=C2-7B2T - 3e-5-with-cuda-bs1 (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/3e-5-with-cuda-bs1/', '--trainer.logger.init_args.name=C2-7B2T - 3e-5-with-cuda-bs1 (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 1610738499\n", + "Seed set to 1610738499\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 02:11:40,898] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:40,912] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:40,950] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:41,053] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:41,057] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:41,064] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:41,112] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 1610738499\n", + "[rank: 4] Seed set to 1610738499\n", + "[rank: 7] Seed set to 1610738499\n", + "[rank: 1] Seed set to 1610738499\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 1610738499\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 1610738499\n", + "[rank: 3] Seed set to 1610738499\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 2] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 5] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 6] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 3] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 4] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 7] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 1] Seed set to 1610738499\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_021240-5lkzr1zc\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 3e-5-with-cuda-bs1 (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/5lkzr1zc\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 3.000e-05 (3e-05)\n", + " - lr_final: 3.000e-05 (3e-05)\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.048139095306396484 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10102510452270508 seconds\n", + "Time to load fused_adam op: 0.1011970043182373 seconds\n", + "Time to load fused_adam op: 0.10113167762756348 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10125136375427246 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10170507431030273 seconds\n", + "Time to load fused_adam op: 0.10161161422729492 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10172152519226074 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 3%| | 400/13278 [2:01:19<65:05:51, 0.05it/s, v_num=r1zc, train/loss=/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n", + " warnings.warn(\n", + "Epoch 0: 3%| | 416/13278 [2:09:59<66:59:00, 0.05it/s, v_num=r1zc, train/loss=`Trainer.fit` stopped: `max_steps=26` reached.\n", + "Epoch 0: 3%| | 416/13278 [2:09:59<66:59:01, 0.05it/s, v_num=r1zc, train/loss=\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: / 0.031 MB of 0.031 MB uploaded\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 █▆▆▆▆▆▆▆▆▆▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss ▆▆▄█▅▅▅▆▃▃▂▇▅▅▄▇▄▆▄▂▄▇▆▂▆▅▄▅▅▂▄▅▇▃▅█▆▅▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss ▆▆▄█▅▅▅▆▃▃▂▇▅▅▄▇▄▆▄▂▄▇▆▂▆▅▄▅▅▂▄▅▇▃▅█▆▅▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx 415\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 6.99436\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 54525.952\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep 3320\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen 16384.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss 0.82031\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss 0.82031\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens 16383.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 25\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate 3e-05\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mC2-7B2T - 3e-5-with-cuda-bs1 (deepspeed_stage_2)\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/5lkzr1zc\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzNjg5NDk3OA==/version_details/v1\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240204_021240-5lkzr1zc/logs\u001b[0m\n", + "Exception in thread IntMsgThr:\n", + "Traceback (most recent call last):\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n", + " self.run()\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/threading.py\", line 982, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/wandb_run.py\", line 300, in check_internal_messages\n", + " self._loop_check_status(\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/wandb_run.py\", line 224, in _loop_check_status\n", + " local_handle = request()\n", + " ^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/interface/interface.py\", line 800, in deliver_internal_messages\n", + " return self._deliver_internal_messages(internal_message)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/interface/interface_shared.py\", line 506, in _deliver_internal_messages\n", + " return self._deliver_record(record)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/interface/interface_shared.py\", line 449, in _deliver_record\n", + " handle = mailbox._deliver_record(record, interface=self)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/lib/mailbox.py\", line 455, in _deliver_record\n", + " interface._publish(record)\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/interface/interface_sock.py\", line 51, in _publish\n", + " self._sock_client.send_record_publish(record)\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/lib/sock_client.py\", line 221, in send_record_publish\n", + " self.send_server_request(server_req)\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/lib/sock_client.py\", line 155, in send_server_request\n", + " self._send_message(msg)\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/lib/sock_client.py\", line 152, in _send_message\n", + " self._sendall_with_error_handle(header + data)\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/wandb/sdk/lib/sock_client.py\", line 130, in _sendall_with_error_handle\n", + " sent = self._sock.send(data)\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + "BrokenPipeError: [Errno 32] Broken pipe\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=1 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/3e-5-cuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/3e-5-cuda.ipynb new file mode 100644 index 00000000..20a566d8 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/3e-5-cuda.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"3e-5-with-cuda\"\n", + "LEARNING_RATE=\"3e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 02:10:48,815] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=1', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=1', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 3963349868\n", + "Seed set to 3963349868\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 1\n", + " - accumulate_grad_batches: 128\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 02:11:31,800] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:31,846] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:31,898] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:31,938] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:31,949] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:31,958] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 02:11:31,983] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 3963349868\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 1] Seed set to 3963349868\n", + "[rank: 6] Seed set to 3963349868\n", + "[rank: 5] Seed set to 3963349868\n", + "[rank: 4] Seed set to 3963349868\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 3963349868\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 7] Seed set to 3963349868\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 6] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 4] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 7] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 1] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 5] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 2] Seed set to 3963349868\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_021235-52pkbyh9\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 3e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/52pkbyh9\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 3.000e-05 (3e-05)\n", + " - lr_final: 3.000e-05 (3e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.04738616943359375 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10104179382324219 seconds\n", + "Time to load fused_adam op: 0.10118889808654785 secondsTime to load fused_adam op: 0.10123348236083984 seconds\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10162854194641113 seconds\n", + "Time to load fused_adam op: 0.10167551040649414 seconds\n", + "Time to load fused_adam op: 0.10173392295837402 seconds\n", + "Loading extension module fused_adam...\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10230803489685059 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 2%| | 2057/106218 [2:40:29<135:27:13, 0.21it/s, v_num=byh9, train/lo^C\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/5e-5-cuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/5e-5-cuda.ipynb new file mode 100644 index 00000000..c87d7a36 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/5e-5-cuda.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"5e-5-with-cuda\"\n", + "LEARNING_RATE=\"5e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 00:47:42,386] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=5e-5', '--model.lr_final=5e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/5e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 5e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=5e-5', '--model.lr_final=5e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/5e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 5e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 3581325923\n", + "Seed set to 3581325923\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 00:48:27,651] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:48:27,686] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:48:27,767] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:48:27,780] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:48:27,785] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:48:27,799] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:48:27,813] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 3581325923\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 3581325923\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 3581325923\n", + "[rank: 4] Seed set to 3581325923\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 1] Seed set to 3581325923\n", + "[rank: 7] Seed set to 3581325923\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 3581325923\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 5] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 3] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 1] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 4] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 2] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 6] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 7] Seed set to 3581325923\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_004931-omz7toab\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 5e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/omz7toab\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 5.000e-05 (5e-05)\n", + " - lr_final: 5.000e-05 (5e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.047918081283569336 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10101819038391113 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10114145278930664 seconds\n", + "Time to load fused_adam op: 0.10138821601867676 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10172271728515625 seconds\n", + "Time to load fused_adam op: 0.10170412063598633 seconds\n", + "Time to load fused_adam op: 0.10173869132995605 seconds\n", + "Time to load fused_adam op: 0.10183525085449219 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 2%| | 259/13278 [1:18:41<65:55:22, 0.05it/s, v_num=toab, train/loss=^C\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/5e-6-cuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/5e-6-cuda.ipynb new file mode 100644 index 00000000..3d57a665 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/5e-6-cuda.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"5e-6-with-cuda\"\n", + "LEARNING_RATE=\"5e-6\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5e-6 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 00:56:02,760] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=5e-6', '--model.lr_final=5e-6', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/5e-6-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 5e-6-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=5e-6', '--model.lr_final=5e-6', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/5e-6-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 5e-6-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 127011471\n", + "Seed set to 127011471\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Creating extension directory /root/.cache/torch_extensions/py311_cu121/wkv5...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "[1/3] c++ -MMD -MF wkv5_op.o.d -DTORCH_EXTENSION_NAME=wkv5 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/TH -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /root/miniconda3/envs/rwkv-infctx/include/python3.11 -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++17 -c /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/src/module/cuda/wkv5_op.cpp -o wkv5_op.o \n", + "/workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/src/module/cuda/wkv5_op.cpp:48:51: note: ‘#pragma message: No SIMD is supported’\n", + " 48 | #pragma message(\"No SIMD is supported\")\n", + " | ^\n", + "[2/3] /usr/local/cuda/bin/nvcc -DTORCH_EXTENSION_NAME=wkv5 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/TH -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /root/miniconda3/envs/rwkv-infctx/include/python3.11 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -res-usage --use_fast_math -O3 -Xptxas -O3 --extra-device-vectorization -D_N_=64 -std=c++17 -c /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/src/module/cuda/wkv5_cuda.cu -o wkv5_cuda.cuda.o \n", + "ptxas info : 1 bytes gmem\n", + "ptxas info : Compiling entry function '_Z15kernel_backwardIfEviiiiPfPKT_S3_S3_PKfS5_S3_S3_PS1_S6_S6_S6_S6_' for 'sm_90'\n", + "ptxas info : Function properties for _Z15kernel_backwardIfEviiiiPfPKT_S3_S3_PKfS5_S3_S3_PS1_S6_S6_S6_S6_\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 166 registers, 1536 bytes smem\n", + "ptxas info : Compiling entry function '_Z15kernel_backwardIN3c104HalfEEviiiiPfPKT_S5_S5_PKfS7_S5_S5_PS3_S8_S8_S8_S8_' for 'sm_90'\n", + "ptxas info : Function properties for _Z15kernel_backwardIN3c104HalfEEviiiiPfPKT_S5_S5_PKfS7_S5_S5_PS3_S8_S8_S8_S8_\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 166 registers, 1536 bytes smem\n", + "ptxas info : Compiling entry function '_Z15kernel_backwardIN3c108BFloat16EEviiiiPfPKT_S5_S5_PKfS7_S5_S5_PS3_S8_S8_S8_S8_' for 'sm_90'\n", + "ptxas info : Function properties for _Z15kernel_backwardIN3c108BFloat16EEviiiiPfPKT_S5_S5_PKfS7_S5_S5_PS3_S8_S8_S8_S8_\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 166 registers, 1536 bytes smem\n", + "ptxas info : Compiling entry function '_Z24kernel_forward_inferenceIfEviiiiPfPKT_S3_S3_PKfS3_PS1_' for 'sm_90'\n", + "ptxas info : Function properties for _Z24kernel_forward_inferenceIfEviiiiPfPKT_S3_S3_PKfS3_PS1_\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 130 registers, 1024 bytes smem\n", + "ptxas info : Compiling entry function '_Z24kernel_forward_inferenceIN3c104HalfEEviiiiPfPKT_S5_S5_PKfS5_PS3_' for 'sm_90'\n", + "ptxas info : Function properties for _Z24kernel_forward_inferenceIN3c104HalfEEviiiiPfPKT_S5_S5_PKfS5_PS3_\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 131 registers, 1024 bytes smem\n", + "ptxas info : Compiling entry function '_Z24kernel_forward_inferenceIN3c108BFloat16EEviiiiPfPKT_S5_S5_PKfS5_PS3_' for 'sm_90'\n", + "ptxas info : Function properties for _Z24kernel_forward_inferenceIN3c108BFloat16EEviiiiPfPKT_S5_S5_PKfS5_PS3_\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 131 registers, 1024 bytes smem\n", + "ptxas info : Compiling entry function '_Z15kernelc_mm8_oneIfEvyyPKT_PKhyPfPKfS7_yy' for 'sm_90'\n", + "ptxas info : Function properties for _Z15kernelc_mm8_oneIfEvyyPKT_PKhyPfPKfS7_yy\n", + " 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads\n", + "ptxas info : Used 32 registers\n", + "[3/3] c++ wkv5_op.o wkv5_cuda.cuda.o -shared -L/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/usr/local/cuda/lib64 -lcudart -o wkv5.so\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 00:57:05,388] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:57:05,391] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:57:05,392] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:57:05,414] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:57:05,433] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:57:05,487] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:57:05,490] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 7] Seed set to 127011471\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 1] Seed set to 127011471\n", + "[rank: 6] Seed set to 127011471\n", + "[rank: 3] Seed set to 127011471\n", + "[rank: 2] Seed set to 127011471\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 4] Seed set to 127011471\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 127011471\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 2] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 5] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 7] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 4] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 3] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 1] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 6] Seed set to 127011471\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_005807-txulfaab\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 5e-6-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/txulfaab\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 5.000e-06 (5e-06)\n", + " - lr_final: 5.000e-06 (5e-06)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Creating extension directory /root/.cache/torch_extensions/py311_cu121/fused_adam...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "[1/3] /usr/local/cuda/bin/nvcc -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -I/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/csrc/includes -I/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/csrc/adam -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/TH -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /root/miniconda3/envs/rwkv-infctx/include/python3.11 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -lineinfo --use_fast_math -gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_90,code=compute_90 -DBF16_AVAILABLE -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -std=c++17 -c /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/csrc/adam/multi_tensor_adam.cu -o multi_tensor_adam.cuda.o \n", + "[2/3] c++ -MMD -MF fused_adam_frontend.o.d -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -I/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/csrc/includes -I/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/csrc/adam -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/torch/csrc/api/include -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/TH -isystem /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /root/miniconda3/envs/rwkv-infctx/include/python3.11 -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++17 -O3 -std=c++17 -g -Wno-reorder -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -DBF16_AVAILABLE -c /root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/csrc/adam/fused_adam_frontend.cpp -o fused_adam_frontend.o \n", + "[3/3] c++ fused_adam_frontend.o multi_tensor_adam.cuda.o -shared -L/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/usr/local/cuda/lib64 -lcudart -o fused_adam.so\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.275224685668945 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.319664001464844 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.321433782577515 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.322482109069824 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.324394941329956 seconds\n", + "Loading extension module fused_adam...\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 20.32595705986023 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 20.324968099594116 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.329177618026733 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 2%| | 229/13278 [1:09:31<66:01:16, 0.05it/s, v_num=faab, train/loss=^C\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/8e-5-cuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/8e-5-cuda.ipynb new file mode 100644 index 00000000..8aaaf32e --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/8e-5-cuda.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"8e-5-with-cuda\"\n", + "LEARNING_RATE=\"8e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 00:48:14,716] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=8e-5', '--model.lr_final=8e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/8e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 8e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=8e-5', '--model.lr_final=8e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/8e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - 8e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 792584448\n", + "Seed set to 792584448\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 00:49:02,485] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:02,511] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:02,556] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:02,565] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:02,575] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:02,682] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 00:49:02,685] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 1] Seed set to 792584448\n", + "[rank: 6] Seed set to 792584448\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 792584448\n", + "[rank: 3] Seed set to 792584448\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 4] Seed set to 792584448\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 2] Seed set to 792584448\n", + "[rank: 7] Seed set to 792584448\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 2] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 7] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 5] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 6] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 4] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 3] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 1] Seed set to 792584448\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_005004-tty2hwsy\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - 8e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/tty2hwsy\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 8.000e-05 (8e-05)\n", + " - lr_final: 8.000e-05 (8e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.05113959312438965 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10113072395324707 seconds\n", + "Time to load fused_adam op: 0.1012580394744873 seconds\n", + "Time to load fused_adam op: 0.1014091968536377 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10148310661315918 seconds/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10175824165344238 seconds\n", + "Time to load fused_adam op: 0.10180974006652832 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.1018514633178711 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 2%| | 257/13278 [1:18:00<65:52:08, 0.05it/s, v_num=hwsy, train/loss=^C\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-1e-5-nocuda.ipynb b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-1e-5-nocuda.ipynb new file mode 100644 index 00000000..e59b798b --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-1e-5-nocuda.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"enwiki-16k-1e-5-no-cuda\"\n", + "LEARNING_RATE=\"1e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enwiki 16 - 1e-5 NO CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 08:17:08,072] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-1e-5-no-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-1e-5-no-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-1e-5-no-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-1e-5-no-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/usr/local/lib/python3.10/dist-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 3134289796\n", + "Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 08:17:48,259] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 08:17:48,296] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 08:17:48,364] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 08:17:48,434] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 08:17:48,497] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 08:17:48,521] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 08:17:48,523] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[rank: 6] Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 1] Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 3134289796\n", + "[rank: 7] Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 4] Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 2] Seed set to 3134289796\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 2] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 6] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 5] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 7] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 4] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 1] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 3134289796\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_081853-8aroejja\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - enwiki-16k-1e-5-no-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/8aroejja\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 1.000e-05 (1e-05)\n", + " - lr_final: 1.000e-05 (1e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.04761028289794922 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...Loading extension module fused_adam...\n", + "\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10117268562316895 seconds\n", + "Time to load fused_adam op: 0.10118985176086426 seconds\n", + "Time to load fused_adam op: 0.10121536254882812 seconds\n", + "Time to load fused_adam op: 0.10122442245483398 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10135841369628906 secondsLoading extension module fused_adam.../usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10133194923400879 seconds\n", + "Loading extension module fused_adam...\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10180020332336426 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 98%|▉| 307/312 [2:02:19<01:59, 0.04it/s, v_num=ejja, train/loss=2.140^C\n", + "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=1 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-1e-5.ipynb b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-1e-5.ipynb new file mode 100644 index 00000000..cb9b7f06 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-1e-5.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"enwiki-16k-1e-5-with-cuda\"\n", + "LEARNING_RATE=\"1e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enwiki 16 - 1e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 05:01:34,110] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-1e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-1e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-1e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-1e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 1807152312\n", + "Seed set to 1807152312\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 05:02:18,691] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 05:02:18,730] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 05:02:18,765] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 05:02:18,832] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 05:02:18,836] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 05:02:18,862] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 05:02:18,897] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 6] Seed set to 1807152312\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 1807152312\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 7] Seed set to 1807152312\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 1] Seed set to 1807152312\n", + "[rank: 4] Seed set to 1807152312\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 1807152312\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 2] Seed set to 1807152312\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 5] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 2] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 3] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 6] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 7] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 4] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 1] Seed set to 1807152312\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_050321-3nyx5g6f\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - enwiki-16k-1e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/3nyx5g6f\u001b[0m\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 1.000e-05 (1e-05)\n", + " - lr_final: 1.000e-05 (1e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.04673123359680176 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10108637809753418 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 0.10121321678161621 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10197234153747559 seconds\n", + "Time to load fused_adam op: 0.10172867774963379 seconds\n", + "Time to load fused_adam op: 0.10169768333435059 seconds\n", + "Time to load fused_adam op: 0.1017763614654541 seconds\n", + "Time to load fused_adam op: 0.10174942016601562 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 100%|█| 312/312 [1:34:35<00:00, 0.05it/s, v_num=5g6f, train/loss=2.200\n", + "Validation: | | 0/? [00:00=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 3.000e-05 (3e-05)\n", + " - lr_final: 3.000e-05 (3e-05)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Creating extension directory /root/.cache/torch_extensions/py310_cu121/fused_adam...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "[1/3] /usr/local/cuda/bin/nvcc -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/includes -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam -isystem /usr/local/lib/python3.10/dist-packages/torch/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.10/dist-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /usr/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -lineinfo --use_fast_math -gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_90,code=compute_90 -DBF16_AVAILABLE -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -std=c++17 -c /usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam/multi_tensor_adam.cu -o multi_tensor_adam.cuda.o \n", + "[2/3] c++ -MMD -MF fused_adam_frontend.o.d -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/includes -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam -isystem /usr/local/lib/python3.10/dist-packages/torch/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.10/dist-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /usr/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++17 -O3 -std=c++17 -g -Wno-reorder -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -DBF16_AVAILABLE -c /usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam/fused_adam_frontend.cpp -o fused_adam_frontend.o \n", + "[3/3] c++ fused_adam_frontend.o multi_tensor_adam.cuda.o -shared -L/usr/local/lib/python3.10/dist-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/usr/local/cuda/lib64 -lcudart -o fused_adam.so\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.588465929031372 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.62997841835022 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.63469910621643 seconds\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.634520530700684 seconds\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.636313438415527 seconds\n", + "Time to load fused_adam op: 20.636080741882324 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 20.635762214660645 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 20.635676383972168 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 100%|█| 312/312 [2:03:37<00:00, 0.04it/s, v_num=x1bx, train/loss=2.090\n", + "Validation: | | 0/? [00:00\n", + " cli_main()\n", + " File \"/workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/lightning_trainer.py\", line 271, in cli_main\n", + " LightningCLI(\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py\", line 386, in __init__\n", + " self._run_subcommand(self.subcommand)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py\", line 677, in _run_subcommand\n", + " fn(**fn_kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 544, in fit\n", + " call._call_and_handle_interrupt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py\", line 43, in _call_and_handle_interrupt\n", + " return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/strategies/launchers/subprocess_script.py\", line 102, in launch\n", + " return function(*args, **kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 580, in _fit_impl\n", + " self._run(model, ckpt_path=ckpt_path)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 989, in _run\n", + " results = self._run_stage()\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 1035, in _run_stage\n", + " self.fit_loop.run()\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py\", line 203, in run\n", + " self.on_advance_end()\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py\", line 374, in on_advance_end\n", + " call._call_callback_hooks(trainer, \"on_train_epoch_end\", monitoring_callbacks=True)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py\", line 208, in _call_callback_hooks\n", + " fn(trainer, trainer.lightning_module, *args, **kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/callbacks/model_checkpoint.py\", line 314, in on_train_epoch_end\n", + " self._save_last_checkpoint(trainer, monitor_candidates)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/callbacks/model_checkpoint.py\", line 681, in _save_last_checkpoint\n", + " self._save_checkpoint(trainer, filepath)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/callbacks/model_checkpoint.py\", line 375, in _save_checkpoint\n", + " trainer.save_checkpoint(filepath, self.save_weights_only)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 1383, in save_checkpoint\n", + " self.strategy.save_checkpoint(checkpoint, filepath, storage_options=storage_options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/strategies/deepspeed.py\", line 769, in save_checkpoint\n", + " self.deepspeed_engine.save_checkpoint(filepath, client_state=checkpoint, tag=\"checkpoint\")\n", + " File \"/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py\", line 3048, in save_checkpoint\n", + " self.checkpoint_engine.makedirs(save_dir, exist_ok=True)\n", + " File \"/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/checkpoint_engine/checkpoint_engine.py\", line 20, in makedirs\n", + " os.makedirs(path, exist_ok=exist_ok)\n", + " File \"/usr/lib/python3.10/os.py\", line 215, in makedirs\n", + " makedirs(head, exist_ok=exist_ok)\n", + " File \"/usr/lib/python3.10/os.py\", line 225, in makedirs\n", + " mkdir(name, mode)\n", + "OSError: [Errno 28] No space left on device: '/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-no-cuda'\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: / 0.041 MB of 0.041 MB uploaded (0.004 MB deduped)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 ▁▆▇▇▇▇▇████████████████████████████████▆\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen ███████████████████████████████████████▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss ▆▆█▆▇▁▅▂▅▂▃▃▄▃▆▁▄▄▃▂▆▃▂▃▂▂▃▃▄▂▃▁▅▅▃▆▂▆▆▂\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss ▆▆█▆▇▁▅▂▅▂▃▃▄▃▆▁▄▄▃▂▆▃▂▃▂▂▃▃▄▂▃▁▅▅▃▆▂▆▆▂\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens ███████████████████████████████████████▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇████\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen ▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss ▅▄▁█\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss ▅▄▁█\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens ███▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx 3\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 5.50155\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 40796.16\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep 2488\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen 4096.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss 2.09375\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss 2.09375\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens 4095.75\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 19\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate 3e-05\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen 16383\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss 2.23438\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss 2.23438\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens 32766\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss 2.15084\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mC2-7B2T - enwiki-16k-3e-5-no-cuda (deepspeed_stage_2)\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/h9pzx1bx\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzNjg5NDk3OA==/version_details/v5\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240204_074044-h9pzx1bx/logs\u001b[0m\n", + "Exception in thread NetStatThr:\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n", + "Exception in thread ChkStopThr:\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n", + " self.run()self.run()\n", + "\n", + " File \"/usr/lib/python3.10/threading.py\", line 953, in run\n", + " File \"/usr/lib/python3.10/threading.py\", line 953, in run\n", + " self._target(*self._args, **self._kwargs)self._target(*self._args, **self._kwargs)\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_run.py\", line 286, in check_stop_status\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_run.py\", line 268, in check_network_status\n", + " self._loop_check_status(self._loop_check_status(\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_run.py\", line 224, in _loop_check_status\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_run.py\", line 224, in _loop_check_status\n", + " local_handle = request()\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface.py\", line 792, in deliver_network_status\n", + " local_handle = request()\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface.py\", line 784, in deliver_stop_status\n", + " return self._deliver_network_status(status) \n", + "return self._deliver_stop_status(status) File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_shared.py\", line 500, in _deliver_network_status\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_shared.py\", line 484, in _deliver_stop_status\n", + " return self._deliver_record(record) \n", + "return self._deliver_record(record) File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_shared.py\", line 449, in _deliver_record\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_shared.py\", line 449, in _deliver_record\n", + " handle = mailbox._deliver_record(record, interface=self)handle = mailbox._deliver_record(record, interface=self)\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/mailbox.py\", line 455, in _deliver_record\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/mailbox.py\", line 455, in _deliver_record\n", + " interface._publish(record)interface._publish(record)\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_sock.py\", line 51, in _publish\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/interface/interface_sock.py\", line 51, in _publish\n", + " self._sock_client.send_record_publish(record)self._sock_client.send_record_publish(record)\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 221, in send_record_publish\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 221, in send_record_publish\n", + " self.send_server_request(server_req) \n", + "self.send_server_request(server_req) File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 155, in send_server_request\n", + "\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 155, in send_server_request\n", + " self._send_message(msg)\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 152, in _send_message\n", + " self._send_message(msg)\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 152, in _send_message\n", + "self._sendall_with_error_handle(header + data)\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 130, in _sendall_with_error_handle\n", + "self._sendall_with_error_handle(header + data)\n", + " File \"/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/sock_client.py\", line 130, in _sendall_with_error_handle\n", + " sent = self._sock.send(data) \n", + "sent = self._sock.send(data)BrokenPipeError\n", + ": BrokenPipeError[Errno 32] Broken pipe: \n", + "[Errno 32] Broken pipe\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=1 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-3e-5.ipynb b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-3e-5.ipynb new file mode 100644 index 00000000..609343fb --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-3e-5.ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"enwiki-16k-3e-5-with-cuda\"\n", + "LEARNING_RATE=\"3e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Map (num_proc=160): 100%|███| 1000000/1000000 [00:21<00:00, 45967.85 examples/s]\n", + "Filter (num_proc=160): 100%|█| 1000000/1000000 [00:05<00:00, 170314.36 examples/\n", + "Map (num_proc=160): 100%|█████| 134693/134693 [00:06<00:00, 20857.01 examples/s]\n", + "Map (num_proc=160): 100%|████████| 19916/19916 [00:05<00:00, 3811.26 examples/s]\n", + "Map (num_proc=160): 100%|████████| 19916/19916 [00:03<00:00, 5217.89 examples/s]\n", + "Map (num_proc=160): 100%|█████████████| 202/202 [00:00<00:00, 377.15 examples/s]\n", + "Saving the dataset (4/4 shards): 100%|█| 19916/19916 [00:06<00:00, 3008.07 examp\n", + "Saving the dataset (1/1 shards): 100%|█| 202/202 [00:00<00:00, 2200.58 examples/\n" + ] + } + ], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enwiki 16 - 3e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 04:35:14,774] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 1776216156\n", + "Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 04:36:01,442] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,503] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,528] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,544] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,571] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,574] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,577] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 1776216156\n", + "[rank: 5] Seed set to 1776216156\n", + "[rank: 1] Seed set to 1776216156\n", + "[rank: 4] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 7] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 4] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 6] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 7] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 2] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 1] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 5] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_043659-qhlfdr18\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/qhlfdr18\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 3.000e-05 (3e-05)\n", + " - lr_final: 3.000e-05 (3e-05)\n", + "\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.050296783447265625 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10118675231933594 seconds\n", + "Time to load fused_adam op: 0.10118412971496582 seconds\n", + "Time to load fused_adam op: 0.10109424591064453 secondsTime to load fused_adam op: 0.10121369361877441 seconds\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.1016843318939209 seconds\n", + "Time to load fused_adam op: 0.10199475288391113 seconds\n", + "Time to load fused_adam op: 0.10179924964904785 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 100%|█| 312/312 [1:34:30<00:00, 0.06it/s, v_num=dr18, train/loss=2.170\n", + "Validation: | | 0/? [00:00=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 5.000e-05 (5e-05)\n", + " - lr_final: 5.000e-05 (5e-05)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Creating extension directory /root/.cache/torch_extensions/py310_cu121/fused_adam...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "[1/3] /usr/local/cuda/bin/nvcc -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/includes -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam -isystem /usr/local/lib/python3.10/dist-packages/torch/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.10/dist-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /usr/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -lineinfo --use_fast_math -gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_90,code=compute_90 -DBF16_AVAILABLE -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -std=c++17 -c /usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam/multi_tensor_adam.cu -o multi_tensor_adam.cuda.o \n", + "[2/3] c++ -MMD -MF fused_adam_frontend.o.d -DTORCH_EXTENSION_NAME=fused_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/includes -I/usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam -isystem /usr/local/lib/python3.10/dist-packages/torch/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.10/dist-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /usr/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++17 -O3 -std=c++17 -g -Wno-reorder -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DVERSION_GE_1_5 -DBF16_AVAILABLE -c /usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/adam/fused_adam_frontend.cpp -o fused_adam_frontend.o \n", + "[3/3] c++ fused_adam_frontend.o multi_tensor_adam.cuda.o -shared -L/usr/local/lib/python3.10/dist-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/usr/local/cuda/lib64 -lcudart -o fused_adam.so\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 20.958332300186157 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 21.03412938117981 seconds\n", + "Loading extension module fused_adam...\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 21.034971714019775 secondsLoading extension module fused_adam...\n", + "\n", + "Time to load fused_adam op: 21.035020112991333 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 21.035765171051025 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 21.036338806152344 seconds\n", + "Loading extension module fused_adam...Time to load fused_adam op: 21.035861253738403 seconds\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Time to load fused_adam op: 21.03908371925354 seconds\n", + "/usr/local/lib/python3.10/dist-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 100%|█| 312/312 [2:03:44<00:00, 0.04it/s, v_num=oche, train/loss=2.090\n", + "Validation: | | 0/? [00:00\n", + " cli_main()\n", + " File \"/workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/lightning_trainer.py\", line 271, in cli_main\n", + " LightningCLI(\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py\", line 386, in __init__\n", + " self._run_subcommand(self.subcommand)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py\", line 677, in _run_subcommand\n", + " fn(**fn_kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 544, in fit\n", + " call._call_and_handle_interrupt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py\", line 43, in _call_and_handle_interrupt\n", + " return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/strategies/launchers/subprocess_script.py\", line 102, in launch\n", + " return function(*args, **kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 580, in _fit_impl\n", + " self._run(model, ckpt_path=ckpt_path)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 989, in _run\n", + " results = self._run_stage()\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 1035, in _run_stage\n", + " self.fit_loop.run()\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py\", line 203, in run\n", + " self.on_advance_end()\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py\", line 374, in on_advance_end\n", + " call._call_callback_hooks(trainer, \"on_train_epoch_end\", monitoring_callbacks=True)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py\", line 208, in _call_callback_hooks\n", + " fn(trainer, trainer.lightning_module, *args, **kwargs)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/callbacks/model_checkpoint.py\", line 314, in on_train_epoch_end\n", + " self._save_last_checkpoint(trainer, monitor_candidates)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/callbacks/model_checkpoint.py\", line 681, in _save_last_checkpoint\n", + " self._save_checkpoint(trainer, filepath)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/callbacks/model_checkpoint.py\", line 375, in _save_checkpoint\n", + " trainer.save_checkpoint(filepath, self.save_weights_only)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/trainer.py\", line 1383, in save_checkpoint\n", + " self.strategy.save_checkpoint(checkpoint, filepath, storage_options=storage_options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/lightning/pytorch/strategies/deepspeed.py\", line 769, in save_checkpoint\n", + " self.deepspeed_engine.save_checkpoint(filepath, client_state=checkpoint, tag=\"checkpoint\")\n", + " File \"/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/engine.py\", line 3048, in save_checkpoint\n", + " self.checkpoint_engine.makedirs(save_dir, exist_ok=True)\n", + " File \"/usr/local/lib/python3.10/dist-packages/deepspeed/runtime/checkpoint_engine/checkpoint_engine.py\", line 20, in makedirs\n", + " os.makedirs(path, exist_ok=exist_ok)\n", + " File \"/usr/lib/python3.10/os.py\", line 215, in makedirs\n", + " makedirs(head, exist_ok=exist_ok)\n", + " File \"/usr/lib/python3.10/os.py\", line 225, in makedirs\n", + " mkdir(name, mode)\n", + "OSError: [Errno 28] No space left on device: '/checkpoint/calibration/Eagle-2T/enwiki-16k-5e-5-no-cuda'\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: / 0.030 MB of 0.030 MB uploaded\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 ▁▅▆▇▇▇▇▇▇██████████████████████████████▆\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen ███████████████████████████████████████▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss ▆▆█▄▆▁▄▃▅▃▅▃▅▃▅▁▄▅▄▃▆▅▃▄▃▃▄▄▄▃▄▂▅▅▄▆▃▆▆▃\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss ▆▆█▄▆▁▄▃▅▃▅▃▅▃▅▁▄▅▄▃▆▅▃▄▃▃▄▄▄▃▄▂▅▅▄▆▃▆▆▃\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens ███████████████████████████████████████▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁▁▁▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇████\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen ▁▁▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss ▅▄▁█\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss ▅▄▁█\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens ███▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx 3\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_per_sec.gpu.0 5.49616\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: perf/kTokens_total.gpu.0 40796.16\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: substep 2488\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_ctxlen 4096.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/data_loss 2.09375\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_loss 2.09375\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train/learn_tokens 4095.75\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 19\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/learning_rate 5e-05\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen 16383\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss 2.23438\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss 2.23438\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens 32766\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss 2.15249\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mC2-7B2T - enwiki-16k-5e-5-no-cuda (deepspeed_stage_2)\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/aqm4oche\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzNjg5NDk3OA==/version_details/v5\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240204_074114-aqm4oche/logs\u001b[0m\n" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=1 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-5e-5.ipynb b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-5e-5.ipynb new file mode 100644 index 00000000..0aa53373 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-5e-5.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"enwiki-16k-5e-5-no-cuda\"\n", + "LEARNING_RATE=\"5e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "^C\n", + "Traceback (most recent call last):\n", + " File \"/workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/preload_datapath.py\", line 5, in \n", + " from src.data import RWKVDataModule\n", + " File \"/workspace/picocreator/RWKV-infctx-trainer/RWKV-v5/src/data.py\", line 1, in \n", + " from lightning import LightningDataModule\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/__init__.py\", line 18, in \n", + " from lightning.fabric.fabric import Fabric # noqa: E402\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/__init__.py\", line 29, in \n", + " from lightning.fabric.fabric import Fabric # noqa: E402\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/fabric.py\", line 35, in \n", + " import torch\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/__init__.py\", line 1504, in \n", + " from . import masked\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/masked/__init__.py\", line 3, in \n", + " from ._ops import (\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/masked/_ops.py\", line 11, in \n", + " from torch._prims_common import corresponding_real_dtype\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_prims_common/__init__.py\", line 23, in \n", + " import sympy\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/__init__.py\", line 74, in \n", + " from .polys import (Poly, PurePoly, poly_from_expr, parallel_poly_from_expr,\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/polys/__init__.py\", line 123, in \n", + " from .partfrac import apart, apart_list, assemble_partfrac_list\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/polys/partfrac.py\", line 13, in \n", + " @xthreaded\n", + " ^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/utilities/decorator.py\", line 76, in xthreaded\n", + " return threaded_factory(func, False)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/utilities/decorator.py\", line 13, in threaded_factory\n", + " from sympy.matrices import MatrixBase\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/matrices/__init__.py\", line 21, in \n", + " from .immutable import ImmutableDenseMatrix, ImmutableSparseMatrix\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/matrices/immutable.py\", line 8, in \n", + " from sympy.matrices.expressions import MatrixExpr\n", + " File \"/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/sympy/matrices/expressions/__init__.py\", line 16, in \n", + " from .hadamard import hadamard_product, HadamardProduct, hadamard_power, HadamardPower\n", + " File \"\", line 1176, in _find_and_load\n", + " File \"\", line 1147, in _find_and_load_unlocked\n", + " File \"\", line 666, in _load_unlocked\n", + "KeyboardInterrupt\n" + ] + } + ], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enwiki 16 - 3e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 04:35:14,774] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 1776216156\n", + "Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 04:36:01,442] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,503] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,528] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,544] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,571] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,574] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,577] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 1776216156\n", + "[rank: 5] Seed set to 1776216156\n", + "[rank: 1] Seed set to 1776216156\n", + "[rank: 4] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 7] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 4] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 6] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 7] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 2] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 1] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 5] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_043659-qhlfdr18\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/qhlfdr18\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 3.000e-05 (3e-05)\n", + " - lr_final: 3.000e-05 (3e-05)\n", + "\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.050296783447265625 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10118675231933594 seconds\n", + "Time to load fused_adam op: 0.10118412971496582 seconds\n", + "Time to load fused_adam op: 0.10109424591064453 secondsTime to load fused_adam op: 0.10121369361877441 seconds\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.1016843318939209 seconds\n", + "Time to load fused_adam op: 0.10199475288391113 seconds\n", + "Time to load fused_adam op: 0.10179924964904785 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 19%|▏| 58/312 [17:38<1:17:15, 0.05it/s, v_num=dr18, train/loss=2.310]" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=1 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-8e-6.ipynb b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-8e-6.ipynb new file mode 100644 index 00000000..a6b9f159 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-8e-6.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eagle 1T - 2nd stage calibration \n", + "\n", + "Various smaller runs to figure out the optimal settings for the model calibration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"C2-7B2T\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"enwiki-16k-8e-6-with-cuda\"\n", + "LEARNING_RATE=\"8e-6\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Map (num_proc=160): 100%|███| 1000000/1000000 [00:21<00:00, 45967.85 examples/s]\n", + "Filter (num_proc=160): 100%|█| 1000000/1000000 [00:05<00:00, 170314.36 examples/\n", + "Map (num_proc=160): 100%|█████| 134693/134693 [00:06<00:00, 20857.01 examples/s]\n", + "Map (num_proc=160): 100%|████████| 19916/19916 [00:05<00:00, 3811.26 examples/s]\n", + "Map (num_proc=160): 100%|████████| 19916/19916 [00:03<00:00, 5217.89 examples/s]\n", + "Map (num_proc=160): 100%|█████████████| 202/202 [00:00<00:00, 377.15 examples/s]\n", + "Saving the dataset (4/4 shards): 100%|█| 19916/19916 [00:06<00:00, 3008.07 examp\n", + "Saving the dataset (1/1 shards): 100%|█| 202/202 [00:00<00:00, 2200.58 examples/\n" + ] + } + ], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enwiki 16 - 8e-5 with WITH CUDA" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 04:35:14,774] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['fit', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=3e-5', '--model.lr_final=3e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/enwiki-16k-3e-5-with-cuda/', '--trainer.logger.init_args.name=C2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 1776216156\n", + "Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 04:36:01,442] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,503] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,528] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,544] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,571] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,574] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 04:36:01,577] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "[rank: 2] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 3] Seed set to 1776216156\n", + "[rank: 5] Seed set to 1776216156\n", + "[rank: 1] Seed set to 1776216156\n", + "[rank: 4] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 6] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 7] Seed set to 1776216156\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 4] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 6] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 7] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 2] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 1] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 5] Seed set to 1776216156\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_043659-qhlfdr18\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mC2-7B2T - enwiki-16k-3e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/qhlfdr18\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "#\n", + "# RWKV lighting_trainer.py important notes \n", + "# https://github.com/RWKV/RWKV-infctx-trainer \n", + "#\n", + "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", + "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", + "# - When resuming from checkpoint, the estimated time is inaccurate\n", + "#\n", + "\n", + "[RWKV.model] Configuring optimizer with\n", + " - lr_init: 3.000e-05 (3e-05)\n", + " - lr_final: 3.000e-05 (3e-05)\n", + "\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Using /root/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", + "Building extension module fused_adam...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.050296783447265625 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.10118675231933594 seconds\n", + "Time to load fused_adam op: 0.10118412971496582 seconds\n", + "Time to load fused_adam op: 0.10109424591064453 secondsTime to load fused_adam op: 0.10121369361877441 seconds\n", + "\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Loading extension module fused_adam...\n", + "Time to load fused_adam op: 0.1016843318939209 seconds\n", + "Time to load fused_adam op: 0.10199475288391113 seconds\n", + "Time to load fused_adam op: 0.10179924964904785 seconds\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "/root/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", + " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", + "\n", + " | Name | Type | Params\n", + "--------------------------------------\n", + "0 | emb | Embedding | 268 M \n", + "1 | blocks | ModuleList | 7.0 B \n", + "2 | ln_out | LayerNorm | 8.2 K \n", + "3 | head | Linear | 268 M \n", + "--------------------------------------\n", + "7.5 B Trainable params\n", + "0 Non-trainable params\n", + "7.5 B Total params\n", + "30,072.177Total estimated model params size (MB)\n", + "Epoch 0: 19%|▏| 58/312 [17:38<1:17:15, 0.05it/s, v_num=dr18, train/loss=2.310]" + ] + } + ], + "source": [ + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py fit \\\n", + " -c \"{NOTEBOOK_DIR}/enwiki-16k-rechunk.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rwkv-infctx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml new file mode 100644 index 00000000..90410b06 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/enwiki-16k-rechunk.yaml @@ -0,0 +1,190 @@ +############################################### +## +## See the full `config-example.yaml` for more +## detailes on the trainer/model configs +## +############################################### + +trainer: + # Multi node training settings + num_nodes: 1 + microbatch_size: 8 + strategy: deepspeed_stage_2 + + # Limit to 1 epoch + # max_epochs: 1 + max_steps: 26 + + # Resonable batch size, for a more realistic it/s rate + # this is currently overwritten in the notebook + target_batch_size: 1024 + + # Logger setting for wandb, if you want to enable wandb, uncomment the whole logger section + # --- + logger: + class_path: lightning.pytorch.loggers.WandbLogger + init_args: + name: 'Eagle-2T-P1' + project: 'RWKV-V5-Eagle-2T-exp' + tags: ['Eagle', 'RWKV-V5'] + + # Checkpoint settings for the training process + callbacks: + class_path: lightning.pytorch.callbacks.ModelCheckpoint + init_args: + # Configure this to the path you want to save your checkpoints to + # note that a subdir will be created with the name `epoch=x-step=y.ckpt` + # + # to convert a checkpoint to a model, you can use the + # `python3 export_checkpoint.py ` script, + # which will create a `rwkv_model.pth` in the checkpoint directory. + # + # Do not use the `zero_to_fp32.py` script as that will have export format issues + dirpath: /checkpoint/calibration/Eagle-2T-p1/ + filename: null + + # Save the top/last K checkpoints + save_top_k: 10 + # Choose the most recent checkpoints by steps + monitor: 'step' + mode: max + + # If enabled (true), save a copy of the latest checkpoint to 'last.ckpt' + # useful to simply checkpoint resume scripts, at a price of disk performance + save_last: true + + # DO NOT set this as true, as the model weight exported will have format issues + # expert as checkpoint, and use the `export_checkpoint.py` script to convert to model instead + save_weights_only: false + + # How frequent you want to save a checkpoint for every step. + # This will happen for every X data sample, where X = every_n_train_steps * accumulate_grad_batches + # + # In general you will want to avoid putting a low number (expecially if accumulate_grad_batches <= 100) + # as the checkpoint process, will pause all the gpu training for some time, slowing down the overall process + # However you do not want to configure too high of a number, where you will lose too much progress if the training crashes + every_n_train_steps: 25 + every_n_epochs: null + save_on_train_epoch_end: true + train_time_interval: null + + # Other pytorch lightning settings, which in most cases you can remove/ignore + # --- + # verbose: false + # auto_insert_metric_name: true + +model: + # The model to load + load_model: /workspace/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth + + # Starting and ending learning rate + lr_init: 3e-5 + lr_final: 3e-5 + + # Training context length, note that the dataset can be + # larger then the context size, in which the trainer + # will process the dataset in chunks + ctx_len: 4096 + + # BPTT learning, this allows you to run the trainer against dataset + # larger then its training context length + bptt_learning: true + bptt_learning_range: -1 + +######################################## +## Training model settings +######################################## +data: + # Skip the datapath setup + # + # ignored if using the preload_datapath.py, useful for speeding up the trainer startup + # provided you have your datasets all properly preinitialized + # --- + skip_datapath_setup: True + + # dataset_path for the prebuilt dataset, using HF `load_from_disk()` + # + # Use this if you have built your own dataset and saved it with `save_to_disk()` + # with source left as null. Other wise configure this to a directory which the + # dataset will be built and tokenized by the huggingface dataset process. + data_path: /datapath/enwiki-16k-rechunk/ + + # Other wise provide the source path, which is used as huggingface dataset path + # this will be used to populate the dataset_path + # + # Use either the following + # - hugging face dataset + # - Directory path to a directory containing dataset files + # - Path to a single dataset file + # - hugging face dataset mode (ie: text,csv,etc - use data_dir, to configure the path then) + # - null + # + # If source is disabled, all other params, except data_path, is ignored + source: "teven/enwiki_100k" + # source: text + # source: /home/ubuntu/RWKV-LM-LoRA/dataset-text/enwik8.txt + + # Use data_dir, if you are using source=text/json/etc + # this should be relative to the trainer script path + source_data_dir: null + + # After loading the dataset, split out test data used for unit-test, + # This process is skipped if the dataset includes a test split + # This process is skipped if set to zero + test_split: 0.01 + test_split_shuffle: false + + # Tokenizer to use, use either the inbuilt 'neox', or 'world' tokenizer + # If using a custom tokenizer, provide the tokenizer file path + # --- + tokenizer: world + + # Minimum / Maximum token size of the dataset to use + # useful for filtering out small noisy data samples from large datasets + # (eg. removal of small articles of less then 512 tokens from wikipedia) + # + # This is ignored, if set to -1 + min_token_size: 1024 + max_token_size: -1 + + # Rechunking of text dataset, this is done only when source is set as 'text' + # and will merge the various sentencees, into larger chunks up to the target size + # + # Defaults to 4096 + # + # This is ignored, if source is not set as text + # This is ignored, if set to zero + # --- + text_rechunk_size: 16384 + + # Apply text rechunk to the dataset, even if its not a 'text' source + # This is done only after dataset filtering, and if source is not 'text' + # --- + text_rechunk_force: true + + # Custom text column to use, useful for dataset with alternative training columns labels + # This is checked before multi column merging, default is null (disabled) + # eg: 'code' + # --- + # custom_text_key: 'code' + + # Multi Column merging process, default setting is used to support and merge + # "instruction", "input", "output", datasets. To disable set multi_column_keys to [] + # + # A minimum of 2 columns is required, with non empty data, for the merge to occur + # If no match is found, this will fallback to the default prompt/completion or text column, + # or throw an error if the default fallback is not found + # --- + # multi_column_keys: ['instruction', 'input', 'output'] + # multi_column_prefix: ['Instruction:\n', 'Input:\n', 'Output:\n'] + # multi_column_train_mask: [true, false, true] + # multi_column_separator: '\n\n' + + # If processing prompt/completion jsonl pairs, the prompt is masked by default + # use this flag to disable this default behaviour + # --- + # disable_prompt_completion_mask: false + +# Path to the current checkpoint to continue training from +# this should be the directory path, and ends with `.ckpt/` +# ckpt_path: /checkpoint/Eagle-2T-p1/last.ckpt diff --git a/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml b/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml new file mode 100644 index 00000000..b837bd65 --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml @@ -0,0 +1,114 @@ +############################################### +## +## See the full `config-example.yaml` for more +## detailes on the trainer/model configs +## +############################################### + +trainer: + # Multi node training settings + num_nodes: 1 + microbatch_size: 8 + strategy: deepspeed_stage_2 + + # Limit to 1 epoch + # max_epochs: 1 + max_steps: 26 + + # Resonable batch size, for a more realistic it/s rate + # this is currently overwritten in the notebook + target_batch_size: 1024 + + # Logger setting for wandb, if you want to enable wandb, uncomment the whole logger section + # --- + logger: + class_path: lightning.pytorch.loggers.WandbLogger + init_args: + name: 'Eagle-2T-P1' + project: 'RWKV-V5-Eagle-2T-exp' + tags: ['Eagle', 'RWKV-V5'] + + # Checkpoint settings for the training process + callbacks: + class_path: lightning.pytorch.callbacks.ModelCheckpoint + init_args: + # Configure this to the path you want to save your checkpoints to + # note that a subdir will be created with the name `epoch=x-step=y.ckpt` + # + # to convert a checkpoint to a model, you can use the + # `python3 export_checkpoint.py ` script, + # which will create a `rwkv_model.pth` in the checkpoint directory. + # + # Do not use the `zero_to_fp32.py` script as that will have export format issues + dirpath: /checkpoint/calibration/Eagle-2T-p1/ + filename: null + + # Save the top/last K checkpoints + save_top_k: 10 + # Choose the most recent checkpoints by steps + monitor: 'step' + mode: max + + # If enabled (true), save a copy of the latest checkpoint to 'last.ckpt' + # useful to simply checkpoint resume scripts, at a price of disk performance + save_last: true + + # DO NOT set this as true, as the model weight exported will have format issues + # expert as checkpoint, and use the `export_checkpoint.py` script to convert to model instead + save_weights_only: false + + # How frequent you want to save a checkpoint for every step. + # This will happen for every X data sample, where X = every_n_train_steps * accumulate_grad_batches + # + # In general you will want to avoid putting a low number (expecially if accumulate_grad_batches <= 100) + # as the checkpoint process, will pause all the gpu training for some time, slowing down the overall process + # However you do not want to configure too high of a number, where you will lose too much progress if the training crashes + every_n_train_steps: 25 + every_n_epochs: null + save_on_train_epoch_end: true + train_time_interval: null + + # Other pytorch lightning settings, which in most cases you can remove/ignore + # --- + # verbose: false + # auto_insert_metric_name: true + +model: + # The model to load + load_model: /workspace/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth + + # Starting and ending learning rate + lr_init: 3e-5 + lr_final: 3e-5 + + # Training context length, note that the dataset can be + # larger then the context size, in which the trainer + # will process the dataset in chunks + ctx_len: 4096 + + # BPTT learning, this allows you to run the trainer against dataset + # larger then its training context length + bptt_learning: true + bptt_learning_range: -1 + +######################################## +## Training model settings +######################################## +data: + # Skip the datapath setup + # + # ignored if using the preload_datapath.py, useful for speeding up the trainer startup + # provided you have your datasets all properly preinitialized + # --- + skip_datapath_setup: True + + # dataset_path for the prebuilt dataset, using HF `load_from_disk()` + # + # Use this if you have built your own dataset and saved it with `save_to_disk()` + # with source left as null. Other wise configure this to a directory which the + # dataset will be built and tokenized by the huggingface dataset process. + data_path: /datapath/world-v2.5-0.01-hfdataset-pt0/ + +# Path to the current checkpoint to continue training from +# this should be the directory path, and ends with `.ckpt/` +# ckpt_path: /checkpoint/Eagle-2T-p1/last.ckpt diff --git a/notebook/major-runs/Eagle-2T-calibration/unpack-datapack.ipynb b/notebook/major-runs/Eagle-2T-calibration/unpack-datapack.ipynb new file mode 100644 index 00000000..c91ce21d --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/unpack-datapack.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unpacking to inspect the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading the dataset... /datapath/world-v2.5-0.01-hfdataset-pt0/\n", + "Dataset 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16384\n", + "Document 6:\n", + "- Keys: dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'dataset_name', 'dataset_index'])\n", + "- input_ids length: 16384\n", + "- input_ids sample: [2430, 2464, 2434, 2464, 195, 131, 2465, 2430, 2464, 2434, 2464, 2429, 2465, 2430, 2464, 2435, 2464, 2423, 2465, 2430, 2464, 2434, 2464, 2434, 4940, 2430, 2464, 2434, 2464, 2444, 2465, 2430, 2464, 2435, 2464, 195, 136, 2465, 2430, 2464, 2434, 2464, 195, 131, 4940, 2430, 2464, 2434, 2464, 2454, 2465, 2430, 2464, 2435, 2464, 195, 141, 2465, 2430, 2464, 2434, 2464, 195, 131, 2465, 2430, 2464, 2434, 2464, 2436, 2465, 2430, 2464, 2434, 2464, 2446, 2465, 2430, 2464, 2435, 2464, 195, 142, 2465, 2430, 2464, 2434, 2464, 2445, 4940, 2430, 2464, 2434, 2464, 2434, 2465, 2430, 2464, 2434, 2464, 2442, 4940, 2430, 2464, 2434, 2464, 2438, 2465, 2430, 2464, 2434, 2464, 2461, 2465, 2430, 2464, 2434, 2464, 195, 150, 2465, 2430, 2464, 2434, 2464, 2448, 2465, 2430, 2464, 2434, 2464, 2434, 2465, 2430, 2464, 2434, 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2464, 2456, 2465, 195, 145, 2464, 2460]\n", + "- attention_mask length: 16384\n", + "- token_type_ids length: 16384\n", + "Document 8:\n", + "- Keys: dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'dataset_name', 'dataset_index'])\n", + "- input_ids length: 16384\n", + "- input_ids sample: [47, 20048, 40212, 30135, 51987, 52869, 52280, 57073, 37810, 4712, 37885, 31650, 1843, 4450, 51713, 45242, 50700, 20267, 25566, 47, 261, 1136, 55452, 4811, 60338, 45, 50361, 4600, 45096, 4596, 22590, 52360, 20872, 1740, 50662, 39082, 21265, 55844, 33948, 53366, 45909, 45, 59708, 39082, 45, 57024, 30917, 22590, 4136, 6985, 116, 21265, 47279, 46883, 47, 261, 1563, 7205, 58965, 116, 11, 65290, 261, 48558, 460, 38328, 40136, 62442, 32462, 32132, 58306, 63798, 31891, 47, 20996, 46486, 32462, 56466, 4450, 332, 51507, 38640, 4706, 50982, 45, 50961, 21265, 62061, 51739, 59841, 302, 47, 318, 47, 36936, 45, 50064, 55412, 45, 44337, 20376, 40429, 45, 3677, 24485, 44690, 45, 50251, 29743, 7424, 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2465, 195, 145, 2464, 2446, 2465, 195, 145, 2464, 2459, 2465, 2425, 2464, 195, 136, 2465, 195]\n", + "- attention_mask length: 16384\n", + "- token_type_ids length: 16384\n" + ] + } + ], + "source": [ + "# Lets load the datapath at {PROJECT_DIR}/datapath/world/Eagle-x-multipack/ via HF dataset\n", + "# and iterate the first 10 documents\n", + "import datasets\n", + "datapath = f\"/datapath/world-v2.5-0.01-hfdataset-pt0/\"\n", + "print(f\"Loading the dataset... {datapath}\")\n", + "\n", + "# Load the dataset\n", + "full_dataset = datasets.load_from_disk(datapath)\n", + "\n", + "# Train dataset\n", + "train_dataset = full_dataset[\"train\"]\n", + "print(f\"Dataset loaded, {len(train_dataset)} samples\")\n", + "\n", + "# Lets iterate the first 10 documents\n", + "iterate_limit = 10\n", + "for idx, doc in enumerate(train_dataset):\n", + " print(f\"Document {idx+1}:\")\n", + " # Print the keys\n", + " print(\"- Keys:\", doc.keys())\n", + " print(\"- input_ids length:\", len(doc[\"input_ids\"]))\n", + " print(\"- input_ids sample:\", doc[\"input_ids\"][512:])\n", + " print(\"- attention_mask length:\", len(doc[\"attention_mask\"]))\n", + " print(\"- token_type_ids length:\", len(doc[\"token_type_ids\"]))\n", + " # print(\"- sample_length:\", doc[\"sample_length\"])\n", + "\n", + " if idx >= iterate_limit:\n", + " break" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/major-runs/Eagle-2T-calibration/validation-runs.ipynb b/notebook/major-runs/Eagle-2T-calibration/validation-runs.ipynb new file mode 100644 index 00000000..8d58533e --- /dev/null +++ b/notebook/major-runs/Eagle-2T-calibration/validation-runs.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perform validation runs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"Eagle-Base Validation\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "EXPERIMENT_NAME=\"Baseline Validation\"\n", + "LEARNING_RATE=\"1e-5\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 09:23:43,108] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['validate', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/Baseline Validation/', '--trainer.logger.init_args.name=Eagle-Base Validation - Baseline Validation (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['validate', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--model.lr_init=1e-5', '--model.lr_final=1e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/Baseline Validation/', '--trainer.logger.init_args.name=Eagle-Base Validation - Baseline Validation (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/usr/local/lib/python3.10/dist-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 2258363414\n", + "Seed set to 2258363414\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 09:24:24,824] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:24:24,843] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:24:24,918] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:24:24,975] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:24:25,017] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:24:25,041] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:24:25,073] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[rank: 6] Seed set to 2258363414\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 7] Seed set to 2258363414\n", + "[rank: 4] Seed set to 2258363414\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 2] Seed set to 2258363414\n", + "[rank: 3] Seed set to 2258363414\n", + "[rank: 1] Seed set to 2258363414\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 2258363414\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 2] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 1] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 3] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 6] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "[rank: 5] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 7] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 4] Seed set to 2258363414\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_092532-xutxi031\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mEagle-Base Validation - Baseline Validation (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/xutxi031\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "Validation DataLoader 0: 100%|████████████████████| 1/1 [00:03<00:00, 0.29it/s]\n", + "────────────────────────────────────────────────────────────────────────────────\n", + " Validate metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────\n", + " validation/loss 1.130859375\n", + "────────────────────────────────────────────────────────────────────────────────\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: / 0.024 MB of 0.024 MB uploaded\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen 16383\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss 1.28906\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss 1.28906\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens 16383\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss 1.13086\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mEagle-Base Validation - Baseline Validation (deepspeed_stage_2)\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/xutxi031\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzNjg5NDk3OA==/version_details/v4\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240204_092532-xutxi031/logs\u001b[0m\n" + ] + } + ], + "source": [ + "# The 7B model\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "\n", + "# Perform the validation\n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py validate \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 09:12:09,440] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['validate', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-5-World-1B5-v2-20231025-ctx4096.pth', '--model.lr_init=8e-5', '--model.lr_final=8e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/8e-5-with-cuda/', '--trainer.logger.init_args.name=Eagle-Base Validation - 8e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['validate', '-c', '/workspace/picocreator/RWKV-infctx-trainer/notebook/major-runs/Eagle-2T-calibration/ref-3e-5.yaml', '--model.load_model=/workspace/main-models/RWKV-5-World-1B5-v2-20231025-ctx4096.pth', '--model.lr_init=8e-5', '--model.lr_final=8e-5', '--data.skip_datapath_setup=True', '--trainer.callbacks.init_args.dirpath=/checkpoint/calibration/Eagle-2T/8e-5-with-cuda/', '--trainer.logger.init_args.name=Eagle-Base Validation - 8e-5-with-cuda (deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.target_batch_size=1024', '--trainer.microbatch_size=8', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "/usr/local/lib/python3.10/dist-packages/lightning/fabric/utilities/seed.py:40: No seed found, seed set to 27755974\n", + "Seed set to 27755974\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 1024\n", + " - num_nodes: 1\n", + " - num_devices: 8\n", + " - microbatch_size: 8\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 1024\n", + "\n", + "[rank: 0] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", + "[2024-02-04 09:12:27,298] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:12:27,320] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:12:27,371] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:12:27,414] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:12:27,459] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:12:27,462] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 09:12:27,512] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.1+cu121'\n", + "[rank: 4] Seed set to 27755974\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 1] Seed set to 27755974\n", + "[rank: 6] Seed set to 27755974\n", + "[rank: 2] Seed set to 27755974\n", + "[rank: 7] Seed set to 27755974\n", + "[rank: 3] Seed set to 27755974\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "[rank: 5] Seed set to 27755974\n", + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/wkv5/build.ninja...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "[rank: 4] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", + "[rank: 3] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", + "[rank: 7] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", + "[rank: 5] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", + "[rank: 1] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", + "[rank: 2] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", + "[rank: 6] Seed set to 27755974\n", + "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_091303-v77aevsm\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mEagle-Base Validation - 8e-5-with-cuda (deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/v77aevsm\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", + "Validation DataLoader 0: 100%|████████████████████| 1/1 [00:02<00:00, 0.40it/s]\n", + "────────────────────────────────────────────────────────────────────────────────\n", + " Validate metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────\n", + " validation/loss 1.31640625\n", + "────────────────────────────────────────────────────────────────────────────────\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: / 0.024 MB of 0.024 MB uploaded\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step ▁▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: batchidx 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: epoch 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: global_rank 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: trainer/global_step 0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_ctxlen 16383\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/data_loss 1.44531\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_loss 1.44531\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/learn_tokens 16383\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: validation/loss 1.31641\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mEagle-Base Validation - 8e-5-with-cuda (deepspeed_stage_2)\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/runs/v77aevsm\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-V5-Eagle-2T-exp/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzNjg5NDk3OA==/version_details/v4\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240204_091303-v77aevsm/logs\u001b[0m\n" + ] + } + ], + "source": [ + "# The 1B5 model\n", + "MODEL_PATH=\"/workspace/main-models/RWKV-5-World-1B5-v2-20231025-ctx4096.pth\"\n", + "\n", + "# Perform the validation\n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " export RWKV_NO_CUDA=0 && \\\n", + " export WANDB_MODE=\"{WANDB_MODE}\" && \\\n", + " python3 lightning_trainer.py validate \\\n", + " -c \"{NOTEBOOK_DIR}/ref-3e-5.yaml\" \\\n", + " --model.load_model=\"{MODEL_PATH}\" \\\n", + " --model.lr_init={LEARNING_RATE} \\\n", + " --model.lr_final={LEARNING_RATE} \\\n", + " --data.skip_datapath_setup=True \\\n", + " --trainer.callbacks.init_args.dirpath=\"/checkpoint/calibration/Eagle-2T/{EXPERIMENT_NAME}/\" \\\n", + " --trainer.logger.init_args.name=\"{WANDB_PREFIX} - {EXPERIMENT_NAME} ({DEEPSPEED_STRAT})\" \\\n", + " --trainer.strategy=\"{DEEPSPEED_STRAT}\" \\\n", + " --trainer.target_batch_size=1024 \\\n", + " --trainer.microbatch_size=8 \\\n", + " --model.ctx_len=4096 \\\n", + " --trainer.devices=\"{GPU_DEVICES}\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/major-runs/README.md b/notebook/major-runs/README.md new file mode 100644 index 00000000..528d177b --- /dev/null +++ b/notebook/major-runs/README.md @@ -0,0 +1,2 @@ +# Reference training process for foundation / coding models + diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/README.md b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/README.md similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/README.md rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/README.md diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-enwiki-4k-64.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-enwiki-4k-64.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-enwiki-4k-64.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-enwiki-4k-64.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-enwiki-4k.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-enwiki-4k.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-enwiki-4k.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-enwiki-4k.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-enwiki-instruct.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-enwiki-instruct.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-enwiki-instruct.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-enwiki-instruct.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-mem-instruct.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-mem-instruct.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-mem-instruct.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-mem-instruct.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-mem-template.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-mem-template.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-mem-template.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-mem-template.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-slimpajama.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-slimpajama.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-slimpajama.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-slimpajama.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-starcoder.yaml b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-starcoder.yaml similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/config/config-starcoder.yaml rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/config/config-starcoder.yaml diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage1-enwiki-100k-64node.ipynb b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage1-enwiki-100k-64node.ipynb similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage1-enwiki-100k-64node.ipynb rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage1-enwiki-100k-64node.ipynb diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage1-enwiki-100k.ipynb b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage1-enwiki-100k.ipynb similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage1-enwiki-100k.ipynb rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage1-enwiki-100k.ipynb diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage2-memory-instruct.ipynb b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage2-memory-instruct.ipynb similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage2-memory-instruct.ipynb rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage2-memory-instruct.ipynb diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage3-memory-tune.ipynb b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage3-memory-tune.ipynb similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage3-memory-tune.ipynb rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage3-memory-tune.ipynb diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage4-slimpajama.ipynb b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage4-slimpajama.ipynb similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage4-slimpajama.ipynb rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage4-slimpajama.ipynb diff --git a/notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage5-starcoder.ipynb b/notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage5-starcoder.ipynb similarity index 100% rename from notebook/foundation-v5-models/3B-L48-D2048-coding-model/stage5-starcoder.ipynb rename to notebook/rwkv-x-exp/v5-3B-L48-D2048-coding-model/stage5-starcoder.ipynb diff --git a/notebook/trainer-v5-validation/7B-LR-loss-validation.ipynb b/notebook/trainer-v5-validation/7B-LR-loss-validation.ipynb new file mode 100644 index 00000000..e2a1226a --- /dev/null +++ b/notebook/trainer-v5-validation/7B-LR-loss-validation.ipynb @@ -0,0 +1,821 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Long Range Loss validation\n", + "\n", + "This is based on the theory, given how the model improve over longer range context length, the loss value approaches 1 over longer context lengths." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENABLE_WANDB: True\n", + "GPU_DEVICES: auto\n", + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/trainer-v5-validation\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" + ] + } + ], + "source": [ + "GPU_DEVICES=\"auto\"\n", + "ENABLE_WANDB=True\n", + "WANDB_PREFIX=\"infctx-v5-LR-validation-loss\"\n", + "DEEPSPEED_STRAT=\"deepspeed_stage_2\"\n", + "\n", + "print(\"ENABLE_WANDB:\", ENABLE_WANDB)\n", + "print(\"GPU_DEVICES:\", GPU_DEVICES)\n", + "\n", + "if ENABLE_WANDB:\n", + " WANDB_MODE=\"online\"\n", + "else:\n", + " WANDB_MODE=\"disabled\"\n", + "\n", + "# The model sizing\n", + "MODEL_NAME=\"RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth\"\n", + "MODEL_URL=\"https://huggingface.co/RWKV/v5-Eagle-7B/resolve/main/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth?download=true\"\n", + "\n", + "# Computing the notebook, and various paths\n", + "import os\n", + "NOTEBOOK_DIR=os.path.dirname(os.path.abspath(\"__file__\"))\n", + "PROJECT_DIR=os.path.abspath(os.path.join(NOTEBOOK_DIR, \"../../\"))\n", + "TRAINER_DIR=os.path.abspath(os.path.join(PROJECT_DIR, \"./RWKV-v5/\"))\n", + "\n", + "print(\"NOTEBOOK_DIR:\", NOTEBOOK_DIR)\n", + "print(\"TRAINER_DIR:\", TRAINER_DIR)\n", + "print(\"PROJECT_DIR:\", PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preload the model and the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File ‘RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth’ already there; not retrieving.\n" + ] + } + ], + "source": [ + "# Lets wget the model files\n", + "!mkdir -p \"{PROJECT_DIR}/model\"\n", + "!cd \"{PROJECT_DIR}/model\" && \\\n", + " wget -O \"{MODEL_NAME}\" -nc \"{MODEL_URL}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Map (num_proc=160): 100%|███| 1000000/1000000 [00:17<00:00, 57881.62 examples/s]\n", + "Filter (num_proc=160): 100%|█| 1000000/1000000 [00:06<00:00, 158000.06 examples/\n", + "Map (num_proc=160): 100%|█████| 134693/134693 [00:06<00:00, 19534.25 examples/s]\n", + "Map (num_proc=160): 100%|███████| 79904/79904 [00:04<00:00, 19456.14 examples/s]\n", + "Saving the dataset (4/4 shards): 100%|█| 79904/79904 [00:04<00:00, 19732.78 exam\n", + "Saving the dataset (1/1 shards): 100%|█| 808/808 [00:00<00:00, 10472.99 examples\n" + ] + } + ], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/config/enwiki_100k-world-4096.yaml\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Map (num_proc=160): 100%|███| 1000000/1000000 [00:17<00:00, 56798.77 examples/s]\n", + "Filter (num_proc=160): 100%|█| 1000000/1000000 [00:06<00:00, 166184.25 examples/\n", + "Map (num_proc=160): 100%|█████| 134693/134693 [00:06<00:00, 20507.12 examples/s]\n", + "Map (num_proc=160): 100%|████████| 19916/19916 [00:03<00:00, 5006.21 examples/s]\n", + "Saving the dataset (4/4 shards): 100%|█| 19916/19916 [00:03<00:00, 5061.16 examp\n", + "Saving the dataset (1/1 shards): 100%|█| 202/202 [00:00<00:00, 2671.59 examples/\n" + ] + } + ], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/config/enwiki_100k-world-16k-rechunk.yaml\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving the dataset (4/4 shards): 100%|█| 9921/9921 [00:04<00:00, 2306.06 example\n", + "Saving the dataset (1/1 shards): 100%|█| 101/101 [00:00<00:00, 1322.34 examples/\n" + ] + } + ], + "source": [ + "# Lets preload the requried dataset \n", + "!cd \"{TRAINER_DIR}\" && \\\n", + " python3 preload_datapath.py \"{NOTEBOOK_DIR}/config/enwiki_100k-world-32k-rechunk.yaml\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Validation loss for 4k / BS=1" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-02-04 12:42:57,250] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", + "/home/picocreator/anaconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['validate', '-c', '/home/picocreator/rwkv-proj/RWKV-infctx-trainer/notebook/trainer-v5-validation/config/enwiki_100k-world-4096.yaml', '--model.load_model=../model/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--data.skip_datapath_setup=True', '--trainer.logger.init_args.name=infctx-v5-LR-validation-loss - 7B - 4k loss validation (bs=1, deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.microbatch_size=1', '--model.ctx_len=4096', '--trainer.devices=auto'], args=['validate', '-c', '/home/picocreator/rwkv-proj/RWKV-infctx-trainer/notebook/trainer-v5-validation/config/enwiki_100k-world-4096.yaml', '--model.load_model=../model/RWKV-v5-Eagle-World-7B-v2-20240128-ctx4096.pth', '--data.skip_datapath_setup=True', '--trainer.logger.init_args.name=infctx-v5-LR-validation-loss - 7B - 4k loss validation (bs=1, deepspeed_stage_2)', '--trainer.strategy=deepspeed_stage_2', '--trainer.microbatch_size=1', '--model.ctx_len=4096', '--trainer.devices=auto'].\n", + "Seed set to 3941088705\n", + "/home/picocreator/anaconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n", + "---\n", + "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", + "Using /home/picocreator/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", + "Detected CUDA files, patching ldflags\n", + "Emitting ninja build file /home/picocreator/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", + "Building extension module wkv5...\n", + "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", + "ninja: no work to do.\n", + "Loading extension module wkv5...\n", + "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", + "---\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + "\n", + "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", + " - target_batch_size: 16\n", + " - num_nodes: 1\n", + " - num_devices: 1\n", + " - microbatch_size: 1\n", + " - accumulate_grad_batches: 16\n", + " - effective_batch_size: 16\n", + "\n", + "[rank: 0] Seed set to 3941088705\n", + "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/1\n", + "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpicocreator\u001b[0m (\u001b[33mrwkv-x-dev\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.2\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m./wandb/run-20240204_124337-88jugzz1\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33minfctx-v5-LR-validation-loss - 7B - 4k loss validation (bs=1, deepspeed_stage_2)\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-infctx-unit-test\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/rwkv-x-dev/RWKV-infctx-unit-test/runs/88jugzz1\u001b[0m\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "Validation DataLoader 0: 0%| | 0/808 [00:00 # endpoint_url: - # Batch size to use to alternate between datasets - # This should be a multiple of the GPU and node count - # - # Uses, `8 * (3 * 4 * 5 * 6 * 7) = 20160` for default, as it should align across - # a large number of batch size combinations. This helps reduce the amount of - # misaligned batches, and thus reduce the amount of wasted training time - batchsize: 2048 - # Mixing mode to use, this is used to alternate between datasets # # - batch : Meaning one dataset worth per batch, partial batches are discarded # - sample : Dataset is mixed on a per sample level - mixing_mode: "batch" + mixing_mode: "shuffle" - # # Mixing distribution to use - # # - weighted : Dataset batches/mixture is distrbuted randomly, but weighted by dataset size - # # - uniform : Dataset batches/mixture is distrbuted randomly, but with uniform probability - # distribution: "weighted" - - # Mixed batch percentage - # - # % of batches which will contain a mixture of records from multiple datasets - # instad of limiting each batch to a single dataset - # - # Use 0, to disable mixed batches, sampled mixing_mode is the equavalent of mixed batch 1.0 - mixed_batch_percentage: 0.2 # # Default settings used across all datasets in the datapack diff --git a/notebook/trainer-v5-validation/config/enwiki_100k-world-16k-rechunk.yaml b/notebook/trainer-v5-validation/config/enwiki_100k-world-16k-rechunk.yaml new file mode 100644 index 00000000..8153e2b1 --- /dev/null +++ b/notebook/trainer-v5-validation/config/enwiki_100k-world-16k-rechunk.yaml @@ -0,0 +1,265 @@ +# lightning.pytorch==2.0.2 +seed_everything: 3941088705 +trainer: + + # + # Configure the deepspeed strategy, we recommend you start with `deepspeed_stage_2_offload` + # and adjust from there according to your training needs. `deepspeed_stage_3_offload` is useful + # for training LoRA on large models on a single GPU. + # + # In general you would want to use the following: + # + # - deepspeed_stage_1 : Each of your GPU has too much vram, and you do not know what to do + # + # - deepspeed_stage_2 : Optimal distributed training strategy, across multiple gpu each with sufficient vram + # - deepspeed_stage_2_offload : Reduce vram usage by offloading the optimizer state and work to cpu + # + # - deepspeed_stage_3 : Split up the model across multiple gpu, useful for large models, at a performance cost + # - deepspeed_stage_3_offload : Additional offloading, for even greater performance cost + # + # For more details see: + # https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed-zero-stage-2 + # + strategy: deepspeed_stage_2_offload + + # Logger setting for wandb, if you want to enable wandb, uncomment the whole logger section + # --- + logger: + class_path: lightning.pytorch.loggers.WandbLogger + init_args: + name: 'infctx-v5-unit-test-baseline (train-ctx=4096, data-ctx=4096)' + project: 'RWKV-infctx-unit-test' + tags: ['RWKV', 'infctx'] + + # Checkpoint settings for the training process + callbacks: + class_path: lightning.pytorch.callbacks.ModelCheckpoint + init_args: + # Configure this to the path you want to save your checkpoints to + # note that a subdir will be created with the name `epoch=x-step=y.ckpt` + # + # to convert a checkpoint to a model, you can use the + # `python3 export_checkpoint.py ` script, + # which will create a `rwkv_model.pth` in the checkpoint directory. + # + # Do not use the `zero_to_fp32.py` script as that will have export format issues + dirpath: ../checkpoint/trainer-validaiton/infctx-v5-enwiki-100k-4096 + filename: null + + # Save the top/last K checkpoints + save_top_k: 3 + # Choose by the most recent checkpoints (step based) + monitor: 'step' + mode: max + + # If enabled (true), save a copy of the latest checkpoint to 'last.ckpt' + # useful to simply checkpoint resume scripts, at a price of disk performance + save_last: false + + # DO NOT set this as true, as the model weight exported will have format issues + # expert as checkpoint, and use the `export_checkpoint.py` script to convert to model instead + save_weights_only: false + + # How frequent you want to save a checkpoint for every step. + # This will happen for every X data sample, where X = every_n_train_steps * accumulate_grad_batches + # + # In general you will want to avoid putting a low number (expecially if accumulate_grad_batches <= 100) + # as the checkpoint process, will pause all the gpu training for some time, slowing down the overall process + # However you do not want to configure too high of a number, where you will lose too much progress if the training crashes + every_n_train_steps: 100 + every_n_epochs: null + save_on_train_epoch_end: true + train_time_interval: null + + # Other settings, you can probably leave alone + verbose: false + auto_insert_metric_name: true + + ######################################## + ## Training run parameter settings + ######################################## + + # Generally what you want to configure is the maximum number of epochs + # Leave it as -1, and it will keep going forever till interrupted + # Or set it as a number, and it will stop after that number of epochs + max_epochs: 1 + min_epochs: null + max_steps: -1 + min_steps: null + max_time: null + + # Number of datasamples to train for each step, a data sample is considered + # a "substep" in wandb logs, and a "step" is tracked as "trainer/global_step" + # + # This decides the number of datasample, to learn together from, before backproping + # any weight changes at the end of the batch. + # + # Recommended to be a big enough number (like 128/256) where it prevents the training + # loss from flucuating in the process. But not too big of a number where the increased + # GPU vRAM / offloaded RAM usage will cause the training to crash. + # + # You are also recommended to configure this to a large enough number to fully utilize + # your GPU processing time %, and avoid idle time for the GPU between batches + # + # This number is divided by the number of GPUs, and nodes configured + # So if you have 4 GPUs, and 2 nodes, and this is configured as 128 + # Each GPU will process 128/4/2 = 16 datasamples per step, via accumulate_grad_batches + target_batch_size: 16 + +######################################## +## Training model settings +######################################## +model: + # Model to start the finetune/training process from + load_model: ../model/L24-D2048-world-v5base-init.pth + + # Context length to use for the training process + # the larger the number (and batch size) the larger the vram usage + # + # Note that if the datasample context length is larger then the ctx_len + # its training process would be split into ctx_len sized chunks. + # + # This allows the training of extreamly large context length (eg. 100k), + # without eating up too much vram by keeping the training context length + # to a resonable number sutible to the current GPU setup + ctx_len: 4096 + + # Data samples would be cut down to the respective max ctx_len_cutoffs + # values if its larger then ctx_len. If the data sample is larger then + # the largest len_cutoff, the remaining data will be discarded + ctx_len_cutoffs: [] + # Experimental settings, number of tokens to skip in the data sample + # prefix, for the respective cutoff length. Used to speed up the process + ctx_len_warmup_steps: [] + + # Learning rate of the training process + # --- + + # Initia learning rate of the process + lr_init: 8e-4 + # Final learning rate after the learning rate period + # learning rate will stay at final value from then onwards + lr_final: 4e-4 + + # Number of epoch to reduce the learning rate from lr_init to lr_final + # 1 means a single epoch (so lr would be lr_final from epoch 2 onwards) + # 0 means lr_final will apply immediately + # -1 means we take the current max_step / max_epoch as the period + lr_period: 1 + # lr_period type if its set, defaults to epoch + lr_period_type: epoch + + # Adam optimizer settings + # You probably want to leave this alone, unless you know what you are doing + beta1: 0.9 + beta2: 0.99 + adam_eps: 1.0e-08 + weight_decay: 0.01 + + # torch.set_float32_matmul_precision, used to optimize operations with tensor cores + # this should be set as null, for non cuda core GPUs + torch_set_float32_matmul_precision: 'high' + # torch_set_float32_matmul_precision: null + + # Segmented based learning, used to work around training of large context length + # beyond what can be supported by the current GPU vram architecture + # + # This is not 1:1 equivalent to the same training process with required vram + # as the training process is split into multiple segments, part by part. + # with limited learnings from the previous segment. + bptt_learning: true + + # Segmented range to performing backprop learning on + # 1 means to apply only for the last segment + # -1 means to apply for all segments + bptt_learning_range: -1 + +data: + # dataset_path for the prebuilt dataset, using HF `load_from_disk()` + # + # Use this if you have built your own dataset and saved it with `save_to_disk()` + # with source left as null. Other wise configure this to a directory which the + # dataset will be built and tokenized by the huggingface dataset process. + data_path: ../datapath/enwiki_100k-world-16k-rechunk/ + + # Other wise provide the source path, which is used as huggingface dataset path + # this will be used to populate the dataset_path + # + # Use either the following + # - hugging face dataset + # - Directory path to a directory containing dataset files + # - Path to a single dataset file + # - hugging face dataset mode (ie: text,csv,etc - use data_dir, to configure the path then) + # - null + # + # If source is disabled, all other params, except data_path, is ignored + source: "teven/enwiki_100k" + # source: text + # source: /home/ubuntu/RWKV-LM-LoRA/dataset-text/enwik8.txt + + # Use data_dir, if you are using source=text/json/etc + # this should be relative to the trainer script path + source_data_dir: null + + # After loading the dataset, split out test data used for unit-test, + # This process is skipped if the dataset includes a test split + # This process is skipped if set to zero + test_split: 0.01 + test_split_shuffle: false + + # Tokenizer to use, use either the inbuilt 'neox', or 'world' tokenizer + # If using a custom tokenizer, provide the tokenizer file path + # --- + tokenizer: world + + # Minimum / Maximum token size of the dataset to use + # useful for filtering out small noisy data samples from large datasets + # (eg. removal of small articles of less then 512 tokens from wikipedia) + # + # This is ignored, if set to -1 + min_token_size: 1024 + max_token_size: -1 + + # Rechunking of text dataset, this is done only when source is set as 'text' + # and will merge the various sentencees, into larger chunks up to the target size + # + # Defaults to 4096 + # + # This is ignored, if source is not set as text + # This is ignored, if set to zero + # --- + text_rechunk_size: 16384 + + # Apply text rechunk to the dataset, even if its not a 'text' source + # This is done only after dataset filtering, and if source is not 'text' + # --- + text_rechunk_force: true + + # Custom text column to use, useful for dataset with alternative training columns labels + # This is checked before multi column merging, default is null (disabled) + # eg: 'code' + # --- + # custom_text_key: 'code' + + # Multi Column merging process, default setting is used to support and merge + # "instruction", "input", "output", datasets. To disable set multi_column_keys to [] + # + # A minimum of 2 columns is required, with non empty data, for the merge to occur + # If no match is found, this will fallback to the default prompt/completion or text column, + # or throw an error if the default fallback is not found + # --- + # multi_column_keys: ['instruction', 'input', 'output'] + # multi_column_prefix: ['Instruction:\n', 'Input:\n', 'Output:\n'] + # multi_column_train_mask: [true, false, true] + # multi_column_separator: '\n\n' + + # If processing prompt/completion jsonl pairs, the prompt is masked by default + # use this flag to disable this default behaviour + # --- + # disable_prompt_completion_mask: false + +# Path to the current checkpoint to continue training from +# Enable this to the last checkpoint after the first run +# (if it crash and you want to resume) +# ckpt_path: ../checkpoint/trainer-validaiton/infctx-unit-test-baseline/epoch=0-step=20.ckpt +ckpt_path: null diff --git a/notebook/trainer-v5-validation/config/enwiki_100k-world-32k-rechunk.yaml b/notebook/trainer-v5-validation/config/enwiki_100k-world-32k-rechunk.yaml new file mode 100644 index 00000000..711ad299 --- /dev/null +++ b/notebook/trainer-v5-validation/config/enwiki_100k-world-32k-rechunk.yaml @@ -0,0 +1,265 @@ +# lightning.pytorch==2.0.2 +seed_everything: 3941088705 +trainer: + + # + # Configure the deepspeed strategy, we recommend you start with `deepspeed_stage_2_offload` + # and adjust from there according to your training needs. `deepspeed_stage_3_offload` is useful + # for training LoRA on large models on a single GPU. + # + # In general you would want to use the following: + # + # - deepspeed_stage_1 : Each of your GPU has too much vram, and you do not know what to do + # + # - deepspeed_stage_2 : Optimal distributed training strategy, across multiple gpu each with sufficient vram + # - deepspeed_stage_2_offload : Reduce vram usage by offloading the optimizer state and work to cpu + # + # - deepspeed_stage_3 : Split up the model across multiple gpu, useful for large models, at a performance cost + # - deepspeed_stage_3_offload : Additional offloading, for even greater performance cost + # + # For more details see: + # https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed-zero-stage-2 + # + strategy: deepspeed_stage_2_offload + + # Logger setting for wandb, if you want to enable wandb, uncomment the whole logger section + # --- + logger: + class_path: lightning.pytorch.loggers.WandbLogger + init_args: + name: 'infctx-v5-unit-test-baseline (train-ctx=4096, data-ctx=4096)' + project: 'RWKV-infctx-unit-test' + tags: ['RWKV', 'infctx'] + + # Checkpoint settings for the training process + callbacks: + class_path: lightning.pytorch.callbacks.ModelCheckpoint + init_args: + # Configure this to the path you want to save your checkpoints to + # note that a subdir will be created with the name `epoch=x-step=y.ckpt` + # + # to convert a checkpoint to a model, you can use the + # `python3 export_checkpoint.py ` script, + # which will create a `rwkv_model.pth` in the checkpoint directory. + # + # Do not use the `zero_to_fp32.py` script as that will have export format issues + dirpath: ../checkpoint/trainer-validaiton/infctx-v5-enwiki-100k-4096 + filename: null + + # Save the top/last K checkpoints + save_top_k: 3 + # Choose by the most recent checkpoints (step based) + monitor: 'step' + mode: max + + # If enabled (true), save a copy of the latest checkpoint to 'last.ckpt' + # useful to simply checkpoint resume scripts, at a price of disk performance + save_last: false + + # DO NOT set this as true, as the model weight exported will have format issues + # expert as checkpoint, and use the `export_checkpoint.py` script to convert to model instead + save_weights_only: false + + # How frequent you want to save a checkpoint for every step. + # This will happen for every X data sample, where X = every_n_train_steps * accumulate_grad_batches + # + # In general you will want to avoid putting a low number (expecially if accumulate_grad_batches <= 100) + # as the checkpoint process, will pause all the gpu training for some time, slowing down the overall process + # However you do not want to configure too high of a number, where you will lose too much progress if the training crashes + every_n_train_steps: 100 + every_n_epochs: null + save_on_train_epoch_end: true + train_time_interval: null + + # Other settings, you can probably leave alone + verbose: false + auto_insert_metric_name: true + + ######################################## + ## Training run parameter settings + ######################################## + + # Generally what you want to configure is the maximum number of epochs + # Leave it as -1, and it will keep going forever till interrupted + # Or set it as a number, and it will stop after that number of epochs + max_epochs: 1 + min_epochs: null + max_steps: -1 + min_steps: null + max_time: null + + # Number of datasamples to train for each step, a data sample is considered + # a "substep" in wandb logs, and a "step" is tracked as "trainer/global_step" + # + # This decides the number of datasample, to learn together from, before backproping + # any weight changes at the end of the batch. + # + # Recommended to be a big enough number (like 128/256) where it prevents the training + # loss from flucuating in the process. But not too big of a number where the increased + # GPU vRAM / offloaded RAM usage will cause the training to crash. + # + # You are also recommended to configure this to a large enough number to fully utilize + # your GPU processing time %, and avoid idle time for the GPU between batches + # + # This number is divided by the number of GPUs, and nodes configured + # So if you have 4 GPUs, and 2 nodes, and this is configured as 128 + # Each GPU will process 128/4/2 = 16 datasamples per step, via accumulate_grad_batches + target_batch_size: 16 + +######################################## +## Training model settings +######################################## +model: + # Model to start the finetune/training process from + load_model: ../model/L24-D2048-world-v5base-init.pth + + # Context length to use for the training process + # the larger the number (and batch size) the larger the vram usage + # + # Note that if the datasample context length is larger then the ctx_len + # its training process would be split into ctx_len sized chunks. + # + # This allows the training of extreamly large context length (eg. 100k), + # without eating up too much vram by keeping the training context length + # to a resonable number sutible to the current GPU setup + ctx_len: 4096 + + # Data samples would be cut down to the respective max ctx_len_cutoffs + # values if its larger then ctx_len. If the data sample is larger then + # the largest len_cutoff, the remaining data will be discarded + ctx_len_cutoffs: [] + # Experimental settings, number of tokens to skip in the data sample + # prefix, for the respective cutoff length. Used to speed up the process + ctx_len_warmup_steps: [] + + # Learning rate of the training process + # --- + + # Initia learning rate of the process + lr_init: 8e-4 + # Final learning rate after the learning rate period + # learning rate will stay at final value from then onwards + lr_final: 4e-4 + + # Number of epoch to reduce the learning rate from lr_init to lr_final + # 1 means a single epoch (so lr would be lr_final from epoch 2 onwards) + # 0 means lr_final will apply immediately + # -1 means we take the current max_step / max_epoch as the period + lr_period: 1 + # lr_period type if its set, defaults to epoch + lr_period_type: epoch + + # Adam optimizer settings + # You probably want to leave this alone, unless you know what you are doing + beta1: 0.9 + beta2: 0.99 + adam_eps: 1.0e-08 + weight_decay: 0.01 + + # torch.set_float32_matmul_precision, used to optimize operations with tensor cores + # this should be set as null, for non cuda core GPUs + torch_set_float32_matmul_precision: 'high' + # torch_set_float32_matmul_precision: null + + # Segmented based learning, used to work around training of large context length + # beyond what can be supported by the current GPU vram architecture + # + # This is not 1:1 equivalent to the same training process with required vram + # as the training process is split into multiple segments, part by part. + # with limited learnings from the previous segment. + bptt_learning: true + + # Segmented range to performing backprop learning on + # 1 means to apply only for the last segment + # -1 means to apply for all segments + bptt_learning_range: -1 + +data: + # dataset_path for the prebuilt dataset, using HF `load_from_disk()` + # + # Use this if you have built your own dataset and saved it with `save_to_disk()` + # with source left as null. Other wise configure this to a directory which the + # dataset will be built and tokenized by the huggingface dataset process. + data_path: ../datapath/enwiki_100k-world-32k-rechunk/ + + # Other wise provide the source path, which is used as huggingface dataset path + # this will be used to populate the dataset_path + # + # Use either the following + # - hugging face dataset + # - Directory path to a directory containing dataset files + # - Path to a single dataset file + # - hugging face dataset mode (ie: text,csv,etc - use data_dir, to configure the path then) + # - null + # + # If source is disabled, all other params, except data_path, is ignored + source: "teven/enwiki_100k" + # source: text + # source: /home/ubuntu/RWKV-LM-LoRA/dataset-text/enwik8.txt + + # Use data_dir, if you are using source=text/json/etc + # this should be relative to the trainer script path + source_data_dir: null + + # After loading the dataset, split out test data used for unit-test, + # This process is skipped if the dataset includes a test split + # This process is skipped if set to zero + test_split: 0.01 + test_split_shuffle: false + + # Tokenizer to use, use either the inbuilt 'neox', or 'world' tokenizer + # If using a custom tokenizer, provide the tokenizer file path + # --- + tokenizer: world + + # Minimum / Maximum token size of the dataset to use + # useful for filtering out small noisy data samples from large datasets + # (eg. removal of small articles of less then 512 tokens from wikipedia) + # + # This is ignored, if set to -1 + min_token_size: 1024 + max_token_size: -1 + + # Rechunking of text dataset, this is done only when source is set as 'text' + # and will merge the various sentencees, into larger chunks up to the target size + # + # Defaults to 4096 + # + # This is ignored, if source is not set as text + # This is ignored, if set to zero + # --- + text_rechunk_size: 32768 + + # Apply text rechunk to the dataset, even if its not a 'text' source + # This is done only after dataset filtering, and if source is not 'text' + # --- + text_rechunk_force: true + + # Custom text column to use, useful for dataset with alternative training columns labels + # This is checked before multi column merging, default is null (disabled) + # eg: 'code' + # --- + # custom_text_key: 'code' + + # Multi Column merging process, default setting is used to support and merge + # "instruction", "input", "output", datasets. To disable set multi_column_keys to [] + # + # A minimum of 2 columns is required, with non empty data, for the merge to occur + # If no match is found, this will fallback to the default prompt/completion or text column, + # or throw an error if the default fallback is not found + # --- + # multi_column_keys: ['instruction', 'input', 'output'] + # multi_column_prefix: ['Instruction:\n', 'Input:\n', 'Output:\n'] + # multi_column_train_mask: [true, false, true] + # multi_column_separator: '\n\n' + + # If processing prompt/completion jsonl pairs, the prompt is masked by default + # use this flag to disable this default behaviour + # --- + # disable_prompt_completion_mask: false + +# Path to the current checkpoint to continue training from +# Enable this to the last checkpoint after the first run +# (if it crash and you want to resume) +# ckpt_path: ../checkpoint/trainer-validaiton/infctx-unit-test-baseline/epoch=0-step=20.ckpt +ckpt_path: null diff --git a/notebook/trainer-v5-validation/datapack-mixing.ipynb b/notebook/trainer-v5-validation/datapack-mixing.ipynb index 9d37d27a..f6b456eb 100644 --- a/notebook/trainer-v5-validation/datapack-mixing.ipynb +++ b/notebook/trainer-v5-validation/datapack-mixing.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -20,9 +20,9 @@ "text": [ "ENABLE_WANDB: True\n", "GPU_DEVICES: auto\n", - "NOTEBOOK_DIR: /home/recursal/RWKV-infctx-trainer/notebook/trainer-v5-validation\n", - "TRAINER_DIR: /home/recursal/RWKV-infctx-trainer/RWKV-v5\n", - "PROJECT_DIR: /home/recursal/RWKV-infctx-trainer\n" + "NOTEBOOK_DIR: /workspace/picocreator/RWKV-infctx-trainer/notebook/trainer-v5-validation\n", + "TRAINER_DIR: /workspace/picocreator/RWKV-infctx-trainer/RWKV-v5\n", + "PROJECT_DIR: /workspace/picocreator/RWKV-infctx-trainer\n" ] } ], @@ -52,14 +52,14 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-01-28 08:49:47,247] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-02-04 14:06:15,966] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", "---- Initializing model ----\n", "No of layers: 6\n", @@ -91,26 +91,26 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ">> Starting datapack build process for: /home/recursal/RWKV-infctx-trainer/notebook/trainer-v5-validation/config/datapack-build.yaml\n", + ">> Starting datapack build process for: /workspace/picocreator/RWKV-infctx-trainer/notebook/trainer-v5-validation/config/datapack-build.yaml\n", ">> Preparing dataset - index: 0\n", "Warning: packing_enable=true, with text rechunking (either auto, or forced) - packing_enable will be treated as false\n", - "num_proc must be <= 6. Reducing num_proc to 6 for dataset of size 6.\n", - "Saving the dataset (1/1 shards): 100%|█| 586/586 [00:00<00:00, 2954.51 examples/\n", - "Saving the dataset (1/1 shards): 100%|███| 6/6 [00:00<00:00, 1768.01 examples/s]\n", + "Saving the dataset (1/1 shards): 100%|█| 586/586 [00:00<00:00, 4641.04 examples/\n", + "Saving the dataset (1/1 shards): 100%|████| 6/6 [00:00<00:00, 270.57 examples/s]\n", ">> Preparing dataset - index: 1\n", - "Saving the dataset (1/1 shards): 100%|█| 9232/9232 [00:03<00:00, 2685.27 example\n", - "Saving the dataset (1/1 shards): 100%|█| 2429/2429 [00:00<00:00, 80126.19 exampl\n", - ">> Total approx train batches ( full | random ) : 4 ( 3 | 1 )\n", + "Map (num_proc=160): 100%|████| 240402/240402 [00:02<00:00, 113556.24 examples/s]\n", + "Saving the dataset (1/1 shards): 100%|█| 9232/9232 [00:01<00:00, 8685.37 example\n", + "Saving the dataset (1/1 shards): 100%|█| 2429/2429 [00:00<00:00, 40807.03 exampl\n", + ">> Dataset Mixing mode: shuffle\n", ">> Saving dataset to data_path : ../datapath/v5-validation/example-datapack/\n", - "Saving the dataset (1/1 shards): 100%|█| 9818/9818 [00:04<00:00, 2330.25 example\n", - "Saving the dataset (1/1 shards): 100%|█| 2435/2435 [00:00<00:00, 73759.12 exampl\n", + "Saving the dataset (1/1 shards): 100%|█| 9818/9818 [00:00<00:00, 12069.53 exampl\n", + "Saving the dataset (1/1 shards): 100%|█| 2435/2435 [00:00<00:00, 38873.99 exampl\n", ">> Dataset saved to data_path\n", ">> -----------------------------------\n", ">> Performing dataset counting\n", @@ -118,6 +118,8 @@ ">> Final dataset count ( train ) : 9,818\n", ">> Final dataset count ( test ) : 2,435\n", ">> -----------------------------------\n", + "Map (num_proc=160): 100%|██████████| 9818/9818 [00:03<00:00, 2496.16 examples/s]\n", + "Map (num_proc=160): 100%|███████████| 2435/2435 [00:03<00:00, 722.67 examples/s]\n", ">> -----------------------------------\n", ">> Final 'train' dataset token count ...\n", ">> - Total tokens : 79,743,969\n", @@ -138,258 +140,71 @@ " python3 datapack_build.py \"{NOTEBOOK_DIR}/config/datapack-build.yaml\"" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Short train" - ] - }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-01-28 09:04:21,218] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/cli.py:518: LightningCLI's args parameter is intended to run from within Python like if it were from the command line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: sys.argv[1:]=['fit', '-c', '/home/recursal/RWKV-infctx-trainer/notebook/trainer-v5-validation/config/datapack-train.yaml', '--model.load_model=../model/L6-D1024-world-v5base-init.pth', '--trainer.callbacks.init_args.dirpath=../checkpoint/datapack-validaiton-train/', '--trainer.logger.init_args.name=infctx-v5-datapack - Multi Datapack Validation - (deepspeed_stage_1)', '--trainer.strategy=deepspeed_stage_1', '--trainer.microbatch_size=8', '--trainer.fast_dev_run=2', '--trainer.devices=auto'], args=['fit', '-c', '/home/recursal/RWKV-infctx-trainer/notebook/trainer-v5-validation/config/datapack-train.yaml', '--model.load_model=../model/L6-D1024-world-v5base-init.pth', '--trainer.callbacks.init_args.dirpath=../checkpoint/datapack-validaiton-train/', '--trainer.logger.init_args.name=infctx-v5-datapack - Multi Datapack Validation - (deepspeed_stage_1)', '--trainer.strategy=deepspeed_stage_1', '--trainer.microbatch_size=8', '--trainer.fast_dev_run=2', '--trainer.devices=auto'].\n", - "Seed set to 3941088705\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Detected CUDA files, patching ldflags\n", - "Emitting ninja build file /home/recursal/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", - "Building extension module wkv5...\n", - "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", - "ninja: no work to do.\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "Running in `fast_dev_run` mode: will run the requested loop using 2 batch(es). Logging and checkpointing is suppressed.\n", - "\n", - "\n", - "[RWKV.Trainer] Applying 'target_batch_size' with the following:\n", - " - target_batch_size: 16\n", - " - num_nodes: 1\n", - " - num_devices: 8\n", - " - microbatch_size: 8\n", - " - accumulate_grad_batches: 1\n", - " - effective_batch_size: 64\n", - "\n", - "[rank: 0] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/8\n", - "[2024-01-28 09:04:27,346] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-01-28 09:04:27,392] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-01-28 09:04:27,406] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-01-28 09:04:27,415] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-01-28 09:04:27,422] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-01-28 09:04:27,423] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[2024-01-28 09:04:27,437] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[RWKV.model] Running RWKV infctx using 'torch-jit' with torch '2.1.2'\n", - "[rank: 3] Seed set to 3941088705\n", - "[rank: 5] Seed set to 3941088705\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "[rank: 7] Seed set to 3941088705\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "[rank: 2] Seed set to 3941088705\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "[rank: 6] Seed set to 3941088705\n", - "[rank: 1] Seed set to 3941088705\n", - "[rank: 4] Seed set to 3941088705\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " return self.fget.__get__(instance, owner)()\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Detected CUDA files, patching ldflags\n", - "Emitting ninja build file /home/recursal/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", - "Building extension module wkv5...\n", - "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "ninja: no work to do.\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "Detected CUDA files, patching ldflags\n", - "Emitting ninja build file /home/recursal/.cache/torch_extensions/py311_cu121/wkv5/build.ninja...\n", - "Building extension module wkv5...\n", - "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "---\n", - "[RWKV.TimeMix] Compiling CUDA kernel with HEAD_SIZE=64\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "ninja: no work to do.\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "Loading extension module wkv5...\n", - "Loading extension module wkv5...\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "[RWKV.TimeMix] CUDA kernel compiled & loaded globally\n", - "---\n", - "[rank: 3] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 3, MEMBER: 4/8\n", - "[rank: 5] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 5, MEMBER: 6/8\n", - "[rank: 7] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 7, MEMBER: 8/8\n", - "[rank: 1] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/8\n", - "[rank: 6] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 6, MEMBER: 7/8\n", - "[rank: 2] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 2, MEMBER: 3/8\n", - "[rank: 4] Seed set to 3941088705\n", - "initializing deepspeed distributed: GLOBAL_RANK: 4, MEMBER: 5/8\n", - "Enabling DeepSpeed BF16. Model parameters and inputs will be cast to `bfloat16`.\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory /home/recursal/RWKV-infctx-trainer/checkpoint/datapack-validaiton-train exists and is not empty.\n", - "LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "\n", - "\n", - "#\n", - "# RWKV lighting_trainer.py important notes \n", - "# https://github.com/RWKV/RWKV-infctx-trainer \n", - "#\n", - "# - Ensure your host is not running cuda 12.0 (use either 11.8, or >=12.1), as this is known to have freeze issues\n", - "# - The terms used in wandb / the progress bar can be confusing, see the github README.md for beter clarifications\n", - "# - When resuming from checkpoint, the estimated time is inaccurate\n", - "#\n", - "\n", - "[RWKV.model] Configuring optimizer with\n", - " - lr_init: 8.000e-04 (0.0008)\n", - " - lr_final: 4.000e-04 (0.0004)\n", - "\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "[WARNING]: unlimited bptt_learning_range across multiple GPU's has a performance penalty with datasets of mixed sizes due to its constant need to keep all GPU's in sync (consider using bptt_learning_range=1 instead)\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Using /home/recursal/.cache/torch_extensions/py311_cu121 as PyTorch extensions root...\n", - "Detected CUDA files, patching ldflags\n", - "Emitting ninja build file /home/recursal/.cache/torch_extensions/py311_cu121/fused_adam/build.ninja...\n", - "Building extension module fused_adam...\n", - "Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n", - "ninja: no work to do.\n", - "Loading extension module fused_adam...\n", - "Time to load fused_adam op: 0.05720949172973633 seconds\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "Time to load fused_adam op: 0.10125899314880371 seconds\n", - "Time to load fused_adam op: 0.10107564926147461 seconds\n", - "Loading extension module fused_adam...\n", - "Loading extension module fused_adam...\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Time to load fused_adam op: 0.10136151313781738 seconds\n", - "Time to load fused_adam op: 0.10109567642211914 secondsLoading `train_dataloader` to estimate number of stepping batches.\n", - "\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Time to load fused_adam op: 0.10142135620117188 seconds\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "Time to load fused_adam op: 0.10141849517822266 seconds\n", - "Time to load fused_adam op: 0.10153317451477051 seconds\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "/home/recursal/miniconda3/envs/rwkv-infctx/lib/python3.11/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400430266/work/torch/csrc/tensor/python_tensor.cpp:83.)\n", - " self._dummy_overflow_buf = get_accelerator().IntTensor([0])\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------\n", - "0 | emb | Embedding | 67.1 M\n", - "1 | blocks | ModuleList | 81.9 M\n", - "2 | ln_out | LayerNorm | 2.0 K \n", - "3 | head | Linear | 67.1 M\n", - "--------------------------------------\n", - "216 M Trainable params\n", - "0 Non-trainable params\n", - "216 M Total params\n", - "864.387 Total estimated model params size (MB)\n", - "Epoch 0: 100%|██████████████████| 2/2 [00:06<00:00, 0.30it/s, train/loss=9.880]\n", - "Validation: | | 0/? [00:00 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset loaded, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[43mdataset\u001b[49m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m documents\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Train dataset\u001b[39;00m\n\u001b[1;32m 12\u001b[0m train_dataset \u001b[38;5;241m=\u001b[39m full_dataset[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" ] } ], + "source": [ + "# Lets load the datapath at {PROJECT_DIR}/datapath/world/Eagle-x-multipack/ via HF dataset\n", + "# and iterate the first 10 documents\n", + "import datasets\n", + "datapath = f\"{PROJECT_DIR}/datapath/v5-validation/example-datapack/\"\n", + "print(f\"Loading the dataset... {datapath}\")\n", + "\n", + "# Load the dataset\n", + "full_dataset = datasets.load_from_disk(datapath)\n", + "print(f\"Dataset loaded, {len(full_dataset)} documents\")\n", + "\n", + "# Train dataset\n", + "train_dataset = full_dataset[\"train\"]\n", + "\n", + "# Lets iterate the first 10 documents\n", + "iterate_limit = 10\n", + "for idx, doc in enumerate(train_dataset):\n", + " print(f\"Document {idx+1}:\")\n", + " # Print the keys\n", + " print(\"- Keys:\", doc.keys())\n", + " print(\"- input_ids length:\", len(doc[\"input_ids\"]))\n", + " print(\"- attention_mask length:\", len(doc[\"attention_mask\"]))\n", + " print(\"- token_type_ids length:\", len(doc[\"token_type_ids\"]))\n", + " # print(\"- sample_length:\", doc[\"sample_length\"])\n", + "\n", + " if idx >= iterate_limit:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Short train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "!cd \"{TRAINER_DIR}\" && \\\n", " export WANDB_MODE=\"disabled\" && \\\n", @@ -722,7 +537,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rwkv-infctx", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -736,7 +551,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.10.12" }, "orig_nbformat": 4 },