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Support weight-only kernel with IPEX for intel GPU (#1153)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Set up the environment for Intel oneAPI DPC++/C++ Compiler | ||
| # ONEAPI_INSTALL_PATH below assumes you installed to the default folder /opt/intel/oneapi | ||
| # If you customized the installation folder, please update ONEAPI_INSTALL_PATH to your custom folder | ||
| ONEAPI_INSTALL_PATH=/opt/intel/oneapi | ||
| source ${ONEAPI_INSTALL_PATH}/setvars.sh |
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examples/huggingface/pytorch/text-generation/quantization/run_generation_gpu_woq.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,227 @@ | ||
| import argparse | ||
| import re | ||
| import time | ||
| import json | ||
| import torch | ||
| from transformers import AutoConfig, AutoTokenizer | ||
| from transformers.generation import GenerationConfig | ||
| import intel_extension_for_pytorch as ipex | ||
| from intel_extension_for_transformers.transformers import AutoModelForCausalLM, WeightOnlyQuantConfig | ||
| from intel_extension_for_transformers.llm.quantization.utils import convert_dtype_str2torch | ||
| from transformers.utils import check_min_version | ||
|
|
||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "--model", nargs="?", default="Qwen/Qwen-7B-Chat", const="Qwen/Qwen-7B-Chat" | ||
| ) | ||
| parser.add_argument("--revision", default=None, type=str) | ||
| parser.add_argument("--trust_remote_code", default=True) | ||
| parser.add_argument( | ||
| "--dataset", nargs="?", default="NeelNanda/pile-10k", const="NeelNanda/pile-10k" | ||
| ) | ||
| parser.add_argument( | ||
| "--max-new-tokens", default=32, type=int, help="output max new tokens" | ||
| ) | ||
| parser.add_argument( | ||
| "--num_beams", default=1, type=int, help="number of beams" | ||
| ) | ||
| parser.add_argument("--output_dir", nargs="?", default="./saved_results") | ||
| parser.add_argument("--int8", action="store_true") | ||
| parser.add_argument( | ||
| "--int8_bf16_mixed", | ||
| action="store_true", | ||
| help="by default it is int8-fp32 mixed, to enable int8 mixed amp bf16 (work on platforms like SPR)", | ||
| ) | ||
| parser.add_argument("--peft_model_id", type=str, default=None, help="model_name_or_path of peft model") | ||
| # ============Benchmark configs============== | ||
| parser.add_argument("--benchmark", action="store_true") | ||
| parser.add_argument("--do_profiling", action="store_true") | ||
| parser.add_argument("--profile_token_latency", action="store_true") | ||
| parser.add_argument("--iters", default=10, type=int, help="num iter") | ||
| parser.add_argument("--num_warmup", default=3, type=int, help="num warmup") | ||
| # ============Accuracy configs============== | ||
| parser.add_argument("--accuracy", action="store_true") | ||
| parser.add_argument("--batch_size", default=1, type=int, | ||
| help="batch size num.") | ||
| parser.add_argument("--save_accuracy_path", default=None, | ||
| help="Save accuracy results path.") | ||
| parser.add_argument("--tasks", nargs='+', default=["lambada_openai"], type=str, \ | ||
| help="tasks list for accuracy validation") | ||
| # ============WeightOnlyQuant configs=============== | ||
| parser.add_argument("--woq", action="store_true") | ||
| parser.add_argument("--woq_algo", default="RTN", choices=['RTN'], | ||
| help="Weight-only parameter.") | ||
| parser.add_argument("--woq_dtype", type=str, default="int4_fullrange", | ||
| choices=["int4_fullrange"]) | ||
| parser.add_argument("--woq_group_size", type=int, default=32) | ||
| parser.add_argument("--woq_scheme", default="sym") | ||
| parser.add_argument("--woq_enable_mse_search", action="store_true") | ||
| parser.add_argument("--device", default="xpu") | ||
| parser.add_argument("--compute_dtype", default="fp16") | ||
| # ============BitsAndBytes configs============== | ||
| parser.add_argument("--bitsandbytes", action="store_true") | ||
| parser.add_argument("--load_in_4bit", type=bool, default=False) | ||
| parser.add_argument("--load_in_8bit", type=bool, default=False) | ||
| # ======================================= | ||
| args = parser.parse_args() | ||
| torch_dtype = convert_dtype_str2torch(args.compute_dtype) | ||
|
|
||
| # transformers version >= 4.32.0 contained the mpt modeling definition. | ||
| # https://github.com/huggingface/transformers/blob/main/src/transformers/models/mpt/modeling_mpt.py | ||
| check_min_version("4.31.0") | ||
|
|
||
| # get model config | ||
| config = AutoConfig.from_pretrained( | ||
| args.model, | ||
| use_cache=True, # to use kv cache. | ||
| trust_remote_code=args.trust_remote_code, | ||
| revision=args.revision, | ||
| ) | ||
| generation_config = GenerationConfig.from_pretrained(args.model, trust_remote_code=args.trust_remote_code) | ||
| generation_config.do_sample = False | ||
| user_model = None | ||
|
|
||
| # tokenizer | ||
| if config.model_type == "llama": | ||
| from transformers import LlamaTokenizer | ||
| tokenizer = LlamaTokenizer.from_pretrained(args.model) | ||
| else: | ||
| tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=args.trust_remote_code) | ||
|
|
||
| quantization_config = None | ||
| if args.woq: | ||
| quantization_config = WeightOnlyQuantConfig( | ||
| compute_dtype=args.compute_dtype, weight_dtype=args.woq_dtype, | ||
| group_size=args.woq_group_size, scale_dtype=args.compute_dtype | ||
| ) #default is A16W4G16 | ||
|
|
||
| # get model | ||
| if quantization_config is not None: | ||
| user_model = AutoModelForCausalLM.from_pretrained(args.model, | ||
| device_map=args.device, | ||
| quantization_config=quantization_config, | ||
| trust_remote_code=args.trust_remote_code, | ||
| fp16=True, | ||
| use_llm_runtime=False | ||
| ) | ||
| elif args.load_in_4bit or args.load_in_8bit: | ||
| # CPU device usage is provided by intel-extension-for-transformers. | ||
| user_model = AutoModelForCausalLM.from_pretrained(args.model, | ||
| device_map=args.device, | ||
| load_in_4bit=args.load_in_4bit, | ||
| load_in_8bit=args.load_in_8bit, | ||
| use_llm_runtime=False | ||
| ) | ||
| if user_model is not None: | ||
| user_model.save_pretrained(args.output_dir) | ||
| tokenizer.save_pretrained(args.output_dir) | ||
|
|
||
| if args.benchmark: | ||
| prompt = "它完成了，并提交了。你可以在Android和网络上玩美味生存。在网络上玩是有效的，但你必须模拟多次触摸才能移动桌子." | ||
|
|
||
| input_size = tokenizer(prompt, return_tensors="pt").input_ids.size(dim=1) | ||
| print("---- Prompt size:", input_size) | ||
|
|
||
| user_model = AutoModelForCausalLM.from_pretrained( | ||
| args.model, trust_remote_code=args.trust_remote_code, device_map=args.device, torch_dtype=torch_dtype) \ | ||
| if user_model is None else user_model | ||
| user_model = ipex.optimize_transformers( | ||
| user_model.eval(), device=args.device, inplace=True, woq=True, dtype=torch_dtype) | ||
| # start | ||
| num_iter = args.iters | ||
| num_warmup = args.num_warmup | ||
| prompt = [prompt] * args.batch_size | ||
| amp_enabled = True | ||
| amp_dtype = torch_dtype | ||
|
|
||
| generate_kwargs = dict(do_sample=False, temperature=0.9, num_beams=args.num_beams) | ||
| if args.profile_token_latency: | ||
| generate_kwargs["token_latency"] = True | ||
|
|
||
| total_time = 0.0 | ||
| total_list = [] | ||
| with torch.inference_mode(), torch.no_grad(), torch.autocast( | ||
| device_type=args.device, | ||
| enabled=amp_enabled, | ||
| dtype=amp_dtype if amp_enabled else None, | ||
| ): | ||
| for i in range(num_iter + num_warmup): | ||
| with torch.autograd.profiler_legacy.profile(enabled=args.do_profiling, use_xpu=(args.device=="xpu"), record_shapes=False) as prof: | ||
| input_ids = tokenizer( | ||
| prompt, return_tensors="pt").input_ids.to(args.device) | ||
| tic = time.time() | ||
| output = user_model.generate( | ||
| input_ids, max_new_tokens=int(args.max_new_tokens), **generate_kwargs | ||
| ) | ||
| toc = time.time() | ||
| gen_ids = output[0] if args.profile_token_latency else output | ||
| gen_text = tokenizer.batch_decode( | ||
| gen_ids, skip_special_tokens=True) | ||
| if args.device == "xpu": | ||
| torch.xpu.synchronize() | ||
| if args.do_profiling and i >= num_warmup and (i == num_warmup or i == num_iter + num_warmup - 1): | ||
| print(f"Save pt for iter {i}") | ||
| torch.save(prof.key_averages().table( | ||
| sort_by="self_xpu_time_total"), f"./profile_{i}.pt") | ||
| # torch.save(prof.table(sort_by="id", row_limit=-1), | ||
| # './profile_id.pt') | ||
| # torch.save(prof.key_averages( | ||
| # group_by_input_shape=True).table(), "./profile_detail.pt") | ||
| prof.export_chrome_trace(f"./trace_{i}.json") | ||
| input_tokens_lengths = [x.shape[0] for x in input_ids] | ||
| output_tokens_lengths = [x.shape[0] for x in gen_ids] | ||
| total_new_tokens = [ | ||
| o - i if user_model.config.model_type != "t5" else o | ||
| for i, o in zip(input_tokens_lengths, output_tokens_lengths) | ||
| ] | ||
| print(gen_text, total_new_tokens, flush=True) | ||
| print("Iteration: %d, Time: %.6f sec" % (i, toc - tic), flush=True) | ||
| if i >= num_warmup: | ||
| total_time += toc - tic | ||
| if args.profile_token_latency: | ||
| total_list.append(output[1]) | ||
|
|
||
| print("\n", "-" * 10, "Summary:", "-" * 10) | ||
| latency = total_time / (num_iter - num_warmup) | ||
| print("Inference latency: %.5f sec." % latency) | ||
| throughput = (args.max_new_tokens + input_size) / latency | ||
| print("Average throughput: {} samples/sec".format(throughput)) | ||
|
|
||
| if args.profile_token_latency: | ||
| import numpy as np | ||
| from itertools import chain | ||
|
|
||
| first_latency = np.mean([x[0] for x in total_list]) | ||
| average_2n = list(chain(*[x[1:] for x in total_list])) | ||
| average_2n.sort() | ||
| average_2n_latency = np.mean(average_2n) | ||
| print("First token average latency: %.5f sec." % first_latency) | ||
| print("Average 2... latency: %.5f sec." % average_2n_latency) | ||
| print(total_list) | ||
|
|
||
|
|
||
| if args.accuracy: | ||
| from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate | ||
| user_model = AutoModelForCausalLM.from_pretrained( | ||
| args.model, trust_remote_code=args.trust_remote_code, device_map=args.device, torch_dtype=torch_dtype) \ | ||
| if user_model is None else user_model | ||
| user_model = ipex.optimize_transformers( | ||
| user_model.eval(), device=args.device, inplace=True, woq=True, dtype=torch_dtype) | ||
| results = evaluate( | ||
| model="hf-causal", | ||
| model_args='pretrained='+args.model+',tokenizer='+args.model+',dtype=float32', | ||
| user_model=user_model, | ||
| batch_size=args.batch_size, | ||
| tasks=args.tasks, | ||
| device=args.device | ||
| ) | ||
| dumped = json.dumps(results, indent=2) | ||
| if args.save_accuracy_path: | ||
| with open(args.save_accuracy_path, "w") as f: | ||
| f.write(dumped) | ||
| for task_name in args.tasks: | ||
| if task_name == "wikitext": | ||
| print("Accuracy for %s is: %s" % (task_name, results["results"][task_name]["word_perplexity"])) | ||
| else: | ||
| print("Accuracy for %s is: %s" % (task_name, results["results"][task_name]["acc"])) | ||
|
|
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