add support for hpu devices

bigcode_eval/generation.py

@@ -76,7 +76,9 @@ def parallel_generations(
     # The input_length / start_length set to 0 for now will be adjusted later
     # Check if the task has a custom check_fn method for the stopping criteria
     if task.stop_words and tokenizer.eos_token:
-        task.stop_words.append(tokenizer.eos_token)    
+        # if padding token is same as eos it cannot be a delimiter as hpu output is padded
+        if not (accelerator.device.type == "hpu" and tokenizer.eos_token == tokenizer.pad_token):
+            task.stop_words.append(tokenizer.eos_token)
     if hasattr(task, "check_fn"):
         stopping_criteria.append(
             EndOfFunctionCriteria(0, task.stop_words, tokenizer, task.check_fn)

main.py

@@ -229,6 +229,14 @@ def get_gpus_max_memory(max_memory, num_gpus):
     return max_memory
 
 
+def use_hpu():
+    import importlib
+    if importlib.util.find_spec("habana_frameworks") is not None:
+        import habana_frameworks.torch.hpu as hthpu
+        if hthpu.is_available():
+            return True
+    return False
+
 def main():
     args = parse_args()
     transformers.logging.set_verbosity_error()
@@ -239,10 +247,19 @@ def main():
     else:
         task_names = pattern_match(args.tasks.split(","), ALL_TASKS)
 
-    accelerator = Accelerator()
+    if use_hpu():
+        from optimum.habana.accelerate import GaudiAccelerator
+        accelerator = GaudiAccelerator()
+    else:
+        accelerator = Accelerator()
+
     if accelerator.is_main_process:
         print(f"Selected Tasks: {task_names}")
 
+    if accelerator.device.type == "hpu":
+        from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
+        adapt_transformers_to_gaudi()
+
     results = {}
     if args.load_generations_path:
         # here we don't generate code but only evaluate previously computed generations
@@ -310,6 +327,10 @@ def main():
                 f"Non valid modeltype {args.modeltype}, choose from: causal, seq2seq"
             )
 
+        if accelerator.device.type == "hpu":
+            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+            model = wrap_in_hpu_graph(model)
+
         if args.peft_model:
             from peft import PeftModel  # dynamic import to avoid dependency on peft