[llama] fix training and inference scripts (#5384)

* [llama] refactor inference example to fit sft

* [llama] fix training script to fit gemini

* [llama] fix inference script

applications/Colossal-LLaMA-2/inference_example.py

@@ -1,17 +1,16 @@
 import argparse
-import os
 
 import torch
+from colossal_llama2.dataset.conversation import default_conversation
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
 from colossalai.logging import get_dist_logger
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
 logger = get_dist_logger()
 
 
 def load_model(model_path, device="cuda", **kwargs):
-    logger.info(
-        "Please check whether the tokenizer and model weights are properly stored in the same folder."
-    )
+    logger.info("Please check whether the tokenizer and model weights are properly stored in the same folder.")
     model = AutoModelForCausalLM.from_pretrained(model_path, **kwargs)
     model.to(device)
 
@@ -27,31 +26,50 @@ def load_model(model_path, device="cuda", **kwargs):
 def generate(args):
     model, tokenizer = load_model(model_path=args.model_path, device=args.device)
 
-    BASE_INFERENCE_SUFFIX = "\n\n->\n\n"
-    input_txt = f"{args.input_txt}{BASE_INFERENCE_SUFFIX}"
+    if args.prompt_style == "sft":
+        conversation = default_conversation.copy()
+        conversation.append_message("Human", args.input_txt)
+        input_txt = conversation.get_prompt()
+    else:
+        BASE_INFERENCE_SUFFIX = "\n\n->\n\n"
+        input_txt = f"{args.input_txt}{BASE_INFERENCE_SUFFIX}"
 
-    inputs = tokenizer(args.input_txt, return_tensors='pt').to(args.device)
-    output = model.generate(**inputs,
-                            max_new_tokens=args.max_new_tokens,
-                            do_sample=args.do_sample,
-                            temperature=args.temperature,
-                            top_k=args.top_k,
-                            top_p=args.top_p,
-                            num_return_sequences=1)
-    response = tokenizer.decode(output.cpu()[0], skip_special_tokens=True)[len(input_txt):]
+    inputs = tokenizer(input_txt, return_tensors="pt").to(args.device)
+    num_input_tokens = inputs["input_ids"].shape[-1]
+    output = model.generate(
+        **inputs,
+        max_new_tokens=args.max_new_tokens,
+        do_sample=args.do_sample,
+        temperature=args.temperature,
+        top_k=args.top_k,
+        top_p=args.top_p,
+        num_return_sequences=1,
+    )
+    response = tokenizer.decode(output.cpu()[0, num_input_tokens:], skip_special_tokens=True)
     logger.info(f"Question: {input_txt} \n\n Answer: \n{response}")
     return response
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Colossal-LLaMA-2 inference Process.")
-    parser.add_argument('--model_path', type=str, default="hpcai-tech/Colossal-LLaMA-2-7b-base", help="HF repo name or local path of the model")
-    parser.add_argument('--device', type=str, default="cuda:0", help="Set the device")
-    parser.add_argument('--max_new_tokens', type=int, default=512, help=" Set maximum numbers of tokens to generate, ignoring the number of tokens in the prompt")
-    parser.add_argument('--do_sample', type=bool, default=True, help="Set whether or not to use sampling")
-    parser.add_argument('--temperature', type=float, default=0.3, help="Set temperature value")
-    parser.add_argument('--top_k', type=int, default=50, help="Set top_k value for top-k-filtering")
-    parser.add_argument('--top_p', type=int, default=0.95, help="Set top_p value for generation")
-    parser.add_argument('--input_txt', type=str, default="明月松间照，", help="The prompt input to the model")
+    parser.add_argument(
+        "--model_path",
+        type=str,
+        default="hpcai-tech/Colossal-LLaMA-2-7b-base",
+        help="HF repo name or local path of the model",
+    )
+    parser.add_argument("--device", type=str, default="cuda:0", help="Set the device")
+    parser.add_argument(
+        "--max_new_tokens",
+        type=int,
+        default=512,
+        help=" Set maximum numbers of tokens to generate, ignoring the number of tokens in the prompt",
+    )
+    parser.add_argument("--do_sample", type=bool, default=True, help="Set whether or not to use sampling")
+    parser.add_argument("--temperature", type=float, default=0.3, help="Set temperature value")
+    parser.add_argument("--top_k", type=int, default=50, help="Set top_k value for top-k-filtering")
+    parser.add_argument("--top_p", type=int, default=0.95, help="Set top_p value for generation")
+    parser.add_argument("--input_txt", type=str, default="明月松间照，", help="The prompt input to the model")
+    parser.add_argument("--prompt_style", choices=["sft", "pretrained"], default="sft", help="The style of the prompt")
     args = parser.parse_args()
     generate(args)

applications/Colossal-LLaMA-2/train.py

@@ -154,6 +154,7 @@ def main() -> None:
             precision=args.mixed_precision,
             initial_scale=2**16,
             max_norm=args.grad_clip,
+            enable_gradient_accumulation=(args.accumulation_steps > 1),
         )
     elif args.plugin == "gemini_auto":
         plugin = GeminiPlugin(
@@ -161,6 +162,7 @@ def main() -> None:
             placement_policy="auto",
             initial_scale=2**16,
             max_norm=args.grad_clip,
+            enable_gradient_accumulation=(args.accumulation_steps > 1),
         )
     elif args.plugin == "zero2":
         plugin = LowLevelZeroPlugin(

colossalai/zero/gemini/gemini_ddp.py

@@ -726,11 +726,13 @@ class GeminiDDP(ModelWrapper):
                 chunk.cpu_shard.copy_(temp_chunk[chunk.shard_begin : chunk.shard_end])
 
             del temp_chunk
-        if self.reuse_fp16_chunk:
-            for chunk_32 in chunk_list:
-                chunk_16 = chunk_32.paired_chunk
-                assert chunk_16 is not None
-                chunk_16.payload.copy_(chunk_32.payload)
+
+        # sync running weights and master weights
+        if self.master_weights:
+            for loaded_chunk in chunk_list:
+                paired_chunk = loaded_chunk.paired_chunk
+                assert paired_chunk is not None
+                paired_chunk.payload.copy_(loaded_chunk.payload)
 
         for name, buf in persistent_buffers.items():
             if buf is not None: