add unfreeze llm option

egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py

@@ -126,6 +126,13 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         help="Whether to use lora to fine-tune llm.",
     )
 
+    parser.add_argument(
+        "--unfreeze-llm",
+        type=str2bool,
+        default=False,
+        help="Whether to unfreeze llm during training.",
+    )
+
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -587,30 +594,30 @@ def train_one_epoch(
                 valid_info.write_summary(
                     tb_writer, "train/valid_", params.batch_idx_train
                 )
-
+            if batch_idx != 0:
-            model.save_checkpoint(
+                model.save_checkpoint(
-                save_dir=params.exp_dir,
+                    save_dir=params.exp_dir,
-                tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
-                client_state={},
-                exclude_frozen_parameters=True
-            )
-
-            if rank == 0:
-                convert_zero_checkpoint_to_fp32_state_dict(
-                    params.exp_dir,
-                    f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}.pt",
                     tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
-                    exclude_frozen_parameters=True,
+                    client_state={},
-                )
+                    exclude_frozen_parameters=True
-                # save sampler state dict into checkpoint
-                sampler_state_dict = train_dl.sampler.state_dict()
-                torch.save(
-                    sampler_state_dict,
-                    f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}-sampler.pt",
-                )
-                os.system(
-                    f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}"
                 )
+
+                if rank == 0:
+                    convert_zero_checkpoint_to_fp32_state_dict(
+                        params.exp_dir,
+                        f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}.pt",
+                        tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
+                        exclude_frozen_parameters=True,
+                    )
+                    # save sampler state dict into checkpoint
+                    sampler_state_dict = train_dl.sampler.state_dict()
+                    torch.save(
+                        sampler_state_dict,
+                        f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}-sampler.pt",
+                    )
+                    os.system(
+                        f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}"
+                    )
         try:
             with torch.cuda.amp.autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
@@ -695,6 +702,9 @@ def run(rank, world_size, args):
     whisper_model = whisper.load_model(params.speech_encoder_path_or_name, "cpu")
     speech_encoder = whisper_model.encoder
     speech_encoder_dim = whisper_model.dims.n_audio_state
+    for name, param in speech_encoder.named_parameters(): 
+        param.requires_grad = False
+    speech_encoder.eval()
 
     tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name)
     if params.use_flash_attn:
@@ -713,16 +723,22 @@ def run(rank, world_size, args):
         attn_implementation=attn_implementation,
         torch_dtype=torch_dtype,
     )
-    if params.use_lora:
+
-        lora_config = LoraConfig(
+    if not params.unfreeze_llm:
-            r=64,
+        for name, param in llm.named_parameters(): 
-            lora_alpha=16,
+            param.requires_grad = False
-            target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"],
+        llm.eval()
-            lora_dropout=0.05,
+    else:
-            task_type="CAUSAL_LM",
+        if params.use_lora:
-        )
+            lora_config = LoraConfig(
-        llm = get_peft_model(llm, lora_config)
+                r=64,
-        llm.print_trainable_parameters()
+                lora_alpha=16,
+                target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"],
+                lora_dropout=0.05,
+                task_type="CAUSAL_LM",
+            )
+            llm = get_peft_model(llm, lora_config)
+            llm.print_trainable_parameters()
 
     special_tokens_dict = {
         "additional_special_tokens": [DEFAULT_SPEECH_TOKEN]
@@ -733,15 +749,6 @@ def run(rank, world_size, args):
 
     encoder_projector = EncoderProjector(speech_encoder_dim, llm.config.hidden_size, params.encoder_projector_ds_rate)
 
-    for name, param in speech_encoder.named_parameters(): 
-        param.requires_grad = False
-    speech_encoder.eval()
-
-    if not params.use_lora:
-        for name, param in llm.named_parameters(): 
-            param.requires_grad = False
-        llm.eval()
-
     model = SPEECH_LLM(
         speech_encoder,
         llm,