fix bugs

egs/speech_llm/ASR_LLM/run.sh

@@ -1,15 +1,14 @@
 
-export PYTHONPATH=$PYTHONPATH:/workspace/asr/icefall
+export PYTHONPATH=$PYTHONPATH:/mnt/samsung-t7/yuekai/asr/icefall_llm
 # pip install k2==1.24.3.dev20230524+cuda11.8.torch2.0.1 -f https://k2-fsa.github.io/k2/cuda.html
 # pip install -r whisper/requirements.txt
-
-method=mask_predict
-# method=cif_ar_distill_embedding
-torchrun --nproc_per_node 8 ./parawhisper/train.py \
-  --max-duration 200 \
-  --exp-dir parawhisper/exp_large_v2_${method} \
-  --model-name large-v2 \
+export CUDA_VISIBLE_DEVICES=0,1
+torchrun --nproc_per_node 2 ./whisper_llm_zh/train.py \
+  --max-duration 80 \
+  --exp-dir ./whisper_llm_zh/exp_test \
+  --speech-encoder-path-or-name tiny \
+  --llm-path-or-name Qwen/Qwen1.5-0.5B-Chat \
   --manifest-dir data/fbank \
-  --method $method \
   --deepspeed \
-  --deepspeed_config ./whisper/ds_config_zero1.json
+  --deepspeed_config ./whisper_llm_zh/ds_config_zero1.json \
+  --use-flash-attn False

egs/speech_llm/ASR_LLM/whisper_llm_zh/model.py

@@ -1,7 +1,8 @@
 from torch import nn
 import torch
+from transformers.trainer_pt_utils import LabelSmoother
 
-DEFAULT_SPEECH_TOKEN = -1997 # "<speech>"
+IGNORE_TOKEN_ID = LabelSmoother.ignore_index
 
 class EncoderProjector(nn.Module):
 
@@ -18,7 +19,6 @@ class EncoderProjector(nn.Module):
         return x
 
 class SPEECH_LLM(nn.Module):
-    # https://github.com/ddlBoJack/SLAM-LLM/blob/main/src/slam_llm/models/slam_model.py
     def __init__(
         self,
         encoder: nn.Module,
@@ -28,8 +28,12 @@ class SPEECH_LLM(nn.Module):
         super().__init__()
 
         self.encoder = encoder
+        for name, param in encoder.named_parameters(): 
+            param.requires_grad = False
         self.encoder.eval()
         self.llm = llm
+        for name, param in llm.named_parameters(): 
+            param.requires_grad = False
         self.llm.eval()
         self.encoder_projector = encoder_projector
         self.encoder_outputs_downsample_rate = 4
@@ -39,11 +43,11 @@ class SPEECH_LLM(nn.Module):
         batch_size, sequence_length = input_ids.shape
         left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.llm.config.pad_token_id))
         # 1. Create a mask to know where special speech tokens are
-        special_speech_token_mask = input_ids == DEFAULT_SPEECH_TOKEN
+        special_speech_token_mask = input_ids == self.llm.config.default_speech_token_id
         num_special_speech_tokens = torch.sum(special_speech_token_mask, dim=-1)
         # Compute the maximum embed dimension
         max_embed_dim = (num_special_speech_tokens.max() * (speech_len - 1)) + sequence_length
-        batch_indices, non_speech_indices = torch.where(input_ids != DEFAULT_SPEECH_TOKEN)
+        batch_indices, non_speech_indices = torch.where(input_ids != self.llm.config.default_speech_token_id)
 
         # 2. Compute the positions where text should be written
         # Calculate new positions for text tokens in merged speech-text sequence.
@@ -65,7 +69,7 @@ class SPEECH_LLM(nn.Module):
         )
         if labels is not None:
             final_labels = torch.full(
-                (batch_size, max_embed_dim), self., dtype=input_ids.dtype, device=input_ids.device
+                (batch_size, max_embed_dim), IGNORE_TOKEN_ID, dtype=input_ids.dtype, device=input_ids.device
             )
         # In case the Vision model or the Language model has been offloaded to CPU, we need to manually
         # set the corresponding tensors into their correct target device.
@@ -128,17 +132,17 @@ class SPEECH_LLM(nn.Module):
             speech_features, inputs_embeds, input_ids, attention_mask, labels
         )
 
-        outputs = self.language_model(
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        logits = outputs[0]
+        # outputs = self.llm(
+        #     attention_mask=attention_mask,
+        #     position_ids=position_ids,
+        #     past_key_values=past_key_values,
+        #     inputs_embeds=inputs_embeds,
+        #     use_cache=use_cache,
+        #     output_attentions=output_attentions,
+        #     output_hidden_states=output_hidden_states,
+        #     return_dict=return_dict,
+        # )
+        # logits = outputs[0]
 
         model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels, position_ids=position_ids)
 

egs/speech_llm/ASR_LLM/whisper_llm_zh/multi_dataset.py

@@ -256,3 +256,15 @@ class MultiDataset:
         )
 
         return aishell_cuts
+
+
+    def aishell_dev_cuts(self) -> CutSet:
+        logging.info("About to get multidataset dev cuts")
+
+        # AISHELL
+        logging.info("Loading Aishell set in lazy mode")
+        aishell_dev_cuts = load_manifest_lazy(
+            self.fbank_dir / "aishell_cuts_dev.jsonl.gz"
+        )
+
+        return aishell_dev_cuts

egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py

@@ -39,13 +39,13 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 import deepspeed
 import k2
-import optim
+# import optim
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 import whisper
 from asr_datamodule import AsrDataModule
-from model import SPEECH_LLM, EncoderProjector
+from model import SPEECH_LLM, EncoderProjector, IGNORE_TOKEN_ID
 from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
 from label_smoothing import LabelSmoothingLoss
 from lhotse import CutSet, load_manifest
@@ -59,7 +59,7 @@ from torch.cuda.amp import GradScaler
 from torch.nn.functional import pad as pad_tensor
 # from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
-from whisper_decoder_forward_monkey_patch import replace_whisper_decoder_forward
+
 from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 
 from icefall import diagnostics
@@ -78,13 +78,12 @@ from icefall.utils import (
 )
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+import transformers
+from transformers.trainer_pt_utils import LabelSmoother
+IGNORE_TOKEN_ID = LabelSmoother.ignore_index
+DEFAULT_SPEECH_TOKEN = "<speech>"
 
-
-def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
-    if isinstance(model, DDP):
-        # get underlying nn.Module
-        model = model.module
+def set_batch_count(model: nn.Module, batch_count: float) -> None:
     for module in model.modules():
         if hasattr(module, "batch_count"):
             module.batch_count = batch_count
@@ -240,6 +239,13 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    parser.add_argument(
+        "--use-flash-attn",
+        type=str2bool,
+        default=True,
+        help="Whether to use flash attention.",
+    )
+
     parser = deepspeed.add_config_arguments(parser)
     add_model_arguments(parser)
 
@@ -272,6 +278,7 @@ def get_params() -> AttributeDict:
     params = AttributeDict(
         {
             "allowed_excess_duration_ratio": 0.1,
+            "subsampling_factor": 2,
             "frame_shift_ms": 10,
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
@@ -357,7 +364,7 @@ def get_params() -> AttributeDict:
 def compute_loss(
     params: AttributeDict,
     tokenizer: AutoTokenizer,
-    model: Union[nn.Module, DDP],
+    model: nn.Module,
     batch: dict,
     is_training: bool,
 ) -> Tuple[Tensor, MetricsTracker]:
@@ -397,7 +404,6 @@ def compute_loss(
             texts.append(
                 tokenizer.apply_chat_template(
                     msg,
-                    chat_template=TEMPLATE,
                     tokenize=True,
                     add_generation_prompt=False,
                     padding="max_length",
@@ -405,8 +411,9 @@ def compute_loss(
                     truncation=True,
                 )
             )
-        # model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
         input_ids = torch.tensor(texts, dtype=torch.int)
+        # response = tokenizer.batch_decode(input_ids, skip_special_tokens=True)[0]
         target_ids = input_ids.clone()
         target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID
         attention_mask = input_ids.ne(tokenizer.pad_token_id)
@@ -453,47 +460,49 @@ def compute_loss(
     allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
     batch = filter_uneven_sized_batch(batch, allowed_max_frames)
 
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    device = next(model.parameters()).device
     feature = batch["inputs"]
 
     assert feature.ndim == 3
     feature = feature.to(device)
     feature = feature.transpose(1, 2)  # (N, C, T)
 
-    # feature_lens = supervisions["num_frames"].to(device)
-
     batch_idx_train = params.batch_idx_train
     supervisions = batch["supervisions"]
     texts = batch["supervisions"]["text"]
     # remove spaces in texts
     texts = [normalize_text_alimeeting(text) for text in texts]
 
-    #     messages = [
-    #     {"role": "system", "content": "You are a helpful assistant."},
-    #     {"role": "user", "content": prompt}
-    # ]
+    messages = []
+    for i, text in enumerate(texts):
+        message = [
+        {"role": "system", "content": "你是一个能处理音频的助手。"},
+        {"role": "user", "content": f"请转写音频为文字 {DEFAULT_SPEECH_TOKEN}"},
+        {"role": "assistant", "content": text},
+        ]
+        messages.append(message)
+
     input_ids, attention_mask, target_ids = preprocess(
-        texts, tokenizer, max_len=512
+        messages, tokenizer, max_len=128
     )
 
-    # decoder_criterion = LabelSmoothingLoss(
-    #     ignore_index=50256, label_smoothing=0.1, reduction="sum"
-    # )
+    target_ids = target_ids.type(torch.LongTensor)
+    input_ids = input_ids.type(torch.LongTensor)
 
-    # # ignore the first 3 tokens, which are always <|lang_id|>, <|transcibe|>, <|notimestampes|>
-    # ignore_prefix_size = 3
-    # with torch.set_grad_enabled(is_training):
-    #     encoder_out = model.encoder(feature)
-    #     text_logits = model.decoder(prev_outputs_tokens.to(device), encoder_out)
-    #     text_logits = text_logits[:, ignore_prefix_size:, :]
-    #     target_tokens = target_tokens[:, ignore_prefix_size:]
-    #     loss = decoder_criterion(text_logits, target_tokens.to(device))
-
-    # assert loss.requires_grad == is_training
+    with torch.set_grad_enabled(is_training):
+        model_outpus = model(
+            fbank=feature,
+            input_ids=input_ids.to(device),
+            attention_mask=attention_mask.to(device),
+            labels=target_ids.to(device),
+        )
+        loss = model_outpus.loss
+    assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
+        feature_lens = supervisions["num_frames"]
         info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
 
     # Note: We use reduction=sum while computing the loss.
@@ -505,7 +514,7 @@ def compute_loss(
 def compute_validation_loss(
     params: AttributeDict,
     tokenizer: whisper.tokenizer.Tokenizer,
-    model: Union[nn.Module, DDP],
+    model: nn.Module,
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
 ) -> MetricsTracker:
@@ -540,9 +549,9 @@ def compute_validation_loss(
 def train_one_epoch(
     params: AttributeDict,
     tokenizer: AutoTokenizer,
-    model: Union[nn.Module, DDP],
+    model: nn.Module,
     optimizer: torch.optim.Optimizer,
-    scheduler: LRSchedulerType,
+    scheduler: torch.optim.lr_scheduler,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     tb_writer: Optional[SummaryWriter] = None,
@@ -609,18 +618,26 @@ def train_one_epoch(
             model.save_checkpoint(
                 save_dir=params.exp_dir,
                 tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
-                client_state={"sampler": train_dl.sampler.state_dict()},
+                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,
+                )
+                # 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(
@@ -698,19 +715,32 @@ def run(rank, world_size, args):
 
     logging.info("About to create model")
     
-    if 'whisper' in params.speech_encoder_path_or_name:
+    # if 'whisper' in params.speech_encoder_path_or_name:
     replace_whisper_encoder_forward()
     # TODO: directly loading from whisper-ft checkpoint
     # whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt
-        speech_encoder = whisper.load_model(params.model_name, "cpu").encoder
-        speech_encoder_dim = speech_encoder.dims.n_audio_ctx
+    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
+    
+    if params.use_flash_attn:
+        attn_implementation = "flash_attention_2"
+
+    else:
+        attn_implementation = "eager"
 
     llm = AutoModelForCausalLM.from_pretrained(
         params.llm_path_or_name,
-        attn_implemented="flash_attention_2",
-        device_map="cpu"
+        attn_implementation=attn_implementation,
     )
     tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name)
+    tokenizer.padding_side  = 'left'
+    special_tokens_dict = {
+        "additional_special_tokens": [DEFAULT_SPEECH_TOKEN]
+    }
+    tokenizer.add_special_tokens(special_tokens_dict)
+    llm.config.pad_token_id = tokenizer.pad_token_id
+    llm.config.default_speech_token_id = tokenizer.convert_tokens_to_ids(DEFAULT_SPEECH_TOKEN)
 
     encoder_projector = EncoderProjector(speech_encoder_dim, llm.config.hidden_size)
 
@@ -723,6 +753,11 @@ def run(rank, world_size, args):
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
+    logging.info("Trainable parameters (excluding model.eval modules):")
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            logging.info(f"{name}: {param.shape}")
+
     if torch.cuda.is_available():
         device = torch.device("cuda", rank)
     else:
@@ -770,12 +805,14 @@ def run(rank, world_size, args):
     #     sampler_state_dict = checkpoints["sampler"]
     # else:
     #     sampler_state_dict = None
+    sampler_state_dict = None
     # TODO: load sampler state dict
     train_dl = data_module.train_dataloaders(
         train_cuts, sampler_state_dict=sampler_state_dict
     )
 
-    valid_cuts = multi_dataset.dev_cuts()
+    # valid_cuts = multi_dataset.dev_cuts()
+    valid_cuts = multi_dataset.aishell_dev_cuts()
     valid_dl = data_module.valid_dataloaders(valid_cuts)
 
     if args.tensorboard and rank == 0:
@@ -818,14 +855,20 @@ def run(rank, world_size, args):
         model.save_checkpoint(
             save_dir=params.exp_dir,
             tag=f"epoch-{params.cur_epoch}",
-            client_state={"sampler": train_dl.sampler.state_dict()},
+            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}.pt",
                 tag=f"epoch-{params.cur_epoch}",
+                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}-sampler.pt")
+    
             os.system(f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}")