feat✨: 添加Gigaspeech数据集支持，更新训练脚本以使用新数据集并优化模型加载逻辑，添加Qwen2audio模型

pyproject.toml

@@ -4,11 +4,14 @@ dependencies = [
   "datasets==3.2.0",
   "deepspeed==0.16.2",
   "evaluate==0.4.3",
+  "librosa>=0.10.2.post1",
   "markupsafe==2.1.5",
+  "numba>=0.60.0",
   "peft==0.14.0",
   "pip==24.3.1",
   "requests==2.32.3",
   "setuptools>=70.0.0",
+  "soundfile>=0.13.0",
   "torch==2.5.1+cu124",
   "torchaudio==2.5.1+cu124",
   "torchvision==0.20.1+cu124",

src/dataset_library/GigaspeechDataset.py

@@ -0,0 +1,97 @@
+from PIL import Image
+from torch.utils.data import Dataset
+import json
+import os
+from datasets import load_dataset
+
+
+class GigaspeechDataset(Dataset):
+    def __init__(self, audio_processor=None, text_processor=None, split="train"):
+        """
+        vis_root (string): Root directory of images (e.g. coco/images/)
+        ann_root (string): directory to store the annotation file
+        """
+
+        self.audio_processor = audio_processor
+        self.text_processor = text_processor
+        gs = load_dataset("speechcolab/gigaspeech", "xs")
+        self.data = gs[split]
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        audio = sample["audio"]["array"]
+        sampling_rate = sample["audio"]["sampling_rate"]
+        text = sample["text"]
+
+        if self.audio_processor is not None:
+            audio = self.audio_processor(audio)
+        if self.text_processor is not None:
+            text = self.text_processor(text)
+
+        chat = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio", "audio_url": ""},
+                    {
+                        "type": "text",
+                        "text": "Please convert the audio to text",
+                    },
+                ],
+            },
+            {"role": "assistant", "content": [{"type": "text", "text": text}]},
+        ]
+        return {
+            "audio": (audio, sampling_rate),
+            "chat": chat,
+        }
+
+
+class GigaspeechDatasetForGeneration(GigaspeechDataset):
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        audio = sample["audio"]["array"]
+        sampling_rate = sample["audio"]["sampling_rate"]
+        text = sample["text"]
+
+        if self.audio_processor is not None:
+            audio = self.audio_processor(audio)
+        if self.text_processor is not None:
+            text = self.text_processor(text)
+
+        chat = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio", "audio_url": ""},
+                    {
+                        "type": "text",
+                        "text": "Please convert the audio to text",
+                    },
+                ],
+            },
+        ]
+
+        return {
+            "audio": (audio, sampling_rate),
+            "chat": chat,
+            "answer": text,
+        }
+
+
+if __name__ == "__main__":
+    dataset = GigaspeechDataset(
+        split="train",
+    )
+    print(len(dataset))
+    print(dataset[0])
+    dataset = GigaspeechDatasetForGeneration(
+        split="train",
+    )
+    print(len(dataset))
+    print(dataset[0])
+    pass

src/dataset_library/factory.py

@@ -48,4 +48,13 @@ def get_dataset(
                 split="test",
             ),
         }
+
+    if dataset_name == "gigaspeech":
+        from .GigaspeechDataset import GigaspeechDataset, GigaspeechDatasetForGeneration
+
+        dataset = {
+            "train": GigaspeechDataset(split="train"),
+            "test": GigaspeechDataset(split="test"),
+            "generation": GigaspeechDatasetForGeneration(split="test"),
+        }
     return dataset

src/model_library/factory.py

@@ -0,0 +1,105 @@
+import torch
+from trl import (
+    get_kbit_device_map,
+    # get_peft_config,
+    get_quantization_config,
+)
+
+
+def get_model(model_args):
+    if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
+        torch_dtype = (
+            model_args.torch_dtype
+            if model_args.torch_dtype in ["auto", None]
+            else getattr(torch, model_args.torch_dtype)
+        )
+        quantization_config = get_quantization_config(model_args)
+        model_kwargs = dict(
+            revision=model_args.model_revision,
+            attn_implementation=model_args.attn_implementation,
+            torch_dtype=torch_dtype,
+            device_map=(
+                get_kbit_device_map() if quantization_config is not None else None
+            ),
+            quantization_config=quantization_config,
+        )
+        from transformers import Qwen2VLProcessor
+        from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified
+
+        model = Qwen2VLForConditionalGeneration_modified.from_pretrained(
+            model_args.model_name_or_path,
+            trust_remote_code=model_args.trust_remote_code,
+            **model_kwargs,
+        )
+        processor = Qwen2VLProcessor.from_pretrained(
+            model_args.model_name_or_path,
+            trust_remote_code=model_args.trust_remote_code,
+            padding_side="left",
+        )
+        print(model)
+        from model_library.qwen2vl import (
+            collate_fn_for_train,
+            collate_fn_for_evaluate,
+        )
+        from functools import partial
+
+        collate_fn_for_train = partial(collate_fn_for_train, processor=processor)
+        collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor)
+
+    if model_args.model_name_or_path == "Qwen/Qwen2-Audio-7B-Instruct":
+        torch_dtype = (
+            model_args.torch_dtype
+            if model_args.torch_dtype in ["auto", None]
+            else getattr(torch, model_args.torch_dtype)
+        )
+        quantization_config = get_quantization_config(model_args)
+        model_kwargs = dict(
+            revision=model_args.model_revision,
+            attn_implementation=model_args.attn_implementation,
+            torch_dtype=torch_dtype,
+            device_map=(
+                get_kbit_device_map() if quantization_config is not None else None
+            ),
+            quantization_config=quantization_config,
+        )
+        from transformers import Qwen2AudioProcessor, Qwen2AudioForConditionalGeneration
+
+        model = Qwen2AudioForConditionalGeneration.from_pretrained(
+            model_args.model_name_or_path,
+            trust_remote_code=model_args.trust_remote_code,
+            **model_kwargs,
+        )
+        processor = Qwen2AudioProcessor.from_pretrained(
+            model_args.model_name_or_path,
+            trust_remote_code=model_args.trust_remote_code,
+            padding_side="left",
+        )
+        print(model)
+        from model_library.qwen2audio import (
+            collate_fn_for_train,
+            collate_fn_for_evaluate,
+        )
+        from functools import partial
+
+        collate_fn_for_train = partial(collate_fn_for_train, processor=processor)
+        collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor)
+
+    if model_args.model_name_or_path == "VITA-MLLM/VITA-1.5":
+        # from transformers import
+        # from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified
+
+        model = Qwen2VLForConditionalGeneration_modified.from_pretrained(
+            model_args.model_name_or_path,
+            trust_remote_code=model_args.trust_remote_code,
+            **model_kwargs,
+        )
+        print(model)
+        from model_library.qwen2vl import (
+            collate_fn_for_train,
+            collate_fn_for_evaluate,
+        )
+        from functools import partial
+
+        collate_fn_for_train = partial(collate_fn_for_train, processor=processor)
+        collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor)
+    return model, processor, collate_fn_for_train, collate_fn_for_evaluate

src/model_library/qwen2audio/__init__.py

@@ -0,0 +1,4 @@
+from .collate_fn import collate_fn_for_evaluate, collate_fn_for_train
+from .model import Qwen2VLForConditionalGeneration_modified
+
+__all__ = ["collate_fn_for_train", "collate_fn_for_evaluate", "Qwen2VLForConditionalGeneration_modified"]

src/model_library/qwen2audio/collate_fn.py

@@ -0,0 +1,87 @@
+from transformers import Qwen2AudioProcessor
+
+
+def collate_fn_for_train(examples, processor: Qwen2AudioProcessor):
+    # Get the texts and images, and apply the chat template
+    texts = [
+        processor.apply_chat_template(example["chat"], tokenize=False)
+        for example in examples
+    ]
+    audios = [example["audio"][0] for example in examples]
+    # Tokenize the texts and process the images
+    batch = processor(
+        text=texts,
+        audios=audios,
+        return_tensors="pt",
+        padding=True,
+        sampling_rate=examples[0]["audio"][1],
+    )
+
+    # The labels are the input_ids, and we mask the padding tokens in the loss computation
+    labels = batch["input_ids"].clone()
+    labels[labels == processor.tokenizer.pad_token_id] = -100  #
+    # Ignore the image token index in the loss computation (model specific)
+    # 对<|im_start|>system *** <|im_end|>\n加掩码
+    im_start_token_id = processor.tokenizer.convert_tokens_to_ids("<|im_start|>")
+    im_end_token_id = processor.tokenizer.convert_tokens_to_ids("<|im_end|>")
+    system_token_id = processor.tokenizer.convert_tokens_to_ids("system")
+    user_token_id = processor.tokenizer.convert_tokens_to_ids("user")
+    assistant_token_id = processor.tokenizer.convert_tokens_to_ids("assistant")
+    # enter_token_id = processor.tokenizer.convert_tokens_to_ids("\n")
+    # print(im_start_token_id, im_end_token_id, system_token_id, user_token_id, assistant_token_id, enter_token_id, processor.tokenizer.pad_token_id)
+    # 151644 151645 8948 872 77091 None 151643
+
+    for i, label in enumerate(labels):
+        now_index = 0
+        while now_index < len(label):
+            if label[now_index] == im_start_token_id:
+                label[now_index] = -100
+                now_index += 1
+                if (
+                    label[now_index] == system_token_id
+                    or label[now_index] == user_token_id
+                ):
+                    while label[now_index] != im_end_token_id:
+                        label[now_index] = -100
+                        now_index += 1
+                    label[now_index] = -100
+                elif label[now_index] == assistant_token_id:
+                    label[now_index] = -100
+                    label[now_index + 1] = -100
+                    now_index += 2
+                    while (
+                        now_index < len(label) and label[now_index] != im_end_token_id
+                    ):
+                        now_index += 1
+            now_index += 1
+    batch["labels"] = labels
+    # batch["task_id"] = torch.tensor([0] * len(labels), dtype=torch.long)
+
+    return batch
+
+
+def collate_fn_for_evaluate(examples, processor: Qwen2AudioProcessor):
+    # Get the texts and images, and apply the chat template
+    texts = [
+        processor.apply_chat_template(
+            example["chat"], tokenize=False, add_generation_prompt=True
+        )
+        for example in examples
+    ]
+    # print(texts)
+    audios = [example["audio"] for example in examples]
+
+    # Tokenize the texts and process the images
+    batch = processor(text=texts, audios=audios, return_tensors="pt", padding=True)
+
+    answers = [example["answer"] for example in examples]
+    answers = processor(text=answers, return_tensors="pt", padding=True)
+    batch["answers_ids"] = answers["input_ids"]
+    batch["answers_mask"] = answers["attention_mask"]
+    # input_ids torch.Size([3, 370])
+    # attention_mask torch.Size([3, 370])
+    # pixel_values torch.Size([3888, 1176])
+    # image_grid_thw torch.Size([3, 3])
+    # answers_ids torch.Size([3, 10])
+    # answers_mask torch.Size([3, 10])
+    return batch

src/model_library/qwen2audio/model.py

@@ -0,0 +1,856 @@
+# from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention,
+from transformers.models.qwen2_vl.modeling_qwen2_vl import (
+    is_flash_attn_2_available,
+    Qwen2VLAttention,
+    Qwen2VLFlashAttention2,
+    Qwen2VLDecoderLayer,
+    Qwen2VLModel,
+    Qwen2VLForConditionalGeneration,
+    Qwen2VLConfig,
+    logger,
+    apply_rotary_pos_emb_vision,
+    Cache,
+    apply_multimodal_rotary_pos_emb,
+    repeat_kv,
+    is_flash_attn_greater_or_equal_2_10,
+    math,
+    Qwen2VLCausalLMOutputWithPast,
+    Qwen2VisionTransformerPretrainedModel,
+)
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_varlen_func
+
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+else:
+    flash_attn_varlen_func = None
+from transformers.cache_utils import DynamicCache
+from transformers.modeling_outputs import BaseModelOutputWithPast
+import torch
+from typing import Optional, List, Union, Tuple
+from torch.nn import CrossEntropyLoss
+
+from torch import nn
+from torch.nn import functional as F
+from torch import Tensor
+
+
+class LinearLayer(nn.Linear):
+    def forward(self, input: Tensor, **kwargs) -> Tensor:
+        return F.linear(input, self.weight, self.bias)
+
+
+class Qwen2VLAttention_modified(Qwen2VLAttention):
+    def __init__(self, config: Qwen2VLConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        self.q_proj = LinearLayer(
+            self.hidden_size, self.num_heads * self.head_dim, bias=True
+        )
+        self.k_proj = LinearLayer(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
+        )
+        self.v_proj = LinearLayer(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
+        )
+        self.o_proj = LinearLayer(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # will become mandatory in v4.46
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
+        )
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "cache_position": cache_position,
+            }  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # Fix precision issues in Qwen2-VL float16 inference
+        # Replace inf values with zeros in attention weights to prevent NaN propagation
+        if query_states.dtype == torch.float16:
+            attn_weights = torch.where(
+                torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights
+            )
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class Qwen2VLSdpaAttention_modified(Qwen2VLAttention_modified):
+    """
+    Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from Qwen2Attention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # will become mandatory in v4.46
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "Qwen2VLModel is using Qwen2VLSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                **kwargs,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states, **kwargs)
+        key_states = self.k_proj(hidden_states, **kwargs)
+        value_states = self.v_proj(hidden_states, **kwargs)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
+        )
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "cache_position": cache_position,
+            }  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+        is_causal = True if causal_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+class Qwen2VLFlashAttention2_modified(Qwen2VLAttention_modified):
+    """
+    Qwen2VL flash attention module, following Qwen2VL attention module. This module inherits from `Qwen2VLAttention`
+    as the weights of the module stays untouched. The only required change would be on the forward pass
+    where it needs to correctly call the public API of flash attention and deal with padding tokens
+    in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
+    config.max_window_layers layers.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # will become mandatory in v4.46
+        **kwargs,
+    ):
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states, **kwargs)
+        key_states = self.k_proj(hidden_states, **kwargs)
+        value_states = self.v_proj(hidden_states, **kwargs)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            cos, sin = self.rotary_emb(value_states, position_ids)
+        else:
+            cos, sin = position_embeddings
+
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
+        )
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "cache_position": cache_position,
+            }  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        if (
+            self.config.use_sliding_window
+            and getattr(self.config, "sliding_window", None) is not None
+            and self.layer_idx >= self.config.max_window_layers
+        ):
+            sliding_window = self.config.sliding_window
+        else:
+            sliding_window = None
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            sliding_window=sliding_window,
+            is_causal=self.is_causal,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+QWEN2_VL_ATTENTION_CLASSES = {
+    "eager": Qwen2VLAttention,
+    "flash_attention_2": Qwen2VLFlashAttention2_modified,
+    "sdpa": Qwen2VLSdpaAttention_modified,
+}
+
+
+class Qwen2VLDecoderLayer_modified(Qwen2VLDecoderLayer):
+    def __init__(self, config: Qwen2VLConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.self_attn = QWEN2_VL_ATTENTION_CLASSES[config._attn_implementation](
+            config, layer_idx
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[
+            Tuple[torch.Tensor, torch.Tensor]
+        ] = None,  # will become mandatory in v4.46
+        **kwargs,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class Qwen2VLModel_modified(Qwen2VLModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [
+                Qwen2VLDecoderLayer_modified(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # torch.jit.trace() doesn't support cache objects in the output
+        if use_cache and past_key_values is None and not torch.jit.is_tracing():
+            past_key_values = DynamicCache()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        # the hard coded `3` is for temporal, height and width.
+        if position_ids is None:
+            position_ids = cache_position.view(1, 1, -1).expand(
+                3, inputs_embeds.shape[0], -1
+            )
+        elif position_ids.dim() == 2:
+            position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask,
+            inputs_embeds,
+            cache_position,
+            past_key_values,
+            output_attentions,
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                    **kwargs,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                    **kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class Qwen2VLForConditionalGeneration_modified(Qwen2VLForConditionalGeneration):
+    def __init__(self, config):
+        super().__init__(config)
+        self.visual = Qwen2VisionTransformerPretrainedModel._from_config(
+            config.vision_config
+        )
+        self.model = Qwen2VLModel_modified(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.rope_deltas = None  # cache rope_deltas here
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        rope_deltas: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Union[Tuple, Qwen2VLCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+
+        >>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+        >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+
+        >>> messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+        ```"""
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.model.embed_tokens(input_ids)
+            if pixel_values is not None:
+                pixel_values = pixel_values.type(self.visual.get_dtype())
+                image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+                n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
+                n_image_features = image_embeds.shape[0]
+                if n_image_tokens != n_image_features:
+                    raise ValueError(
+                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                    )
+                image_mask = (
+                    (input_ids == self.config.image_token_id)
+                    .unsqueeze(-1)
+                    .expand_as(inputs_embeds)
+                    .to(inputs_embeds.device)
+                )
+                image_embeds = image_embeds.to(
+                    inputs_embeds.device, inputs_embeds.dtype
+                )
+                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+            if pixel_values_videos is not None:
+                pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
+                video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+                n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
+                n_video_features = video_embeds.shape[0]
+                if n_video_tokens != n_video_features:
+                    raise ValueError(
+                        f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
+                    )
+                video_mask = (
+                    (input_ids == self.config.video_token_id)
+                    .unsqueeze(-1)
+                    .expand_as(inputs_embeds)
+                    .to(inputs_embeds.device)
+                )
+                video_embeds = video_embeds.to(
+                    inputs_embeds.device, inputs_embeds.dtype
+                )
+                inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
+        if (
+            position_ids is None
+            and input_ids is not None
+            and (attention_mask is None or attention_mask.ndim == 2)
+        ):
+            # calculate RoPE index once per generation in the pre-fill stage only
+            if (
+                cache_position is not None and cache_position[0] == 0
+            ) or self.rope_deltas is None:
+                position_ids, rope_deltas = self.get_rope_index(
+                    input_ids, image_grid_thw, video_grid_thw, attention_mask
+                )
+                self.rope_deltas = rope_deltas
+            # then use the prev pre-calculated rope-deltas to get the correct position ids
+            else:
+                batch_size, seq_length, _ = inputs_embeds.shape
+                delta = (
+                    cache_position[0] + self.rope_deltas
+                    if cache_position is not None
+                    else 0
+                )
+                position_ids = torch.arange(seq_length, device=inputs_embeds.device)
+                position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+                if cache_position is not None:  # otherwise `deltas` is an int `0`
+                    delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
+                position_ids = position_ids.add(delta)
+                position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
+        outputs = self.model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            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,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return Qwen2VLCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            rope_deltas=self.rope_deltas,
+        )

src/train.py

@@ -1,16 +1,10 @@
-import torch
 from dataset_library.factory import get_dataset
 from transformers import (
-    AutoModelForVision2Seq,
-    AutoProcessor,
     TrainingArguments,
 )
 
 from trl import (
     TrlParser,
-    get_kbit_device_map,
-    # get_peft_config,
-    get_quantization_config,
 )
 from peft_library import get_peft_model
 
@@ -32,64 +26,12 @@ if __name__ == "__main__":
     training_args.remove_unused_columns = False
     training_args.dataset_kwargs = {"skip_prepare_dataset": True}
 
-    # peft_config = get_peft_config(dict(**vars(model_args)))
+    from model_library.factory import get_model
 
-    torch_dtype = (
+    model, processor, collate_fn_for_train, collate_fn_for_evaluate = get_model(
-        model_args.torch_dtype
+        model_args
-        if model_args.torch_dtype in ["auto", None]
-        else getattr(torch, model_args.torch_dtype)
-    )
-    quantization_config = get_quantization_config(model_args)
-    model_kwargs = dict(
-        revision=model_args.model_revision,
-        attn_implementation=model_args.attn_implementation,
-        torch_dtype=torch_dtype,
-        device_map=get_kbit_device_map() if quantization_config is not None else None,
-        quantization_config=quantization_config,
-    )
-    processor = AutoProcessor.from_pretrained(
-        model_args.model_name_or_path,
-        trust_remote_code=model_args.trust_remote_code,
-        padding_side="left",
     )
 
-    if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
-        # from transformers import Qwen2VLForConditionalGeneration
-        from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified
-
-        model = Qwen2VLForConditionalGeneration_modified.from_pretrained(
-            model_args.model_name_or_path,
-            trust_remote_code=model_args.trust_remote_code,
-            **model_kwargs,
-        )
-        print(model)
-        from model_library.qwen2vl import (
-            collate_fn_for_train,
-            collate_fn_for_evaluate,
-        )
-        from functools import partial
-
-        collate_fn_for_train = partial(collate_fn_for_train, processor=processor)
-        collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor)
-
-    if model_args.model_name_or_path == "VITA-MLLM/VITA-1.5":
-        # from transformers import 
-        # from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified
-
-        model = Qwen2VLForConditionalGeneration_modified.from_pretrained(
-            model_args.model_name_or_path,
-            trust_remote_code=model_args.trust_remote_code,
-            **model_kwargs,
-        )
-        print(model)
-        from model_library.qwen2vl import (
-            collate_fn_for_train,
-            collate_fn_for_evaluate,
-        )
-        from functools import partial
-
-        collate_fn_for_train = partial(collate_fn_for_train, processor=processor)
-        collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor)
     ################
     # Dataset
     ################
@@ -110,7 +52,7 @@ if __name__ == "__main__":
     elif model_args.peft_type == "LORA":
         from peft.tuners.lora import LoraConfig
 
-        peft_config = LoraConfig(target_modules=model_args.lora_target_modules)
+        peft_config = LoraConfig(target_modules=model_args.lora_target_modules, r=2)
 
         model = get_peft_model(model, peft_config)
 
@@ -122,7 +64,6 @@ if __name__ == "__main__":
 
     for dataset_name in script_args.dataset_name:
         dataset = get_dataset(dataset_name)
-        print(dataset)
         model.train()
 
         trainer = ContinualTrainer(

src/train.sh

@@ -1,14 +1,13 @@
 #!/bin/bash
 
 accelerate launch --config_file configs/accelerate_configs/deepspeed_zero2.yaml train.py \
-    --dataset_name OCR-VQA-200K \
+    --dataset_name gigaspeech \
     --use_peft \
-    --peft_type MMOELORA \
+    --peft_type LORA \
-    --model_name_or_path Qwen/Qwen2-VL-7B-Instruct \
+    --model_name_or_path Qwen/Qwen2-Audio-7B-Instruct \
     --lora_target_modules q_proj v_proj \
     --per_device_train_batch_size 1 \
     --per_device_eval_batch_size 2 \
-    --attn_implementation flash_attention_2 \
     --gradient_accumulation_steps 16 \
     --output_dir checkpoint/sft-llava-1.5-7b-hf \
     --bf16 \

src/utils/trainer.py

@@ -52,7 +52,6 @@ class ContinualTrainer(Trainer):
         # TODO: this needs to be fixed and made cleaner later.
         if self.args.past_index >= 0:
             self._past = outputs[self.args.past_index]
-        print(labels)
 
         if labels is not None:
             unwrapped_model = self.accelerator.unwrap_model(model)

uv.lock

@@ -124,6 +124,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "audioread"
+version = "3.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/db/d2/87016ca9f083acadffb2d8da59bfa3253e4da7eeb9f71fb8e7708dc97ecd/audioread-3.0.1.tar.gz", hash = "sha256:ac5460a5498c48bdf2e8e767402583a4dcd13f4414d286f42ce4379e8b35066d", size = 116513 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/57/8d/30aa32745af16af0a9a650115fbe81bde7c610ed5c21b381fca0196f3a7f/audioread-3.0.1-py3-none-any.whl", hash = "sha256:4cdce70b8adc0da0a3c9e0d85fb10b3ace30fbdf8d1670fd443929b61d117c33", size = 23492 },
+]
+
 [[package]]
 name = "certifi"
 version = "2024.12.14"
@@ -133,6 +142,51 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a5/32/8f6669fc4798494966bf446c8c4a162e0b5d893dff088afddf76414f70e1/certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56", size = 164927 },
 ]
 
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621 }
+wheels = [
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+]
+
 [[package]]
 name = "charset-normalizer"
 version = "3.4.1"
@@ -190,11 +244,14 @@ dependencies = [
     { name = "datasets" },
     { name = "deepspeed" },
     { name = "evaluate" },
+    { name = "librosa" },
     { name = "markupsafe" },
+    { name = "numba" },
     { name = "peft" },
     { name = "pip" },
     { name = "requests" },
     { name = "setuptools" },
+    { name = "soundfile" },
     { name = "torch" },
     { name = "torchaudio" },
     { name = "torchvision" },
@@ -215,11 +272,14 @@ requires-dist = [
     { name = "deepspeed", specifier = "==0.16.2" },
     { name = "evaluate", specifier = "==0.4.3" },
     { name = "flash-attn", marker = "extra == 'compile'", specifier = ">=2.7.2.post1" },
+    { name = "librosa", specifier = ">=0.10.2.post1" },
     { name = "markupsafe", specifier = "==2.1.5", index = "https://download.pytorch.org/whl/cu124" },
+    { name = "numba", specifier = ">=0.60.0" },
     { name = "peft", specifier = "==0.14.0" },
     { name = "pip", specifier = "==24.3.1" },
     { name = "requests", specifier = "==2.32.3", index = "https://pypi.org/simple" },
     { name = "setuptools", specifier = ">=70.0.0" },
+    { name = "soundfile", specifier = ">=0.13.0" },
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