Merge branch 'feature/QwenForMoreInput' into develop

src/dataset_library/chem.py

@@ -0,0 +1,181 @@
+from PIL import Image
+from torch.utils.data import Dataset
+import json
+import os
+
+
+class ChemDataseet(Dataset):
+    def __init__(
+        self, vis_root, ann_path, vis_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.vis_root = vis_root
+
+        self.vis_processor = vis_processor
+        self.text_processor = text_processor
+        if split == "train":
+            self.data = self.create_data(ann_path, split=1)[:200]
+        elif split == "test":
+            self.data = self.create_data(ann_path, split=3)[:200]
+
+    def create_data(self, ann_path, split=1):
+        processed_data = []
+        with open(ann_path, "r") as f:
+            data = json.load(f)
+        for k in data.keys():
+            if data[k]["split"] != split:
+                continue  # 1 for training, 2 for validation, 3 for test
+            ext = os.path.splitext(data[k]["imageURL"])[1]
+            imageFile = k + ext
+            assert len(data[k]["questions"]) == len(data[k]["answers"])
+            for q, a in zip(data[k]["questions"], data[k]["answers"]):
+                if os.path.exists(os.path.join(self.vis_root, imageFile)):
+                    processed_data.append(
+                        {
+                            "question": q,
+                            "answer": a,
+                            "image_path": imageFile,
+                            "image_id": k,
+                            "title": data[k]["title"],
+                            "genre": data[k]["genre"],
+                        }
+                    )
+        return processed_data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        image = Image.open(os.path.join(self.vis_root, sample["image_path"])).convert(
+            "RGB"
+        )
+        question = sample["question"]
+        answer = sample["answer"]
+        if self.vis_processor is not None:
+            image = self.vis_processor(image)
+        if self.text_processor is not None:
+            question = self.text_processor(question)
+            answer = self.text_processor(answer)
+
+        chat = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {
+                        "type": "text",
+                        "text": f"[vqa] Based on the image, respond to this question with a short answer: {question}",
+                    },
+                ],
+            },
+            {"role": "assistant", "content": [{"type": "text", "text": answer}]},
+        ]
+        return {
+            "image": image,
+            "chat": chat,
+            "image_id": sample["image_id"],
+        }
+
+
+class OCRVQADatasetForGeneration(Dataset):
+    def __init__(
+        self, vis_root, ann_path, vis_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.vis_root = vis_root
+
+        self.vis_processor = vis_processor
+        self.text_processor = text_processor
+        if split == "train":
+            self.data = self.create_data(ann_path, split=1)[:200]
+        elif split == "test":
+            self.data = self.create_data(ann_path, split=3)[:200]
+
+        # self.instruction_pool = [
+        #     "[vqa] {}",
+        #     "[vqa] Based on the image, respond to this question with a short answer: {}",
+        # ]
+
+    def create_data(self, ann_path, split=1):
+        processed_data = []
+        with open(ann_path, "r") as f:
+            data = json.load(f)
+        for k in data.keys():
+            if data[k]["split"] != split:
+                continue  # 1 for training, 2 for validation, 3 for test
+            ext = os.path.splitext(data[k]["imageURL"])[1]
+            imageFile = k + ext
+            assert len(data[k]["questions"]) == len(data[k]["answers"])
+            for q, a in zip(data[k]["questions"], data[k]["answers"]):
+                if os.path.exists(os.path.join(self.vis_root, imageFile)):
+                    processed_data.append(
+                        {
+                            "question": q,
+                            "answer": a,
+                            "image_path": imageFile,
+                            "image_id": k,
+                            "title": data[k]["title"],
+                            "genre": data[k]["genre"],
+                        }
+                    )
+        return processed_data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        image = Image.open(os.path.join(self.vis_root, sample["image_path"])).convert(
+            "RGB"
+        )
+        question = sample["question"]
+        answer = sample["answer"]
+        if self.vis_processor is not None:
+            image = self.vis_processor(image)
+        if self.text_processor is not None:
+            question = self.text_processor(question)
+            answer = self.text_processor(answer)
+
+        chat = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {
+                        "type": "text",
+                        "text": f"[vqa] Based on the image, respond to this question with a short answer: {question}",
+                    },
+                ],
+            }
+            # {"role": "assistant", "content": answer},
+        ]
+        return {
+            "image": image,
+            "chat": chat,
+            "answer": answer,
+            "image_id": sample["image_id"],
+        }
+
+if __name__ == "__main__":
+    dataset = ChemDataseet(
+        "/home/zyy/research/accelerate/dataset/chem/images/",
+        "/home/zyy/research/accelerate/dataset/chem/qwen_data/conversations_loc_train.jsonl",
+        split="train",
+    )
+    print(len(dataset))
+    print(dataset[0])
+    dataset = OCRVQADatasetForGeneration(
+        "/home/zyy/research/accelerate/dataset/OCR-VQA-200K/images",
+        "/home/zyy/research/accelerate/dataset/OCR-VQA-200K/dataset.json",
+        split="train",
+    )
+    print(len(dataset))
+    print(dataset[0])
+    pass

src/evaluation.py

@@ -51,7 +51,7 @@ if __name__ == "__main__":
     print(model)
 
     if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
-        from collatefn_library.qwen2 import (
+        from model_library.qwen2 import (
             collate_fn_for_train,
             collate_fn_for_evaluate,
         )

src/model_library/qwen2vl/__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/qwen2vl/collate_fn.py

@@ -10,7 +10,6 @@ def collate_fn_for_train(examples, processor: Qwen2VLProcessor):
     ]
     # print(texts)
     images = [example["image"] for example in examples]
-
     # Tokenize the texts and process the images
     batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
 
@@ -52,7 +51,7 @@ def collate_fn_for_train(examples, processor: Qwen2VLProcessor):
                         now_index += 1
             now_index += 1
     batch["labels"] = labels
-    # batch["task_id"] = torch.tensor([0] * len(labels), dtype=torch.long)
+    batch["task_id"] = torch.tensor([0] * len(labels), dtype=torch.long)
 
     return batch
 
@@ -80,48 +79,5 @@ def collate_fn_for_evaluate(examples, processor: Qwen2VLProcessor):
     # 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]) 
+    # answers_mask torch.Size([3, 10])
     return batch
-
-
-if __name__ == "__main__":
-    from transformers import Qwen2VLProcessor
-    from dataset_library.OCRVQADataset import OCRVQADatasetForGeneration
-
-    processor = Qwen2VLProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-    dataset = OCRVQADatasetForGeneration(
-        vis_root="/home/zyy/research/accelerate/dataset/OCR-VQA-200K/images",
-        ann_path="/home/zyy/research/accelerate/dataset/OCR-VQA-200K/dataset.json",
-        split="train",
-    )
-    examples = [dataset[i] for i in range(3)]
-    # print(collate_fn_for_evaluate(examples, processor))
-    from transformers import AutoModelForCausalLM
-    
-    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-    from accelerate import Accelerator
-    from torch.utils.data import DataLoader
-    
-    val_dataloader = DataLoader(
-        dataset,
-        batch_size=3,
-        collate_fn=lambda x: collate_fn_for_evaluate(x, processor),
-    )
-    accelerator = Accelerator()
-    model = accelerator.prepare(model)
-    val_dataloader = accelerator.prepare(val_dataloader)
-    
-    for batch in val_dataloader:
-        completion = model.generate(
-            input_ids=batch["input_ids"],
-            attention_mask=batch["attention_mask"],
-            pixel_values=batch["pixel_values"],
-            image_grid_thw=batch["image_grid_thw"],
-            max_length=100,
-        )
-        target = batch["answers_ids"]
-        generated_text = [out_ids[len(in_ids) :] for out_ids, in_ids in zip(completion, batch["input_ids"])]
-        generated_text = processor.tokenizer.batch_decode(generated_text)
-        target_text = processor.tokenizer.batch_decode(target)
-        print(generated_text, target_text)
-        

src/model_library/qwen2vl/model.py

@@ -0,0 +1,696 @@
+# 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 *
+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
+
+
+QWEN2_VL_ATTENTION_CLASSES = {
+    "eager": Qwen2VLAttention,
+    "flash_attention_2": Qwen2VLFlashAttention2,
+    "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/peft_library/tuners/mmoelora/layer.py

@@ -151,9 +151,10 @@ class MMOELoraLinear(nn.Module, MMOELoraLayer):
     def forward(self, x: torch.Tensor, *args, **kwargs):
         self._check_forward_args(x, *args, **kwargs)
         adapter_names = kwargs.pop("adapter_names", None)
-        task_id = kwargs.pop(
-            "task_id", torch.tensor([0] * len(x), dtype=torch.long).to(x.device)
-        )
+        # task_id = kwargs.pop(
+        #     "task_id", torch.tensor([0] * len(x), dtype=torch.long).to(x.device)
+        # )
+        task_id = kwargs.pop("task_id", torch.tensor([0] * len(x), dtype=torch.long))
         previous_dtype = x.dtype
 
         if self.disable_adapters:  # No adapter

src/train.py

@@ -53,14 +53,16 @@ if __name__ == "__main__":
         padding_side="left",
     )
 
-    model = AutoModelForVision2Seq.from_pretrained(
-        model_args.model_name_or_path,
-        trust_remote_code=model_args.trust_remote_code,
-        **model_kwargs,
-    )
-
     if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
-        from collatefn_library.qwen2 import (
+        # 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,
+        )
+        from model_library.qwen2vl import (
             collate_fn_for_train,
             collate_fn_for_evaluate,
         )

src/utils/args.py

@@ -1,7 +1,6 @@
 from dataclasses import dataclass, field
 from typing import Optional
 from trl import ScriptArguments, ModelConfig
-from transformers import TrainingArguments
 
 
 @dataclass