Upload VisionPixtralEncoderDecoder

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
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+<!-- Provide the basic links for the model. -->
+
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+[More Information Needed]
+
+### Downstream Use [optional]
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+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+### Out-of-Scope Use
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+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+**BibTeX:**
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+**APA:**
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+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
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+## More Information [optional]
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+[More Information Needed]
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+## Model Card Authors [optional]
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+## Model Card Contact
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config.json

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+{
+  "architectures": [
+    "VisionPixtralEncoderDecoder"
+  ],
+  "auto_map": {
+    "AutoModel": "modeling.VisionPixtralEncoderDecoder"
+  },
+  "decoder": {
+    "_attn_implementation_autoset": true,
+    "activation_dropout": 0.0,
+    "activation_function": "relu",
+    "add_cross_attention": true,
+    "attention_dropout": 0.0,
+    "classifier_dropout": 0.0,
+    "cross_attention_hidden_size": 1024,
+    "d_model": 1024,
+    "decoder_attention_heads": 16,
+    "decoder_ffn_dim": 4096,
+    "decoder_layerdrop": 0.0,
+    "decoder_layers": 12,
+    "dropout": 0.1,
+    "init_std": 0.02,
+    "is_decoder": true,
+    "layernorm_embedding": false,
+    "max_position_embeddings": 1024,
+    "model_type": "trocr",
+    "scale_embedding": true,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float32",
+    "use_cache": false,
+    "use_learned_position_embeddings": false,
+    "vocab_size": 50265
+  },
+  "encoder": {
+    "_attn_implementation_autoset": true,
+    "architectures": [
+      "PixtralVisionModelBatch"
+    ],
+    "attention_dropout": 0.0,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "image_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_composition": true,
+    "model_type": "pixtral_batch",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "patch_size": 16,
+    "rope_theta": 10000.0,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float32"
+  },
+  "is_encoder_decoder": true,
+  "model_type": "vision-encoder-decoder",
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3"
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "eos_token_id": 2,
+  "pad_token_id": 1,
+  "transformers_version": "4.51.3",
+  "use_cache": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e5153c6c3b48b4b6b75b5bbf7f2b0f0e413cced9f0d213525f23f6323f1c3dd
+size 2832038364

modeling.py

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+import torch
+import torch.nn as nn
+
+from typing import Optional, Union, Tuple
+
+from transformers.models.vision_encoder_decoder.modeling_vision_encoder_decoder import (
+    shift_tokens_right,
+    VisionEncoderDecoderModel
+)
+from transformers.modeling_outputs import Seq2SeqLMOutput
+from transformers import PreTrainedModel
+from transformers.models.pixtral.modeling_pixtral import apply_rotary_pos_emb, PixtralAttention, PixtralVisionModel
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import BaseModelOutput
+
+from pixtral_encoder_decoder.config import PixtralVisionModelBatchConfig, VisionPixtralEncoderDecoderConfig
+
+
+def position_ids_in_meshgrid_batch(patch_embeds, max_width):
+    """get the position ids of the batch. """
+    # unlike flattened patch_embeds, we use the padded ones, which mean each entry has the same w/h and thus the same ids
+    height, width = patch_embeds.shape[-2:]
+    mesh = torch.meshgrid(torch.arange(height), torch.arange(width), indexing="ij")
+    h_grid, v_grid = torch.stack(mesh, dim=-1).reshape(-1, 2).chunk(2, -1)
+    ids = h_grid * max_width + v_grid
+    # expand ids to batch size
+    ids = ids.reshape(1, -1).repeat(patch_embeds.shape[0], 1)
+    return ids
+
+
+def create_attention_mask_batch(w, h, image_sizes, patch_size):
+    def foo(i, j):
+        return ((torch.arange(h).unsqueeze(1) < i) & (torch.arange(w).unsqueeze(0) < j)).float()
+
+    mask = [foo(size[0] // patch_size, size[1] // patch_size) for size in image_sizes]
+    return torch.stack(mask, dim=0)
+
+
+def pixtral_attention_fix_forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    """Input shape: Batch x Time x Channel"""
+
+    batch_size, patches, _ = 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(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
+    value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
+
+    cos, sin = position_embeddings
+    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=1)
+
+    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
+
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    # 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.dropout, training=self.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(batch_size, patches, -1)
+
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, attn_weights
+
+
+# monkey patch a fix for unsqueeze dim for position embedds (since our input is batched and the old one is not)
+PixtralAttention.forward = pixtral_attention_fix_forward
+
+
+class PixtralVisionModelBatch(PixtralVisionModel):
+    config_class = PixtralVisionModelBatchConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+    def forward(
+            self,
+            pixel_values: torch.Tensor,
+            image_sizes: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            *args,
+            **kwargs,
+    ) -> Union[Tuple, BaseModelOutput]:
+        """
+        Returns:
+            pixel_values: tensor of token features for
+                all tokens of all images of shape (N_toks, D)
+        """
+        if attention_mask is None and image_sizes is None:
+            raise ValueError("Either `attention_mask` or `image_sizes` must be defined")
+        # pass images through initial convolution independently
+        patch_embeds = self.patch_conv(pixel_values)
+        # build attention mask based on image_sizes if not provided
+        if attention_mask is None:
+            h, w = patch_embeds.shape[-2:]
+            attention_mask = create_attention_mask_batch(w, h, image_sizes, self.patch_size).to(patch_embeds.device)
+            attention_mask = attention_mask.flatten(start_dim=-2)
+
+        # positional embeddings
+        position_ids = position_ids_in_meshgrid_batch(
+            patch_embeds, max_width=self.config.image_size // self.config.patch_size
+        )
+        position_embeddings = self.patch_positional_embedding(patch_embeds, position_ids)
+
+        # flatten patch_embeds
+        # seq_len = (h*w); hidden x seq_len -> seq_len x hidden.
+        patch_embeds = patch_embeds.flatten(start_dim=-2).transpose(-1, -2)
+
+        attention_mask = _prepare_4d_attention_mask(attention_mask, torch.float)
+
+        patch_embeds = self.ln_pre(patch_embeds)
+
+        out = self.transformer(
+            patch_embeds,
+            attention_mask=attention_mask,
+            position_embeddings=position_embeddings,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+            return_dict=return_dict,
+        )
+        return out
+
+
+class VisionPixtralEncoderDecoder(VisionEncoderDecoderModel):
+    config_class = VisionPixtralEncoderDecoderConfig
+
+    def __init__(self, config,
+                 encoder: Optional[PixtralVisionModelBatch] = None,
+                 decoder: Optional[PreTrainedModel] = None):
+        super().__init__(config, encoder, decoder)
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.Tensor] = None,
+            image_sizes: Optional[torch.Tensor] = None,
+            encoder_attention_mask: Optional[torch.Tensor] = None,
+            decoder_input_ids: Optional[torch.LongTensor] = None,
+            decoder_attention_mask: Optional[torch.BoolTensor] = None,
+            encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            decoder_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,
+            **kwargs,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # num_items_in_batch is only needed for loss computation
+        num_items_in_batch = kwargs.pop("num_items_in_batch", None)
+
+        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
+
+        kwargs_decoder = {
+            argument[len("decoder_"):]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+        }
+
+        if encoder_outputs is None:
+            if pixel_values is None:
+                raise ValueError("You have to specify pixel_values")
+            if encoder_attention_mask is None and image_sizes is None:
+                raise ValueError("Either `encoder_attention_mask` or `image_sizes` must be defined")
+            if encoder_attention_mask is None:
+                h, w = pixel_values.shape[-2:]
+                h = h // self.encoder.patch_size  # simulate convolution to get num_patches
+                w = w // self.encoder.patch_size  # simulate convolution to get num_patches
+                encoder_attention_mask = create_attention_mask_batch(w, h, image_sizes, self.encoder.patch_size)
+                encoder_attention_mask = encoder_attention_mask.to(pixel_values.device)
+                encoder_attention_mask = encoder_attention_mask.flatten(start_dim=-2)
+
+            encoder_outputs = self.encoder(
+                pixel_values=pixel_values,
+                image_sizes=image_sizes,
+                attention_mask=encoder_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs_encoder,
+            )
+        elif isinstance(encoder_outputs, tuple):
+            encoder_outputs = BaseModelOutput(*encoder_outputs)
+
+        encoder_hidden_states = encoder_outputs[0]
+
+        # optionally project encoder_hidden_states
+        if (
+                self.encoder.config.hidden_size != self.decoder.config.hidden_size
+                and self.decoder.config.cross_attention_hidden_size is None
+        ):
+            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
+
+        # else:
+        # encoder_attention_mask = None
+
+        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+            decoder_input_ids = shift_tokens_right(
+                labels, self.config.pad_token_id, self.config.decoder_start_token_id
+            )
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            use_cache=use_cache,
+            past_key_values=past_key_values,
+            return_dict=return_dict,
+            **kwargs_decoder,
+        )
+
+        # Compute loss independent from decoder (as some shift the logits inside them)
+        loss = None
+        if labels is not None:
+            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
+
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.decoder.config.vocab_size,
+                num_items_in_batch=num_items_in_batch,
+            )
+
+        if not return_dict:
+            if loss is not None:
+                return (loss,) + decoder_outputs + encoder_outputs
+            else:
+                return decoder_outputs + encoder_outputs
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=decoder_outputs.logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )