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__init__.py

@@ -0,0 +1,42 @@
+"""AshishOCR model package - Custom OCR model based on vision-language architecture."""
+
+from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoProcessor, AutoImageProcessor
+
+from .configuration_ashish_ocr import (
+    AshishOcrConfig,
+    AshishOcrTextConfig,
+    AshishOcrVisionConfig,
+)
+from .modeling_ashish_ocr import (
+    AshishOcrPreTrainedModel,
+    AshishOcrTextModel,
+    AshishOcrVisionEncoder,
+    AshishOcrForConditionalGeneration,
+)
+from .processing_ashish_ocr import (
+    AshishOcrImageProcessor,
+    AshishOcrProcessor,
+)
+
+# Register model with transformers Auto classes
+AutoConfig.register("ashish_ocr", AshishOcrConfig)
+AutoConfig.register("ashish_ocr_text", AshishOcrTextConfig)
+AutoConfig.register("ashish_ocr_vision", AshishOcrVisionConfig)
+
+AutoModel.register(AshishOcrConfig, AshishOcrForConditionalGeneration)
+AutoModelForCausalLM.register(AshishOcrConfig, AshishOcrForConditionalGeneration)
+
+AutoImageProcessor.register(AshishOcrConfig, AshishOcrImageProcessor)
+AutoProcessor.register(AshishOcrConfig, AshishOcrProcessor)
+
+__all__ = [
+    "AshishOcrConfig",
+    "AshishOcrTextConfig",
+    "AshishOcrVisionConfig",
+    "AshishOcrPreTrainedModel",
+    "AshishOcrTextModel",
+    "AshishOcrVisionEncoder",
+    "AshishOcrForConditionalGeneration",
+    "AshishOcrImageProcessor",
+    "AshishOcrProcessor",
+]

config.json

@@ -2,6 +2,14 @@
   "architectures": [
     "AshishOcrForConditionalGeneration"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration_ashish_ocr.AshishOcrConfig",
+    "AutoModel": "modeling_ashish_ocr.AshishOcrForConditionalGeneration",
+    "AutoModelForCausalLM": "modeling_ashish_ocr.AshishOcrForConditionalGeneration",
+    "AutoModelForVision2Seq": "modeling_ashish_ocr.AshishOcrForConditionalGeneration",
+    "AutoProcessor": "processing_ashish_ocr.AshishOcrProcessor",
+    "AutoImageProcessor": "processing_ashish_ocr.AshishOcrImageProcessor"
+  },
   "model_type": "ashish_ocr",
   "text_config": {
     "model_type": "ashish_ocr_text",

configuration_ashish_ocr.py

@@ -0,0 +1,135 @@
+"""AshishOCR model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class AshishOcrVisionConfig(PretrainedConfig):
+    """Configuration class for AshishOCR vision encoder."""
+
+    model_type = "ashish_ocr_vision"
+
+    def __init__(
+        self,
+        hidden_size=1024,
+        depth=24,
+        num_heads=16,
+        attention_bias=True,
+        intermediate_size=4096,
+        hidden_act="silu",
+        hidden_dropout_prob=0.0,
+        initializer_range=0.02,
+        image_size=336,
+        patch_size=14,
+        out_hidden_size=1536,
+        rms_norm_eps=1e-05,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.depth = depth
+        self.num_heads = num_heads
+        self.attention_bias = attention_bias
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.initializer_range = initializer_range
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.out_hidden_size = out_hidden_size
+        self.rms_norm_eps = rms_norm_eps
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+
+
+class AshishOcrTextConfig(PretrainedConfig):
+    """Configuration class for AshishOCR text decoder."""
+
+    model_type = "ashish_ocr_text"
+
+    def __init__(
+        self,
+        vocab_size=59392,
+        hidden_size=1536,
+        intermediate_size=4608,
+        num_hidden_layers=16,
+        num_attention_heads=16,
+        num_key_value_heads=8,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        tie_word_embeddings=False,
+        attention_bias=False,
+        attention_dropout=0.0,
+        pad_token_id=59246,
+        eos_token_id=None,
+        num_nextn_predict_layers=1,
+        rope_parameters=None,
+        dtype="bfloat16",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.pad_token_id = pad_token_id
+        self.eos_token_id = eos_token_id if eos_token_id is not None else [59246, 59253]
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.rope_parameters = rope_parameters
+        self.dtype = dtype
+
+
+class AshishOcrConfig(PretrainedConfig):
+    """Configuration class for AshishOCR multimodal model."""
+
+    model_type = "ashish_ocr"
+    sub_configs = {"text_config": AshishOcrTextConfig, "vision_config": AshishOcrVisionConfig}
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_start_token_id=59256,
+        image_end_token_id=59257,
+        video_start_token_id=59258,
+        video_end_token_id=59259,
+        image_token_id=59280,
+        video_token_id=59281,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        if text_config is None:
+            text_config = {}
+        if vision_config is None:
+            vision_config = {}
+
+        self.text_config = AshishOcrTextConfig(**text_config)
+        self.vision_config = AshishOcrVisionConfig(**vision_config)
+
+        self.image_start_token_id = image_start_token_id
+        self.image_end_token_id = image_end_token_id
+        self.video_start_token_id = video_start_token_id
+        self.video_end_token_id = video_end_token_id
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+
+        # Inherit key parameters from text config
+        self.vocab_size = self.text_config.vocab_size
+        self.hidden_size = self.text_config.hidden_size

modeling_ashish_ocr.py

@@ -0,0 +1,579 @@
+"""AshishOCR model implementation."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_ashish_ocr import AshishOcrConfig, AshishOcrTextConfig, AshishOcrVisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class AshishOcrRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class AshishOcrRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=131072, base=10000, device=None):
+        super().__init__()
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.float32, device=device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        cos = emb.cos()
+        sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class AshishOcrMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class AshishOcrAttention(nn.Module):
+    def __init__(self, config: AshishOcrTextConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.attention_dropout = config.attention_dropout
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = AshishOcrRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+        )
+
+    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,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+        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)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
+
+        # Repeat k/v heads for grouped query attention
+        key_states = key_states.repeat_interleave(self.num_key_value_groups, dim=1)
+        value_states = value_states.repeat_interleave(self.num_key_value_groups, dim=1)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = F.dropout(attn_weights, p=self.attention_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(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 AshishOcrDecoderLayer(nn.Module):
+    def __init__(self, config: AshishOcrTextConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = AshishOcrAttention(config, layer_idx)
+        self.mlp = AshishOcrMLP(config)
+        self.input_layernorm = AshishOcrRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = AshishOcrRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    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,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        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,
+        )
+        hidden_states = residual + hidden_states
+
+        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
+
+
+# ==================== Vision Encoder ====================
+
+class AshishOcrVisionMLP(nn.Module):
+    def __init__(self, config: AshishOcrVisionConfig):
+        super().__init__()
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.act = ACT2FN[config.hidden_act]
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states):
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class AshishOcrVisionAttention(nn.Module):
+    def __init__(self, config: AshishOcrVisionConfig):
+        super().__init__()
+        self.num_heads = config.num_heads
+        self.head_dim = config.hidden_size // config.num_heads
+
+        self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.attention_bias)
+        self.proj = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        bsz, seq_len, _ = hidden_states.size()
+
+        qkv = self.qkv(hidden_states)
+        qkv = qkv.reshape(bsz, seq_len, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+
+        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        attn_output = torch.matmul(attn_weights, v)
+
+        attn_output = attn_output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+class AshishOcrVisionBlock(nn.Module):
+    def __init__(self, config: AshishOcrVisionConfig):
+        super().__init__()
+        self.norm1 = AshishOcrRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attn = AshishOcrVisionAttention(config)
+        self.norm2 = AshishOcrRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = AshishOcrVisionMLP(config)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(self.norm1(hidden_states))
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+class AshishOcrPatchEmbed(nn.Module):
+    def __init__(self, config: AshishOcrVisionConfig):
+        super().__init__()
+        self.patch_size = config.patch_size
+        self.temporal_patch_size = config.temporal_patch_size
+        self.proj = nn.Conv3d(
+            3,
+            config.hidden_size,
+            kernel_size=(config.temporal_patch_size, config.patch_size, config.patch_size),
+            stride=(config.temporal_patch_size, config.patch_size, config.patch_size),
+            bias=False,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # hidden_states: (B, C, T, H, W)
+        hidden_states = self.proj(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)  # (B, N, D)
+        return hidden_states
+
+
+class AshishOcrPatchMerger(nn.Module):
+    def __init__(self, config: AshishOcrVisionConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.out_hidden_size = config.out_hidden_size
+        self.spatial_merge_size = config.spatial_merge_size
+
+        self.mlp = nn.Sequential(
+            AshishOcrRMSNorm(config.hidden_size * config.spatial_merge_size ** 2, eps=config.rms_norm_eps),
+            nn.Linear(config.hidden_size * config.spatial_merge_size ** 2, config.out_hidden_size, bias=False),
+            nn.GELU(),
+            nn.Linear(config.out_hidden_size, config.out_hidden_size, bias=False),
+        )
+
+    def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        # Merge spatial patches
+        batch_size = hidden_states.shape[0]
+        merged_states = []
+
+        for b in range(batch_size):
+            t, h, w = grid_thw[b].tolist() if grid_thw.dim() > 1 else grid_thw.tolist()
+            states = hidden_states[b, :t*h*w]
+            states = states.view(t, h, w, -1)
+
+            # Merge spatial patches
+            h_new = h // self.spatial_merge_size
+            w_new = w // self.spatial_merge_size
+            states = states.view(t, h_new, self.spatial_merge_size, w_new, self.spatial_merge_size, -1)
+            states = states.permute(0, 1, 3, 2, 4, 5).contiguous()
+            states = states.view(t * h_new * w_new, -1)
+            merged_states.append(states)
+
+        hidden_states = torch.stack(merged_states, dim=0)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states
+
+
+class AshishOcrVisionEncoder(nn.Module):
+    def __init__(self, config: AshishOcrVisionConfig):
+        super().__init__()
+        self.config = config
+        self.patch_embed = AshishOcrPatchEmbed(config)
+        self.blocks = nn.ModuleList([AshishOcrVisionBlock(config) for _ in range(config.depth)])
+        self.merger = AshishOcrPatchMerger(config)
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_thw: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.patch_embed(pixel_values)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states)
+
+        if grid_thw is not None:
+            hidden_states = self.merger(hidden_states, grid_thw)
+
+        return hidden_states
+
+
+# ==================== Main Model ====================
+
+class AshishOcrPreTrainedModel(PreTrainedModel):
+    config_class = AshishOcrConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["AshishOcrDecoderLayer", "AshishOcrVisionBlock"]
+
+    def _init_weights(self, module):
+        std = self.config.text_config.initializer_range if hasattr(self.config, 'text_config') else 0.02
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+
+
+class AshishOcrTextModel(AshishOcrPreTrainedModel):
+    def __init__(self, config: AshishOcrTextConfig):
+        super().__init__(config)
+        self.config = config
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList(
+            [AshishOcrDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = AshishOcrRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = 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,
+    ) -> 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 inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        batch_size, seq_length = inputs_embeds.shape[:2]
+
+        if position_ids is None:
+            position_ids = torch.arange(seq_length, device=inputs_embeds.device).unsqueeze(0)
+
+        if past_key_values is None:
+            past_key_values = DynamicCache()
+
+        # Create causal mask
+        if attention_mask is None:
+            attention_mask = torch.ones((batch_size, seq_length), device=inputs_embeds.device)
+
+        causal_mask = self._prepare_attention_mask(attention_mask, seq_length, inputs_embeds.dtype, inputs_embeds.device)
+
+        hidden_states = inputs_embeds
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            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,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _prepare_attention_mask(self, attention_mask, seq_length, dtype, device):
+        # Create causal mask
+        causal_mask = torch.triu(torch.ones((seq_length, seq_length), device=device), diagonal=1)
+        causal_mask = causal_mask.masked_fill(causal_mask == 1, float("-inf"))
+        causal_mask = causal_mask.unsqueeze(0).unsqueeze(0)
+
+        # Expand attention mask
+        if attention_mask.dim() == 2:
+            extended_mask = attention_mask[:, None, None, :]
+            extended_mask = (1.0 - extended_mask) * float("-inf")
+            causal_mask = causal_mask + extended_mask
+
+        return causal_mask.to(dtype)
+
+
+class AshishOcrForConditionalGeneration(AshishOcrPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: AshishOcrConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.visual = AshishOcrVisionEncoder(config.vision_config)
+        self.model = AshishOcrTextModel(config.text_config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+
+        self.image_token_id = config.image_token_id
+        self.video_token_id = config.video_token_id
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = 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,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        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)
+
+            # Process images if provided
+            if pixel_values is not None:
+                image_embeds = self.visual(pixel_values, image_grid_thw)
+                image_mask = input_ids == self.image_token_id
+                inputs_embeds = inputs_embeds.clone()
+                inputs_embeds[image_mask] = image_embeds.view(-1, image_embeds.shape[-1])
+
+            # Process videos if provided
+            if pixel_values_videos is not None:
+                video_embeds = self.visual(pixel_values_videos, video_grid_thw)
+                video_mask = input_ids == self.video_token_id
+                inputs_embeds = inputs_embeds.clone()
+                inputs_embeds[video_mask] = video_embeds.view(-1, video_embeds.shape[-1])
+
+        outputs = self.model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            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 CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        pixel_values_videos=None,
+        image_grid_thw=None,
+        video_grid_thw=None,
+        **kwargs,
+    ):
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+
+        model_inputs = {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "attention_mask": attention_mask,
+            "inputs_embeds": inputs_embeds,
+            "pixel_values": pixel_values,
+            "pixel_values_videos": pixel_values_videos,
+            "image_grid_thw": image_grid_thw,
+            "video_grid_thw": video_grid_thw,
+        }
+        return model_inputs

preprocessor_config.json

@@ -1,4 +1,8 @@
 {
+    "auto_map": {
+        "AutoImageProcessor": "processing_ashish_ocr.AshishOcrImageProcessor",
+        "AutoProcessor": "processing_ashish_ocr.AshishOcrProcessor"
+    },
     "size": {"shortest_edge": 12544, "longest_edge": 9633792},
     "do_rescale": true,
     "patch_size": 14,
@@ -6,6 +10,6 @@
     "merge_size": 2,
     "image_mean": [0.48145466, 0.4578275, 0.40821073],
     "image_std": [0.26862954, 0.26130258, 0.27577711],
-    "image_processor_type": "Ashish46VImageProcessor",
-    "processor_class": "Ashish46VProcessor"
+    "image_processor_type": "AshishOcrImageProcessor",
+    "processor_class": "AshishOcrProcessor"
 }

processing_ashish_ocr.py

@@ -0,0 +1,164 @@
+"""AshishOCR processor for handling image and text inputs."""
+
+from typing import List, Optional, Union
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+class AshishOcrImageProcessor(BaseImageProcessor):
+    """Image processor for AshishOCR model."""
+
+    model_input_names = ["pixel_values", "image_grid_thw"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: dict = None,
+        do_rescale: bool = True,
+        rescale_factor: float = 1/255,
+        do_normalize: bool = True,
+        image_mean: list = None,
+        image_std: list = None,
+        min_pixels: int = 56 * 56,
+        max_pixels: int = 28 * 28 * 1280,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        merge_size: int = 2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.do_resize = do_resize
+        self.size = size if size is not None else {"shortest_edge": 336}
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073]
+        self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711]
+        self.min_pixels = min_pixels
+        self.max_pixels = max_pixels
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.merge_size = merge_size
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        **kwargs,
+    ) -> BatchFeature:
+        import numpy as np
+        import torch
+        from PIL import Image
+
+        if not isinstance(images, list):
+            images = [images]
+
+        processed_images = []
+        grid_thw = []
+
+        for image in images:
+            if isinstance(image, str):
+                image = Image.open(image).convert("RGB")
+            elif not isinstance(image, Image.Image):
+                image = Image.fromarray(np.array(image))
+
+            # Resize
+            width, height = image.size
+            target_size = self.size.get("shortest_edge", 336)
+
+            # Calculate resize dimensions
+            if width < height:
+                new_width = target_size
+                new_height = int(height * target_size / width)
+            else:
+                new_height = target_size
+                new_width = int(width * target_size / height)
+
+            # Ensure dimensions are divisible by patch_size
+            new_width = (new_width // self.patch_size) * self.patch_size
+            new_height = (new_height // self.patch_size) * self.patch_size
+
+            image = image.resize((new_width, new_height), Image.BILINEAR)
+
+            # Convert to tensor
+            image_array = np.array(image).astype(np.float32)
+
+            if self.do_rescale:
+                image_array = image_array * self.rescale_factor
+
+            if self.do_normalize:
+                image_array = (image_array - np.array(self.image_mean)) / np.array(self.image_std)
+
+            # HWC to CHW
+            image_tensor = torch.tensor(image_array).permute(2, 0, 1)
+
+            # Add temporal dimension for 3D conv: (C, H, W) -> (C, T, H, W)
+            image_tensor = image_tensor.unsqueeze(1).repeat(1, self.temporal_patch_size, 1, 1)
+
+            processed_images.append(image_tensor)
+
+            # Calculate grid size (T, H, W in patches)
+            t = 1
+            h = new_height // self.patch_size
+            w = new_width // self.patch_size
+            grid_thw.append([t, h, w])
+
+        pixel_values = torch.stack(processed_images, dim=0)
+        image_grid_thw = torch.tensor(grid_thw, dtype=torch.long)
+
+        return BatchFeature(data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw})
+
+
+class AshishOcrProcessor(ProcessorMixin):
+    """Processor for AshishOCR that combines image processor and tokenizer."""
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "AshishOcrImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        images: ImageInput = None,
+        videos: ImageInput = None,
+        padding: bool = False,
+        truncation: bool = None,
+        max_length: int = None,
+        return_tensors: str = None,
+        **kwargs,
+    ) -> BatchFeature:
+        encoding = BatchFeature()
+
+        if images is not None:
+            image_features = self.image_processor(images, **kwargs)
+            encoding.update(image_features)
+
+        if text is not None:
+            text_encoding = self.tokenizer(
+                text,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                return_tensors=return_tensors,
+                **kwargs,
+            )
+            encoding.update(text_encoding)
+
+        return encoding
+
+    def batch_decode(self, *args, **kwargs):
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

tokenizer_config.json

@@ -44,6 +44,6 @@
   "model_max_length": 655380,
   "pad_token": "<|endoftext|>",
   "padding_side": "left",
-  "processor_class": "Ashish46VProcessor",
+  "processor_class": "AshishOcrProcessor",
   "tokenizer_class": "TokenizersBackend"
 }