# coding=utf-8 # Copyright 2024 the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # You may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Mllama model.""" import math import os from typing import List, Optional, Tuple, Union import habana_frameworks.torch.core as htcore import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from transformers.cache_utils import Cache from transformers.modeling_attn_mask_utils import AttentionMaskConverter from transformers.modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.models.mllama.configuration_mllama import ( MllamaConfig, MllamaTextConfig, ) from transformers.models.mllama.modeling_mllama import ( MllamaCrossAttentionDecoderLayer, MllamaForCausalLM, MllamaForConditionalGeneration, MllamaSelfAttentionDecoderLayer, MllamaTextCrossAttention, MllamaTextModel, MllamaTextRMSNorm, MllamaTextSelfAttention, MllamaVisionAttention, MllamaVisionConfig, MllamaVisionEncoder, MllamaVisionEncoderLayer, MllamaVisionModel, _prepare_aspect_ratio_attention_mask, apply_rotary_pos_emb, repeat_kv, ) from transformers.utils import logging from ...modeling_attn_mask_utils import _gaudi_prepare_4d_causal_attention_mask try: from habana_frameworks.torch.hpex.normalization import FusedRMSNorm as FusedRMSNorm except ImportError: print("Not using HPU fused kernel for RMSNorm") FusedRMSNorm = None logger = logging.get_logger(__name__) try: from habana_frameworks.torch.hpex.kernels import FusedSDPA except ImportError: print("Not using HPU fused scaled dot-product attention kernel.") FusedSDPA = None class GaudiMllamaTextRMSNorm(MllamaTextRMSNorm): def __init__(self, hidden_size, eps=1e-6): """ MllamaTextRMSNorm is equivalent to T5LayerNorm """ super().__init__(hidden_size, eps) self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): """Copied from MllamaTextRMSNorm::forward https://github.com/huggingface/transformers/blob/53fad641cfdb5105e2470bcf3ef17ea8e25cc300/src/transformers/models/mllama/modeling_mllama.py#L475. The only differences are: - Using FusedRMSNorm""" orig_dtype = hidden_states.dtype if FusedRMSNorm is not None: hidden_states = FusedRMSNorm.apply(hidden_states.float(), self.weight.float(), self.variance_epsilon) return hidden_states.to(orig_dtype) else: 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(orig_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class ModuleFusedSDPA(torch.nn.Module): def __init__(self, fusedSDPA): super().__init__() self._hpu_kernel_fsdpa = fusedSDPA def forward(self, query, key, value, attn_mask, dropout_p, is_casual, scale): return self._hpu_kernel_fsdpa.apply(query, key, value, attn_mask, dropout_p, is_casual, scale) def _prepare_cross_attention_mask( cross_attention_mask: torch.Tensor, num_vision_tokens: int, dtype: str, token_idx: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Copied from _prepare_cross_attention_mask: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L99 The only differences are: - if there's pading in cross_attention_mask in the right. do not masked it, or else it will impact softmax in crossattention """ # reshape so it can be used by attn module # Updated cross_attention_mask alignment logic to ensure memory alignment with dtype size (256-byte boundary) cross_attention_mask = cross_attention_mask.to(dtype) dtype_size = ( torch.finfo(dtype).bits if torch.is_floating_point(torch.tensor(0, dtype=dtype)) else torch.iinfo(dtype).bits ) alignment = int(256 / (dtype_size / 8)) aligned_num_vision_tokens = math.ceil(num_vision_tokens / alignment) * alignment batch_size, text_total_length, _, original_dim = cross_attention_mask.shape cross_attention_mask = cross_attention_mask.repeat_interleave(aligned_num_vision_tokens, dim=3) cross_attention_mask = cross_attention_mask.view(batch_size, text_total_length, -1) cross_attention_mask = cross_attention_mask[:, :, : num_vision_tokens * original_dim] cross_attention_mask = cross_attention_mask.unsqueeze(1) # invert the mask inverted_cross_attn_mask = (1.0 - cross_attention_mask).to(dtype) cross_attention_mask = inverted_cross_attn_mask.masked_fill( inverted_cross_attn_mask.to(torch.bool), torch.finfo(dtype).min ) # apply full-row bias, which return 4D tensor of shape [B, H, S1, 1] where value is 0 if the a full row in cross attn mask's # last dimension contains negative infinity values, otherwise it's 1 negative_inf_value = torch.finfo(dtype).min full_text_row_masked_out_mask = ( (cross_attention_mask != negative_inf_value).any(dim=-1).type_as(cross_attention_mask)[..., None] ) if token_idx is not None: cross_attention_mask_2 = cross_attention_mask[:, :, token_idx:, 1] cross_attention_mask *= full_text_row_masked_out_mask cross_attention_mask[:, :, token_idx:, 1] = cross_attention_mask_2 else: cross_attention_mask *= full_text_row_masked_out_mask return cross_attention_mask, full_text_row_masked_out_mask class GaudiMllamaVisionSdpaAttention(MllamaVisionAttention): def __init__(self, config: MllamaVisionConfig): super().__init__(config) self.fused_scaled_dot_product_attention = ModuleFusedSDPA(FusedSDPA) if FusedSDPA else None # Adapted from MllamaVisionAttention def forward( self, hidden_state: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, use_flash_attention: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. """ Copied from MllamaVisionSdpaAttention::forward:https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L283 The only differences are: - add use_flash_attention """ if output_attentions: logger.warning_once( "MllamaModel is using MllamaVisionSdpaAttention, 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_state=hidden_state, attention_mask=attention_mask, output_attentions=output_attentions, ) query = self.q_proj(hidden_state) key = self.k_proj(hidden_state) value = self.v_proj(hidden_state) batch_size, q_seq_len, _ = query.shape _, kv_seq_len, _ = key.shape query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim) key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim) value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim) query = query.transpose(1, 2) key = key.transpose(1, 2) value = value.transpose(1, 2) if use_flash_attention and FusedSDPA: attn_output = self.fused_scaled_dot_product_attention(query, key, value, attention_mask, 0.0, False, None) else: attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(batch_size, q_seq_len, -1) output = self.o_proj(attn_output) return output, None class GaudiMllamaVisionEncoderLayer(MllamaVisionEncoderLayer): def __init__(self, config: MllamaVisionConfig, is_gated: bool = False): super(GaudiMllamaVisionEncoderLayer, self).__init__(config=config, is_gated=is_gated) self.self_attn = GaudiMllamaVisionSdpaAttention(config) def forward( self, hidden_state: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, use_flash_attention: Optional[bool] = False, ): """ Copied from MllamaVisionEncoderLayer::forward:https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L348 The only differences are: - add use_flash_attention """ # Self Attention residual = hidden_state hidden_state = self.input_layernorm(hidden_state) hidden_state, attn_weights = self.self_attn( hidden_state, attention_mask=attention_mask, use_flash_attention=use_flash_attention ) if self.is_gated: hidden_state = self.gate_attn.tanh() * hidden_state hidden_state = residual + hidden_state # Feed forward residual = hidden_state hidden_state = self.post_attention_layernorm(hidden_state) hidden_state = self.mlp(hidden_state) if self.is_gated: hidden_state = self.gate_ffn.tanh() * hidden_state hidden_state = residual + hidden_state outputs = (hidden_state,) if output_attentions: outputs += (attn_weights,) return outputs class GaudiMllamaVisionEncoder(MllamaVisionEncoder): def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, use_flash_attention: Optional[bool] = False, ) -> Union[Tuple, BaseModelOutput]: """ Copied from MllamaVisionEncoder::forward:https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L394 The only differences are: - add use_flash_attention """ 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 encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for encoder_layer in self.layers: if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_state=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, use_flash_attention=use_flash_attention, ) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) htcore.mark_step() hidden_states = layer_outputs[0] if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class GaudiMllamaTextCrossAttention(MllamaTextCrossAttention): def __init__(self, config: Optional[MllamaTextConfig] = None, layer_idx: Optional[int] = None): super().__init__(config, layer_idx) self.fused_scaled_dot_product_attention = ModuleFusedSDPA(FusedSDPA) if FusedSDPA else None def forward( self, hidden_states: torch.Tensor, cross_attention_states: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, attention_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, token_idx: Optional[torch.Tensor] = None, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """ Copied from MllamaTextCrossAttention::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L512 The only differences are: - add token_idx support - add support if past_key_value is not Cache - cache position is None - add use_flash_attention and flash_attention_recompute """ """Input shape: Batch x Time x Channel""" bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) query_states = self.q_norm(query_states) if cross_attention_states is not None: key_states = self.k_proj(cross_attention_states) value_states = self.v_proj(cross_attention_states) key_states = key_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2) if not (FusedSDPA and use_flash_attention): key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) key_states = self.k_norm(key_states) if past_key_value is not None: # if we have a new image + new tokens, we only computed key_states on that new image # we still update the cross key states, past_image, new_image. And use it! if isinstance(past_key_value, Cache): key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, {"cache_position": cache_position} ) else: if token_idx is not None: past_key_value[0].index_copy_(2, token_idx - 1, key_states) past_key_value[1].index_copy_(2, token_idx - 1, value_states) key_states = past_key_value[0] value_states = past_key_value[1] else: key_states = torch.cat((past_key_value[0], key_states), dim=2) value_states = torch.cat((past_key_value[1], value_states), dim=2) if use_cache and not isinstance(past_key_value, Cache): past_key_value = [key_states, value_states] elif not isinstance(past_key_value, Cache) and past_key_value is not None: key_states, value_states = (past_key_value[0], past_key_value[1]) elif cache_position is not None and cache_position[0] != 0: key_states, value_states = ( past_key_value.key_cache[self.layer_idx], past_key_value.value_cache[self.layer_idx], ) else: raise ValueError( "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!" ) if FusedSDPA and use_flash_attention: import habana_frameworks.torch.hpu as ht if q_len == 1: # next token use_recompute = True if os.getenv("QUANT_CONFIG", "") else False with ht.sdp_kernel(enable_recompute=use_recompute): attn_output = self.fused_scaled_dot_product_attention( query_states, key_states, value_states, attention_mask, 0.0, False, None ) else: with ht.sdp_kernel(enable_recompute=flash_attention_recompute): attn_output = self.fused_scaled_dot_product_attention( query_states, key_states, value_states, attention_mask, 0.0, False, None ) else: 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 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(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 GaudiMllamaTextSelfAttention(MllamaTextSelfAttention): def __init__(self, config: Optional[MllamaTextConfig] = None, layer_idx: Optional[int] = None): super().__init__(config, layer_idx) self.fused_scaled_dot_product_attention = ModuleFusedSDPA(FusedSDPA) if FusedSDPA else None def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, position_embeddings: torch.Tensor, output_attentions: bool = False, use_cache: bool = False, past_key_value=None, cache_position=None, token_idx: Optional[torch.Tensor] = None, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, **kwargs, ): """ Copied from MllamaTextSelfAttention::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L733 The only differences are: - add token_idx support - add support if past_key_value is not Cache - add use_flash_attention and flash_attention_recompute """ 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 = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} if isinstance(past_key_value, Cache): key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, cache_kwargs ) else: if token_idx is not None: past_key_value[0].index_copy_(2, token_idx - 1, key_states) past_key_value[1].index_copy_(2, token_idx - 1, value_states) key_states = past_key_value[0] value_states = past_key_value[1] else: key_states = torch.cat((past_key_value[0], key_states), dim=2) value_states = torch.cat((past_key_value[1], value_states), dim=2) if use_cache and not isinstance(past_key_value, Cache): past_key_value = [key_states, value_states] if FusedSDPA and use_flash_attention: import habana_frameworks.torch.hpu as ht if q_len == 1: # next token use_recompute = True if os.getenv("QUANT_CONFIG", "") else False with ht.sdp_kernel(enable_recompute=use_recompute): attn_output = self.fused_scaled_dot_product_attention( query_states, key_states, value_states, attention_mask, 0.0, False, None ) else: with ht.sdp_kernel(enable_recompute=flash_attention_recompute): attn_output = self.fused_scaled_dot_product_attention( query_states, key_states, value_states, attention_mask, 0.0, False, None ) else: 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 # 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.view(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 # Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer class GaudiMllamaSelfAttentionDecoderLayer(MllamaSelfAttentionDecoderLayer): def __init__(self, config: MllamaTextConfig, layer_idx: int) -> None: super(GaudiMllamaSelfAttentionDecoderLayer, self).__init__(config, layer_idx) self.self_attn = GaudiMllamaTextSelfAttention(config, layer_idx=layer_idx) self.input_layernorm = GaudiMllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, cross_attention_states: Optional[torch.Tensor] = None, cross_attention_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = 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 v4.45 token_idx: Optional[torch.Tensor] = None, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Copied from MllamaSelfAttentionDecoderLayer::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L904 The only differences are: - add token_idx input - add use_flash_attention and flash_attention_recompute """ 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, token_idx=token_idx, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, ) 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 GaudiMllamaCrossAttentionDecoderLayer(MllamaCrossAttentionDecoderLayer): def __init__(self, config: MllamaTextConfig, layer_idx: int) -> None: super(GaudiMllamaCrossAttentionDecoderLayer, self).__init__(config, layer_idx) self.cross_attn = GaudiMllamaTextCrossAttention(config, layer_idx=layer_idx) def forward( self, hidden_states: torch.Tensor, cross_attention_states: torch.Tensor, cross_attention_mask: torch.Tensor, attention_mask: torch.Tensor, full_text_row_masked_out_mask: Tuple[torch.Tensor, torch.Tensor], position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[torch.Tensor] = None, token_idx: Optional[torch.Tensor] = None, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, ) -> Tuple[torch.Tensor]: """ Copied from MllamaCrossAttentionDecoderLayer::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L989 The only differences are: - add token_idx support - pass use_cache to cross_attn - add use_flash_attention and flash_attention_recompute """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states, attn_weights, past_key_value = self.cross_attn( hidden_states=hidden_states, attention_mask=cross_attention_mask, cross_attention_states=cross_attention_states, past_key_value=past_key_value, output_attentions=output_attentions, cache_position=cache_position, use_cache=use_cache, token_idx=token_idx, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, ) hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) if full_text_row_masked_out_mask is not None: hidden_states = full_text_row_masked_out_mask[:, 0] * hidden_states # type: ignore hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) if use_cache: outputs += (past_key_value,) return outputs class GaudiMllamaTextModel(MllamaTextModel): def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, cross_attention_states: Optional[torch.FloatTensor] = None, cross_attention_mask: Optional[torch.Tensor] = None, full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, past_key_values: Optional[Union[Cache, 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, token_idx: Optional[torch.Tensor] = None, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, ) -> Union[Tuple, BaseModelOutputWithPast]: """ Copied from MllamaTextModel::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L1617 The only differences are: - add token_idx support - add support if past_key_value is not Cache - add use_flash_attention and flash_attention_recompute """ 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 and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) hidden_states = inputs_embeds if isinstance(past_key_values, Cache): past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 else: past_seen_tokens = past_key_values[0][0].shape[2] if past_key_values is not None else 0 ignore_cache_position = True # Ignoring cache position for HPU, or else hpu graph may has issue if ignore_cache_position is False: if cache_position is None: cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) else: if position_ids is None: position_ids = torch.arange( past_seen_tokens, inputs_embeds.shape[1] + past_seen_tokens, dtype=torch.long, device=inputs_embeds.device, ) position_ids = position_ids.unsqueeze(0) cache_position = None causal_mask = _gaudi_prepare_4d_causal_attention_mask( attention_mask, input_ids.shape, inputs_embeds, past_seen_tokens, ) # 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 if isinstance(past_key_values, Cache) else () for idx, decoder_layer in enumerate(self.layers): if not self.training and ( not torch.distributed.is_initialized() or torch.distributed.get_world_size() == 1 ): htcore.mark_step() if output_hidden_states: all_hidden_states += (hidden_states,) # For text-only path we should skip cross attention layers. # Let's check if the layer is cross attention layer and if we have cross attention states # or cached cross attention states. is_cross_attention_layer = idx in self.cross_attention_layers is_cross_attention_cache_empty = past_key_values is None or ( past_key_values is not None and past_key_values.get_seq_length(idx) == 0 if isinstance(past_key_values, Cache) else False ) if is_cross_attention_layer and cross_attention_states is None and is_cross_attention_cache_empty: continue if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, cross_attention_states, cross_attention_mask, causal_mask, full_text_row_masked_out_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: if isinstance(past_key_values, Cache): past_key_value = past_key_values else: past_key_value = None if past_key_values is None else past_key_values[idx] layer_outputs = decoder_layer( hidden_states, cross_attention_states=cross_attention_states, cross_attention_mask=cross_attention_mask, attention_mask=causal_mask, full_text_row_masked_out_mask=full_text_row_masked_out_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, token_idx=token_idx, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, ) hidden_states = layer_outputs[0] if use_cache: if isinstance(past_key_values, Cache): next_decoder_cache = layer_outputs[2 if output_attentions else 1] else: 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, ) def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, ): """ Copied from MllamaTextModel::_update_causal_mask: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L1768 The only differences are: - add support if past_key_value is not Cache """ if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. if isinstance(past_key_values, Cache): past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 else: past_seen_tokens = past_key_values[0][0].shape[2] if past_key_values is not None else 0 # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward # TODO: we have only SDPA currently and there's a bug when attn-bias is passed. Need to add eager attn and return the line # self.config._attn_implementation == "sdpa" and if self.config._attn_implementation == "sdpa" and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, min_dtype=min_dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type == "cuda" and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask class GaudiMllamaForCausalLM(MllamaForCausalLM): def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, cross_attention_states: Optional[torch.LongTensor] = None, cross_attention_mask: Optional[torch.LongTensor] = None, full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, past_key_values: Optional[Union[Cache, 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, token_idx: Optional[torch.Tensor] = None, trim_logits: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, logits_bf16: Optional[bool] = False, **loss_kwargs, ) -> Union[Tuple, CausalLMOutputWithPast]: """ Copied from MllamaForCausalLM::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L1871 The only differences are: - add token_idx input - add logits handle if token_idx is not None - add use_flash_attention and flash_attention_recompute """ 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, cross_attention_states=cross_attention_states, attention_mask=attention_mask, position_ids=position_ids, cross_attention_mask=cross_attention_mask, full_text_row_masked_out_mask=full_text_row_masked_out_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, token_idx=token_idx, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, ) hidden_states = outputs[0] _, seq_len, _ = hidden_states.shape if seq_len > 1 and trim_logits and not self.training: if token_idx is not None: hidden_states = hidden_states.index_select(1, token_idx - 1) else: hidden_states = hidden_states[:, -1, :] if token_idx is None and logits_to_keep != 0: slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) else: logits = self.lm_head(hidden_states) if not logits_bf16: logits = logits.float() loss = None if labels is not None: loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs) 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, ) class GaudiMllamaForConditionalGeneration(MllamaForConditionalGeneration): def __init__(self, config: MllamaConfig): # sdpa is better for vision model in HPU config._attn_implementation = "sdpa" super().__init__(config) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, aspect_ratio_mask: Optional[torch.Tensor] = None, aspect_ratio_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_mask: Optional[torch.Tensor] = None, cross_attention_states: 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, token_idx: Optional[torch.Tensor] = None, trim_logits: Optional[bool] = False, use_flash_attention: Optional[bool] = False, flash_attention_recompute: Optional[bool] = False, logits_bf16: Optional[bool] = False, **loss_kwargs, ) -> Union[Tuple, CausalLMOutputWithPast]: """ Copied from MllamaForConditionalGeneration::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L2077 The only differences are: - add token_idx input - add use_flash_attention and flash_attention_recompute """ 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 (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if pixel_values is not None and inputs_embeds is not None: raise ValueError( "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one" ) if pixel_values is not None and cross_attention_states is not None: raise ValueError("`pixel_values` and `cross_attention_states` cannot be provided simultaneously") if pixel_values is not None: if aspect_ratio_ids is None: raise ValueError("`aspect_ratio_ids` must be provided if `pixel_values` is provided") # get vision tokens from vision model vision_outputs = self.vision_model( pixel_values=pixel_values, aspect_ratio_ids=aspect_ratio_ids, aspect_ratio_mask=aspect_ratio_mask, output_hidden_states=output_hidden_states, output_attentions=output_attentions, return_dict=return_dict, use_flash_attention=use_flash_attention, ) cross_attention_states = vision_outputs[0] cross_attention_states = self.multi_modal_projector(cross_attention_states).reshape( -1, cross_attention_states.shape[-2], self.hidden_size ) if cross_attention_mask is not None: cross_attention_mask, full_text_row_masked_out_mask = _prepare_cross_attention_mask( cross_attention_mask, num_vision_tokens=self.vision_model.num_patches, dtype=self.dtype, token_idx=token_idx, ) else: full_text_row_masked_out_mask = None if cross_attention_mask is not None: if cache_position is not None: cross_attention_mask = cross_attention_mask[:, :, cache_position] full_text_row_masked_out_mask = full_text_row_masked_out_mask[:, :, cache_position] elif past_key_values is not None: if token_idx is not None: cross_attention_mask = torch.index_select(cross_attention_mask, -2, token_idx - 1) full_text_row_masked_out_mask = torch.index_select( full_text_row_masked_out_mask, -2, token_idx - 1 ) else: cross_attention_mask = cross_attention_mask[:, :, -1:] full_text_row_masked_out_mask = full_text_row_masked_out_mask[:, :, -1:] outputs = self.language_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, cross_attention_states=cross_attention_states, cross_attention_mask=cross_attention_mask, full_text_row_masked_out_mask=full_text_row_masked_out_mask, past_key_values=past_key_values, use_cache=use_cache, inputs_embeds=inputs_embeds, labels=labels, output_hidden_states=output_hidden_states, output_attentions=output_attentions, return_dict=return_dict, cache_position=cache_position, logits_to_keep=logits_to_keep, token_idx=token_idx, trim_logits=trim_logits, use_flash_attention=use_flash_attention, flash_attention_recompute=flash_attention_recompute, logits_bf16=logits_bf16, **loss_kwargs, ) return outputs def prepare_inputs_for_generation( self, input_ids=None, inputs_embeds=None, attention_mask=None, position_ids=None, pixel_values=None, aspect_ratio_ids=None, aspect_ratio_mask=None, cross_attention_mask=None, past_key_values=None, use_cache=False, cache_position=None, logits_to_keep=None, **kwargs, ): """ Copied from MllamaForConditionalGeneration::prepare_inputs_for_generation: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L2208 The only differences are: - add token_idx handling - add bucket_internal handling - add use_flash_attention and flash_attention_recompute """ token_idx = kwargs.get("token_idx", None) bucket_internal = kwargs.get("bucket_internal", None) if past_key_values is not None: if token_idx is not None: input_ids = torch.index_select(input_ids, 1, token_idx - 1) elif inputs_embeds is not None: # Exception 1 input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] elif bucket_internal and token_idx is not None: # for the 1st token we can slice the inputs till token idx for the fwd pass. input_ids = input_ids[:, :token_idx] attention_mask = attention_mask[:, :token_idx] if cross_attention_mask is not None: cross_attention_mask = cross_attention_mask[:, :token_idx, ...] # TODO: we have no attention_mask so this won't work, check if we really won't need attention mask and find another way if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: if token_idx is not None: position_ids = torch.index_select(position_ids, 1, token_idx - 1) else: position_ids = position_ids[:, -input_ids.shape[1] :] # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture. position_ids = position_ids.clone(memory_format=torch.contiguous_format) # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None} else: # The clone here is for the same reason as for `position_ids`. model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None} if logits_to_keep is not None: model_inputs["logits_to_keep"] = logits_to_keep # keep cache_position implementation as None for HPU cache_position = None model_inputs.update( { "position_ids": position_ids, "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, "cross_attention_mask": cross_attention_mask, "token_idx": token_idx, "trim_logits": kwargs.get("trim_logits"), "use_flash_attention": kwargs.get("use_flash_attention"), "flash_attention_recompute": kwargs.get("flash_attention_recompute"), "logits_bf16": kwargs.get("logits_bf16"), } ) # If we're in pre-fill or cacheless decoding step, then we need pixel_values and aspect ratios # to compute image hidden states, otherwise they are cached within each cross attn layer if (input_ids == self.config.image_token_index).any(): model_inputs["pixel_values"] = pixel_values model_inputs["aspect_ratio_ids"] = aspect_ratio_ids model_inputs["aspect_ratio_mask"] = aspect_ratio_mask return model_inputs def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder, **kwargs): """ Copied from MllamaForConditionalGeneration::_update_model_kwargs_for_generation: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L2274 The only differences are: - add token_idx handling """ cross_attention_mask_prev = model_kwargs.get("cross_attention_mask", None) model_kwargs = super(MllamaForConditionalGeneration, self)._update_model_kwargs_for_generation( outputs=outputs, model_kwargs=model_kwargs, is_encoder_decoder=is_encoder_decoder, **kwargs, ) # add cross-attn mask for new token if cross_attention_mask_prev is not None: token_idx = model_kwargs.get("token_idx", None) token_idx_cpu = model_kwargs.get( "token_idx_cpu", None ) # returns an integer so following slicing ops happen using int instead of tensor if token_idx is not None: mask = cross_attention_mask_prev[:, token_idx_cpu - 2 : token_idx_cpu - 1, ...] cross_attention_mask_prev.index_copy_(1, token_idx - 1, mask) model_kwargs["cross_attention_mask"] = cross_attention_mask_prev else: model_kwargs["cross_attention_mask"] = torch.cat( [cross_attention_mask_prev, cross_attention_mask_prev[:, -1:, ...]], dim=1 ) return model_kwargs class GaudiMllamaVisionModel(MllamaVisionModel): def forward( self, pixel_values: torch.Tensor, aspect_ratio_ids: torch.Tensor, aspect_ratio_mask: torch.Tensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, use_flash_attention: Optional[bool] = False, ) -> Union[BaseModelOutput, Tuple[torch.Tensor, ...]]: """ Copied from MllamaVisionModel::forward: https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/models/mllama/modeling_mllama.py#L1425 The only differences are: - optimize perf of stage "Collect intermediate layer outputs from encoder output" """ 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 batch_size, num_concurrent_media, num_tiles, num_channels, height, width = pixel_values.shape pixel_values = pixel_values.reshape(batch_size * num_concurrent_media * num_tiles, num_channels, height, width) aspect_ratio_ids = aspect_ratio_ids.reshape(batch_size * num_concurrent_media, -1) # Patch embedding target_dtype = self.patch_embedding.weight.dtype target_device = self.patch_embedding.weight.device patch_embeds = self.patch_embedding(pixel_values.to(target_device, target_dtype)) hidden_state = patch_embeds.flatten(2).transpose(1, 2) # Tile embeddings _, num_patches, dim = hidden_state.shape hidden_state = hidden_state.reshape(batch_size * num_concurrent_media, num_tiles, -1, dim) hidden_state = self.pre_tile_positional_embedding(hidden_state, aspect_ratio_ids) # Add cls token hidden_state = hidden_state.reshape(batch_size * num_concurrent_media * num_tiles, num_patches, dim) hidden_state = self.apply_class_embedding(hidden_state) num_patches += 1 # Position embeddings hidden_state = hidden_state.reshape(batch_size * num_concurrent_media, num_tiles, num_patches, dim) hidden_state = self.gated_positional_embedding(hidden_state, aspect_ratio_ids) hidden_state = self.layernorm_pre(hidden_state) # Compute the number of tokens to pad num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8 # Compute padding tuple for pad function padding = (0, 0, 0, num_padding_patches) # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2) # Pad the tensor hidden_state = F.pad(hidden_state, padding, mode="constant", value=0) slice_index = -num_padding_patches if num_padding_patches > 0 else None # Prepare attention mask attention_mask = aspect_ratio_mask.reshape(batch_size * num_concurrent_media, -1) attention_mask = _prepare_aspect_ratio_attention_mask( aspect_ratio_mask=attention_mask, num_patches=self.num_patches, target_length=hidden_state.shape[2], dtype=self.dtype, ) # Apply encoder hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1, dim) output = self.transformer( hidden_state, attention_mask=attention_mask, output_hidden_states=True, output_attentions=output_attentions, use_flash_attention=use_flash_attention, ) hidden_state = output[0] hidden_state = self.layernorm_post(hidden_state) # Apply global encoder hidden_state = hidden_state.reshape( batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, dim ) hidden_state = self.post_tile_positional_embedding(hidden_state, aspect_ratio_ids) hidden_state = hidden_state.reshape( batch_size * num_concurrent_media, num_tiles * (num_patches + num_padding_patches), dim ) global_output = self.global_transformer( hidden_state, attention_mask=attention_mask, output_hidden_states=output_hidden_states, output_attentions=output_attentions, use_flash_attention=use_flash_attention, ) hidden_state = global_output[0] # Remove padding form hidden state hidden_state = hidden_state.reshape( batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, dim ) hidden_state = hidden_state[:, :, :slice_index] hidden_state = hidden_state.reshape(batch_size, num_concurrent_media, num_tiles, num_patches, dim) # Collect intermediate layer outputs from encoder output all_intermediate_hidden_states = [output[1][i] for i in self.intermediate_layers_indices] intermediate_hidden_states = torch.stack(all_intermediate_hidden_states, dim=-1) """ intermediate_hidden_states = torch.stack(all_intermediate_hidden_states, dim=-1) intermediate_hidden_states = intermediate_hidden_states[..., self.intermediate_layers_indices] """ # Remove padding from intermediate hidden states intermediate_hidden_states = intermediate_hidden_states.reshape( batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, -1 ) intermediate_hidden_states = intermediate_hidden_states[:, :, :slice_index] intermediate_hidden_states = intermediate_hidden_states.reshape( batch_size, num_concurrent_media, num_tiles, num_patches, -1 ) # Concatenate final hidden state and intermediate hidden states hidden_state = torch.cat([hidden_state, intermediate_hidden_states], dim=-1) if output_hidden_states: hidden_states = tuple(all_intermediate_hidden_states) + tuple(global_output[1]) else: hidden_states = None if output_attentions: # global transformer in contrast to `self.transformer` doesn't always return hidden states so we might go index out-of-range global_attn = tuple(global_output[2]) if output_hidden_states else tuple(global_output[1]) attentions = tuple(output[2]) + global_attn else: attentions = None if not return_dict: return tuple(v for v in [hidden_state, hidden_states, attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_state, hidden_states=hidden_states, attentions=attentions, )