276 lines
11 KiB
Python
276 lines
11 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mllama/modeling_mllama.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from typing import Optional, Tuple, Union
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from transformers.cache_utils import Cache
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.models.mllama.modeling_mllama import MllamaVisionAttention
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from transformers.models.mllama.modeling_mllama import MllamaTextSelfAttention
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.common import merge_qkv_base, attention_softmax
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
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from ipex_llm.transformers.utils import invalidInputError
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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, MllamaVisionAttention)
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merge_qkv_base(module, MllamaTextSelfAttention)
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def mllama_vision_attention_forward(
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self,
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hidden_state: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = None,
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):
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bsz, q_len, _ = hidden_state.size()
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qkv = self.qkv_proj(hidden_state)
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qkv = qkv.view(bsz, q_len, 3 * self.num_heads, self.head_dim)
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qkv = qkv.transpose(1, 2)
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query, key, value = qkv.chunk(3, dim=1)
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attn_weights = None
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attn_output = scaled_dot_product_attention(
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query, key.contiguous(), value.contiguous(),
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attention_softmax, False
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, -1)
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output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return output, attn_weights
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def mllama_text_model_forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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cross_attention_states: Optional[torch.FloatTensor] = None,
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cross_attention_mask: Optional[torch.Tensor] = None,
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full_text_row_masked_out_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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# IPEX-LLM OPT start: kv cache and quantize kv cache
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inputs = input_ids if input_ids is not None else inputs_embeds
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use_cache = True if inputs.device.type == "xpu" else use_cache
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use_quantize_kv = use_quantize_kv_cache(
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self.layers[0].mlp.down_proj, inputs,
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self.config.num_attention_heads // self.config.num_key_value_heads
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)
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if use_cache:
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if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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# IPEX-LLM OPT end
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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invalidInputError((input_ids is None) ^ (inputs_embeds is None),
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"You cannot specify both input_ids and inputs_embeds at the same time, "
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"and must specify either one")
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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hidden_states = inputs_embeds
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1],
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device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
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)
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# create position embeddings to be shared across the decoder layers
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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# IPEX-LLM OPT start: use fused rope
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if (should_use_fuse_rope(hidden_states, position_ids, False)
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and self.rotary_emb.rope_type == "llama3"):
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position_embeddings = self.rotary_emb.inv_freq
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# IEPX_LLM OPT end
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for idx, decoder_layer in enumerate(self.layers):
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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# For text-only path we should skip cross attention layers.
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# Let's check if the layer is cross attention layer and if we have cross attention states
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# or cached cross attention states.
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is_cross_attention_layer = idx in self.cross_attention_layers
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# IPEX-LLM change start
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if is_cross_attention_layer and cross_attention_states is None:
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if past_key_values is None:
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# use_cache=False
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continue
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elif len(past_key_values.key_cache) <= idx:
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# first token but no cross_attention_states, means no image inputs
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past_key_values.key_cache.append([])
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past_key_values.value_cache.append([])
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continue
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elif past_key_values.key_cache[idx] == []:
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# next token but no cross kv cache, means no image inputs
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continue
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# IPEX-LLM change end
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layer_outputs = decoder_layer(
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hidden_states,
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cross_attention_states=cross_attention_states,
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cross_attention_mask=cross_attention_mask,
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attention_mask=causal_mask,
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full_text_row_masked_out_mask=full_text_row_masked_out_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
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if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def mllama_cross_attention_forward(
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self,
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hidden_states: torch.Tensor,
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cross_attention_states: Optional[torch.Tensor] = None,
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past_key_value: Optional[Cache] = None,
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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use_cache: bool = None,
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cache_position: Optional[torch.LongTensor] = None,
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):
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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query_states = self.q_norm(query_states.view(-1, self.head_dim))
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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if cross_attention_states is not None:
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key_states = self.k_proj(cross_attention_states)
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value_states = self.v_proj(cross_attention_states)
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key_states = self.k_norm(key_states.view(-1, self.head_dim))
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key_states = key_states.view(bsz, -1, self.num_key_value_heads,
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self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, -1, self.num_key_value_heads,
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self.head_dim).transpose(1, 2)
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# if we have a new image + new tokens, we only computed key_states on that new image
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# we still update the cross key states, past_image, new_image. And use it!
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, None
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)
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else:
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key_states, value_states = (
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past_key_value.key_cache[self.layer_idx],
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past_key_value.value_cache[self.layer_idx],
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)
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attn_weights = None
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attn_output = scaled_dot_product_attention(
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query_states, key_states, value_states,
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attention_mask, q_len == key_states.size(2)
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, -1)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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