461 lines
20 KiB
Python
461 lines
20 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/cohere/modeling_cohere.py
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# coding=utf-8
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# Copyright 2024 Cohere team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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# This file is based on the LLama model definition file in transformers
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"""PyTorch Cohere model."""
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import math
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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import torch.utils.checkpoint
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from typing import Optional, Tuple, List
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from ipex_llm.transformers.models.llama import repeat_kv
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from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
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from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import use_decoding_fast_path
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
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from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.kv import DynamicFp8Cache
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from ipex_llm.transformers.models.qwen2 import should_use_fuse_rope
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from ipex_llm.utils.common import invalidInputError
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try:
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from transformers.cache_utils import Cache, DynamicCache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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def cohere_model_forward(
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self,
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input_ids: 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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past_key_values: Optional[List[torch.FloatTensor]] = 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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):
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use_cache = use_cache if use_cache is not None \
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else self.config.use_cache
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if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
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if not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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output_attentions = output_attentions if output_attentions is not None \
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else self.config.output_attentions
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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invalidInputError(False,
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"You cannot specify both input_ids and inputs_embeds at the same time")
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if self.gradient_checkpointing and self.training and use_cache:
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invalidInputError(False,
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"`use_cache=True` is incompatible "
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"with gradient checkpointing. Setting `use_cache=False`.")
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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past_seen_tokens = 0
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if use_cache: # kept for BC (cache positions)
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if not isinstance(past_key_values, Cache):
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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past_seen_tokens = past_key_values.get_seq_length()
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if cache_position is None:
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if isinstance(past_key_values, Cache):
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invalidInputError(False, "cache_position is a required argument when using Cache.")
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], 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(attention_mask,
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inputs_embeds, cache_position, past_seen_tokens)
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# embed positions
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hidden_states = inputs_embeds
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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 decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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causal_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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cache_position,
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)
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else:
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# ipex-llm changes
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curr_device = decoder_layer.input_layernorm.weight.device
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if causal_mask is not None:
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causal_mask = causal_mask.to(curr_device)
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if position_ids is not None:
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position_ids = position_ids.to(curr_device)
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# ipex-llm changes end
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_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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)
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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,
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all_hidden_states, all_self_attns] 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 cohere_attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if use_quantize_kv_cache(self.q_proj, hidden_states):
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forward_function = cohere_attention_forward_quantized
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else:
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forward_function = cohere_attention_forward_origin
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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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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**kwargs,
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)
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def cohere_attention_forward_quantized(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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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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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
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if self.use_qk_norm:
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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past_key_value = getattr(self, "past_key_value", past_key_value)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, position_ids)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; position_ids needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx,
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cache_kwargs, new_layout=True)
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if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
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and not hidden_states.requires_grad:
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import linear_q4_0
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attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
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attention_mask)
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attn_weights = None
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else:
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key_states, value_states = restore_fp8_kv_cache(key_states,
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value_states, query_states.dtype)
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights,
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dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights,
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p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
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"`attn_output` should be of size "
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f"{(bsz, self.num_heads, q_len, self.head_dim)},"
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f" but is {attn_output.size()}")
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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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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def cohere_attention_forward_origin(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
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decoding_fast_path = use_decoding_fast_path(self.q_proj,
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use_fuse_rope,
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enough_kv_room,
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bsz * q_len)
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if decoding_fast_path:
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hidden_states = hidden_states.view(1, -1)
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cache_k = past_key_value.key_cache[self.layer_idx]
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cache_v = past_key_value.value_cache[self.layer_idx]
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kv_seq_len = cache_k.shape[-2]
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import linear_q4_0
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query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
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self.q_proj.weight,
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self.k_proj.weight,
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self.v_proj.weight,
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position_ids,
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cache_k, cache_v,
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self.q_proj.weight.qtype,
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self.v_proj.weight.qtype,
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kv_seq_len,
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self.head_dim,
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self.rotary_emb.base,)
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kv_seq_len += 1
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# update past_key_value's seem_tokens and kv caches.
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if self.layer_idx == 0:
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past_key_value._seen_tokens = kv_seq_len
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past_key_value.key_cache[self.layer_idx] = key_states
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past_key_value.value_cache[self.layer_idx] = value_states
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else:
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
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if self.use_qk_norm:
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
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self.head_dim).transpose(1, 2)
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past_key_value = getattr(self, "past_key_value", past_key_value)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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if self.layer_idx is None:
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invalidInputError(
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False,
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"The cache structure has changed since version v4.36. "
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f"If you are using {self.__class__.__name__} "
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"for auto-regressive decoding with k/v caching, "
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"please make sure to initialize the attention class with a layer index."
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)
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, position_ids)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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if self.layer_idx == 0:
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past_key_value._seen_tokens += key_states.shape[-2]
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if len(past_key_value.key_cache) <= self.layer_idx:
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past_key_value.key_cache.append(key_states)
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past_key_value.value_cache.append(value_states)
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else:
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cache_k = past_key_value.key_cache[self.layer_idx]
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cache_v = past_key_value.value_cache[self.layer_idx]
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if not enough_kv_room:
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# allocate new
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new_c_k, new_c_v = extend_kv_cache(bsz,
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self.num_key_value_heads, # Support GQA
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self.head_dim,
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cache_k.size(2),
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kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device)
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new_c_k[:] = cache_k
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new_c_v[:] = cache_v
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cache_k = new_c_k
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cache_v = new_c_v
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key_states, value_states = append_kv_cache(cache_k,
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cache_v,
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key_states,
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value_states)
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# update past_key_value
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past_key_value.key_cache[self.layer_idx] = key_states
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past_key_value.value_cache[self.layer_idx] = value_states
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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if not self.training and not hidden_states.requires_grad and \
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use_flash_attention(query_states, key_states, attention_mask):
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attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
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key_states.to(device, dtype=torch.float16),
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value_states.to(device, dtype=torch.float16),
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is_causal=True)
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attn_weights = None
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elif not self.training and not hidden_states.requires_grad and \
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use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
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import linear_q4_0
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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else:
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causal_mask = None
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attn_output = linear_q4_0.sdp(query_states, key_states, value_states, causal_mask)
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attn_output = attn_output.view(query_states.shape)
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attn_weights = None
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else:
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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|
|
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
|
|
|
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# upcast attention to fp32
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|
attn_weights = nn.functional.softmax(attn_weights,
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dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights,
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|
p=self.attention_dropout, training=self.training)
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|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
|
"`attn_output` should be of size "
|
|
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
|
f" but is {attn_output.size()}")
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|