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# 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. | ||
"""Modules for the Mistral architecture.""" | ||
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import warnings | ||
from typing import Optional, Tuple | ||
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import torch | ||
from transformers import MistralConfig | ||
from transformers.cache_utils import Cache | ||
from transformers.models.llama.modeling_mistral import ( | ||
MistralAttention, | ||
apply_rotary_pos_emb, | ||
repeat_kv, | ||
) | ||
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from ..utils.require_utils import requires_neuronx_distributed | ||
from .core import CoreAttention, NeuronAttention | ||
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class NeuronMistralAttention(MistralAttention, NeuronAttention): | ||
def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None): | ||
super().__init__(config, layer_idx=layer_idx) | ||
self.core_attn = CoreAttention() | ||
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@classmethod | ||
def from_original(cls, orig_module: torch.nn.Module, **options) -> "NeuronMistralAttention": | ||
orig_module.core_attn = CoreAttention() | ||
orig_module.__class__ = cls | ||
return orig_module | ||
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@requires_neuronx_distributed | ||
def forward( | ||
self, | ||
hidden_states: torch.Tensor, | ||
attention_mask: Optional[torch.Tensor] = None, | ||
position_ids: Optional[torch.LongTensor] = None, | ||
past_key_value: Optional[Cache] = None, | ||
output_attentions: bool = False, | ||
use_cache: bool = False, | ||
**kwargs, | ||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: | ||
from neuronx_distributed.kernels.flash_attn import nki_flash_attn_func | ||
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if "padding_mask" in kwargs: | ||
warnings.warn( | ||
"Passing `padding_mask` is deprecated and removed since `transformers` v4.37. Please make sure to " | ||
"use `attention_mask` instead.`" | ||
) | ||
query_states = self.q_proj(hidden_states) | ||
key_states = self.k_proj(hidden_states) | ||
value_states = self.v_proj(hidden_states) | ||
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if self.sequence_parallel_enabled: | ||
q_len, bsz, _ = query_states.size() | ||
else: | ||
bsz, q_len, _ = query_states.size() | ||
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if self.sequence_parallel_enabled: | ||
# [S, B, hidden_dim] -> [S, B, num_heads, head_dim] -> [B, num_heads, S, head_dim] | ||
query_states = query_states.view(q_len, bsz, self.num_heads, self.head_dim).permute(1, 2, 0, 3) | ||
key_states = key_states.view(q_len, bsz, self.num_key_value_heads, self.head_dim).permute(1, 2, 0, 3) | ||
value_states = value_states.view(q_len, bsz, self.num_key_value_heads, self.head_dim).permute(1, 2, 0, 3) | ||
else: | ||
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) | ||
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kv_seq_len = key_states.shape[-2] | ||
if past_key_value is not None: | ||
if self.layer_idx is None: | ||
raise ValueError( | ||
"The cache structure has changed since `transformers` v4.36. If you are using " | ||
f"{self.__class__.__name__} for auto-regressive decoding with k/v caching, please make sure to " | ||
"initialize the attention class with a layer index." | ||
) | ||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) | ||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) | ||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) | ||
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if past_key_value is not None: | ||
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models | ||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) | ||
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# repeat k/v heads if n_kv_heads < n_heads | ||
key_states = repeat_kv(key_states, self.num_key_value_groups) | ||
value_states = repeat_kv(value_states, self.num_key_value_groups) | ||
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# if attention_mask is not None: | ||
# if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): | ||
# raise ValueError( | ||
# f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" | ||
# ) | ||
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# attn_weights = attn_weights + attention_mask | ||
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attn_output = ( | ||
nki_flash_attn_func(query_states, key_states, value_states, droupout_p=self.attention_dropout) | ||
if self.flash_attention_enabled | ||
else self.core_attn(query_states, key_states, value_states, attention_dropout=self.attention_dropout) | ||
) | ||
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): | ||
raise ValueError( | ||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" | ||
f" {attn_output.size()}" | ||
) | ||
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if self.sequence_parallel_enabled: | ||
# [B, num_heads, S, head_dim] -> [S, B, num_heads, head_dim] | ||
attn_output = attn_output.permute(2, 0, 1, 3) | ||
attn_output = attn_output.reshape(q_len, bsz, -1) | ||
else: | ||
attn_output = attn_output.transpose(1, 2).contiguous() | ||
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: | ||
attn_weights = None | ||
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return attn_output, attn_weights, past_key_value |