923 lines
44 KiB
Python
923 lines
44 KiB
Python
#
|
|
# Copyright 2016 The BigDL Authors.
|
|
#
|
|
# 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.
|
|
#
|
|
# Some parts of this file is adapted from
|
|
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py
|
|
#
|
|
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
|
# and OPT implementations in this library. It has been modified from its
|
|
# original forms to accommodate minor architectural differences compared
|
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
|
#
|
|
# 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 Mistral model."""
|
|
import math
|
|
from typing import List, Optional, Tuple, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
from transformers.modeling_outputs import BaseModelOutputWithPast
|
|
from transformers.models.mistral.modeling_mistral import MistralModel
|
|
from ipex_llm.utils.common import invalidInputError
|
|
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
|
|
from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
|
|
restore_fp8_kv_cache, use_quantize_kv_cache
|
|
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, \
|
|
apply_rotary_pos_emb_no_cache_xpu
|
|
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
|
|
is_enough_kv_cache_room_4_36
|
|
from ipex_llm.transformers.low_bit_linear import SYM_INT4, FP8E5, IQ2_XXS
|
|
from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
|
|
from ipex_llm.transformers.models.llama import llama_decoding_fast_path_qtype_check
|
|
from ipex_llm.transformers.models.llama import should_use_xetla_mm_qkv
|
|
from ipex_llm.transformers.models.llama import fuse_qkv_weight_xetla
|
|
try:
|
|
from transformers.cache_utils import Cache
|
|
except ImportError:
|
|
Cache = Tuple[torch.Tensor]
|
|
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
|
|
|
|
|
|
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
|
"""
|
|
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
|
|
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
|
|
to (batch, num_attention_heads, seqlen, head_dim)
|
|
"""
|
|
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
|
if n_rep == 1:
|
|
return hidden_states
|
|
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
|
|
n_rep, slen, head_dim)
|
|
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
|
|
|
|
|
def should_use_fuse_rope(self, hidden_states, position_ids):
|
|
use_fuse_rope = hidden_states.device.type == "xpu"
|
|
use_fuse_rope = use_fuse_rope and not (self.training and hidden_states.requires_grad)
|
|
use_fuse_rope = use_fuse_rope and position_ids is not None
|
|
return use_fuse_rope
|
|
|
|
|
|
def use_decoding_fast_path(proj, use_fuse_rope, enough_kv_room, bs):
|
|
return llama_decoding_fast_path_qtype_check(proj) and \
|
|
use_fuse_rope and enough_kv_room and bs == 1 and \
|
|
not proj.enable_xetla
|
|
|
|
|
|
def compute_attn_outputs_weights(query_states, key_states, value_states, bsz, q_len, kv_seq_len,
|
|
num_heads, head_dim, hidden_size, attention_mask):
|
|
attn_weights = torch.matmul(
|
|
query_states.to(key_states.dtype),
|
|
key_states.transpose(2, 3)) / math.sqrt(head_dim)
|
|
|
|
if attn_weights.size() != (bsz, num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size {(bsz, num_heads, q_len, kv_seq_len)},"
|
|
f" but is {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.\
|
|
softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
attn_output = torch.matmul(attn_weights, value_states.to(query_states.dtype))
|
|
|
|
if attn_output.size() != (bsz, num_heads, q_len, head_dim):
|
|
invalidInputError(
|
|
False,
|
|
f"`attn_output` should be of size {(bsz, num_heads, q_len, head_dim)},"
|
|
f" but is {attn_output.size()}"
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, hidden_size)
|
|
|
|
return attn_output, attn_weights
|
|
|
|
|
|
def mistral_model_forward_4_36(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[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,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
from ipex_llm.transformers.kv import DynamicFp8Cache
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
|
|
if not isinstance(past_key_values, DynamicFp8Cache):
|
|
past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
|
|
return MistralModel.forward(
|
|
self=self,
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
|
|
def mistral_attention_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor]=None,
|
|
position_ids: Optional[torch.LongTensor]=None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]]=None,
|
|
output_attentions: bool=False,
|
|
use_cache: bool=False,
|
|
padding_mask: Optional[torch.Tensor]=None,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
if use_quantize_kv_cache(self.q_proj, hidden_states):
|
|
forward_function = mistral_attention_forward_quantized
|
|
else:
|
|
forward_function = mistral_attention_forward_original
|
|
return forward_function(
|
|
self=self,
|
|
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,
|
|
padding_mask=padding_mask
|
|
)
|
|
|
|
|
|
def mistral_attention_forward_quantized(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor]=None,
|
|
position_ids: Optional[torch.LongTensor]=None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]]=None,
|
|
output_attentions: bool=False,
|
|
use_cache: bool=False,
|
|
padding_mask: Optional[torch.Tensor]=None,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, hidden_size = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.dtype
|
|
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value)
|
|
decoding_fast_path = use_decoding_fast_path(self.q_proj,
|
|
use_fuse_rope,
|
|
enough_kv_room,
|
|
bsz * q_len)
|
|
|
|
if decoding_fast_path:
|
|
hidden_states = hidden_states.view(1, -1)
|
|
tmp_cache_k, tmp_cache_v = init_kv_cache(
|
|
bsz,
|
|
self.num_key_value_heads,
|
|
self.head_dim,
|
|
0,
|
|
1,
|
|
dtype=hidden_states.dtype,
|
|
device=device
|
|
)
|
|
import linear_q4_0
|
|
query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
|
|
self.q_proj.weight,
|
|
self.k_proj.weight,
|
|
self.v_proj.weight,
|
|
position_ids,
|
|
tmp_cache_k, tmp_cache_v,
|
|
self.q_proj.weight.qtype,
|
|
self.v_proj.weight.qtype,
|
|
0,
|
|
self.head_dim)
|
|
else:
|
|
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)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
kv_seq_len += past_key_value[0].shape[-2]
|
|
|
|
if use_fuse_rope:
|
|
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
|
|
key_states,
|
|
position_ids,
|
|
"mistral")
|
|
else:
|
|
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, "mistral")
|
|
|
|
if not self.training and not hidden_states.requires_grad:
|
|
fsdp_flag = use_flash_attention(query_states, key_states)
|
|
else:
|
|
fsdp_flag = False
|
|
if fsdp_flag:
|
|
attention_dtype = torch.float16 # use fp16 for flash attention
|
|
else:
|
|
attention_dtype = original_dtype
|
|
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is None:
|
|
attn_weights = torch.matmul(query_states.to(key_states.dtype),
|
|
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1,
|
|
dtype=torch.float32).to(query_states.dtype)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
if use_cache:
|
|
k_cache, v_cache = init_fp8_kv_cache(
|
|
bsz, self.num_heads, kv_seq_len, self.head_dim,
|
|
device=query_states.device, new_layout=True
|
|
)
|
|
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
|
|
key_states, value_states)
|
|
past_key_value = (key_states, value_states)
|
|
else:
|
|
k_cache, v_cache = past_key_value
|
|
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
|
|
key_states, value_states, new_layout=True)
|
|
kv_seq_len = key_states.shape[-2]
|
|
past_key_value = (key_states, value_states)
|
|
|
|
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
|
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
|
|
|
attn_weights = attn_weights / math.sqrt(self.head_dim)
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1,
|
|
dtype=torch.float32).to(query_states.dtype)
|
|
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
else:
|
|
import linear_q4_0
|
|
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
|
|
attention_mask)
|
|
attn_weights = None
|
|
|
|
attn_output_size = (bsz, self.num_heads, q_len, self.head_dim)
|
|
if attn_output.size() != attn_output_size:
|
|
invalidInputError(False,
|
|
f"`attn_output` should be of size {attn_output_size},"
|
|
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(original_dtype), attn_weights, past_key_value
|
|
|
|
|
|
def mistral_attention_forward_original(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor]=None,
|
|
position_ids: Optional[torch.LongTensor]=None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]]=None,
|
|
output_attentions: bool=False,
|
|
use_cache: bool=False,
|
|
padding_mask: Optional[torch.Tensor]=None,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
bsz, q_len, hidden_size = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.dtype
|
|
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value)
|
|
decoding_fast_path = use_decoding_fast_path(self.q_proj,
|
|
use_fuse_rope,
|
|
enough_kv_room,
|
|
bsz * q_len)
|
|
|
|
if decoding_fast_path:
|
|
hidden_states = hidden_states.view(1, -1)
|
|
kv_seq_len = past_key_value[0].shape[-2]
|
|
cache_k = past_key_value[0]
|
|
cache_v = past_key_value[1]
|
|
import linear_q4_0
|
|
query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
|
|
self.q_proj.weight,
|
|
self.k_proj.weight,
|
|
self.v_proj.weight,
|
|
position_ids,
|
|
cache_k, cache_v,
|
|
self.q_proj.weight.qtype,
|
|
self.v_proj.weight.qtype,
|
|
kv_seq_len,
|
|
self.head_dim)
|
|
kv_seq_len += 1
|
|
else:
|
|
|
|
if should_use_xetla_mm_qkv(self, device):
|
|
if not hasattr(self, "qkv_proj_qweight"):
|
|
self.qkv_proj_qweight = fuse_qkv_weight_xetla(self.q_proj,
|
|
self.k_proj,
|
|
self.v_proj,
|
|
self.q_proj.qtype)
|
|
import linear_q4_0
|
|
q_out_len = self.q_proj.out_len
|
|
k_out_len = self.k_proj.out_len
|
|
v_out_len = self.v_proj.out_len
|
|
qkv_states = linear_q4_0.mm_xetla(hidden_states,
|
|
self.qkv_proj_qweight,
|
|
self.q_proj.qtype)
|
|
query_states = qkv_states[:, :, :q_out_len]
|
|
key_states = qkv_states[:, :, q_out_len:q_out_len + k_out_len]
|
|
value_states = qkv_states[:, :, q_out_len + k_out_len:]
|
|
else:
|
|
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)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
kv_seq_len += past_key_value[0].shape[-2]
|
|
|
|
if use_fuse_rope:
|
|
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
|
|
key_states,
|
|
position_ids,
|
|
"mistral")
|
|
else:
|
|
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, "mistral")
|
|
|
|
if past_key_value is not None:
|
|
# reuse k, v, self_attention
|
|
cache_k = past_key_value[0]
|
|
cache_v = past_key_value[1]
|
|
if not enough_kv_room:
|
|
# allocate new
|
|
new_cache_k, new_cache_v = extend_kv_cache(bsz,
|
|
self.num_key_value_heads, # Support GQA
|
|
self.head_dim,
|
|
cache_k.size(2),
|
|
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=cache_k.dtype,
|
|
device=device)
|
|
|
|
new_cache_k[:] = cache_k
|
|
new_cache_v[:] = cache_v
|
|
cache_k = new_cache_k
|
|
cache_v = new_cache_v
|
|
|
|
key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
|
|
|
|
elif use_cache:
|
|
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
|
|
new_key_states, new_value_states = init_kv_cache(bsz,
|
|
self.num_key_value_heads,
|
|
self.head_dim,
|
|
kv_seq_len,
|
|
max_cache_length,
|
|
dtype=key_states.dtype,
|
|
device=device)
|
|
new_key_states[:] = key_states
|
|
new_value_states[:] = value_states
|
|
key_states = new_key_states
|
|
value_states = new_value_states
|
|
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
|
|
|
if not self.training and not hidden_states.requires_grad:
|
|
fsdp_flag = use_flash_attention(query_states, key_states)
|
|
else:
|
|
fsdp_flag = False
|
|
if fsdp_flag:
|
|
attention_dtype = torch.float16 # use fp16 for flash attention
|
|
else:
|
|
attention_dtype = original_dtype
|
|
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
|
|
if fsdp_flag:
|
|
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype),
|
|
key_states,
|
|
value_states,
|
|
is_causal=True)
|
|
attn_weights = None
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
elif use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
|
import linear_fp16_esimd
|
|
attn_output = linear_fp16_esimd.sdp_forward(query_states,
|
|
key_states,
|
|
value_states)
|
|
attn_output = attn_output.view(query_states.shape)
|
|
attn_weights = None
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
else:
|
|
attn_output, attn_weights = compute_attn_outputs_weights(query_states,
|
|
key_states,
|
|
value_states,
|
|
bsz, q_len, kv_seq_len,
|
|
self.num_heads, self.head_dim,
|
|
self.hidden_size, attention_mask)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output.to(original_dtype), attn_weights, past_key_value
|
|
|
|
|
|
def mistral_attention_forward_4_36(
|
|
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[Cache]]:
|
|
if use_quantize_kv_cache(self.q_proj, hidden_states):
|
|
forward_function = mistral_attention_forward_4_36_quantized
|
|
else:
|
|
forward_function = mistral_attention_forward_4_36_original
|
|
return forward_function(
|
|
self=self,
|
|
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,
|
|
kwargs=kwargs
|
|
)
|
|
|
|
|
|
def mistral_attention_forward_4_36_quantized(
|
|
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[Cache]]:
|
|
bsz, q_len, hidden_size = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.dtype
|
|
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx, seq_len=q_len)
|
|
decoding_fast_path = use_decoding_fast_path(self.q_proj,
|
|
use_fuse_rope,
|
|
enough_kv_room,
|
|
bsz * q_len)
|
|
|
|
if decoding_fast_path:
|
|
hidden_states = hidden_states.view(1, -1)
|
|
tmp_cache_k, tmp_cache_v = init_kv_cache(
|
|
bsz,
|
|
self.num_key_value_heads,
|
|
self.head_dim,
|
|
0,
|
|
1,
|
|
dtype=hidden_states.dtype,
|
|
device=device
|
|
)
|
|
import linear_q4_0
|
|
query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
|
|
self.q_proj.weight,
|
|
self.k_proj.weight,
|
|
self.v_proj.weight,
|
|
position_ids,
|
|
tmp_cache_k, tmp_cache_v,
|
|
self.q_proj.weight.qtype,
|
|
self.v_proj.weight.qtype,
|
|
0,
|
|
self.head_dim)
|
|
else:
|
|
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)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
if self.layer_idx is None:
|
|
invalidInputError(
|
|
False,
|
|
f"The cache structure has changed since version v4.36. "
|
|
"If you are using {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)
|
|
|
|
if use_fuse_rope:
|
|
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
|
|
key_states,
|
|
position_ids,
|
|
"mistral")
|
|
else:
|
|
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, "mistral")
|
|
|
|
if not self.training and not hidden_states.requires_grad:
|
|
fsdp_flag = use_flash_attention(query_states, key_states)
|
|
else:
|
|
fsdp_flag = False
|
|
if fsdp_flag:
|
|
attention_dtype = torch.float16 # use fp16 for flash attention
|
|
else:
|
|
attention_dtype = original_dtype
|
|
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
kv_seq_len = key_states.shape[-2]
|
|
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
attn_weights = torch.matmul(query_states.to(key_states.dtype),
|
|
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1,
|
|
dtype=torch.float32).to(query_states.dtype)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
if use_cache:
|
|
cache_kwargs = None
|
|
key_states, value_states = past_key_value.update(key_states, value_states,
|
|
self.layer_idx, cache_kwargs,
|
|
new_layout=True)
|
|
else:
|
|
cache_kwargs = None # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states,
|
|
self.layer_idx, cache_kwargs,
|
|
new_layout=True)
|
|
kv_seq_len = key_states.shape[-2]
|
|
if query_states.size(2) != 1 or query_states.device.type != 'xpu':
|
|
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
|
|
query_states.dtype)
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
|
|
|
attn_weights = attn_weights / math.sqrt(self.head_dim)
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
invalidInputError(
|
|
False,
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
|
|
f" but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1,
|
|
dtype=torch.float32).to(query_states.dtype)
|
|
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
else:
|
|
import linear_q4_0
|
|
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
|
|
attention_mask)
|
|
attn_weights = None
|
|
|
|
attn_output_size = (bsz, self.num_heads, q_len, self.head_dim)
|
|
if attn_output.size() != attn_output_size:
|
|
invalidInputError(False,
|
|
f"`attn_output` should be of size {attn_output_size},"
|
|
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(original_dtype), attn_weights, past_key_value
|
|
|
|
|
|
def mistral_attention_forward_4_36_original(
|
|
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[Cache]]:
|
|
bsz, q_len, hidden_size = hidden_states.size()
|
|
device = hidden_states.device
|
|
# for flash attention
|
|
original_dtype = hidden_states.dtype
|
|
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
|
|
decoding_fast_path = use_decoding_fast_path(self.q_proj,
|
|
use_fuse_rope,
|
|
enough_kv_room,
|
|
bsz * q_len)
|
|
decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
|
|
|
|
if decoding_fast_path:
|
|
hidden_states = hidden_states.view(1, -1)
|
|
|
|
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
|
|
kv_seq_len = cache_k.shape[-2]
|
|
|
|
import linear_q4_0
|
|
query_states, key_states, value_states = linear_q4_0.forward_qkv(hidden_states,
|
|
self.q_proj.weight,
|
|
self.k_proj.weight,
|
|
self.v_proj.weight,
|
|
position_ids,
|
|
cache_k, cache_v,
|
|
self.q_proj.weight.qtype,
|
|
self.v_proj.weight.qtype,
|
|
kv_seq_len,
|
|
self.head_dim)
|
|
kv_seq_len += 1
|
|
|
|
# update past_key_value's seem_tokens and kv caches.
|
|
if self.layer_idx == 0:
|
|
past_key_value.seen_tokens = kv_seq_len
|
|
past_key_value.key_cache[self.layer_idx] = key_states
|
|
past_key_value.value_cache[self.layer_idx] = value_states
|
|
|
|
else:
|
|
if should_use_xetla_mm_qkv(self, device):
|
|
if not hasattr(self, "qkv_proj_qweight"):
|
|
self.qkv_proj_qweight = fuse_qkv_weight_xetla(self.q_proj,
|
|
self.k_proj,
|
|
self.v_proj,
|
|
self.q_proj.qtype)
|
|
import linear_q4_0
|
|
q_out_len = self.q_proj.out_len
|
|
k_out_len = self.k_proj.out_len
|
|
v_out_len = self.v_proj.out_len
|
|
qkv_states = linear_q4_0.mm_xetla(hidden_states,
|
|
self.qkv_proj_qweight,
|
|
self.q_proj.qtype)
|
|
query_states = qkv_states[:, :, :q_out_len]
|
|
key_states = qkv_states[:, :, q_out_len:q_out_len + k_out_len]
|
|
value_states = qkv_states[:, :, q_out_len + k_out_len:]
|
|
else:
|
|
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)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
|
|
if past_key_value is not None:
|
|
if self.layer_idx is None:
|
|
invalidInputError(False,
|
|
"The cache structure has changed since version v4.36. "
|
|
f"If you are using {self.__class__.__name__} for "
|
|
"auto-regressive decodingwith 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)
|
|
|
|
if use_fuse_rope:
|
|
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
|
|
key_states,
|
|
position_ids,
|
|
"mistral")
|
|
else:
|
|
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, "mistral")
|
|
|
|
if past_key_value is not None:
|
|
# update the number of seen tokens
|
|
if self.layer_idx == 0:
|
|
past_key_value.seen_tokens += key_states.shape[-2]
|
|
|
|
# reuse k, v, self_attention
|
|
# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
|
|
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
past_key_value.key_cache.append(key_states)
|
|
past_key_value.value_cache.append(value_states)
|
|
else:
|
|
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
|
|
if not enough_kv_room:
|
|
# allocate new
|
|
new_c_k, new_c_v = extend_kv_cache(bsz,
|
|
self.num_key_value_heads, # Support GQA
|
|
self.head_dim,
|
|
cache_k.size(2),
|
|
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=cache_k.dtype,
|
|
device=device)
|
|
|
|
new_c_k[:] = cache_k
|
|
new_c_v[:] = cache_v
|
|
cache_k = new_c_k
|
|
cache_v = new_c_v
|
|
|
|
key_states, value_states = append_kv_cache(cache_k, cache_v,
|
|
key_states, value_states)
|
|
|
|
# update past_key_value
|
|
past_key_value.key_cache[self.layer_idx] = key_states
|
|
past_key_value.value_cache[self.layer_idx] = value_states
|
|
|
|
if not self.training and not hidden_states.requires_grad:
|
|
fsdp_flag = use_flash_attention(query_states, key_states)
|
|
else:
|
|
fsdp_flag = False
|
|
if fsdp_flag:
|
|
attention_dtype = torch.float16 # use fp16 for flash attention
|
|
else:
|
|
attention_dtype = original_dtype
|
|
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
|
|
dtype=attention_dtype)
|
|
|
|
if fsdp_flag:
|
|
attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype),
|
|
key_states,
|
|
value_states,
|
|
is_causal=True)
|
|
attn_weights = None
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
elif use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
|
import linear_fp16_esimd
|
|
attn_output = linear_fp16_esimd.sdp_forward(query_states,
|
|
key_states,
|
|
value_states)
|
|
attn_output = attn_output.view(query_states.shape)
|
|
attn_weights = None
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
else:
|
|
attn_output, attn_weights = compute_attn_outputs_weights(query_states,
|
|
key_states,
|
|
value_states,
|
|
bsz, q_len, kv_seq_len,
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.hidden_size,
|
|
attention_mask)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output.to(original_dtype), attn_weights, past_key_value
|