ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

LLM: Optimize cohere model (#10878)

Browse source 
* use mlp and rms

* optimize kv_cache

* add fuse qkv

* add flash attention and fp16 sdp

* error fp8 sdp

* fix optimized

* fix style

* update

* add for pp
This commit is contained in:
Wang, Jian4 2024-05-07 10:19:50 +08:00 committed by GitHub
parent 13a44cdacb
commit 191b184341
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 475 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

18
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -1282,6 +1282,24 @@ def _optimize_post(model, lightweight_bmm=False):
convert_forward(model,
module.Qwen2MoeAttention,
qwen2moe_attention_forward)
elif model.config.model_type == "cohere":
# for CohereForAI/c4ai-command-r-v01
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.cohere import cohere_attention_forward
from ipex_llm.transformers.models.cohere import cohere_model_forward
convert_forward(model,
module.CohereModel,
cohere_model_forward)
convert_forward(model,
module.CohereAttention,
cohere_attention_forward)
convert_forward(model,
module.CohereLayerNorm,
llama_rms_norm_forward)
convert_forward(model,
module.CohereMLP,
llama_mlp_forward)
elif model.config.model_type == "aquila":
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)

457
python/llm/src/ipex_llm/transformers/models/cohere.py Normal file
View file

@ -0,0 +1,457 @@
#
# 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/cohere/modeling_cohere.py
# coding=utf-8
# Copyright 2024 Cohere 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.
# This file is based on the LLama model definition file in transformers
"""PyTorch Cohere model."""
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
from typing import Optional, Tuple, List
from ipex_llm.transformers.models.llama import repeat_kv
from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
from ipex_llm.transformers.models.utils import use_decoding_fast_path
from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from ipex_llm.transformers.kv import DynamicFp8Cache
from ipex_llm.transformers.models.qwen2 import should_use_fuse_rope
from transformers.modeling_outputs import BaseModelOutputWithPast
from ipex_llm.utils.common import invalidInputError
try:
from transformers.cache_utils import Cache, DynamicCache
except ImportError:
Cache = Tuple[torch.Tensor]
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
def cohere_model_forward(
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,
cache_position: Optional[torch.LongTensor] = None,
):
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)
output_attentions = output_attentions if output_attentions is not None \
else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
invalidInputError(False,
"You cannot specify both input_ids and inputs_embeds at the same time")
if self.gradient_checkpointing and self.training and use_cache:
invalidInputError(False,
"`use_cache=True` is incompatible "
"with gradient checkpointing. Setting `use_cache=False`.")
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
past_seen_tokens = 0
if use_cache: # kept for BC (cache positions)
if not isinstance(past_key_values, Cache):
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_seen_tokens = past_key_values.get_seq_length()
if cache_position is None:
if isinstance(past_key_values, Cache):
invalidInputError(False, "cache_position is a required argument when using Cache.")
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask,
inputs_embeds, cache_position, past_seen_tokens)
# embed positions
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
)
else:
# ipex-llm changes
curr_device = decoder_layer.input_layernorm.weight.device
if causal_mask is not None:
causal_mask = causal_mask.to(curr_device)
if position_ids is not None:
position_ids = position_ids.to(curr_device)
# ipex-llm changes end
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache,
all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
def cohere_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,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if use_quantize_kv_cache(self.q_proj, hidden_states):
forward_function = cohere_attention_forward_quantized
else:
forward_function = cohere_attention_forward_origin
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,
cache_position=cache_position,
**kwargs,
)
def cohere_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,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
if self.use_qk_norm:
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.view(bsz, q_len,
self.num_key_value_heads, self.head_dim).transpose(1, 2)
past_key_value = getattr(self, "past_key_value", past_key_value)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx,
cache_kwargs, new_layout=True)
if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
and not hidden_states.requires_grad:
import linear_q4_0
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states,
attention_mask)
attn_weights = None
else:
key_states, value_states = restore_fp8_kv_cache(key_states,
value_states, query_states.dtype)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights,
dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights,
p=self.attention_dropout, training=self.training)
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, attn_weights, past_key_value
def cohere_attention_forward_origin(
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,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
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)
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,
self.rotary_emb.base,)
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:
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)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
if self.use_qk_norm:
query_states = self.q_norm(query_states)
key_states = self.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim).transpose(1, 2)
past_key_value = getattr(self, "past_key_value", past_key_value)
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 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, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
if self.layer_idx == 0:
past_key_value._seen_tokens += key_states.shape[-2]
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
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if not self.training and not hidden_states.requires_grad and \
use_flash_attention(query_states, key_states, attention_mask):
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
key_states.to(device, dtype=torch.float16),
value_states.to(device, dtype=torch.float16),
is_causal=True)
attn_weights = None
elif not self.training and not hidden_states.requires_grad and \
use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
import linear_q4_0
attn_output = linear_q4_0.sdp_fp16(query_states, key_states, value_states, attention_mask)
attn_output = attn_output.view(query_states.shape)
attn_weights = None
else:
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights,
dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights,
p=self.attention_dropout, training=self.training)
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