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Optimize StableLM (#10619)

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* Initial commit for stablelm optimizations

* Small style fix

* add dependency

* Add mlp optimizations

* Small fix

* add attention forward

* Remove quantize kv for now as head_dim=80

* Add merged qkv

* fix lisence

* Python style fix

---------

Co-authored-by: qiuxin2012 <qiuxin2012cs@gmail.com>
This commit is contained in:
Yuwen Hu 2024-04-02 18:58:38 +08:00 committed by GitHub
parent 27be448920
commit fd384ddfb8
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GPG key ID: B5690EEEBB952194
3 changed files with 261 additions and 2 deletions
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15
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -632,6 +632,10 @@ def _optimize_pre(model):
module.rope_base = rope_base
del module.c_attn
model.apply(split_qkv_proj_func)
if model.config.model_type == "stablelm":
from ipex_llm.transformers.models.stablelm import merge_qkv
model.apply(merge_qkv)
return model
@ -1336,5 +1340,16 @@ def _optimize_post(model, lightweight_bmm=False):
convert_forward(model,
module.BertEncoder,
encoder_forward)
elif model.config.model_type == 'stablelm':
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.stablelm import stablelm_attention_forward
convert_forward(model,
module.StableLmAttention,
stablelm_attention_forward
)
convert_forward(model,
module.StableLmMLP,
llama_mlp_forward)
return model

244
python/llm/src/ipex_llm/transformers/models/stablelm.py Normal file
View file

@ -0,0 +1,244 @@
#
# 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/v4.38.0/src/transformers/models/stablelm/modeling_stablelm.py
# which is licensed under Apache License 2.0:
#
# Copyright 2024 EleutherAI 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.
#
import math
from typing import Optional, Tuple
import torch
from torch import nn
import torch.nn.functional as F
from transformers.models.stablelm.modeling_stablelm import StableLmAttention
from ipex_llm.utils.common import invalidInputError
from ipex_llm.transformers.models.utils import extend_kv_cache, append_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_36
from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
from ipex_llm.transformers.models.mistral import should_use_fuse_rope, repeat_kv
try:
from transformers.cache_utils import Cache
except ImportError:
Cache = Tuple[torch.Tensor]
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
def merge_qkv(module: torch.nn.Module):
if isinstance(module, StableLmAttention):
new_weight = torch.cat([
module.q_proj.weight.data,
module.k_proj.weight.data,
module.v_proj.weight.data,
], dim=0)
qkv_proj = torch.nn.Linear(0, 0, bias=False)
qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
qkv_proj.in_features = new_weight.size(1)
qkv_proj.out_features = new_weight.size(0)
module.qkv_proj = qkv_proj
del module.q_proj, module.k_proj, module.v_proj
def stablelm_attention_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[Cache]]:
bsz, q_len, _ = 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)
qkv = self.qkv_proj(hidden_states)
qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
qkv = qkv.transpose(1, 2)
query_states, key_states, value_states = qkv.split([self.num_heads,
self.num_heads,
self.num_heads], dim=1)
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)
# Partial rotary embedding
query_rot, query_pass = (
query_states[..., : self.rotary_emb.dim],
query_states[..., self.rotary_emb.dim:],
)
key_rot, key_pass = (
key_states[..., : self.rotary_emb.dim],
key_states[..., self.rotary_emb.dim:],
)
if use_fuse_rope:
query_rot, key_rot = apply_rotary_pos_emb_no_cache_xpu(query_rot,
key_rot,
position_ids,
"stablelm")
else:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
query_rot, key_rot = apply_rotary_pos_emb(query_rot,
key_rot,
cos,
sin,
position_ids,
"stablelm")
# [batch_size, seq_length, num_heads, head_dim]
query_states = torch.cat((query_rot, query_pass), dim=-1)
key_states = torch.cat((key_rot, key_pass), dim=-1)
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
# 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)
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, attention_mask):
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
else:
attn_weights = torch.matmul(
query_states,
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 {(bsz, self.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, dtype=torch.float32, dim=-1).to(query_states.dtype)
attn_weights = self.attention_dropout(attn_weights)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
invalidInputError(
False,
f"`attn_output` should be of size {(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(original_dtype), attn_weights, past_key_value

4
python/llm/src/ipex_llm/transformers/models/utils.py
View file

@ -168,7 +168,7 @@ def rotate_every_two(x):
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family):
if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral",
"mixtral", "qwen2", "yuan"]:
"mixtral", "qwen2", "yuan", "stablelm"]:
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
@ -207,7 +207,7 @@ def apply_rotary_pos_emb_no_cache_xpu(q, k, position_ids, model_family, rope_the
q_embed = torch.empty(q.shape, dtype=q.dtype, device=q.device)
k_embed = torch.empty(k.shape, dtype=k.dtype, device=k.device)
if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral",
"mixtral"]:
"mixtral", "stablelm"]:
linear_q4_0.apply_rotary_embedding_half_q_and_k(q, k, position_ids,
q_embed, k_embed, rope_theta)
return q_embed, k_embed