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

Optimize stablelm on NPU (#11512)

Browse source 
* stablelm_optimize
This commit is contained in:
Zhao Changmin 2024-07-05 14:21:57 +08:00 committed by GitHub
parent 7d8bc83415
commit 24de13fc45
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 208 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

15
python/llm/src/ipex_llm/transformers/npu_models/convert.py
View file

@ -118,3 +118,18 @@ def optimize_llm(model: torch.nn.Module):
convert_forward(model, module.MiniCPMForCausalLM, minicpm_model_causal_lm_forward)
convert_forward(model, module.MiniCPMAttention, minicpm_attention_forward)
convert_forward(model, module.MiniCPMMLP, minicpm_mlp_forward)
elif model.config.model_type == "stablelm":
from ipex_llm.transformers.npu_models.stablelm import merge_qkv
from ipex_llm.transformers.npu_models.stablelm import merge_mlp
model.apply(merge_qkv)
model.apply(merge_mlp)
from ipex_llm.transformers.npu_models.stablelm import stablelm_model_forward
from ipex_llm.transformers.npu_models.stablelm import stablelm_attention_forward
from ipex_llm.transformers.npu_models.stablelm import stablelm_mlp_forward
from transformers.models.stablelm.modeling_stablelm import StableLmModel
from transformers.models.stablelm.modeling_stablelm import StableLmAttention
from transformers.models.stablelm.modeling_stablelm import StableLmMLP
convert_forward(model, StableLmModel, stablelm_model_forward)
convert_forward(model, StableLmAttention, stablelm_attention_forward)
convert_forward(model, StableLmMLP, stablelm_mlp_forward)

193
python/llm/src/ipex_llm/transformers/npu_models/stablelm.py Normal file
View file

@ -0,0 +1,193 @@
#
# 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/v4.40.0/src/transformers/models/llama/modeling_llama.py
# which is licensed under Apache License 2.0:
#
# Copyright 2021 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.
from typing import Optional, Tuple, List
import math
import torch
from transformers.cache_utils import Cache
from transformers.models.stablelm.modeling_stablelm import repeat_kv, apply_rotary_pos_emb
from transformers.models.stablelm.modeling_stablelm import StableLmAttention, StableLmMLP, \
StableLmModel
from ipex_llm.transformers.npu_models.common import merge_linear
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)
if module.q_proj.bias is not None:
qkv_proj = torch.nn.Linear(0, 0, bias=True)
new_bias = torch.cat([
module.q_proj.bias.data,
module.k_proj.bias.data,
module.v_proj.bias.data,
], dim=0)
qkv_proj.bias = torch.nn.Parameter(new_bias, requires_grad=False)
else:
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 merge_mlp(module: torch.nn.Module):
if isinstance(module, StableLmMLP):
gate_up_proj = merge_linear([
module.gate_proj,
module.up_proj,
])
module.gate_up_proj = gate_up_proj
del module.gate_proj, module.up_proj
def stablelm_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,
):
# ipex-llm changes start
from ipex_llm.transformers.kv import DynamicNormalCache
# IPEX-LLM OPT: kv cache and quantize kv cache
use_cache = use_cache if use_cache is not None else self.config.use_cache
if use_cache:
if not isinstance(past_key_values, DynamicNormalCache):
past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
return StableLmModel.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 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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
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_key_value_heads,
self.num_key_value_heads], dim=1)
# For stablelm-2-12b's qk per-head norm
if getattr(self, "qk_layernorm", False):
query_states = self.q_layernorm(query_states)
key_states = self.k_layernorm(key_states)
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)
# Partial rotary embedding
# [batch_size, num_heads, seq_length, head_dim * config.partial_rotary_factor]
rot_dim = self.rotary_emb.dim
query_rot, query_pass = query_states[..., :rot_dim], query_states[..., rot_dim:]
key_rot, key_pass = key_states[..., :rot_dim], key_states[..., rot_dim:]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_rot, key_rot = apply_rotary_pos_emb(query_rot,
key_rot,
cos,
sin,
position_ids)
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:
# Specific to RoPE models with partial rotation
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, cache_kwargs)
# 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)
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(value_states.dtype)
attn_weights = self.attention_dropout(attn_weights)
attn_output = torch.matmul(attn_weights, value_states)
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 stablelm_mlp_forward(self, x):
gate_up_proj = self.gate_up_proj(x)
gate_proj, up_proj = gate_up_proj.chunk(2, dim=-1)
down_proj = self.down_proj(self.act_fn(gate_proj) * up_proj)
return down_proj