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optimize starcoder2-3b (#10625)

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Yishuo Wang 2024-04-02 17:16:29 +08:00 committed by GitHub
parent a10f5a1b8d
commit ba8cc6bd68
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3 changed files with 224 additions and 1 deletions
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13
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -595,6 +595,10 @@ def _optimize_pre(model):
):
from ipex_llm.transformers.models.bert import merge_qkv
model.apply(merge_qkv)
# for starcoder2
if model.config.model_type == "starcoder2":
from ipex_llm.transformers.models.starcoder2 import merge_qkv
model.apply(merge_qkv)
if model.config.model_type == "qwen":
rope_base = model.config.rotary_emb_base
from accelerate.big_modeling import init_empty_weights
@ -1295,6 +1299,15 @@ def _optimize_post(model, lightweight_bmm=False):
module.GPTBigCodeAttention,
"_attn",
_attn)
elif model.config.model_type == "starcoder2":
# starcoder2
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.starcoder2 import attention_forward
from ipex_llm.transformers.models.starcoder2 import model_forward
convert_forward(model, module.Starcoder2Attention, attention_forward)
convert_forward(model, module.Starcoder2Model, model_forward)
elif model.config.model_type == 'yuan':
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)

7
python/llm/src/ipex_llm/transformers/kv.py
View file

@ -35,7 +35,12 @@ class DynamicFp8Cache(DynamicCache):
batch_size, num_heads, seq_len, head_dim = key_states.shape
if layer_idx == 0:
self.seen_tokens += seq_len
if hasattr(self, "_seen_tokens"):
# 4.39 uses `_seen_tokens`
self._seen_tokens += seq_len
else:
# 4.37 uses `seen_tokens`
self.seen_tokens += seq_len
# Update the cache
if len(self.key_cache) <= layer_idx:

205
python/llm/src/ipex_llm/transformers/models/starcoder2.py Normal file
View file

@ -0,0 +1,205 @@
#
# 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/v4.39.0/src/transformers/models/starcoder2/modeling_starcoder2.py
# which is licensed under Apache License 2.0:
#
# Copyright 2024 BigCode 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
import torch
import warnings
from ipex_llm.transformers.models.utils import (
use_quantize_kv_cache, restore_fp8_kv_cache,
apply_rotary_pos_emb_no_cache_xpu
)
from ipex_llm.transformers.kv import DynamicFp8Cache
from ipex_llm.utils.common.log4Error import invalidInputError
from typing import Optional, Tuple, List
from transformers.cache_utils import Cache
from transformers.models.starcoder2.modeling_starcoder2 import repeat_kv, apply_rotary_pos_emb
from transformers.models.starcoder2.modeling_starcoder2 import Starcoder2Model, Starcoder2Attention
def should_use_fuse_rope(self, hidden_states, position_ids):
use_fuse_rope = (
hidden_states.device.type == "xpu" and
not (self.training and hidden_states.requires_grad) and
position_ids is not None
)
return use_fuse_rope
def merge_qkv(module: torch.nn.Module):
if isinstance(module, Starcoder2Attention):
new_weight = torch.cat([
module.q_proj.weight.data,
module.k_proj.weight.data,
module.v_proj.weight.data,
], dim=0)
new_bias = torch.cat([
module.q_proj.bias.data,
module.k_proj.bias.data,
module.v_proj.bias.data,
], dim=-1)
qkv_proj = torch.nn.Linear(0, 0, bias=True)
qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
qkv_proj.bias = torch.nn.Parameter(new_bias, 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 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[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
"Please make sure use `attention_mask` instead.`"
)
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)
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)
# IPEX-LLM OPT: fuse rope
if should_use_fuse_rope(self, hidden_states, position_ids):
query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
key_states,
position_ids,
"mistral",
self.rope_theta)
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)
# IPEX-LLM OPT: kv cache and quantize kv cache
invalidInputError(past_key_value is not None,
"`past_key_value` cannot be None")
use_quantize_kv = use_quantize_kv_cache(self.o_proj, hidden_states)
if use_quantize_kv:
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, None, new_layout=True)
else:
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, None)
if use_quantize_kv and q_len == 1:
import linear_q4_0
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states, attention_mask)
attn_weights = None
else:
if use_quantize_kv:
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype)
# 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(query_states.dtype)
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
training=self.training)
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)
attn_output = torch.nn.functional.dropout(attn_output, p=self.residual_dropout,
training=self.training)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def 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,
):
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.c_fc, input_ids):
if not isinstance(past_key_values, DynamicFp8Cache):
past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
return Starcoder2Model.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,
)