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add phi3 optimization (#10871)

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Yishuo Wang 2024-04-24 15:17:40 +08:00 committed by GitHub
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3 changed files with 216 additions and 1 deletions
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14
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -653,6 +653,9 @@ def _optimize_pre(model):
if model.config.model_type == "phi":
from ipex_llm.transformers.models.phi import merge_qkv
model.apply(merge_qkv)
if model.config.model_type == "phi3":
from ipex_llm.transformers.models.phi3 import split_mlp
model.apply(split_mlp)
if model.config.model_type == "qwen":
rope_base = model.config.rotary_emb_base
from accelerate.big_modeling import init_empty_weights
@ -1426,6 +1429,17 @@ def _optimize_post(model, lightweight_bmm=False):
from ipex_llm.transformers.models.phi import model_forward
convert_forward(model, module.PhiAttention, attention_forward)
convert_forward(model, module.PhiModel, model_forward)
elif model.config.model_type == "phi3":
# for phi-3
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)
from ipex_llm.transformers.models.phi3 import attention_forward
convert_forward(model, module.Phi3Attention, attention_forward)
from ipex_llm.transformers.models.phi3 import mlp_forward
convert_forward(model, module.Phi3MLP, mlp_forward)
from ipex_llm.transformers.models.phi3 import model_forward_wrapper
model_forward = model_forward_wrapper(module.Phi3Model.forward)
convert_forward(model, module.Phi3Model, model_forward)
elif model.config.model_type == 'yuan':
modeling_module_name = model.__class__.__module__
module = importlib.import_module(modeling_module_name)

193
python/llm/src/ipex_llm/transformers/models/phi3.py Normal file
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@ -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://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/modeling_phi3.py
# which is licensed under Apache License 2.0:
#
# Copyright 2024 Microsoft and 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.
import math
import torch
import warnings
from ipex_llm.transformers.models.utils import (
rotate_half, should_use_fuse_rope,
apply_rotary_pos_emb_cache_freq_xpu
)
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
from ipex_llm.transformers.kv import DynamicNormalCache
from typing import Optional, Tuple, List
from transformers.models.phi.modeling_phi import repeat_kv
from transformers.cache_utils import Cache
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
warnings.warn("You are not running the flash-attention implementation, "
"expect numerical differences.")
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)
cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
# IPEX-LLM OPT: fuse rope
if should_use_fuse_rope(hidden_states, position_ids, self.training):
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
sin, cos, "phi3")
else:
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
if past_key_value is not None:
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, None)
# 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 = 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)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def split_mlp(module: torch.nn.Module):
if module.__class__.__name__ == "Phi3MLP":
gate_weight, up_weight = module.gate_up_proj.weight.data.chunk(2, dim=0)
gate_proj = torch.nn.Linear(0, 0, bias=False)
gate_proj.weight = torch.nn.Parameter(gate_weight, requires_grad=False)
gate_proj.in_features = gate_weight.size(1)
gate_proj.out_features = gate_weight.size(0)
up_proj = torch.nn.Linear(0, 0, bias=False)
up_proj.weight = torch.nn.Parameter(up_weight, requires_grad=False)
up_proj.in_features = up_weight.size(1)
up_proj.out_features = up_weight.size(0)
module.gate_proj = gate_proj
module.up_proj = up_proj
del module.gate_up_proj
def mlp_forward(
self,
hidden_states: torch.FloatTensor
) -> torch.FloatTensor:
x_2d = hidden_states.view(-1, hidden_states.shape[-1])
qtype = getattr(self.gate_proj, "qtype", None)
if mlp_fusion_check(x_2d, qtype, self.training):
x_2d = x_2d.contiguous()
import linear_q4_0
return self.down_proj(linear_q4_0.mlp_forward_xpu(
x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_features,
SILU, qtype
))
return self.down_proj(
self.activation_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states)
)
def model_forward_wrapper(origin_model_forward):
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,
):
# IPEX-LLM OPT: kv cache but no sdp (its head_dim 96, cannot use sdp)
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 origin_model_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,
)
return model_forward

10
python/llm/src/ipex_llm/transformers/models/utils.py
View file

@ -167,6 +167,14 @@ def rotate_every_two(x):
return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')
def should_use_fuse_rope(hidden_states, position_ids, training):
return (
hidden_states.device.type == "xpu"
and not training and not hidden_states.requires_grad
and position_ids is not None
)
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", "stablelm", "qwen2_moe"]:
@ -234,7 +242,7 @@ def apply_rotary_pos_emb_cache_freq_xpu(q, k, sin, cos, model_family, position_i
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
linear_q4_0.apply_rotary_embedding_half_q_and_k_cache_freq(q, k, sin, cos, q_embed, k_embed)
elif model_family in ["gemma"]:
elif model_family in ["gemma", "phi3"]:
cos = cos.unsqueeze(1)
sin = sin.unsqueeze(1)
linear_q4_0.apply_rotary_embedding_half_q_and_k_cache_freq(q, k, sin, cos, q_embed, k_embed)