add phi-2 optimization (#10843)
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Yishuo Wang
GitHub
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2 changed files with 203 additions and 1 deletions
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@ -650,6 +650,9 @@ def _optimize_pre(model):
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if model.config.model_type == "starcoder2":
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from ipex_llm.transformers.models.starcoder2 import merge_qkv
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model.apply(merge_qkv)
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if model.config.model_type == "phi":
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from ipex_llm.transformers.models.phi import merge_qkv
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model.apply(merge_qkv)
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if model.config.model_type == "qwen":
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rope_base = model.config.rotary_emb_base
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from accelerate.big_modeling import init_empty_weights
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@ -1415,7 +1418,14 @@ def _optimize_post(model, lightweight_bmm=False):
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from ipex_llm.transformers.models.starcoder2 import model_forward
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convert_forward(model, module.Starcoder2Attention, attention_forward)
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convert_forward(model, module.Starcoder2Model, model_forward)
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elif model.config.model_type == 'phi':
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# for phi-2
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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from ipex_llm.transformers.models.phi import attention_forward
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from ipex_llm.transformers.models.phi import model_forward
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convert_forward(model, module.PhiAttention, attention_forward)
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convert_forward(model, module.PhiModel, model_forward)
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elif model.config.model_type == 'yuan':
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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192
python/llm/src/ipex_llm/transformers/models/phi.py
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192
python/llm/src/ipex_llm/transformers/models/phi.py
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@ -0,0 +1,192 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/v4.37.0/src/transformers/models/phi/modeling_phi.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import torch
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu
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from ipex_llm.transformers.kv import DynamicNormalCache
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from ipex_llm.utils.common.log4Error import invalidInputError
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from typing import Optional, Tuple, List
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from transformers.cache_utils import Cache
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from transformers.models.phi.modeling_phi import repeat_kv, apply_rotary_pos_emb
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from transformers.models.phi.modeling_phi import PhiModel
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def should_use_fuse_rope(self, hidden_states, position_ids):
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use_fuse_rope = (
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hidden_states.device.type == "xpu" and
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hidden_states.numel() == hidden_states.size(-1) and
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not (self.training and hidden_states.requires_grad) and
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position_ids is not None
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)
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return use_fuse_rope
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def merge_qkv(module: torch.nn.Module):
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if module.__class__.__name__ == "PhiAttention":
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new_weight = torch.cat([
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module.q_proj.weight.data,
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module.k_proj.weight.data,
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module.v_proj.weight.data,
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], dim=0)
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new_bias = torch.cat([
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module.q_proj.bias.data,
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module.k_proj.bias.data,
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module.v_proj.bias.data,
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], dim=-1)
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qkv_proj = torch.nn.Linear(0, 0, bias=True)
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qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
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qkv_proj.bias = torch.nn.Parameter(new_bias, requires_grad=False)
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qkv_proj.in_features = new_weight.size(1)
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qkv_proj.out_features = new_weight.size(0)
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module.qkv_proj = qkv_proj
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del module.q_proj, module.k_proj, module.v_proj
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def attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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invalidInputError(not self.qk_layernorm, "`qk_layernorm` must be false")
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qkv = self.qkv_proj(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
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qkv = qkv.transpose(1, 2)
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query_states, key_states, value_states = qkv.split([self.num_heads,
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self.num_key_value_heads,
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self.num_key_value_heads], dim=1)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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# Partial rotary embedding
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query_rot, query_pass = (
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query_states[..., : self.rotary_emb.dim],
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query_states[..., self.rotary_emb.dim:],
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)
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key_rot, key_pass = (
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key_states[..., : self.rotary_emb.dim],
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key_states[..., self.rotary_emb.dim:],
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)
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# IPEX-LLM OPT: fuse rope
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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
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if use_fuse_rope:
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query_rot, key_rot = apply_rotary_pos_emb_cache_freq_xpu(query_rot, key_rot, sin,
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cos, "stablelm", position_ids)
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else:
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query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
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# [batch_size, seq_length, num_heads, head_dim]
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query_states = torch.cat((query_rot, query_pass), dim=-1)
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key_states = torch.cat((key_rot, key_pass), dim=-1)
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invalidInputError(past_key_value is not None,
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"`past_key_value` cannot be None")
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, None, new_layout=True)
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
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attn_weights = torch.matmul(
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query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
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) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(value_states.dtype)
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attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
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training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.dense(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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def model_forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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# IPEX-LLM OPT: kv cache but no sdp (its head_dim 80, cannot use sdp)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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if use_cache:
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if not isinstance(past_key_values, DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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return PhiModel.forward(
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self=self,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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