From fe5a082b840a5da2f6b4343a806bb3b9ef100688 Mon Sep 17 00:00:00 2001 From: Yishuo Wang Date: Mon, 22 Apr 2024 18:56:47 +0800 Subject: [PATCH] add phi-2 optimization (#10843) --- .../llm/src/ipex_llm/transformers/convert.py | 12 +- .../src/ipex_llm/transformers/models/phi.py | 192 ++++++++++++++++++ 2 files changed, 203 insertions(+), 1 deletion(-) create mode 100644 python/llm/src/ipex_llm/transformers/models/phi.py diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py index 376c1793..9d7f803d 100644 --- a/python/llm/src/ipex_llm/transformers/convert.py +++ b/python/llm/src/ipex_llm/transformers/convert.py @@ -650,6 +650,9 @@ def _optimize_pre(model): if model.config.model_type == "starcoder2": from ipex_llm.transformers.models.starcoder2 import merge_qkv model.apply(merge_qkv) + if model.config.model_type == "phi": + from ipex_llm.transformers.models.phi 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 @@ -1415,7 +1418,14 @@ def _optimize_post(model, lightweight_bmm=False): 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 == 'phi': + # for phi-2 + modeling_module_name = model.__class__.__module__ + module = importlib.import_module(modeling_module_name) + from ipex_llm.transformers.models.phi import attention_forward + 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 == 'yuan': modeling_module_name = model.__class__.__module__ module = importlib.import_module(modeling_module_name) diff --git a/python/llm/src/ipex_llm/transformers/models/phi.py b/python/llm/src/ipex_llm/transformers/models/phi.py new file mode 100644 index 00000000..43a63827 --- /dev/null +++ b/python/llm/src/ipex_llm/transformers/models/phi.py @@ -0,0 +1,192 @@ +# +# 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/v4.37.0/src/transformers/models/phi/modeling_phi.py +# which is licensed under Apache License 2.0: +# +# Copyright 2023 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 + +from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu +from ipex_llm.transformers.kv import DynamicNormalCache +from ipex_llm.utils.common.log4Error import invalidInputError + +from typing import Optional, Tuple, List +from transformers.cache_utils import Cache +from transformers.models.phi.modeling_phi import repeat_kv, apply_rotary_pos_emb +from transformers.models.phi.modeling_phi import PhiModel + + +def should_use_fuse_rope(self, hidden_states, position_ids): + use_fuse_rope = ( + hidden_states.device.type == "xpu" and + hidden_states.numel() == hidden_states.size(-1) 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 module.__class__.__name__ == "PhiAttention": + 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, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + invalidInputError(not self.qk_layernorm, "`qk_layernorm` must be false") + + 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, seq_len=kv_seq_len) + + # 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:], + ) + + # IPEX-LLM OPT: fuse rope + use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids) + + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + if use_fuse_rope: + query_rot, key_rot = apply_rotary_pos_emb_cache_freq_xpu(query_rot, key_rot, sin, + cos, "stablelm", position_ids) + else: + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) + + # [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) + + invalidInputError(past_key_value is not None, + "`past_key_value` cannot be None") + key_states, value_states = past_key_value.update(key_states, value_states, + self.layer_idx, None, new_layout=True) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow + attn_weights = torch.matmul( + query_states.to(torch.float32), key_states.to(torch.float32).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.dense(attn_output) + + 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, +): + # IPEX-LLM OPT: kv cache but no sdp (its head_dim 80, 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 PhiModel.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, + )