From ba8cc6bd688960d29460d2fb7d806982f74e783d Mon Sep 17 00:00:00 2001 From: Yishuo Wang Date: Tue, 2 Apr 2024 17:16:29 +0800 Subject: [PATCH] optimize starcoder2-3b (#10625) --- .../llm/src/ipex_llm/transformers/convert.py | 13 ++ python/llm/src/ipex_llm/transformers/kv.py | 7 +- .../transformers/models/starcoder2.py | 205 ++++++++++++++++++ 3 files changed, 224 insertions(+), 1 deletion(-) create mode 100644 python/llm/src/ipex_llm/transformers/models/starcoder2.py diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py index d607dbaaba4..d48bc28ba77 100644 --- a/python/llm/src/ipex_llm/transformers/convert.py +++ b/python/llm/src/ipex_llm/transformers/convert.py @@ -595,6 +595,10 @@ def merge_qk_proj_func(module): ): 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 safe_bmm_fwd(*args, **kwargs): 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) diff --git a/python/llm/src/ipex_llm/transformers/kv.py b/python/llm/src/ipex_llm/transformers/kv.py index 71aa6c9f838..f0b4d65778a 100644 --- a/python/llm/src/ipex_llm/transformers/kv.py +++ b/python/llm/src/ipex_llm/transformers/kv.py @@ -35,7 +35,12 @@ def update( 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: diff --git a/python/llm/src/ipex_llm/transformers/models/starcoder2.py b/python/llm/src/ipex_llm/transformers/models/starcoder2.py new file mode 100644 index 00000000000..de48d11b4b6 --- /dev/null +++ b/python/llm/src/ipex_llm/transformers/models/starcoder2.py @@ -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, + )