205 lines
8.6 KiB
Python
205 lines
8.6 KiB
Python
#
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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/v4.39.0/src/transformers/models/starcoder2/modeling_starcoder2.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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import warnings
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from ipex_llm.transformers.models.utils import (
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use_quantize_kv_cache, restore_fp8_kv_cache,
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apply_rotary_pos_emb_no_cache_xpu
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)
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from ipex_llm.transformers.kv import DynamicFp8Cache, 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.starcoder2.modeling_starcoder2 import repeat_kv, apply_rotary_pos_emb
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from transformers.models.starcoder2.modeling_starcoder2 import Starcoder2Model, Starcoder2Attention
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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 isinstance(module, Starcoder2Attention):
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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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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37. "
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"Please make sure use `attention_mask` instead.`"
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)
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bsz, q_len, _ = hidden_states.size()
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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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# IPEX-LLM OPT: fuse rope
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if should_use_fuse_rope(self, hidden_states, position_ids):
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"mistral",
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self.rope_theta)
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(
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query_states, key_states, cos, sin, position_ids)
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# IPEX-LLM OPT: kv cache and quantize kv cache
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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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use_quantize_kv = use_quantize_kv_cache(self.o_proj, hidden_states)
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, None)
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if use_quantize_kv and q_len == 1:
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import linear_q4_0
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attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states, attention_mask)
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attn_weights = None
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else:
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if use_quantize_kv:
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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# repeat k/v heads if n_kv_heads < n_heads
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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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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / 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(query_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.o_proj(attn_output)
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attn_output = torch.nn.functional.dropout(attn_output, p=self.residual_dropout,
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training=self.training)
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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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use_cache = use_cache if use_cache is not None else self.config.use_cache
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use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.c_fc, input_ids)
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if use_cache:
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if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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if not use_quantize_kv and 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 Starcoder2Model.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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