171 lines
7.3 KiB
Python
171 lines
7.3 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/main/src/transformers/models/mistral/modeling_mistral.py
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#
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# Copyright 2023 Mistral AI 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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from typing import Optional, Tuple, Union, List
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import os
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import torch
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from transformers.cache_utils import Cache
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.models.mistral.modeling_mistral import MistralModel, MistralAttention
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from ipex_llm.transformers.models.common import merge_qkv_base
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache
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from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
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from ipex_llm.transformers.kv import DynamicCompressCache, DynamicCompressFp8Cache
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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def mistral_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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) -> Union[Tuple, BaseModelOutputWithPast]:
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# ipex-llm changes start
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# IPEX-LLM OPT: kv cache and quantize kv cache
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inputs = input_ids if input_ids is not None else inputs_embeds
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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use_cache = use_cache or inputs.device.type == 'xpu'
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use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs,
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self.config.num_attention_heads,
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self.config.num_key_value_heads)
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use_compress_kv = should_use_compresskv(inputs, inputs.size(1)) or \
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isinstance(past_key_values, DynamicCompressCache)
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if use_cache:
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if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
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if use_quantize_kv:
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past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
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else:
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past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
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elif 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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elif (
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not use_quantize_kv
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and not use_compress_kv
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and not isinstance(past_key_values, DynamicNormalCache)
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):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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# ipex-llm changes end
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return MistralModel.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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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, MistralAttention)
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def mistral_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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):
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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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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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# IPEX OPT: fuse rope
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if should_use_fuse_rope(hidden_states, position_ids, self.training):
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import xe_addons
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xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
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query_states, key_states)
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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(query_states, key_states,
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cos, sin, position_ids, "mistral")
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if isinstance(past_key_value, DynamicCompressCache):
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx, q_len)
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx,
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query_states, attention_mask, self.num_key_value_groups,
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self.config, enough_kv_room, KV_CACHE_ALLOC_BLOCK_LENGTH
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)
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else:
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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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# IPEX-LLM OPT: sdpa
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attn_weights = None
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attn_output = scaled_dot_product_attention(
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query_states, key_states, value_states,
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attention_mask, q_len == kv_seq_len
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)
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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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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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