9d65dcd7ef
* Fix deepseek coder with linear rope type * Style fix * Move to optimize_pre * Small fix * Small fix * Small fix to not affect other cases * Style fixes * Update function name * Small fix * Small fix * Small fix * Fix for low transformers version first * Style fix * Small fix
206 lines
8.9 KiB
Python
206 lines
8.9 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/llama/modeling_llama.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2022 EleutherAI 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 torch
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from typing import Optional, Tuple, Union
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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.llama.modeling_llama import LlamaModel, LlamaAttention
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from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
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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 make_cache_contiguous_inplaced
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache
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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.kv import DynamicNormalCache, DynamicFp8Cache
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from ipex_llm.transformers.kv import DynamicCompressCache, DynamicCompressFp8Cache
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def llama_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[Cache] = 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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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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# IPEX-LLM OPT start: 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 = True if inputs.device.type == "xpu" else use_cache
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use_quantize_kv = use_quantize_kv_cache(
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self.layers[0].mlp.down_proj, inputs,
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self.config.num_attention_heads, self.config.num_key_value_heads
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)
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use_compresskv = should_use_compresskv(inputs, inputs.shape[1]) or \
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isinstance(past_key_values, DynamicCompressCache)
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# disable llama3.2 1b for prefill performance and output quality
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use_compresskv = use_compresskv and self.config.hidden_size != 2048
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if use_cache:
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if use_compresskv 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_compresskv
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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 OPT end
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# `cache_position` is required after transformers 4.38
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if cache_position is not None:
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kwargs = {"cache_position": cache_position}
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else:
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kwargs = {}
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return LlamaModel.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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**kwargs
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)
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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, LlamaAttention)
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def pre_compute_inv_freq(module: torch.nn.Module):
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if module.__class__.__name__ == "LlamaLinearScalingRotaryEmbedding":
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if hasattr(module, "scaling_factor"):
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module.register_buffer("inv_freq_scaled", None, persistent=False)
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module.inv_freq_scaled = module.inv_freq / module.scaling_factor
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def llama_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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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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**kwargs,
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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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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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if query_states.device.type == "xpu":
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import xe_addons
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if hasattr(self, "rotary_emb"):
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# transformers < 4.46
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if hasattr(self.rotary_emb, "inv_freq_scaled"):
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xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq_scaled, position_ids,
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query_states, key_states)
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else:
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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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# transformers >= 4.46
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cos, sin = position_embeddings
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make_cache_contiguous_inplaced(cos, sin)
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xe_addons.rotary_half_with_cache_inplaced(query_states, key_states, cos, sin)
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else:
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if position_embeddings is None:
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if isinstance(getattr(self.rotary_emb, "cos_cached", None), torch.Tensor):
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# transformers < 4.38
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cos, sin = self.rotary_emb(value_states, kv_seq_len)
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else:
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# transformers >= 4.38
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids)
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if past_key_value is not None:
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# [CompressKV]
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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, 256)
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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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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 == key_states.size(2)
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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, -1)
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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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