331 lines
14 KiB
Python
331 lines
14 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/v4.37.0/src/transformers/models/qwen2/modeling_qwen2.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 The Qwen team, Alibaba Group 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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#
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import os
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from typing import Optional, Tuple, Union, List
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import torch
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from torch.nn import CrossEntropyLoss
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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 SILU, mlp_fusion_check
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, \
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should_use_compresskv, is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache, \
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DynamicCompressCache, DynamicCompressFp8Cache
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Model, Qwen2Attention, Qwen2MLP
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from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.cache_utils import Cache
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def qwen2_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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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 = self.config.hidden_size != 3584 and 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_compress_kv = should_use_compresskv(inputs, inputs.shape[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 use_compress_kv and not isinstance(past_key_values,
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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 use_compress_kv and not isinstance(past_key_values,
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DynamicNormalCache):
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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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# `cache_position` is required after transformers 4.42
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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 Qwen2Model.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 qwen2_causal_lm_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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labels: Optional[torch.LongTensor] = 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, # for transformers >= 4.42
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) -> Union[Tuple, CausalLMOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = self.model(
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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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cache_position=cache_position,
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)
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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# ipex-llm changes start: remove `logits.float()` to reduce memory usage with long input
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# logits = logits.float()
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# ipex-llm changes end
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (logits,) + outputs[1:]
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return (loss,) + output if loss is not None else output
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, Qwen2Attention)
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if isinstance(module, Qwen2Attention) and os.environ.get("IPEX_LLM_LOW_MEM", None) == "1":
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del module.rotary_emb.cos_cached
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del module.rotary_emb.sin_cached
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def padding_mlp(module: torch.nn.Module):
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# for qwen 1.5 14B
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if isinstance(module, Qwen2MLP):
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hidden_size = module.gate_proj.weight.shape[1]
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intermediate_size = module.gate_proj.weight.shape[0]
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padding_intermediate_size = (intermediate_size + 256 - 1) // 256 * 256
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if intermediate_size % 256 == 0:
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return
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gate_weight = module.gate_proj.weight.data
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new_gate_weight = torch.zeros([padding_intermediate_size, hidden_size],
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dtype=gate_weight.dtype, device=gate_weight.device)
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new_gate_weight[:intermediate_size, :] = gate_weight
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if hasattr(module.gate_proj, 'out_features'):
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module.gate_proj.out_features = padding_intermediate_size
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module.gate_proj.weight = torch.nn.Parameter(new_gate_weight, requires_grad=False)
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up_weight = module.up_proj.weight.data
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new_up_weight = torch.zeros([padding_intermediate_size, hidden_size],
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dtype=up_weight.dtype, device=up_weight.device)
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new_up_weight[:intermediate_size, :] = up_weight
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if hasattr(module.gate_proj, 'out_features'):
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module.up_proj.out_features = padding_intermediate_size
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module.up_proj.weight = torch.nn.Parameter(new_up_weight, requires_grad=False)
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down_weight = module.down_proj.weight.data
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new_down_weight = torch.zeros([hidden_size, padding_intermediate_size],
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dtype=down_weight.dtype, device=down_weight.device)
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new_down_weight[:, :intermediate_size] = down_weight
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if hasattr(module.gate_proj, 'out_features'):
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module.down_proj.in_features = padding_intermediate_size
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module.down_proj.weight = torch.nn.Parameter(new_down_weight, requires_grad=False)
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def qwen2_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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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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# [CompressKV]
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from ipex_llm.transformers.kv import DynamicCompressCache
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use_compresskv = isinstance(past_key_value, DynamicCompressCache)
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if hasattr(self, 'qkv_proj') and self.qkv_proj is not None:
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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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else:
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# when quant_method is 'gptq'
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) \
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.transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim) \
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.transpose(1, 2)
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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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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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cos, sin = cos.to(device), sin.to(device)
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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 use_compresskv:
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx,
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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 == 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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def qwen2_mlp_forward(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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x_2d = x.view(-1, x.shape[-1])
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qtype = getattr(self.gate_proj, "qtype", None)
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if mlp_fusion_check(x_2d, qtype, self.training):
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import xe_linear
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return self.down_proj(xe_linear.mlp_forward_xpu(
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x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
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x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
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SILU, qtype
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))
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elif x.device.type == "xpu" and not self.training:
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import xe_addons
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gate = self.gate_proj(x)
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up = self.up_proj(x)
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xe_addons.mlp_silu_mul_inplaced(gate, up)
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return self.down_proj(gate)
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else:
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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