726 lines
32 KiB
Python
726 lines
32 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 math
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import warnings
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from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ipex_llm.transformers.models.llama import repeat_kv
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from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_cache_freq_xpu
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from ipex_llm.transformers.kv import DynamicFp8Cache
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Model, apply_rotary_pos_emb
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask_for_sdpa
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from transformers.models.qwen2.modeling_qwen2 import _prepare_4d_causal_attention_mask
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from ipex_llm.transformers.models.utils import decoding_fast_path_qtype_check
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try:
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from transformers.cache_utils import Cache, DynamicCache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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import logging
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from transformers import logging
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logger = logging.get_logger(__name__)
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import os
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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def should_use_fuse_rope(self, query_states, position_ids):
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use_fuse_rope = query_states.device.type == "xpu"
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use_fuse_rope = use_fuse_rope and not (self.training and query_states.requires_grad)
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use_fuse_rope = use_fuse_rope and position_ids is not None
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return use_fuse_rope
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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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):
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
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if not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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return qwen2_model_forward_internal(
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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 qwen2_model_forward_internal(
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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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output_attentions = output_attentions if output_attentions is not None else \
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self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else
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self.config.output_hidden_states
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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invalidInputError(False,
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"You cannot specify both decoder_input_ids and "
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"decoder_inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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invalidInputError(False,
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"You have to specify either decoder_input_ids or decoder_inputs_embeds")
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. "
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"Setting `use_cache=False`..."
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)
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use_cache = False
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past_key_values_length = 0
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if use_cache:
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use_legacy_cache = not isinstance(past_key_values, Cache)
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if use_legacy_cache:
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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past_key_values_length = past_key_values.get_usable_length(seq_length)
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length,
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dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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flash_attn_2 = self._attn_implementation == "flash_attention_2"
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if attention_mask is not None and flash_attn_2 and use_cache:
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is_padding_right = attention_mask[:, -1].sum().item() != batch_size
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if is_padding_right:
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invalidInputError(
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False,
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"You are attempting to perform batched generation with padding_side='right'"
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" this may lead to unexpected behaviour for Flash Attention version of Qwen2."
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" Make sure to call `tokenizer.padding_side = 'left'` before tokenizing "
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"the input. "
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)
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if self._attn_implementation == "flash_attention_2":
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# 2d mask is passed through the layers
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attention_mask = attention_mask if (attention_mask is not None and
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0 in attention_mask) else None
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elif self._attn_implementation == "sdpa" and not output_attentions:
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# output_attentions=True can not be supported when using SDPA, and we fall back on
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# the manual implementation that requires a 4D causal mask in all cases.
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attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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)
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else:
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# 4d mask is passed through the layers
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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sliding_window=self.config.sliding_window,
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)
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hidden_states = inputs_embeds
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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attention_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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)
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else:
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# bigdl-llm changes
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curr_device = decoder_layer.input_layernorm.weight.device
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if attention_mask is not None:
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attention_mask = attention_mask.to(curr_device)
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if position_ids is not None:
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position_ids = position_ids.to(curr_device)
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# bigdl-llm changes end
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = None
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if use_cache:
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next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else \
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next_decoder_cache
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache,
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all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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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[Tuple[torch.Tensor]] = 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 use_quantize_kv_cache(self.q_proj, hidden_states):
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forward_function = qwen2_attention_forward_quantized
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elif hidden_states.device.type == "cpu":
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forward_function = qwen2_sdpa_attention_forward
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else:
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forward_function = qwen2_attention_forward_origin
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return forward_function(
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self=self,
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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**kwargs,
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)
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def qwen2_attention_forward_quantized(
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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[DynamicFp8Cache] = 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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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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bsz, q_len, _ = hidden_states.size()
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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,
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self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).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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invalidInputError(self.layer_idx is not None,
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"The cache structure has changed since version v4.36. "
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f"If you are using {self.__class__.__name__} "
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"for auto-regressive decoding with k/v caching, "
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"please make sure to initialize the attention class "
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"with a layer index.")
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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if use_fuse_rope:
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query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
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sin, cos, "qwen2",
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position_ids)
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else:
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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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cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, cache_kwargs,
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new_layout=True)
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if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
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and not hidden_states.requires_grad:
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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,
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attention_mask)
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attn_weights = None
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else:
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key, value = restore_fp8_kv_cache(key_states, value_states, query_states.dtype)
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key = repeat_kv(key, self.num_key_value_groups)
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value = repeat_kv(value, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key.transpose(2, 3))
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attn_weights = attn_weights / math.sqrt(self.head_dim)
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invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
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("Attention weights should be of size "
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f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
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"but is {attn_weights.size()}"))
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if attention_mask is not None:
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invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
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(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
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f" but is {attention_mask.size()}"))
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(query_states.dtype)
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attn_weights = 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)
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invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
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"`attn_output` should be of size "
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f"{(bsz, self.num_heads, q_len, self.head_dim)},"
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f" but is {attn_output.size()}")
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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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from ipex_llm.ggml.quantize import ggml_tensor_qtype
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SYM_INT4 = ggml_tensor_qtype["sym_int4"]
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FP8E5 = ggml_tensor_qtype["fp8_e5m2"]
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def qwen2_attention_forward_origin(
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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[Tuple[torch.Tensor]] = 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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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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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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device = hidden_states.device
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enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
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qtype_check = decoding_fast_path_qtype_check(self.q_proj)
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decoding_fast_path = (qtype_check and use_fuse_rope
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and enough_kv_room and bsz * q_len == 1)
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if decoding_fast_path:
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hidden_states = hidden_states.view(1, -1)
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|
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
kv_seq_len = cache_k.shape[-2]
|
|
import linear_q4_0
|
|
args = [hidden_states, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight,
|
|
self.q_proj.bias, self.k_proj.bias, self.v_proj.bias, position_ids, cache_k,
|
|
cache_v, self.q_proj.weight.qtype, self.v_proj.weight.qtype, kv_seq_len,
|
|
self.head_dim, self.rotary_emb.base]
|
|
query_states, key_states, value_states = linear_q4_0.forward_qkv_bias(*args)
|
|
kv_seq_len += 1
|
|
if self.layer_idx == 0:
|
|
past_key_value.seen_tokens = kv_seq_len
|
|
past_key_value.key_cache[self.layer_idx] = key_states
|
|
past_key_value.value_cache[self.layer_idx] = value_states
|
|
|
|
else:
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = \
|
|
key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = \
|
|
value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
if self.layer_idx is None:
|
|
invalidInputError(
|
|
False,
|
|
"The cache structure has changed since version v4.36. "
|
|
f"If you are using {self.__class__.__name__} "
|
|
"for auto-regressive decoding with k/v caching, "
|
|
"please make sure to initialize the attention class with a layer index."
|
|
)
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
if use_fuse_rope:
|
|
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
|
|
sin, cos, "qwen2",
|
|
position_ids)
|
|
else:
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
|
cos, sin, position_ids)
|
|
|
|
if past_key_value is not None:
|
|
# update the number of seen tokens
|
|
if self.layer_idx == 0:
|
|
past_key_value.seen_tokens += key_states.shape[-2]
|
|
|
|
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
past_key_value.key_cache.append(key_states)
|
|
past_key_value.value_cache.append(value_states)
|
|
else:
|
|
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
|
|
if not enough_kv_room:
|
|
# allocate new
|
|
new_c_k, new_c_v = extend_kv_cache(bsz,
|
|
self.num_key_value_heads, # Support GQA
|
|
self.head_dim,
|
|
cache_k.size(2),
|
|
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=cache_k.dtype,
|
|
device=device)
|
|
|
|
new_c_k[:] = cache_k
|
|
new_c_v[:] = cache_v
|
|
cache_k = new_c_k
|
|
cache_v = new_c_v
|
|
|
|
key_states, value_states = append_kv_cache(cache_k,
|
|
cache_v,
|
|
key_states,
|
|
value_states)
|
|
|
|
# update past_key_value
|
|
past_key_value.key_cache[self.layer_idx] = key_states
|
|
past_key_value.value_cache[self.layer_idx] = value_states
|
|
|
|
# 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)
|
|
|
|
if not self.training and not hidden_states.requires_grad and \
|
|
use_flash_attention(query_states, key_states, attention_mask):
|
|
attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
|
|
key_states.to(device, dtype=torch.float16),
|
|
value_states.to(device, dtype=torch.float16),
|
|
is_causal=True)
|
|
attn_weights = None
|
|
elif not self.training and not hidden_states.requires_grad and \
|
|
use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
|
|
import linear_fp16_esimd
|
|
attn_output = linear_fp16_esimd.sdp_forward(query_states,
|
|
key_states,
|
|
value_states)
|
|
attn_output = attn_output.view(query_states.shape)
|
|
attn_weights = None
|
|
else:
|
|
attn_weights = torch.matmul(query_states,
|
|
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
|
|
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
|
|
("Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
|
|
"but is {attn_weights.size()}"))
|
|
|
|
if attention_mask is not None:
|
|
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
|
|
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
|
|
f" but is {attention_mask.size()}"))
|
|
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = \
|
|
nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
attn_weights = nn.functional.dropout(attn_weights,
|
|
p=self.attention_dropout,
|
|
training=self.training)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
|
|
"`attn_output` should be of size "
|
|
f"{(bsz, self.num_heads, q_len, self.head_dim)},"
|
|
f" but is {attn_output.size()}")
|
|
|
|
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)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
|
|
|
|
|
|
def qwen2_sdpa_attention_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
|
|
use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
|
|
|
|
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()
|
|
device = hidden_states.device
|
|
|
|
enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
|
|
qtype_check = decoding_fast_path_qtype_check(self.q_proj)
|
|
decoding_fast_path = (qtype_check and use_fuse_rope
|
|
and enough_kv_room and bsz * q_len == 1)
|
|
if decoding_fast_path:
|
|
hidden_states = hidden_states.view(1, -1)
|
|
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
kv_seq_len = cache_k.shape[-2]
|
|
import linear_q4_0
|
|
args = [hidden_states, self.q_proj.weight, self.k_proj.weight, self.v_proj.weight,
|
|
self.q_proj.bias, self.k_proj.bias, self.v_proj.bias, position_ids, cache_k,
|
|
cache_v, self.q_proj.weight.qtype, self.v_proj.weight.qtype, kv_seq_len,
|
|
self.head_dim, self.rotary_emb.base]
|
|
query_states, key_states, value_states = linear_q4_0.forward_qkv_bias(*args)
|
|
kv_seq_len += 1
|
|
if self.layer_idx == 0:
|
|
past_key_value.seen_tokens = kv_seq_len
|
|
past_key_value.key_cache[self.layer_idx] = key_states
|
|
past_key_value.value_cache[self.layer_idx] = value_states
|
|
|
|
else:
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = \
|
|
key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = \
|
|
value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
if self.layer_idx is None:
|
|
invalidInputError(
|
|
False,
|
|
"The cache structure has changed since version v4.36. "
|
|
f"If you are using {self.__class__.__name__} "
|
|
"for auto-regressive decoding with k/v caching, "
|
|
"please make sure to initialize the attention class with a layer index."
|
|
)
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
if use_fuse_rope:
|
|
query_states, key_states = apply_rotary_pos_emb_cache_freq_xpu(query_states, key_states,
|
|
sin, cos, "qwen2",
|
|
position_ids)
|
|
else:
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
|
|
cos, sin, position_ids)
|
|
|
|
if past_key_value is not None:
|
|
# update the number of seen tokens
|
|
if self.layer_idx == 0:
|
|
past_key_value.seen_tokens += key_states.shape[-2]
|
|
|
|
if len(past_key_value.key_cache) <= self.layer_idx:
|
|
past_key_value.key_cache.append(key_states)
|
|
past_key_value.value_cache.append(value_states)
|
|
else:
|
|
cache_k = past_key_value.key_cache[self.layer_idx]
|
|
cache_v = past_key_value.value_cache[self.layer_idx]
|
|
|
|
if not enough_kv_room:
|
|
# allocate new
|
|
new_c_k, new_c_v = extend_kv_cache(bsz,
|
|
self.num_key_value_heads, # Support GQA
|
|
self.head_dim,
|
|
cache_k.size(2),
|
|
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=cache_k.dtype,
|
|
device=device)
|
|
|
|
new_c_k[:] = cache_k
|
|
new_c_v[:] = cache_v
|
|
cache_k = new_c_k
|
|
cache_v = new_c_v
|
|
|
|
key_states, value_states = append_kv_cache(cache_k,
|
|
cache_v,
|
|
key_states,
|
|
value_states)
|
|
|
|
# update past_key_value
|
|
past_key_value.key_cache[self.layer_idx] = key_states
|
|
past_key_value.value_cache[self.layer_idx] = value_states
|
|
|
|
# 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)
|
|
|
|
invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
|
|
("Attention weights should be of size "
|
|
f"{(bsz, self.num_heads, q_len, kv_seq_len)},"
|
|
"but is {attn_weights.size()}"))
|
|
|
|
if attention_mask is not None:
|
|
invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
|
|
(f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}"
|
|
f" but is {attention_mask.size()}"))
|
|
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
from torch.nn.functional import scaled_dot_product_attention as sdpa
|
|
attn_output = sdpa(query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=attention_mask,
|
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
is_causal=self.is_causal and attention_mask is None and q_len > 1)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
return attn_output, None, past_key_value
|