114 lines
4.6 KiB
Python
114 lines
4.6 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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from typing import Optional, List, Tuple
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from transformers.processing_utils import Unpack
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from transformers.cache_utils import Cache
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from transformers.modeling_outputs import MoeModelOutputWithPast
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.models.qwen3.modeling_qwen3 import apply_rotary_pos_emb
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from transformers.models.qwen3.modeling_qwen3 import Qwen3Model, Qwen3Attention
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from ipex_llm.transformers.kv import DynamicNormalCache
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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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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, Qwen3Attention)
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def qwen3_model_forward(
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self,
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input_ids: Optional[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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output_router_logits: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
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) -> MoeModelOutputWithPast:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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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 device.type == "xpu" else use_cache
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if use_cache and not isinstance(past_key_values, DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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return Qwen3Model.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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output_router_logits=output_router_logits,
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cache_position=cache_position,
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**flash_attn_kwargs,
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)
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def qwen3_attention_forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: Tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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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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qkv = self.qkv_proj(hidden_states)
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qkv = qkv.view(bsz, q_len, -1, 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.config.num_attention_heads,
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self.config.num_key_value_heads,
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self.config.num_key_value_heads], dim=1)
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query_states = self.q_norm(query_states)
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key_states = self.k_norm(key_states)
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cos, sin = position_embeddings
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if device.type == "xpu":
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from ipex_llm.transformers.models.common import rotary_half_with_cache_inplaced
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rotary_half_with_cache_inplaced(query_states, key_states, cos, sin)
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else:
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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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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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), self.scaling
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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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return attn_output, attn_weights
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