122 lines
5.1 KiB
Python
122 lines
5.1 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/v4.31.0/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 2021 The HuggingFace Inc. team. All rights reserved.
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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
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import torch.nn.functional as F
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from ipex_llm.transformers.models.utils import repeat_kv
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb
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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 update_past_key_value
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from ipex_llm.utils.common import invalidInputError
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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 decilm_attention_forward_4_35_2(
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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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padding_mask: Optional[torch.LongTensor] = 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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is_decode = past_key_value is not None
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device = hidden_states.device
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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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kv_seq_len += past_key_value[0].shape[-2]
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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.maybe_rotary.inv_freq, position_ids,
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query_states, key_states)
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "llama")
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key_states, value_states = update_past_key_value(
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past_key_value, key_states, value_states,
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kv_seq_len, False, device
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)
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past_key_value = (key_states, value_states) if use_cache else None
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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if is_decode:
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states,
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is_causal=False,
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attn_mask=attention_mask)
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attn_output = attn_output.contiguous().view(bsz, q_len, self.hidden_size)
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else:
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states,
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is_causal=attention_mask is None,
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attn_mask=attention_mask)
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invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
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f"`attn_output` should be of size "
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f"{(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}")
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attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, self.hidden_size)
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attn_output = attn_output.to(hidden_states.dtype)
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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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