182 lines
8.5 KiB
Python
182 lines
8.5 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 init_kv_cache, extend_kv_cache, append_kv_cache
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from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
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apply_rotary_pos_emb
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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from ipex_llm.transformers.models.llama import should_use_fuse_rope, repeat_kv
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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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use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
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enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=q_len)
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if self.config.pretraining_tp > 1:
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key_value_slicing = ((self.num_key_value_heads * self.head_dim) //
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self.config.pretraining_tp)
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query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim)
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// self.config.pretraining_tp, dim=0)
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
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query_states = [F.linear(hidden_states, query_slices[i])
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for i in range(self.config.pretraining_tp)]
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query_states = torch.cat(query_states, dim=-1)
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key_states = [F.linear(hidden_states, key_slices[i])
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for i in range(self.config.pretraining_tp)]
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key_states = torch.cat(key_states, dim=-1)
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value_states = [F.linear(hidden_states, value_slices[i])
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for i in range(self.config.pretraining_tp)]
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value_states = torch.cat(value_states, dim=-1)
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else:
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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 use_fuse_rope:
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"llama")
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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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if past_key_value is not None:
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# reuse k, v, self_attention
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cache_k = past_key_value[0]
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cache_v = past_key_value[1]
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if not enough_kv_room:
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(bsz,
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self.num_key_value_heads, # Support GQA
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self.head_dim,
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cache_k.size(2),
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kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device)
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new_cache_k[:] = cache_k
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new_cache_v[:] = cache_v
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cache_k = new_cache_k
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cache_v = new_cache_v
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key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
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elif use_cache:
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max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(bsz,
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self.num_key_value_heads,
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self.head_dim,
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kv_seq_len,
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max_cache_length,
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dtype=key_states.dtype,
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device=device)
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new_key_states[:] = key_states
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new_value_states[:] = value_states
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key_states = new_key_states
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value_states = new_value_states
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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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if self.config.pretraining_tp > 1:
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attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
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o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp,
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dim=1)
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attn_output = sum([F.linear(attn_output[i], o_proj_slices[i])
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for i in range(self.config.pretraining_tp)])
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else:
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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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