246 lines
10 KiB
Python
246 lines
10 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/v4.38.0/src/transformers/models/stablelm/modeling_stablelm.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 EleutherAI 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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from typing import Optional, Tuple
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import torch
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from torch import nn
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import torch.nn.functional as F
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from transformers.models.stablelm.modeling_stablelm import StableLmAttention
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from ipex_llm.utils.common import invalidInputError
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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 apply_rotary_pos_emb, \
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apply_rotary_pos_emb_cache_freq_xpu
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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 use_flash_attention, use_esimd_sdp
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from ipex_llm.transformers.models.mistral import should_use_fuse_rope, repeat_kv
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try:
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from transformers.cache_utils import Cache
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except ImportError:
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Cache = Tuple[torch.Tensor]
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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def merge_qkv(module: torch.nn.Module):
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if isinstance(module, StableLmAttention):
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new_weight = torch.cat([
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module.q_proj.weight.data,
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module.k_proj.weight.data,
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module.v_proj.weight.data,
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], dim=0)
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qkv_proj = torch.nn.Linear(0, 0, bias=False)
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qkv_proj.weight = torch.nn.Parameter(new_weight, requires_grad=False)
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qkv_proj.in_features = new_weight.size(1)
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qkv_proj.out_features = new_weight.size(0)
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module.qkv_proj = qkv_proj
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del module.q_proj, module.k_proj, module.v_proj
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def stablelm_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[Cache]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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# for flash attention
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original_dtype = hidden_states.dtype
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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_36(past_key_value, self.layer_idx)
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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_heads,
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self.num_heads], dim=1)
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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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if self.layer_idx is None:
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invalidInputError(False,
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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__} for "
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"auto-regressive decodingwith k/v caching, please make sure "
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"to initialize the attention class 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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# Partial rotary embedding
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query_rot, query_pass = (
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query_states[..., : self.rotary_emb.dim],
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query_states[..., self.rotary_emb.dim:],
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)
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key_rot, key_pass = (
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key_states[..., : self.rotary_emb.dim],
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key_states[..., self.rotary_emb.dim:],
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)
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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_rot, key_rot = apply_rotary_pos_emb_cache_freq_xpu(query_rot,
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key_rot,
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sin,
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cos,
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"stablelm",
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position_ids)
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else:
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# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
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query_rot, key_rot = apply_rotary_pos_emb(query_rot,
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key_rot,
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cos,
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sin,
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position_ids,
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"stablelm")
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# [batch_size, seq_length, num_heads, head_dim]
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query_states = torch.cat((query_rot, query_pass), dim=-1)
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key_states = torch.cat((key_rot, key_pass), dim=-1)
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if past_key_value is not None:
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# update the number of seen tokens
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if self.layer_idx == 0:
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past_key_value.seen_tokens += key_states.shape[-2]
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# reuse k, v, self_attention
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# update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
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if len(past_key_value.key_cache) <= self.layer_idx:
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past_key_value.key_cache.append(key_states)
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past_key_value.value_cache.append(value_states)
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else:
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cache_k = past_key_value.key_cache[self.layer_idx]
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cache_v = past_key_value.value_cache[self.layer_idx]
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if not enough_kv_room:
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# allocate new
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new_c_k, new_c_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_c_k[:] = cache_k
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new_c_v[:] = cache_v
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cache_k = new_c_k
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cache_v = new_c_v
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key_states, value_states = append_kv_cache(cache_k, cache_v,
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key_states, value_states)
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# update past_key_value
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past_key_value.key_cache[self.layer_idx] = key_states
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past_key_value.value_cache[self.layer_idx] = value_states
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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 not self.training and not hidden_states.requires_grad and \
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use_flash_attention(query_states, key_states, attention_mask):
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attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
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key_states.to(device, dtype=torch.float16),
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value_states.to(device, dtype=torch.float16),
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is_causal=True)
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attn_weights = None
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elif not self.training and not hidden_states.requires_grad and \
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use_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states, attention_mask):
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import linear_fp16_esimd
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attn_output = linear_fp16_esimd.sdp_forward(query_states,
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key_states,
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value_states)
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attn_output = attn_output.view(query_states.shape)
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attn_weights = None
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else:
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attn_weights = torch.matmul(
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query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
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f" but is {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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invalidInputError(
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False,
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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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)
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = \
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nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query_states.dtype)
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attn_weights = self.attention_dropout(attn_weights)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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invalidInputError(
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False,
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
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f" but is {attn_output.size()}"
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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.to(original_dtype), attn_weights, past_key_value
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