829 lines
33 KiB
Python
829 lines
33 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/falcon/modeling_falcon.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2023 the Falcon authors and 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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"""PyTorch Falcon model."""
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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.nn import functional as F
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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import warnings
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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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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For
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example, this can be used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze
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cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the
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dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids]
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have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape
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[batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k.
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Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim],
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then set unsqueeze_dim=2.
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Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary
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Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def rw_attention_forward_7b(
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self,
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hidden_states: torch.Tensor,
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alibi: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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head_mask: Optional[torch.Tensor]=None,
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use_cache: bool=False,
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output_attentions: bool=False,
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):
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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, q_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim
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)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size * self.num_kv,
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q_length,
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self.head_dim,
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)
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value_layer = value_layer.transpose(1, 2).reshape(
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batch_size * self.num_kv,
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q_length,
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self.head_dim
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)
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# query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
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_, seq_len, _ = query_layer.shape
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if layer_past is not None:
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_, seq_len_past, _ = layer_past[0].shape
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seq_len = seq_len + seq_len_past
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query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, seq_len)
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_, kv_length, _ = key_layer.shape
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if layer_past is not None:
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kv_length += layer_past[0].shape[-2]
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query_layer = query_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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key_layer = key_layer.view(batch_size, self.num_kv, q_length, self.head_dim)
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value_layer = value_layer.view(batch_size, self.num_kv, q_length, self.head_dim)
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device = hidden_states.device
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if layer_past is not None:
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# reuse k, v, self_attention
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cache_k = layer_past[0].view(batch_size, self.num_kv, -1, self.head_dim)
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cache_v = layer_past[1].view(batch_size, self.num_kv, -1, self.head_dim)
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if cache_k.stride()[1] < kv_length * cache_k.size(3):
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(
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batch_size,
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self.num_kv,
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self.head_dim,
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cache_k.size(2),
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kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device
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)
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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_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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max_cache_length = kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(
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batch_size,
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self.num_kv,
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self.head_dim,
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kv_length,
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max_cache_length,
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dtype=key_layer.dtype,
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device=device
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)
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new_key_states[:] = key_layer
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new_value_states[:] = value_layer
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key_layer = new_key_states
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value_layer = new_value_states
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query_layer = query_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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key_layer = key_layer.view(batch_size*self.num_kv, -1, self.head_dim)
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value_layer = value_layer.view(batch_size*self.num_kv, -1, self.head_dim)
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_, kv_length, _ = key_layer.shape
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if use_cache is True:
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present = (key_layer, value_layer)
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else:
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present = None
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if alibi is None:
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query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
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key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
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value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
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# attn_output = F.scaled_dot_product_attention(
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# query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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# )
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if layer_past is not None:
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L = query_layer_.shape[-2]
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S = key_layer_.shape[-2]
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attn_mask = torch.ones(L, S, dtype=torch.bool, device=query_layer_.device)
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, attn_mask, 0.0, is_causal=False
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)
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else:
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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)
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x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
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x = x.permute(0, 2, 1, 3)
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attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
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output_tensor = self.dense(attn_output)
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outputs = (output_tensor, present)
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if output_attentions:
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invalidInputError(False,
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f"'output_attentions' are not supported yet")
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return outputs
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else:
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attention_mask_float = (attention_mask * 1.0) \
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.masked_fill(attention_mask, -1e9).to(torch.bfloat16)
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matmul_result = query_layer @ key_layer.transpose(-1, -2)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
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# cast attention scores to fp32,
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# compute scaled softmax and cast back to initial dtype
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# - [batch_size, num_heads, q_length, kv_length]
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input_dtype = attention_scores.dtype
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# `float16` has a minimum value of -65504.0,
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# whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
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if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
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attention_scores = attention_scores.to(torch.float32)
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# attn_weights = torch. \
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# masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
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attention_probs = F.softmax(
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(attention_scores + alibi) * self.inv_norm_factor + attention_mask_float,
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dim=-1,
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dtype=hidden_states.dtype,
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)
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# [batch_size, num_heads, q_length, kv_length]
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attention_probs = self.attention_dropout(attention_probs)
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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# change view [batch_size x num_heads, q_length, kv_length]
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attention_probs_reshaped = attention_probs.view(
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batch_size * self.num_heads,
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q_length,
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kv_length
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)
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# matmul: [batch_size * num_heads, q_length, head_dim]
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context_layer = attention_probs_reshaped @ value_layer
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# change view [batch_size, num_heads, q_length, head_dim]
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context_layer = self._merge_heads(context_layer)
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output_tensor = self.dense(context_layer)
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outputs = (output_tensor, present)
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if output_attentions:
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outputs += (attention_probs,)
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return outputs
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def rw_attention_forward_40b(
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self,
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hidden_states: torch.Tensor,
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alibi: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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head_mask: Optional[torch.Tensor]=None,
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use_cache: bool=False,
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output_attentions: bool=False,
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):
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# [batch_size, seq_length, 3 x hidden_size]
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fused_qkv = self.query_key_value(hidden_states)
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, q_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim
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)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim,
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)
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value_layer = value_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim
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)
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# query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
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_, seq_len, _ = query_layer.shape
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if layer_past is not None:
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_, seq_len_past, _ = layer_past[0].shape
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seq_len = seq_len + seq_len_past
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query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, seq_len)
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_, kv_length, _ = key_layer.shape
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if layer_past is not None:
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kv_length += layer_past[0].shape[-2]
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query_layer = query_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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key_layer = key_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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value_layer = value_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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device = hidden_states.device
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if layer_past is not None:
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# reuse k, v, self_attention
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cache_k = layer_past[0].view(batch_size, self.num_heads, -1, self.head_dim)
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cache_v = layer_past[1].view(batch_size, self.num_heads, -1, self.head_dim)
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if cache_k.stride()[1] < kv_length * cache_k.size(3):
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(
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batch_size,
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self.num_heads,
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self.head_dim,
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cache_k.size(2),
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kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device
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)
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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_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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max_cache_length = kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(
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batch_size,
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self.num_heads,
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self.head_dim,
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kv_length,
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max_cache_length,
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dtype=key_layer.dtype,
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device=device
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)
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new_key_states[:] = key_layer
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new_value_states[:] = value_layer
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key_layer = new_key_states
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value_layer = new_value_states
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query_layer = query_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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key_layer = key_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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value_layer = value_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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_, kv_length, _ = key_layer.shape
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if use_cache is True:
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present = (key_layer, value_layer)
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else:
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present = None
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if alibi is None:
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query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
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key_layer_ = key_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
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value_layer_ = value_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
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# attn_output = F.scaled_dot_product_attention(
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# query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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# )
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if present is not None:
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L = query_layer_.shape[-2]
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S = key_layer_.shape[-2]
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attn_mask = torch.ones(L, S, dtype=torch.bool, device=query_layer_.device)
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, attn_mask, 0.0, is_causal=False
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)
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else:
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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)
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x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
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x = x.permute(0, 2, 1, 3)
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attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
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output_tensor = self.dense(attn_output)
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outputs = (output_tensor, present)
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if output_attentions:
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invalidInputError(False,
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f"'output_attentions' are not supported yet")
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return outputs
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else:
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attention_mask_float = (attention_mask * 1.0) \
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.masked_fill(attention_mask, -1e9).to(torch.bfloat16)
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matmul_result = query_layer @ key_layer.transpose(-1, -2)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
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# cast attention scores to fp32,
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# compute scaled softmax and cast back to initial dtype
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# - [batch_size, num_heads, q_length, kv_length]
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input_dtype = attention_scores.dtype
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# `float16` has a minimum value of -65504.0,
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# whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
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if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
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attention_scores = attention_scores.to(torch.float32)
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# attn_weights = torch \
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# .masked_fill(
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# attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
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attention_probs = F.softmax(
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(attention_scores + alibi.view(batch_size, self.num_heads, 1, -1))
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* self.inv_norm_factor + attention_mask_float,
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dim=-1,
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dtype=hidden_states.dtype,
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)
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# [batch_size, num_heads, q_length, kv_length]
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attention_probs = self.attention_dropout(attention_probs)
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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# change view [batch_size x num_heads, q_length, kv_length]
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attention_probs_reshaped = attention_probs.view(
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batch_size * self.num_heads,
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q_length,
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|
kv_length
|
|
)
|
|
|
|
# matmul: [batch_size * num_heads, q_length, head_dim]
|
|
context_layer = attention_probs_reshaped @ value_layer
|
|
|
|
# change view [batch_size, num_heads, q_length, head_dim]
|
|
context_layer = self._merge_heads(context_layer)
|
|
|
|
output_tensor = self.dense(context_layer)
|
|
|
|
outputs = (output_tensor, present)
|
|
if output_attentions:
|
|
outputs += (attention_probs,)
|
|
return outputs
|
|
|
|
|
|
def falcon_attention_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
alibi: Optional[torch.Tensor],
|
|
attention_mask: torch.Tensor,
|
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
|
|
head_mask: Optional[torch.Tensor]=None,
|
|
use_cache: bool=False,
|
|
output_attentions: bool=False,
|
|
):
|
|
# [batch_size, seq_length, 3 x hidden_size]
|
|
fused_qkv = self.query_key_value(hidden_states)
|
|
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
|
|
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
|
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
|
|
|
batch_size, query_length, _, _ = query_layer.shape
|
|
|
|
query_layer = query_layer.transpose(1, 2).reshape(
|
|
batch_size * self.num_heads,
|
|
query_length,
|
|
self.head_dim
|
|
)
|
|
key_layer = key_layer.transpose(1, 2).reshape(
|
|
batch_size * num_kv_heads,
|
|
query_length,
|
|
self.head_dim,
|
|
)
|
|
value_layer = value_layer.transpose(1, 2).reshape(
|
|
batch_size * num_kv_heads,
|
|
query_length,
|
|
self.head_dim
|
|
)
|
|
|
|
past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
|
|
query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
|
|
|
|
_, kv_length, _ = key_layer.shape
|
|
if layer_past is not None:
|
|
kv_length += layer_past[0].shape[-2]
|
|
query_layer = query_layer.view(batch_size, self.num_heads, query_length, self.head_dim)
|
|
key_layer = key_layer.view(batch_size, num_kv_heads, query_length, self.head_dim)
|
|
value_layer = value_layer.view(batch_size, num_kv_heads, query_length, self.head_dim)
|
|
device = hidden_states.device
|
|
if layer_past is not None:
|
|
# reuse k, v, self_attention
|
|
cache_k = layer_past[0].view(batch_size, num_kv_heads, -1, self.head_dim)
|
|
cache_v = layer_past[1].view(batch_size, num_kv_heads, -1, self.head_dim)
|
|
if cache_k.stride()[1] < kv_length * cache_k.size(3):
|
|
# allocate new
|
|
new_cache_k, new_cache_v = extend_kv_cache(
|
|
batch_size,
|
|
num_kv_heads,
|
|
self.head_dim,
|
|
cache_k.size(2),
|
|
kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=cache_k.dtype,
|
|
device=device
|
|
)
|
|
new_cache_k[:] = cache_k
|
|
new_cache_v[:] = cache_v
|
|
cache_k = new_cache_k
|
|
cache_v = new_cache_v
|
|
|
|
key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
|
|
|
|
elif use_cache:
|
|
max_cache_length = kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH
|
|
new_key_states, new_value_states = init_kv_cache(
|
|
batch_size,
|
|
num_kv_heads,
|
|
self.head_dim,
|
|
kv_length,
|
|
max_cache_length,
|
|
dtype=key_layer.dtype,
|
|
device=device
|
|
)
|
|
new_key_states[:] = key_layer
|
|
new_value_states[:] = value_layer
|
|
key_layer = new_key_states
|
|
value_layer = new_value_states
|
|
|
|
query_layer = query_layer.view(batch_size * self.num_heads, -1, self.head_dim)
|
|
key_layer = key_layer.view(batch_size * num_kv_heads, -1, self.head_dim)
|
|
value_layer = value_layer.view(batch_size * num_kv_heads, -1, self.head_dim)
|
|
_, kv_length, _ = key_layer.shape
|
|
if use_cache:
|
|
present = (key_layer, value_layer)
|
|
else:
|
|
present = None
|
|
|
|
attention_mask_float = (attention_mask * 1.0) \
|
|
.masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
|
|
|
|
query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
|
key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
|
|
value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
|
|
|
|
if alibi is None:
|
|
if output_attentions:
|
|
# F.scaled_dot_product_attention doesn't return the attention weights, so we have
|
|
# to do it by hand if we want them
|
|
attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
|
|
attention_scores /= math.sqrt(self.head_dim)
|
|
|
|
attention_scores = F.softmax(
|
|
attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
|
|
)
|
|
attn_output = attention_scores @ value_layer_
|
|
else:
|
|
attn_output = F.scaled_dot_product_attention(
|
|
query_layer_,
|
|
key_layer_,
|
|
value_layer_,
|
|
attention_mask_float,
|
|
0.0,
|
|
is_causal=False
|
|
)
|
|
attention_scores = None
|
|
|
|
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
|
|
attn_output = attn_output.permute(0, 2, 1, 3)
|
|
attn_output = attn_output.reshape(
|
|
batch_size,
|
|
query_length,
|
|
self.num_heads * self.head_dim
|
|
)
|
|
|
|
output_tensor = self.dense(attn_output)
|
|
|
|
if output_attentions:
|
|
return output_tensor, present, attention_scores
|
|
else:
|
|
return output_tensor, present
|
|
|
|
else:
|
|
matmul_result = query_layer_ @ key_layer_.transpose(-1, -2)
|
|
|
|
# change view to [batch_size, num_heads, q_length, kv_length]
|
|
attention_scores = matmul_result.view(
|
|
batch_size,
|
|
self.num_heads,
|
|
query_length,
|
|
kv_length
|
|
)
|
|
|
|
# cast attention scores to fp32,
|
|
# compute scaled softmax and cast back to initial dtype
|
|
# - [batch_size, num_heads, q_length, kv_length]
|
|
input_dtype = attention_scores.dtype
|
|
# `float16` has a minimum value of -65504.0,
|
|
# whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
|
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
|
attention_scores = attention_scores.to(torch.float32)
|
|
# Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
|
|
# adding (alibi * self.inv_norm_factor) to attention_mask_float.
|
|
# I think this would be mathematically
|
|
# equivalent and more performant, but there might be a numerical difference.
|
|
# If you're reading this
|
|
# and you'd like to experiment and maybe file a PR, feel free!
|
|
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
|
|
attention_logits *= self.inv_norm_factor
|
|
attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1,
|
|
dtype=hidden_states.dtype)
|
|
# [batch_size, num_heads, q_length, kv_length]
|
|
attention_probs = self.attention_dropout(attention_probs)
|
|
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
# change view [batch_size, num_heads, q_length, kv_length]
|
|
attention_probs_reshaped = attention_probs.view(
|
|
batch_size,
|
|
self.num_heads,
|
|
query_length,
|
|
kv_length
|
|
)
|
|
|
|
# matmul: [batch_size * num_heads, q_length, head_dim]
|
|
context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1)
|
|
|
|
# change view [batch_size, q_length, num_heads * head_dim]
|
|
context_layer = self._merge_heads(context_layer)
|
|
|
|
output_tensor = self.dense(context_layer)
|
|
|
|
if output_attentions:
|
|
return output_tensor, present, attention_probs
|
|
else:
|
|
return output_tensor, present
|
|
|
|
|
|
def falcon_attention_forward_4_36(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
alibi: Optional[torch.Tensor],
|
|
attention_mask: torch.Tensor,
|
|
position_ids: Optional[torch.LongTensor]=None,
|
|
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
|
|
head_mask: Optional[torch.Tensor]=None,
|
|
use_cache: bool=False,
|
|
output_attentions: bool=False,
|
|
**kwargs,
|
|
):
|
|
""" based on transformers==4.36.0
|
|
https://github.com/huggingface/transformers/blob/v4.36.0/src/transformers/models/falcon/modeling_falcon.py
|
|
"""
|
|
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.`"
|
|
)
|
|
|
|
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
|
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
|
|
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
|
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
|
|
|
batch_size, query_length, _, _ = query_layer.shape
|
|
|
|
query_layer = query_layer.transpose(1, 2).reshape(
|
|
batch_size, self.num_heads, query_length, self.head_dim)
|
|
key_layer = key_layer.transpose(1, 2).reshape(
|
|
batch_size, num_kv_heads, query_length, self.head_dim)
|
|
value_layer = value_layer.transpose(1, 2).reshape(
|
|
batch_size, num_kv_heads, query_length, self.head_dim)
|
|
|
|
kv_seq_len = key_layer.shape[-2]
|
|
device = hidden_states.device
|
|
|
|
if layer_past is not None:
|
|
kv_seq_len += layer_past[0].shape[-2]
|
|
|
|
if alibi is None:
|
|
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
|
|
query_layer, key_layer = apply_rotary_pos_emb(
|
|
query_layer, key_layer, cos, sin, position_ids)
|
|
|
|
if layer_past is not None:
|
|
# reuse k, v, self_attention
|
|
cache_k = layer_past[0].view(batch_size, self.num_heads, -1, self.head_dim)
|
|
cache_v = layer_past[1].view(batch_size, self.num_heads, -1, self.head_dim)
|
|
if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
|
|
# allocate new
|
|
new_cache_k, new_cache_v = extend_kv_cache(
|
|
batch_size,
|
|
self.num_heads,
|
|
self.head_dim,
|
|
cache_k.size(2),
|
|
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
|
dtype=cache_k.dtype,
|
|
device=device
|
|
)
|
|
new_cache_k[:] = cache_k
|
|
new_cache_v[:] = cache_v
|
|
cache_k = new_cache_k
|
|
cache_v = new_cache_v
|
|
|
|
key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
|
|
|
|
elif use_cache:
|
|
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
|
|
new_key_states, new_value_states = init_kv_cache(
|
|
batch_size,
|
|
self.num_heads,
|
|
self.head_dim,
|
|
kv_seq_len,
|
|
max_cache_length,
|
|
dtype=key_layer.dtype,
|
|
device=device
|
|
)
|
|
new_key_states[:] = key_layer
|
|
new_value_states[:] = value_layer
|
|
key_layer = new_key_states
|
|
value_layer = new_value_states
|
|
|
|
query_layer = query_layer.view(batch_size, self.num_heads, -1, self.head_dim)
|
|
key_layer = key_layer.view(batch_size, self.num_heads, -1, self.head_dim)
|
|
value_layer = value_layer.view(batch_size, self.num_heads, -1, self.head_dim)
|
|
|
|
kv_length = key_layer.shape[-2]
|
|
if use_cache:
|
|
present = (key_layer, value_layer)
|
|
else:
|
|
present = None
|
|
|
|
# SDPA with memory-efficient backend is currently (torch==2.1.2)
|
|
# bugged with non-contiguous inputs with custom attn_mask,
|
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
|
if query_layer.device.type == "cuda" and attention_mask is not None:
|
|
query_layer = query_layer.contiguous()
|
|
key_layer = key_layer.contiguous()
|
|
value_layer = value_layer.contiguous()
|
|
|
|
if alibi is None:
|
|
if self._use_sdpa and not output_attentions:
|
|
attn_output = F.scaled_dot_product_attention(
|
|
query_layer,
|
|
key_layer,
|
|
value_layer,
|
|
attention_mask,
|
|
0.0,
|
|
# The query_length > 1 is necessary to match with
|
|
# AttentionMaskConverter.to_causal_4d that does not create a causal mask in case
|
|
# query_length == 1.
|
|
is_causal=self.is_causal and attention_mask is None and query_length > 1,
|
|
)
|
|
attention_scores = None
|
|
else:
|
|
attention_scores = query_layer @ key_layer.transpose(-1, -2)
|
|
attention_scores /= math.sqrt(self.head_dim)
|
|
|
|
attention_scores = F.softmax(
|
|
attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
|
|
# It is unclear why neither dropout nor head_mask is applied here
|
|
# (while it is with alibi).
|
|
attn_output = attention_scores @ value_layer
|
|
|
|
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
|
|
attn_output = attn_output.permute(0, 2, 1, 3)
|
|
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
|
|
|
|
attn_output = self.dense(attn_output)
|
|
|
|
if output_attentions:
|
|
return attn_output, present, attention_scores
|
|
else:
|
|
return attn_output, present
|
|
|
|
else:
|
|
if self._use_sdpa and not output_attentions and head_mask is None:
|
|
attn_output = F.scaled_dot_product_attention(
|
|
query_layer,
|
|
key_layer,
|
|
value_layer,
|
|
attn_mask=attention_mask,
|
|
dropout_p=self.attention_dropout.p if self.training else 0.0,
|
|
is_causal=self.is_causal and attention_mask is None and query_length > 1,
|
|
)
|
|
attn_output = attn_output.transpose(1, 2)
|
|
attn_output = attn_output.reshape(
|
|
batch_size, query_length, self.num_heads * self.head_dim)
|
|
|
|
attn_output = self.dense(attn_output)
|
|
else:
|
|
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
|
|
|
# change view to [batch_size, num_heads, q_length, kv_length]
|
|
attention_scores = matmul_result.view(
|
|
batch_size, self.num_heads, query_length, kv_length)
|
|
|
|
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype -
|
|
# [batch_size, num_heads, q_length, kv_length]
|
|
input_dtype = attention_scores.dtype
|
|
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a
|
|
# minimum value of `-3.4e+38`
|
|
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
|
attention_scores = attention_scores.to(torch.float32)
|
|
|
|
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
|
|
attention_logits *= self.inv_norm_factor
|
|
attention_probs = F.softmax(
|
|
attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
|
|
# [batch_size, num_heads, q_length, kv_length]
|
|
attention_probs = self.attention_dropout(attention_probs)
|
|
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
# change view [batch_size, num_heads, q_length, kv_length]
|
|
attention_probs_reshaped = attention_probs.view(
|
|
batch_size, self.num_heads, query_length, kv_length)
|
|
|
|
# matmul: [batch_size * num_heads, q_length, head_dim]
|
|
attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
|
|
|
|
# change view [batch_size, q_length, num_heads * head_dim]
|
|
attn_output = self._merge_heads(attn_output)
|
|
|
|
attn_output = self.dense(attn_output)
|
|
|
|
if output_attentions:
|
|
return attn_output, present, attention_probs
|
|
else:
|
|
return attn_output, present
|