mirror of
https://github.com/vale981/ray
synced 2025-03-09 12:56:46 -04:00
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
"""
|
|
[1] - Attention Is All You Need - Vaswani, Jones, Shazeer, Parmar,
|
|
Uszkoreit, Gomez, Kaiser - Google Brain/Research, U Toronto - 2017.
|
|
https://arxiv.org/pdf/1706.03762.pdf
|
|
"""
|
|
from ray.rllib.utils.framework import try_import_torch
|
|
from ray.rllib.models.torch.misc import SlimFC
|
|
from ray.rllib.utils.torch_ops import sequence_mask
|
|
|
|
torch, nn = try_import_torch()
|
|
|
|
|
|
class MultiHeadAttention(nn.Module):
|
|
"""A multi-head attention layer described in [1]."""
|
|
|
|
def __init__(self, in_dim, out_dim, num_heads, head_dim, **kwargs):
|
|
"""
|
|
in_dim (int): Dimension of input
|
|
out_dim (int): Dimension of output
|
|
num_heads (int): Number of attention heads
|
|
head_dim (int): Output dimension of each attention head
|
|
"""
|
|
super().__init__(**kwargs)
|
|
|
|
# No bias or non-linearity.
|
|
self._num_heads = num_heads
|
|
self._head_dim = head_dim
|
|
self._qkv_layer = SlimFC(
|
|
in_size=in_dim, out_size=3 * num_heads * head_dim, use_bias=False)
|
|
|
|
self._linear_layer = SlimFC(
|
|
in_size=num_heads * head_dim, out_size=out_dim, use_bias=False)
|
|
|
|
def forward(self, inputs):
|
|
L = list(inputs.size())[1] # length of segment
|
|
H = self._num_heads # number of attention heads
|
|
D = self._head_dim # attention head dimension
|
|
|
|
qkv = self._qkv_layer(inputs)
|
|
|
|
queries, keys, values = torch.chunk(input=qkv, chunks=3, dim=-1)
|
|
queries = queries[:, -L:] # only query based on the segment
|
|
|
|
queries = torch.reshape(queries, [-1, L, H, D])
|
|
keys = torch.reshape(keys, [-1, L, H, D])
|
|
values = torch.reshape(values, [-1, L, H, D])
|
|
|
|
score = torch.einsum("bihd,bjhd->bijh", queries, keys)
|
|
score = score / D**0.5
|
|
|
|
# causal mask of the same length as the sequence
|
|
mask = sequence_mask(torch.arange(1, L + 1), dtype=score.dtype)
|
|
mask = mask[None, :, :, None]
|
|
mask = mask.float()
|
|
|
|
masked_score = score * mask + 1e30 * (mask - 1.)
|
|
wmat = nn.functional.softmax(masked_score, dim=2)
|
|
|
|
out = torch.einsum("bijh,bjhd->bihd", wmat, values)
|
|
shape = list(out.size())[:2] + [H * D]
|
|
# temp = torch.cat(temp2, [H * D], dim=0)
|
|
out = torch.reshape(out, shape)
|
|
return self._linear_layer(out)
|