385 lines
12 KiB
Python
385 lines
12 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. 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 math
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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def scaled_dot_product_attention(q,
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k,
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v,
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mask=None,
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dropout=0.0,
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training=True):
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r"""Scaled dot product attention with masking.
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Assume that q, k, v all have the same leading dimensions (denoted as * in
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descriptions below). Dropout is applied to attention weights before
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weighted sum of values.
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Parameters
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-----------
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q : Tensor [shape=(\*, T_q, d)]
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the query tensor.
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k : Tensor [shape=(\*, T_k, d)]
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the key tensor.
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v : Tensor [shape=(\*, T_k, d_v)]
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the value tensor.
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mask : Tensor, [shape=(\*, T_q, T_k) or broadcastable shape], optional
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the mask tensor, zeros correspond to paddings. Defaults to None.
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Returns
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----------
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out : Tensor [shape=(\*, T_q, d_v)]
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the context vector.
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attn_weights : Tensor [shape=(\*, T_q, T_k)]
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the attention weights.
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"""
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d = q.shape[-1] # we only support imperative execution
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qk = paddle.matmul(q, k, transpose_y=True)
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scaled_logit = paddle.scale(qk, 1.0 / math.sqrt(d))
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if mask is not None:
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scaled_logit += paddle.scale((1.0 - mask), -1e9) # hard coded here
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attn_weights = F.softmax(scaled_logit, axis=-1)
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attn_weights = F.dropout(attn_weights, dropout, training=training)
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out = paddle.matmul(attn_weights, v)
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return out, attn_weights
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def drop_head(x, drop_n_heads, training=True):
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"""Drop n context vectors from multiple ones.
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Parameters
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----------
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x : Tensor [shape=(batch_size, num_heads, time_steps, channels)]
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The input, multiple context vectors.
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drop_n_heads : int [0<= drop_n_heads <= num_heads]
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Number of vectors to drop.
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training : bool
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A flag indicating whether it is in training. If `False`, no dropout is
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applied.
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Returns
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-------
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Tensor
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The output.
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"""
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if not training or (drop_n_heads == 0):
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return x
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batch_size, num_heads, _, _ = x.shape
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# drop all heads
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if num_heads == drop_n_heads:
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return paddle.zeros_like(x)
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mask = np.ones([batch_size, num_heads])
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mask[:, :drop_n_heads] = 0
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for subarray in mask:
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np.random.shuffle(subarray)
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scale = float(num_heads) / (num_heads - drop_n_heads)
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mask = scale * np.reshape(mask, [batch_size, num_heads, 1, 1])
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out = x * paddle.to_tensor(mask)
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return out
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def _split_heads(x, num_heads):
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batch_size, time_steps, _ = x.shape
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x = paddle.reshape(x, [batch_size, time_steps, num_heads, -1])
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x = paddle.transpose(x, [0, 2, 1, 3])
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return x
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def _concat_heads(x):
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batch_size, _, time_steps, _ = x.shape
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x = paddle.transpose(x, [0, 2, 1, 3])
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x = paddle.reshape(x, [batch_size, time_steps, -1])
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return x
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# Standard implementations of Monohead Attention & Multihead Attention
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class MonoheadAttention(nn.Layer):
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"""Monohead Attention module.
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Parameters
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----------
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model_dim : int
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Feature size of the query.
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dropout : float, optional
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Dropout probability of scaled dot product attention and final context
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vector. Defaults to 0.0.
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k_dim : int, optional
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Feature size of the key of each scaled dot product attention. If not
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provided, it is set to `model_dim / num_heads`. Defaults to None.
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v_dim : int, optional
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Feature size of the key of each scaled dot product attention. If not
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provided, it is set to `model_dim / num_heads`. Defaults to None.
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"""
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def __init__(self,
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model_dim: int,
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dropout: float=0.0,
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k_dim: int=None,
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v_dim: int=None):
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super(MonoheadAttention, self).__init__()
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k_dim = k_dim or model_dim
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v_dim = v_dim or model_dim
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self.affine_q = nn.Linear(model_dim, k_dim)
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self.affine_k = nn.Linear(model_dim, k_dim)
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self.affine_v = nn.Linear(model_dim, v_dim)
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self.affine_o = nn.Linear(v_dim, model_dim)
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self.model_dim = model_dim
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self.dropout = dropout
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def forward(self, q, k, v, mask):
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"""Compute context vector and attention weights.
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Parameters
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-----------
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q : Tensor [shape=(batch_size, time_steps_q, model_dim)]
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The queries.
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k : Tensor [shape=(batch_size, time_steps_k, model_dim)]
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The keys.
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v : Tensor [shape=(batch_size, time_steps_k, model_dim)]
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The values.
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mask : Tensor [shape=(batch_size, times_steps_q, time_steps_k] or broadcastable shape
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The mask.
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Returns
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----------
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out : Tensor [shape=(batch_size, time_steps_q, model_dim)]
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The context vector.
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attention_weights : Tensor [shape=(batch_size, times_steps_q, time_steps_k)]
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The attention weights.
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"""
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q = self.affine_q(q) # (B, T, C)
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k = self.affine_k(k)
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v = self.affine_v(v)
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context_vectors, attention_weights = scaled_dot_product_attention(
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q, k, v, mask, self.dropout, self.training)
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out = self.affine_o(context_vectors)
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return out, attention_weights
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class MultiheadAttention(nn.Layer):
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"""Multihead Attention module.
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Parameters
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-----------
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model_dim: int
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The feature size of query.
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num_heads : int
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The number of attention heads.
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dropout : float, optional
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Dropout probability of scaled dot product attention and final context
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vector. Defaults to 0.0.
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k_dim : int, optional
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Feature size of the key of each scaled dot product attention. If not
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provided, it is set to ``model_dim / num_heads``. Defaults to None.
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v_dim : int, optional
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Feature size of the key of each scaled dot product attention. If not
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provided, it is set to ``model_dim / num_heads``. Defaults to None.
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Raises
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---------
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ValueError
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If ``model_dim`` is not divisible by ``num_heads``.
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"""
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def __init__(self,
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model_dim: int,
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num_heads: int,
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dropout: float=0.0,
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k_dim: int=None,
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v_dim: int=None):
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super(MultiheadAttention, self).__init__()
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if model_dim % num_heads != 0:
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raise ValueError("model_dim must be divisible by num_heads")
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depth = model_dim // num_heads
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k_dim = k_dim or depth
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v_dim = v_dim or depth
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self.affine_q = nn.Linear(model_dim, num_heads * k_dim)
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self.affine_k = nn.Linear(model_dim, num_heads * k_dim)
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self.affine_v = nn.Linear(model_dim, num_heads * v_dim)
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self.affine_o = nn.Linear(num_heads * v_dim, model_dim)
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self.num_heads = num_heads
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self.model_dim = model_dim
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self.dropout = dropout
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def forward(self, q, k, v, mask):
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"""Compute context vector and attention weights.
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Parameters
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-----------
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q : Tensor [shape=(batch_size, time_steps_q, model_dim)]
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The queries.
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k : Tensor [shape=(batch_size, time_steps_k, model_dim)]
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The keys.
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v : Tensor [shape=(batch_size, time_steps_k, model_dim)]
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The values.
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mask : Tensor [shape=(batch_size, times_steps_q, time_steps_k] or broadcastable shape
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The mask.
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Returns
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----------
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out : Tensor [shape=(batch_size, time_steps_q, model_dim)]
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The context vector.
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attention_weights : Tensor [shape=(batch_size, times_steps_q, time_steps_k)]
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The attention weights.
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"""
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q = _split_heads(self.affine_q(q), self.num_heads) # (B, h, T, C)
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k = _split_heads(self.affine_k(k), self.num_heads)
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v = _split_heads(self.affine_v(v), self.num_heads)
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mask = paddle.unsqueeze(mask, 1) # unsqueeze for the h dim
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context_vectors, attention_weights = scaled_dot_product_attention(
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q, k, v, mask, self.dropout, self.training)
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# NOTE: there is more sophisticated implementation: Scheduled DropHead
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context_vectors = _concat_heads(context_vectors) # (B, T, h*C)
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out = self.affine_o(context_vectors)
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return out, attention_weights
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class LocationSensitiveAttention(nn.Layer):
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"""Location Sensitive Attention module.
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Reference: `Attention-Based Models for Speech Recognition <https://arxiv.org/pdf/1506.07503.pdf>`_
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Parameters
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-----------
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d_query: int
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The feature size of query.
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d_key : int
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The feature size of key.
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d_attention : int
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The feature size of dimension.
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location_filters : int
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Filter size of attention convolution.
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location_kernel_size : int
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Kernel size of attention convolution.
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"""
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def __init__(self,
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d_query: int,
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d_key: int,
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d_attention: int,
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location_filters: int,
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location_kernel_size: int):
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super().__init__()
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self.query_layer = nn.Linear(d_query, d_attention, bias_attr=False)
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self.key_layer = nn.Linear(d_key, d_attention, bias_attr=False)
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self.value = nn.Linear(d_attention, 1, bias_attr=False)
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#Location Layer
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self.location_conv = nn.Conv1D(
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2,
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location_filters,
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location_kernel_size,
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1,
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int((location_kernel_size - 1) / 2),
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1,
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bias_attr=False,
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data_format='NLC')
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self.location_layer = nn.Linear(
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location_filters, d_attention, bias_attr=False)
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def forward(self,
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query,
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processed_key,
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value,
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attention_weights_cat,
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mask=None):
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"""Compute context vector and attention weights.
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Parameters
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-----------
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query : Tensor [shape=(batch_size, d_query)]
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The queries.
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processed_key : Tensor [shape=(batch_size, time_steps_k, d_attention)]
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The keys after linear layer.
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value : Tensor [shape=(batch_size, time_steps_k, d_key)]
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The values.
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attention_weights_cat : Tensor [shape=(batch_size, time_step_k, 2)]
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Attention weights concat.
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mask : Tensor, optional
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The mask. Shape should be (batch_size, times_steps_q, time_steps_k) or broadcastable shape.
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Defaults to None.
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Returns
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----------
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attention_context : Tensor [shape=(batch_size, time_steps_q, d_attention)]
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The context vector.
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attention_weights : Tensor [shape=(batch_size, times_steps_q, time_steps_k)]
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The attention weights.
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"""
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processed_query = self.query_layer(paddle.unsqueeze(query, axis=[1]))
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processed_attention_weights = self.location_layer(
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self.location_conv(attention_weights_cat))
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alignment = self.value(
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paddle.tanh(processed_attention_weights + processed_key +
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processed_query))
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if mask is not None:
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alignment = alignment + (1.0 - mask) * -1e9
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attention_weights = F.softmax(alignment, axis=1)
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attention_context = paddle.matmul(
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attention_weights, value, transpose_x=True)
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attention_weights = paddle.squeeze(attention_weights, axis=[-1])
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attention_context = paddle.squeeze(attention_context, axis=[1])
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return attention_context, attention_weights
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