1. fix format errors and typos
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iclementine
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d78a8b4e1e
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310366bb54
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@ -24,7 +24,7 @@ def scaled_dot_product_attention(q,
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mask=None,
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dropout=0.0,
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training=True):
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"""Scaled dot product attention with masking.
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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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@ -33,24 +33,24 @@ def scaled_dot_product_attention(q,
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Parameters
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-----------
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q : Tensor [shape=(*, T_q, d)]
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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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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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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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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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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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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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@ -208,16 +208,16 @@ class MultiheadAttention(nn.Layer):
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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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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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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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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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@ -151,7 +151,7 @@ class STFT(nn.Layer):
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Returns
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------------
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Tensor [shape=(B, C, 1, T)]
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The power spectrum. (C = 1 + `n_fft` // 2)
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The power spectrum.
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"""
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real, imag = self(x)
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power = real**2 + imag**2
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@ -168,7 +168,7 @@ class STFT(nn.Layer):
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Returns
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------------
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Tensor [shape=(B, C, 1, T)]
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The magnitude of the spectrum. (C = 1 + `n_fft` // 2)
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The magnitude of the spectrum.
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"""
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power = self.power(x)
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magnitude = paddle.sqrt(power)
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@ -6,18 +6,18 @@ def shuffle_dim(x, axis, perm=None):
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Parameters
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----------
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x : Tensor
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The input tensor.
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axis : int
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The axis to shuffle.
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perm : List[int], ndarray, optional
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The order to reorder the tensor along the `axis`-th dimension.
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It is a permutation of ``[0, d)``, where d is the size of the
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``axis``-th dimension of the input tensor. If not provided,
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a random permutation is used. Defaults to None.
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x : Tensor
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The input tensor.
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axis : int
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The axis to shuffle.
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perm : List[int], ndarray, optional
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The order to reorder the tensor along the ``axis``-th dimension.
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It is a permutation of ``[0, d)``, where d is the size of the
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``axis``-th dimension of the input tensor. If not provided,
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a random permutation is used. Defaults to None.
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Returns
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---------
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@ -18,8 +18,8 @@ def weighted_mean(input, weight):
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-----------
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input : Tensor
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The input tensor.
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weight : Tensor [broadcastable shape with the input]
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The weight tensor.
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weight : Tensor
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The weight tensor with broadcastable shape with the input.
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Returns
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----------
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@ -54,7 +54,7 @@ def feature_mask(input, axis, dtype="bool"):
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Returns
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-------
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Tensor
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The geenrated mask with `spatial` shape as mentioned above.
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The geenrated mask with ``spatial`` shape as mentioned above.
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It has one less dimension than ``input`` does.
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"""
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@ -103,7 +103,7 @@ def future_mask(time_steps, dtype="bool"):
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time_steps : int
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Decoder time steps.
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dtype : str, optional
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The data type of the generate mask, by default "bool"
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The data type of the generate mask, by default "bool".
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Returns
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-------
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@ -43,16 +43,16 @@ class PositionwiseFFN(nn.Layer):
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self.hidden_szie = hidden_size
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def forward(self, x):
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"""Forward pass of positionwise feed forward network.
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r"""Forward pass of positionwise feed forward network.
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Parameters
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----------
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x : Tensor [shape=(*, input_size)]
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x : Tensor [shape=(\*, input_size)]
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The input tensor, where ``\*`` means arbitary shape.
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Returns
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-------
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Tensor [shape=(*, input_size)]
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Tensor [shape=(\*, input_size)]
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The output tensor.
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"""
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l1 = self.dropout(F.relu(self.linear1(x)))
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@ -104,8 +104,9 @@ class TransformerEncoderLayer(nn.Layer):
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x : Tensor [shape=(batch_size, time_steps, d_model)]
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The input.
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mask : Tensor [shape=(batch_size, time_steps, time_steps) or broadcastable shape]
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The padding mask.
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mask : Tensor
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The padding mask. The shape is (batch_size, time_steps,
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time_steps) or broadcastable shape.
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Returns
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-------
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