fix sth

paddlespeech/t2s/modules/fftconv1d.py

@@ -20,35 +20,37 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 
+from ...utils import satisfy_paddle_version
+
 __all__ = [
     "fft_conv1d",
     "FFTConv1D",
 ]
 
 
-def __unfold(_input, kernel_size: int, stride: int):
+def __unfold(x, kernel_size: int, stride: int):
     """1D only unfolding similar to the one from Paddlepaddle.
 
     Notes
     ------
-    Given an _input tensor of size `[*, T]` this will return
+    Given a tensor `x` of size `[*, T]` this will return
     a tensor `[*, F, K]` with `K` the kernel size, and `F` the number
     of frames. The i-th frame is a view onto `i * stride: i * stride + kernel_size`.
-    This will automatically pad the _input to cover at least once all entries in `_input`.
+    This will automatically pad `x` to cover at least once all entries in `x`.
 
     Args:
-        _input (Tensor): 
+        x (Tensor): 
             tensor for which to return the frames.
         kernel_size (int): 
             size of each frame.
         stride (int): 
             stride between each frame.
     """
-    shape = list(_input.shape)
+    shape = list(x.shape)
     length = shape.pop(-1)
     n_frames = math.ceil((max(length, kernel_size) - kernel_size) / stride) + 1
     tgt_length = (n_frames - 1) * stride + kernel_size
-    padded = F.pad(_input, (0, tgt_length - length), data_format="NCL")
+    padded = F.pad(x, (0, tgt_length - length), data_format="NCL")
     strides: typing.List[int] = []
     for dim in range(padded.dim()):
         strides.append(padded.strides[dim])
@@ -58,7 +60,7 @@ def __unfold(_input, kernel_size: int, stride: int):
 
 
 def fft_conv1d(
-        _input: paddle.Tensor,
+        x: paddle.Tensor,
         weight: paddle.Tensor,
         bias: Optional[paddle.Tensor]=None,
         stride: int=1,
@@ -77,8 +79,8 @@ def fft_conv1d(
     more memory than the default Conv1d implementation.
 
     Args:
-        _input (Tensor): 
-            _input signal of shape `[B, C, T]`.
+        x (Tensor): 
+            x signal of shape `[B, C, T]`.
         weight (Tensor): 
             weight of the convolution `[D, C, K]` with `D` the number of output channels.
         bias (Tensor or None): 
@@ -86,17 +88,17 @@ def fft_conv1d(
         stride (int): 
             stride of convolution.
         padding (int): 
-            padding to apply to the _input.
+            padding to apply to x.
         block_ratio (float): 
-            can be tuned for speed. The _input is splitted in chunks with a size of `int(block_ratio * kernel_size)`.
+            can be tuned for speed. x is splitted in chunks with a size of `int(block_ratio * kernel_size)`.
 
     Shape:
 
-        - Inputs: `_input` is `[B, C, T]`, `weight` is `[D, C, K]` and bias is `[D]`.
+        - Inputs: `x` is `[B, C, T]`, `weight` is `[D, C, K]` and bias is `[D]`.
         - Output: `(*, T)`
     """
-    _input = F.pad(_input, (padding, padding), data_format="NCL")
-    batch, _, length = _input.shape
+    x = F.pad(x, (padding, padding), data_format="NCL")
+    batch, _, length = x.shape
     out_channels, _, kernel_size = weight.shape
 
     if length < kernel_size:
@@ -118,8 +120,8 @@ def fft_conv1d(
 
     weight_z = paddle.fft.rfft(weight, axis=-1)
 
-    # We pad the _input and get the different frames, on which
-    frames = __unfold(_input, block_size, fold_stride)
+    # We pad `x` and get the different frames, on which
+    frames = __unfold(x, block_size, fold_stride)
 
     frames_z = paddle.fft.rfft(frames, axis=-1)
     weight_z_coml = paddle.conj(weight_z)
@@ -152,7 +154,7 @@ class FFTConv1D(paddle.nn.Layer):
 
     Args:
         in_channels (int): 
-            number of _input channels.
+            number of `x` channels.
         out_channels (int): 
             number of output channels.
         kernel_size (int): 
@@ -160,7 +162,7 @@ class FFTConv1D(paddle.nn.Layer):
         stride (int): 
             stride of convolution.
         padding (int): 
-            padding to apply to the _input.
+            padding to apply to `x`.
         bias_attr (bool): 
             if True, use a bias term.
 
@@ -200,16 +202,13 @@ class FFTConv1D(paddle.nn.Layer):
         else:
             self.bias = None
 
-    def forward(self, _input: paddle.Tensor):
-        return fft_conv1d(_input, self.weight, self.bias, self.stride,
-                          self.padding)
+    def forward(self, x: paddle.Tensor):
+        return fft_conv1d(x, self.weight, self.bias, self.stride, self.padding)
 
 
 # Currently, the API unfold in Paddle is extremely slow, so __unfold is implemented 
 # using the `.strides` and `.as_strided` APIs. However, these are only supported in 
 # Paddle version 2.6 and above, so F.conv1d and Conv1D are used as replacements.
-version = paddle.__version__
-
-if version < '2.6':
+if not satisfy_paddle_version('2.6'):
     fft_conv1d = F.conv1d
     FFTConv1D = nn.Conv1D

tests/unit/tts/test_fftconv1d.py

@@ -59,7 +59,7 @@ class TestFFTConv1D(unittest.TestCase):
         out_conv1d = conv1d(x)
         out_fft_conv1d = fft_conv1d(x)
         self.assertTrue(
-            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-5))
+            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-6))
 
     def test_fft_conv1d_vs_conv1d_no_padding(self):
         x, conv1d, fft_conv1d = self._init_models(
@@ -68,7 +68,7 @@ class TestFFTConv1D(unittest.TestCase):
         out_conv1d = conv1d(x)
         out_fft_conv1d = fft_conv1d(x)
         self.assertTrue(
-            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-5))
+            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-6))
 
     def test_fft_conv1d_vs_conv1d_large_kernel(self):
         kernel_size = 256
@@ -79,7 +79,7 @@ class TestFFTConv1D(unittest.TestCase):
         out_conv1d = conv1d(x)
         out_fft_conv1d = fft_conv1d(x)
         self.assertTrue(
-            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-5))
+            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-6))
 
     def test_fft_conv1d_vs_conv1d_stride_2(self):
         x, conv1d, fft_conv1d = self._init_models(
@@ -88,7 +88,7 @@ class TestFFTConv1D(unittest.TestCase):
         out_conv1d = conv1d(x)
         out_fft_conv1d = fft_conv1d(x)
         self.assertTrue(
-            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-5))
+            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-6))
 
     def test_fft_conv1d_vs_conv1d_different_input_length(self):
         input_length = 1024
@@ -99,7 +99,7 @@ class TestFFTConv1D(unittest.TestCase):
         out_conv1d = conv1d(x)
         out_fft_conv1d = fft_conv1d(x)
         self.assertTrue(
-            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-5))
+            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-6))
 
     def test_fft_conv1d_vs_conv1d_no_bias(self):
         conv1d = paddle.nn.Conv1D(
@@ -121,7 +121,7 @@ class TestFFTConv1D(unittest.TestCase):
         out_conv1d = conv1d(x)
         out_fft_conv1d = fft_conv1d(x)
         self.assertTrue(
-            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-5))
+            np.allclose(out_conv1d.numpy(), out_fft_conv1d.numpy(), atol=1e-6))
 
 
 if __name__ == '__main__':