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ef658d691e
* fixes for `diagnostics` Replace `2 ** 22` with `512` as the default value of `diagnostics.TensorDiagnosticOptions` also black formatted some scripts * fixed formatting issues
407 lines
13 KiB
Python
407 lines
13 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Daniel Povey,
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# Zengwei Yao)
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#
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# See ../../../../LICENSE for clarification regarding multiple 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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from typing import Tuple
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import warnings
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import torch
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from torch import Tensor, nn
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from scaling import (
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Balancer,
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BiasNorm,
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Dropout3,
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FloatLike,
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Optional,
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ScaledConv2d,
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ScaleGrad,
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ScheduledFloat,
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SwooshL,
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SwooshR,
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Whiten,
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)
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class ConvNeXt(nn.Module):
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"""
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Our interpretation of the ConvNeXt module as used in https://arxiv.org/pdf/2206.14747.pdf
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"""
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def __init__(
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self,
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channels: int,
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hidden_ratio: int = 3,
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kernel_size: Tuple[int, int] = (7, 7),
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layerdrop_rate: FloatLike = None,
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):
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super().__init__()
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self.padding = ((kernel_size[0] - 1) // 2, (kernel_size[1] - 1) // 2)
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hidden_channels = channels * hidden_ratio
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if layerdrop_rate is None:
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layerdrop_rate = ScheduledFloat((0.0, 0.2), (20000.0, 0.015))
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self.layerdrop_rate = layerdrop_rate
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self.depthwise_conv = nn.Conv2d(
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in_channels=channels,
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out_channels=channels,
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groups=channels,
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kernel_size=kernel_size,
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padding=self.padding,
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)
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self.pointwise_conv1 = nn.Conv2d(
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in_channels=channels, out_channels=hidden_channels, kernel_size=1
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)
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self.hidden_balancer = Balancer(
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hidden_channels,
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channel_dim=1,
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min_positive=0.3,
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max_positive=1.0,
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min_abs=0.75,
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max_abs=5.0,
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)
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self.activation = SwooshL()
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self.pointwise_conv2 = ScaledConv2d(
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in_channels=hidden_channels,
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out_channels=channels,
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kernel_size=1,
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initial_scale=0.01,
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)
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self.out_balancer = Balancer(
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channels,
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channel_dim=1,
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min_positive=0.4,
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max_positive=0.6,
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min_abs=1.0,
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max_abs=6.0,
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)
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self.out_whiten = Whiten(
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num_groups=1,
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whitening_limit=5.0,
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prob=(0.025, 0.25),
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grad_scale=0.01,
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)
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def forward(self, x: Tensor) -> Tensor:
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if torch.jit.is_scripting() or torch.jit.is_tracing() or not self.training:
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return self.forward_internal(x)
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layerdrop_rate = float(self.layerdrop_rate)
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if layerdrop_rate != 0.0:
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batch_size = x.shape[0]
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mask = (
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torch.rand((batch_size, 1, 1, 1), dtype=x.dtype, device=x.device)
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> layerdrop_rate
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)
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else:
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mask = None
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# turns out this caching idea does not work with --world-size > 1
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# return caching_eval(self.forward_internal, x, mask)
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return self.forward_internal(x, mask)
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def forward_internal(
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self, x: Tensor, layer_skip_mask: Optional[Tensor] = None
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) -> Tensor:
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"""
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x layout: (N, C, H, W), i.e. (batch_size, num_channels, num_frames, num_freqs)
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The returned value has the same shape as x.
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"""
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bypass = x
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x = self.depthwise_conv(x)
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x = self.pointwise_conv1(x)
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x = self.hidden_balancer(x)
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x = self.activation(x)
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x = self.pointwise_conv2(x)
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if layer_skip_mask is not None:
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x = x * layer_skip_mask
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x = bypass + x
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x = self.out_balancer(x)
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if x.requires_grad:
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x = x.transpose(1, 3) # (N, W, H, C); need channel dim to be last
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x = self.out_whiten(x)
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x = x.transpose(1, 3) # (N, C, H, W)
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return x
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def streaming_forward(
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self,
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x: Tensor,
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cached_left_pad: Tensor,
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) -> Tuple[Tensor, Tensor]:
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"""
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Args:
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x layout: (N, C, H, W), i.e. (batch_size, num_channels, num_frames, num_freqs)
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cached_left_pad: (batch_size, num_channels, left_pad, num_freqs)
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Returns:
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- The returned value has the same shape as x.
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- Updated cached_left_pad.
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"""
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padding = self.padding
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# The length without right padding for depth-wise conv
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T = x.size(2) - padding[0]
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bypass = x[:, :, :T, :]
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# Pad left side
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assert cached_left_pad.size(2) == padding[0], (
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cached_left_pad.size(2),
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padding[0],
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)
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x = torch.cat([cached_left_pad, x], dim=2)
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# Update cached left padding
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cached_left_pad = x[:, :, T : padding[0] + T, :]
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# depthwise_conv
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x = torch.nn.functional.conv2d(
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x,
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weight=self.depthwise_conv.weight,
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bias=self.depthwise_conv.bias,
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padding=(0, padding[1]),
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groups=self.depthwise_conv.groups,
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)
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x = self.pointwise_conv1(x)
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x = self.hidden_balancer(x)
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x = self.activation(x)
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x = self.pointwise_conv2(x)
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x = bypass + x
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return x, cached_left_pad
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class Conv2dSubsampling(nn.Module):
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"""Convolutional 2D subsampling (to 1/2 length).
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Convert an input of shape (N, T, idim) to an output
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with shape (N, T', odim), where
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T' = (T-3)//2 - 2 == (T-7)//2
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It is based on
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https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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layer1_channels: int = 8,
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layer2_channels: int = 32,
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layer3_channels: int = 128,
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dropout: FloatLike = 0.1,
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) -> None:
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"""
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Args:
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in_channels:
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Number of channels in. The input shape is (N, T, in_channels).
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Caution: It requires: T >=7, in_channels >=7
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out_channels
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Output dim. The output shape is (N, (T-3)//2, out_channels)
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layer1_channels:
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Number of channels in layer1
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layer1_channels:
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Number of channels in layer2
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bottleneck:
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bottleneck dimension for 1d squeeze-excite
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"""
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assert in_channels >= 7
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super().__init__()
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# The ScaleGrad module is there to prevent the gradients
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# w.r.t. the weight or bias of the first Conv2d module in self.conv from
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# exceeding the range of fp16 when using automatic mixed precision (amp)
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# training. (The second one is necessary to stop its bias from getting
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# a too-large gradient).
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=1,
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out_channels=layer1_channels,
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kernel_size=3,
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padding=(0, 1), # (time, freq)
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),
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ScaleGrad(0.2),
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Balancer(layer1_channels, channel_dim=1, max_abs=1.0),
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SwooshR(),
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nn.Conv2d(
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in_channels=layer1_channels,
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out_channels=layer2_channels,
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kernel_size=3,
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stride=2,
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padding=0,
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),
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Balancer(layer2_channels, channel_dim=1, max_abs=4.0),
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SwooshR(),
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nn.Conv2d(
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in_channels=layer2_channels,
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out_channels=layer3_channels,
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kernel_size=3,
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stride=(1, 2), # (time, freq)
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),
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Balancer(layer3_channels, channel_dim=1, max_abs=4.0),
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SwooshR(),
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)
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# just one convnext layer
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self.convnext = ConvNeXt(layer3_channels, kernel_size=(7, 7))
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# (in_channels-3)//4
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self.out_width = (((in_channels - 1) // 2) - 1) // 2
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self.layer3_channels = layer3_channels
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self.out = nn.Linear(self.out_width * layer3_channels, out_channels)
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# use a larger than normal grad_scale on this whitening module; there is
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# only one such module, so there is not a concern about adding together
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# many copies of this extra gradient term.
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self.out_whiten = Whiten(
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num_groups=1,
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whitening_limit=ScheduledFloat((0.0, 4.0), (20000.0, 8.0), default=4.0),
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prob=(0.025, 0.25),
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grad_scale=0.02,
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)
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# max_log_eps=0.0 is to prevent both eps and the output of self.out from
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# getting large, there is an unnecessary degree of freedom.
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self.out_norm = BiasNorm(out_channels)
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self.dropout = Dropout3(dropout, shared_dim=1)
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def forward(
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self, x: torch.Tensor, x_lens: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Subsample x.
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Args:
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x:
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Its shape is (N, T, idim).
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x_lens:
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A tensor of shape (batch_size,) containing the number of frames in
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Returns:
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- a tensor of shape (N, (T-7)//2, odim)
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- output lengths, of shape (batch_size,)
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"""
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# On entry, x is (N, T, idim)
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x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
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# scaling x by 0.1 allows us to use a larger grad-scale in fp16 "amp" (automatic mixed precision)
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# training, since the weights in the first convolution are otherwise the limiting factor for getting infinite
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# gradients.
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x = self.conv(x)
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x = self.convnext(x)
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# Now x is of shape (N, odim, (T-7)//2, (idim-3)//4)
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b, c, t, f = x.size()
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x = x.transpose(1, 2).reshape(b, t, c * f)
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# now x: (N, (T-7)//2, out_width * layer3_channels))
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x = self.out(x)
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# Now x is of shape (N, (T-7)//2, odim)
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x = self.out_whiten(x)
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x = self.out_norm(x)
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x = self.dropout(x)
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if torch.jit.is_scripting() or torch.jit.is_tracing():
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x_lens = (x_lens - 7) // 2
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else:
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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x_lens = (x_lens - 7) // 2
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assert x.size(1) == x_lens.max().item(), (x.size(1), x_lens.max())
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return x, x_lens
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def streaming_forward(
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self,
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x: torch.Tensor,
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x_lens: torch.Tensor,
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cached_left_pad: Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Subsample x.
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Args:
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x:
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Its shape is (N, T, idim).
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x_lens:
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A tensor of shape (batch_size,) containing the number of frames in
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Returns:
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- a tensor of shape (N, (T-7)//2, odim)
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- output lengths, of shape (batch_size,)
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- updated cache
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"""
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# On entry, x is (N, T, idim)
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x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
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# T' = (T-7)//2
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x = self.conv(x)
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# T' = (T-7)//2-3
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x, cached_left_pad = self.convnext.streaming_forward(
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x, cached_left_pad=cached_left_pad
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)
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# Now x is of shape (N, odim, T', ((idim-1)//2 - 1)//2)
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b, c, t, f = x.size()
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x = x.transpose(1, 2).reshape(b, t, c * f)
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# now x: (N, T', out_width * layer3_channels))
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x = self.out(x)
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# Now x is of shape (N, T', odim)
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x = self.out_norm(x)
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if torch.jit.is_scripting() or torch.jit.is_tracing():
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assert self.convnext.padding[0] == 3
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# The ConvNeXt module needs 3 frames of right padding after subsampling
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x_lens = (x_lens - 7) // 2 - 3
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else:
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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# The ConvNeXt module needs 3 frames of right padding after subsampling
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assert self.convnext.padding[0] == 3
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x_lens = (x_lens - 7) // 2 - 3
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assert x.size(1) == x_lens.max().item(), (x.shape, x_lens.max())
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return x, x_lens, cached_left_pad
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@torch.jit.export
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def get_init_states(
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self,
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batch_size: int = 1,
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device: torch.device = torch.device("cpu"),
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) -> Tensor:
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"""Get initial states for Conv2dSubsampling module.
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It is the cached left padding for ConvNeXt module,
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of shape (batch_size, num_channels, left_pad, num_freqs)
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"""
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left_pad = self.convnext.padding[0]
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freq = self.out_width
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channels = self.layer3_channels
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cached_embed_left_pad = torch.zeros(batch_size, channels, left_pad, freq).to(
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device
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)
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return cached_embed_left_pad
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