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675 lines
20 KiB
Python
Executable File
675 lines
20 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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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import torch
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import torch.nn as nn
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from transducer.rnn import (
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LayerNormGRU,
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LayerNormGRUCell,
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LayerNormGRULayer,
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LayerNormLSTM,
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LayerNormLSTMCell,
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LayerNormLSTMLayer,
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)
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def assert_allclose(a: torch.Tensor, b: torch.Tensor, **kwargs):
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assert torch.allclose(a, b, **kwargs), f"{(a - b).abs().max()}, {a.numel()}"
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def test_layernorm_lstm_cell_jit():
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input_size = 10
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hidden_size = 20
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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cell = LayerNormLSTMCell(
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input_size=input_size, hidden_size=hidden_size, bias=bias
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)
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torch.jit.script(cell)
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def test_layernorm_lstm_cell_constructor():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
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self_cell = LayerNormLSTMCell(input_size, hidden_size, ln=nn.Identity)
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torch_cell = nn.LSTMCell(input_size, hidden_size)
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for name, param in self_cell.named_parameters():
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assert param.shape == getattr(torch_cell, name).shape
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assert len(self_cell.state_dict()) == len(torch_cell.state_dict())
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def test_layernorm_lstm_cell_with_projection_jit():
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input_size = 10
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hidden_size = 20
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proj_size = 5
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self_cell = LayerNormLSTMCell(input_size, hidden_size, proj_size=proj_size)
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torch.jit.script(self_cell)
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def test_layernorm_lstm_cell_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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self_cell = LayerNormLSTMCell(
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input_size, hidden_size, bias=bias, ln=nn.Identity
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)
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torch_cell = nn.LSTMCell(input_size, hidden_size, bias=bias)
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with torch.no_grad():
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for name, torch_param in torch_cell.named_parameters():
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self_param = getattr(self_cell, name)
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torch_param.copy_(self_param)
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N = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, input_size).requires_grad_()
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h = torch.rand(N, hidden_size)
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c = torch.rand(N, hidden_size)
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x_clone = x.detach().clone().requires_grad_()
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self_h, self_c = self_cell(x.clone(), (h, c))
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torch_h, torch_c = torch_cell(x_clone, (h, c))
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assert_allclose(self_h, torch_h)
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assert_allclose(self_c, torch_c)
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self_hc = self_h * self_c
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torch_hc = torch_h * torch_c
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(self_hc.reshape(-1) * torch.arange(self_hc.numel())).sum().backward()
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(torch_hc.reshape(-1) * torch.arange(torch_hc.numel())).sum().backward()
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assert_allclose(x.grad, x_clone.grad)
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def test_layernorm_lstm_cell_with_projection_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=10, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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proj_size = torch.randint(low=2, high=hidden_size, size=(1,)).item()
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self_cell = LayerNormLSTMCell(
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input_size,
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hidden_size,
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bias=bias,
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ln=nn.Identity,
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proj_size=proj_size,
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)
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torch_cell = nn.LSTM(
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input_size,
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hidden_size,
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bias=bias,
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proj_size=proj_size,
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batch_first=True,
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)
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with torch.no_grad():
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for name, self_param in self_cell.named_parameters():
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getattr(torch_cell, f"{name}_l0").copy_(self_param)
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N = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, input_size).requires_grad_()
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h = torch.rand(N, proj_size)
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c = torch.rand(N, hidden_size)
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x_clone = x.detach().clone().requires_grad_()
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self_h, self_c = self_cell(x.clone(), (h, c))
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_, (torch_h, torch_c) = torch_cell(
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x_clone.unsqueeze(1), (h.unsqueeze(0), c.unsqueeze(0))
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)
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torch_h = torch_h.squeeze(0)
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torch_c = torch_c.squeeze(0)
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assert_allclose(self_h, torch_h)
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assert_allclose(self_c, torch_c)
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(self_h.sum() * self_c.sum()).backward()
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(torch_h.sum() * torch_c.sum()).backward()
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assert_allclose(x.grad, x_clone.grad)
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def test_layernorm_lstm_layer_jit():
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input_size = 10
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hidden_size = 20
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layer = LayerNormLSTMLayer(input_size, hidden_size=hidden_size)
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torch.jit.script(layer)
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def test_layernorm_lstm_layer_with_project_jit():
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input_size = 10
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hidden_size = 20
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proj_size = 5
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layer = LayerNormLSTMLayer(
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input_size,
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hidden_size=hidden_size,
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proj_size=proj_size,
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)
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torch.jit.script(layer)
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def test_layernorm_lstm_layer_with_projection_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=10, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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proj_size = torch.randint(low=2, high=hidden_size, size=(1,)).item()
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self_layer = LayerNormLSTMLayer(
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input_size,
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hidden_size,
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bias=bias,
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proj_size=proj_size,
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ln=nn.Identity,
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)
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N = torch.randint(low=2, high=100, size=(1,))
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T = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, T, input_size).requires_grad_()
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h = torch.rand(N, proj_size)
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c = torch.rand(N, hidden_size)
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x_clone = x.detach().clone().requires_grad_()
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self_y, (self_h, self_c) = self_layer(x, (h, c))
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torch_layer = nn.LSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=1,
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bias=bias,
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proj_size=proj_size,
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batch_first=True,
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dropout=0,
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bidirectional=False,
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)
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with torch.no_grad():
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for name, self_param in self_layer.cell.named_parameters():
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getattr(torch_layer, f"{name}_l0").copy_(self_param)
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torch_y, (torch_h, torch_c) = torch_layer(
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x_clone, (h.unsqueeze(0), c.unsqueeze(0))
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)
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assert_allclose(self_y, torch_y)
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assert_allclose(self_h, torch_h)
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assert_allclose(self_c, torch_c)
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self_hc = self_h * self_c.sum()
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torch_hc = torch_h * torch_c.sum()
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self_hc_sum = (self_hc.reshape(-1) * torch.arange(self_hc.numel())).sum()
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torch_hc_sum = (torch_hc.reshape(-1) * torch.arange(torch_hc.numel())).sum()
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self_y_sum = (self_y.reshape(-1) * torch.arange(self_y.numel())).sum()
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torch_y_sum = (torch_y.reshape(-1) * torch.arange(torch_y.numel())).sum()
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(self_hc_sum * self_y_sum).backward()
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(torch_hc_sum * torch_y_sum).backward()
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assert_allclose(x.grad, x_clone.grad, atol=1e-5)
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def test_layernorm_lstm_layer_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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self_layer = LayerNormLSTMLayer(
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input_size,
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hidden_size,
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bias=bias,
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ln=nn.Identity,
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)
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N = torch.randint(low=2, high=100, size=(1,))
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T = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, T, input_size).requires_grad_()
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h = torch.rand(N, hidden_size)
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c = torch.rand(N, hidden_size)
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x_clone = x.detach().clone().requires_grad_()
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self_y, (self_h, self_c) = self_layer(x, (h, c))
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torch_layer = nn.LSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=1,
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bias=bias,
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batch_first=True,
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dropout=0,
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bidirectional=False,
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)
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with torch.no_grad():
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for name, self_param in self_layer.cell.named_parameters():
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getattr(torch_layer, f"{name}_l0").copy_(self_param)
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torch_y, (torch_h, torch_c) = torch_layer(
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x_clone, (h.unsqueeze(0), c.unsqueeze(0))
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)
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assert_allclose(self_y, torch_y)
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assert_allclose(self_h, torch_h)
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assert_allclose(self_c, torch_c)
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self_hc = self_h * self_c
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torch_hc = torch_h * torch_c
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self_hc_sum = (self_hc.reshape(-1) * torch.arange(self_hc.numel())).sum()
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torch_hc_sum = (torch_hc.reshape(-1) * torch.arange(torch_hc.numel())).sum()
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self_y_sum = (self_y.reshape(-1) * torch.arange(self_y.numel())).sum()
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torch_y_sum = (torch_y.reshape(-1) * torch.arange(torch_y.numel())).sum()
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(self_hc_sum * self_y_sum).backward()
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(torch_hc_sum * torch_y_sum).backward()
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assert_allclose(x.grad, x_clone.grad, atol=1e-5)
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def test_layernorm_lstm_jit():
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input_size = 2
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hidden_size = 3
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num_layers = 4
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bias = True
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lstm = LayerNormLSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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bias=bias,
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ln=nn.Identity,
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)
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torch.jit.script(lstm)
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def test_layernorm_lstm_with_projection_jit():
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input_size = 2
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hidden_size = 5
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proj_size = 3
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num_layers = 4
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bias = True
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lstm = LayerNormLSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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bias=bias,
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proj_size=proj_size,
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ln=nn.Identity,
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)
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torch.jit.script(lstm)
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def test_layernorm_lstm_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
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num_layers = torch.randint(low=2, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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self_lstm = LayerNormLSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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bias=bias,
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ln=nn.Identity,
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)
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torch_lstm = nn.LSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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bias=bias,
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batch_first=True,
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bidirectional=False,
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)
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assert len(self_lstm.state_dict()) == len(torch_lstm.state_dict())
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with torch.no_grad():
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for name, param in self_lstm.named_parameters():
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# name has the form layers.0.cell.weight_hh
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parts = name.split(".")
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layer_num = parts[1]
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getattr(torch_lstm, f"{parts[-1]}_l{layer_num}").copy_(param)
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N = torch.randint(low=2, high=100, size=(1,))
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T = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, T, input_size).requires_grad_()
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hs = [torch.rand(N, hidden_size) for _ in range(num_layers)]
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cs = [torch.rand(N, hidden_size) for _ in range(num_layers)]
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states = list(zip(hs, cs))
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x_clone = x.detach().clone().requires_grad_()
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self_y, self_states = self_lstm(x, states)
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h = torch.stack(hs)
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c = torch.stack(cs)
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torch_y, (torch_h, torch_c) = torch_lstm(x_clone, (h, c))
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assert_allclose(self_y, torch_y)
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self_h = torch.stack([s[0] for s in self_states])
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self_c = torch.stack([s[1] for s in self_states])
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assert_allclose(self_h, torch_h)
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assert_allclose(self_c, torch_c)
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s = self_y.reshape(-1)
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t = torch_y.reshape(-1)
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s_sum = (s * torch.arange(s.numel())).sum()
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t_sum = (t * torch.arange(t.numel())).sum()
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shc_sum = s_sum * self_h.sum() * self_c.sum()
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thc_sum = t_sum * torch_h.sum() * torch_c.sum()
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shc_sum.backward()
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thc_sum.backward()
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assert_allclose(x.grad, x_clone.grad)
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def test_layernorm_lstm_with_projection_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=10, high=100, size=(1,)).item()
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proj_size = torch.randint(low=2, high=hidden_size, size=(1,)).item()
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num_layers = torch.randint(low=2, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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self_lstm = LayerNormLSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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bias=bias,
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proj_size=proj_size,
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ln=nn.Identity,
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)
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torch_lstm = nn.LSTM(
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input_size=input_size,
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hidden_size=hidden_size,
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num_layers=num_layers,
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bias=bias,
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proj_size=proj_size,
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batch_first=True,
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bidirectional=False,
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)
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assert len(self_lstm.state_dict()) == len(torch_lstm.state_dict())
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with torch.no_grad():
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for name, param in self_lstm.named_parameters():
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# name has the form layers.0.cell.weight_hh
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parts = name.split(".")
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layer_num = parts[1]
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getattr(torch_lstm, f"{parts[-1]}_l{layer_num}").copy_(param)
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N = torch.randint(low=2, high=100, size=(1,))
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T = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, T, input_size).requires_grad_()
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hs = [torch.rand(N, proj_size) for _ in range(num_layers)]
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cs = [torch.rand(N, hidden_size) for _ in range(num_layers)]
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states = list(zip(hs, cs))
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x_clone = x.detach().clone().requires_grad_()
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self_y, self_states = self_lstm(x, states)
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h = torch.stack(hs)
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c = torch.stack(cs)
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torch_y, (torch_h, torch_c) = torch_lstm(x_clone, (h, c))
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assert_allclose(self_y, torch_y)
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self_h = torch.stack([s[0] for s in self_states])
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self_c = torch.stack([s[1] for s in self_states])
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assert_allclose(self_h, torch_h)
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assert_allclose(self_c, torch_c)
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s = self_y.reshape(-1)
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t = torch_y.reshape(-1)
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s_sum = (s * torch.arange(s.numel())).sum()
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t_sum = (t * torch.arange(t.numel())).sum()
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shc_sum = s_sum * self_h.sum() * self_c.sum()
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thc_sum = t_sum * torch_h.sum() * torch_c.sum()
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shc_sum.backward()
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thc_sum.backward()
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assert_allclose(x.grad, x_clone.grad)
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def test_layernorm_gru_cell_jit():
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input_size = 10
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hidden_size = 20
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cell = LayerNormGRUCell(
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input_size=input_size, hidden_size=hidden_size, bias=True
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)
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|
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torch.jit.script(cell)
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|
|
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def test_layernorm_gru_cell_constructor():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
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|
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self_cell = LayerNormGRUCell(input_size, hidden_size, ln=nn.Identity)
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torch_cell = nn.GRUCell(input_size, hidden_size)
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|
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for name, param in self_cell.named_parameters():
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assert param.shape == getattr(torch_cell, name).shape
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assert len(self_cell.state_dict()) == len(torch_cell.state_dict())
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|
|
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def test_layernorm_gru_cell_forward():
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input_size = torch.randint(low=2, high=100, size=(1,)).item()
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hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
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bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
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|
|
|
self_cell = LayerNormGRUCell(
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|
input_size, hidden_size, bias=bias, ln=nn.Identity
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|
)
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torch_cell = nn.GRUCell(input_size, hidden_size, bias=bias)
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|
with torch.no_grad():
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for name, torch_param in torch_cell.named_parameters():
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self_param = getattr(self_cell, name)
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torch_param.copy_(self_param)
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|
|
|
N = torch.randint(low=2, high=100, size=(1,))
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x = torch.rand(N, input_size).requires_grad_()
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h = torch.rand(N, hidden_size)
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|
|
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x_clone = x.detach().clone().requires_grad_()
|
|
|
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self_h = self_cell(x.clone(), h)
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|
torch_h = torch_cell(x_clone, h)
|
|
|
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assert_allclose(self_h, torch_h, atol=1e-5)
|
|
|
|
(self_h.reshape(-1) * torch.arange(self_h.numel())).sum().backward()
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|
(torch_h.reshape(-1) * torch.arange(torch_h.numel())).sum().backward()
|
|
|
|
assert_allclose(x.grad, x_clone.grad, atol=1e-4)
|
|
|
|
|
|
def test_layernorm_gru_layer_jit():
|
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input_size = 10
|
|
hidden_size = 20
|
|
layer = LayerNormGRULayer(input_size, hidden_size=hidden_size)
|
|
torch.jit.script(layer)
|
|
|
|
|
|
def test_layernorm_gru_layer_forward():
|
|
input_size = torch.randint(low=2, high=100, size=(1,)).item()
|
|
hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
|
|
bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
|
|
self_layer = LayerNormGRULayer(
|
|
input_size,
|
|
hidden_size,
|
|
bias=bias,
|
|
ln=nn.Identity,
|
|
)
|
|
|
|
N = torch.randint(low=2, high=100, size=(1,))
|
|
T = torch.randint(low=2, high=100, size=(1,))
|
|
|
|
x = torch.rand(N, T, input_size).requires_grad_()
|
|
h = torch.rand(N, hidden_size)
|
|
|
|
x_clone = x.detach().clone().requires_grad_()
|
|
|
|
self_y, self_h = self_layer(x, h.clone())
|
|
|
|
torch_layer = nn.GRU(
|
|
input_size=input_size,
|
|
hidden_size=hidden_size,
|
|
num_layers=1,
|
|
bias=bias,
|
|
batch_first=True,
|
|
dropout=0,
|
|
bidirectional=False,
|
|
)
|
|
with torch.no_grad():
|
|
for name, self_param in self_layer.cell.named_parameters():
|
|
getattr(torch_layer, f"{name}_l0").copy_(self_param)
|
|
|
|
torch_y, torch_h = torch_layer(x_clone, h.unsqueeze(0))
|
|
assert_allclose(self_y, torch_y, atol=1e-6)
|
|
assert_allclose(self_h, torch_h, atol=1e-6)
|
|
|
|
self_y_sum = (self_y.reshape(-1) * torch.arange(self_y.numel())).sum()
|
|
torch_y_sum = (torch_y.reshape(-1) * torch.arange(torch_y.numel())).sum()
|
|
|
|
self_y_sum.backward()
|
|
torch_y_sum.backward()
|
|
|
|
assert_allclose(x.grad, x_clone.grad, atol=0.1)
|
|
|
|
|
|
def test_layernorm_gru_jit():
|
|
input_size = 2
|
|
hidden_size = 3
|
|
num_layers = 4
|
|
bias = True
|
|
|
|
gru = LayerNormGRU(
|
|
input_size=input_size,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
bias=bias,
|
|
ln=nn.Identity,
|
|
)
|
|
torch.jit.script(gru)
|
|
|
|
|
|
def test_layernorm_gru_forward():
|
|
input_size = torch.randint(low=2, high=100, size=(1,)).item()
|
|
hidden_size = torch.randint(low=2, high=100, size=(1,)).item()
|
|
num_layers = torch.randint(low=2, high=100, size=(1,)).item()
|
|
bias = torch.randint(low=0, high=1000, size=(1,)).item() & 2 == 0
|
|
|
|
self_gru = LayerNormGRU(
|
|
input_size=input_size,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
bias=bias,
|
|
ln=nn.Identity,
|
|
)
|
|
torch_gru = nn.GRU(
|
|
input_size=input_size,
|
|
hidden_size=hidden_size,
|
|
num_layers=num_layers,
|
|
bias=bias,
|
|
batch_first=True,
|
|
bidirectional=False,
|
|
)
|
|
assert len(self_gru.state_dict()) == len(torch_gru.state_dict())
|
|
with torch.no_grad():
|
|
for name, param in self_gru.named_parameters():
|
|
# name has the form layers.0.cell.weight_hh
|
|
parts = name.split(".")
|
|
layer_num = parts[1]
|
|
getattr(torch_gru, f"{parts[-1]}_l{layer_num}").copy_(param)
|
|
|
|
N = torch.randint(low=2, high=100, size=(1,))
|
|
T = torch.randint(low=2, high=100, size=(1,))
|
|
|
|
x = torch.rand(N, T, input_size).requires_grad_()
|
|
states = [torch.rand(N, hidden_size) for _ in range(num_layers)]
|
|
|
|
x_clone = x.detach().clone().requires_grad_()
|
|
|
|
self_y, self_states = self_gru(x, states)
|
|
|
|
torch_y, torch_states = torch_gru(x_clone, torch.stack(states))
|
|
|
|
assert_allclose(self_y, torch_y, atol=1e-6)
|
|
|
|
self_states = torch.stack(self_states)
|
|
|
|
assert_allclose(self_states, torch_states, atol=1e-6)
|
|
|
|
s = self_y.reshape(-1)
|
|
t = torch_y.reshape(-1)
|
|
|
|
s_sum = (s * torch.arange(s.numel())).sum()
|
|
t_sum = (t * torch.arange(t.numel())).sum()
|
|
s_state_sum = s_sum + self_states.sum()
|
|
t_state_sum = t_sum + torch_states.sum()
|
|
|
|
s_state_sum.backward()
|
|
t_state_sum.backward()
|
|
assert_allclose(x.grad, x_clone.grad, atol=1e-6)
|
|
|
|
|
|
def test_lstm():
|
|
test_layernorm_lstm_cell_jit()
|
|
test_layernorm_lstm_cell_constructor()
|
|
test_layernorm_lstm_cell_with_projection_jit()
|
|
test_layernorm_lstm_cell_forward()
|
|
test_layernorm_lstm_cell_with_projection_forward()
|
|
#
|
|
test_layernorm_lstm_layer_jit()
|
|
test_layernorm_lstm_layer_with_project_jit()
|
|
test_layernorm_lstm_layer_forward()
|
|
test_layernorm_lstm_layer_with_projection_forward()
|
|
|
|
test_layernorm_lstm_jit()
|
|
test_layernorm_lstm_with_projection_jit()
|
|
test_layernorm_lstm_forward()
|
|
test_layernorm_lstm_with_projection_forward()
|
|
|
|
|
|
def test_gru():
|
|
test_layernorm_gru_cell_jit()
|
|
test_layernorm_gru_cell_constructor()
|
|
test_layernorm_gru_cell_forward()
|
|
#
|
|
test_layernorm_gru_layer_jit()
|
|
test_layernorm_gru_layer_forward()
|
|
#
|
|
test_layernorm_gru_jit()
|
|
test_layernorm_gru_forward()
|
|
|
|
|
|
def main():
|
|
test_lstm()
|
|
test_gru()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
torch.manual_seed(20211202)
|
|
main()
|