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https://github.com/k2-fsa/icefall.git
synced 2025-08-26 10:16:14 +00:00
Fix test failures for torch 1.8.0
This commit is contained in:
Fangjun Kuang
parent
b3a5b04e13
commit
cafd06e909
@ -34,8 +34,10 @@ def get_devices():
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return devices
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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 assert_allclose(a: torch.Tensor, b: torch.Tensor, atol=1e-6, **kwargs):
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assert torch.allclose(
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a, b, atol=atol, **kwargs
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), f"{(a - b).abs().max()}, {a.numel()}"
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def test_layernorm_lstm_cell_jit(device="cpu"):
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@ -65,8 +67,7 @@ def test_layernorm_lstm_cell_constructor(device="cpu"):
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torch_cell = nn.LSTMCell(
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input_size,
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hidden_size,
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device=device,
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)
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).to(device)
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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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@ -103,8 +104,7 @@ def test_layernorm_lstm_cell_forward(device="cpu"):
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input_size,
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hidden_size,
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bias=bias,
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device=device,
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)
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).to(device)
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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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@ -121,7 +121,7 @@ def test_layernorm_lstm_cell_forward(device="cpu"):
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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, atol=1e-6)
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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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@ -155,8 +155,7 @@ def test_layernorm_lstm_cell_with_projection_forward(device="cpu"):
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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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device=device,
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)
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).to(device)
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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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@ -177,7 +176,7 @@ def test_layernorm_lstm_cell_with_projection_forward(device="cpu"):
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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, atol=1e-6)
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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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@ -244,8 +243,7 @@ def test_layernorm_lstm_layer_with_projection_forward(device="cpu"):
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batch_first=True,
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dropout=0,
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bidirectional=False,
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device=device,
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)
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).to(device)
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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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@ -253,9 +251,9 @@ def test_layernorm_lstm_layer_with_projection_forward(device="cpu"):
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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, atol=1e-6)
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assert_allclose(self_h, torch_h, atol=1e-6)
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assert_allclose(self_c, torch_c, atol=1e-6)
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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_y.sum().backward()
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torch_y.sum().backward()
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@ -294,8 +292,7 @@ def test_layernorm_lstm_layer_forward(device="cpu"):
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batch_first=True,
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dropout=0,
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bidirectional=False,
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device=device,
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)
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).to(device)
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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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@ -303,9 +300,9 @@ def test_layernorm_lstm_layer_forward(device="cpu"):
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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, atol=1e-6)
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assert_allclose(self_h, torch_h, atol=1e-6)
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assert_allclose(self_c, torch_c, atol=1e-6)
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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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@ -386,8 +383,7 @@ def test_layernorm_lstm_forward(device="cpu"):
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bias=bias,
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batch_first=True,
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bidirectional=False,
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device=device,
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)
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).to(device)
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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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@ -457,8 +453,7 @@ def test_layernorm_lstm_with_projection_forward(device="cpu"):
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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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device=device,
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)
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).to(device)
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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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@ -483,13 +478,13 @@ def test_layernorm_lstm_with_projection_forward(device="cpu"):
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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, atol=1e-6)
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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, atol=1e-6)
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assert_allclose(self_c, torch_c, atol=1e-6)
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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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@ -501,7 +496,7 @@ def test_layernorm_lstm_with_projection_forward(device="cpu"):
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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, atol=1e-6)
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assert_allclose(x.grad, x_clone.grad)
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def test_layernorm_gru_cell_jit(device="cpu"):
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@ -530,8 +525,7 @@ def test_layernorm_gru_cell_constructor(device="cpu"):
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torch_cell = nn.GRUCell(
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input_size,
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hidden_size,
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device=device,
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)
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).to(device)
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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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@ -555,8 +549,7 @@ def test_layernorm_gru_cell_forward(device="cpu"):
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input_size,
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hidden_size,
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bias=bias,
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device=device,
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)
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).to(device)
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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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@ -580,7 +573,7 @@ def test_layernorm_gru_cell_forward(device="cpu"):
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torch_h.reshape(-1) * torch.arange(torch_h.numel(), device=device)
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).sum().backward()
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assert_allclose(x.grad, x_clone.grad, atol=1e-4)
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assert_allclose(x.grad, x_clone.grad, atol=1e-3)
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def test_layernorm_gru_layer_jit(device="cpu"):
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@ -624,15 +617,14 @@ def test_layernorm_gru_layer_forward(device="cpu"):
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batch_first=True,
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dropout=0,
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bidirectional=False,
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device=device,
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)
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).to(device)
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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_layer(x_clone, h.unsqueeze(0))
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assert_allclose(self_y, torch_y, atol=1e-6)
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assert_allclose(self_h, torch_h, atol=1e-6)
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assert_allclose(self_y, torch_y)
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assert_allclose(self_h, torch_h)
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self_y_sum = (
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self_y.reshape(-1) * torch.arange(self_y.numel(), device=device)
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@ -685,8 +677,7 @@ def test_layernorm_gru_forward(device="cpu"):
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bias=bias,
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batch_first=True,
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bidirectional=False,
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device=device,
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)
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).to(device)
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assert len(self_gru.state_dict()) == len(torch_gru.state_dict())
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with torch.no_grad():
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for name, param in self_gru.named_parameters():
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@ -709,11 +700,11 @@ def test_layernorm_gru_forward(device="cpu"):
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torch_y, torch_states = torch_gru(x_clone, torch.stack(states))
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assert_allclose(self_y, torch_y, atol=1e-6)
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assert_allclose(self_y, torch_y)
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self_states = torch.stack(self_states)
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assert_allclose(self_states, torch_states, atol=1e-6)
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assert_allclose(self_states, torch_states)
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s = self_y.reshape(-1)
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t = torch_y.reshape(-1)
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@ -758,6 +749,10 @@ def _test_gru(device):
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test_layernorm_gru_forward(device)
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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def main():
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for device in get_devices():
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print("device", device)
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