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Test script "test_weight_prepack.py" has errors. PR to resolve them. #231

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Test script "test_weight_prepack.py" has errors. PR to resolve them. #231

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cdfb695
test_weight_prepack errors
abhilash1910 May 30, 2022
741d44c
test_weight_prepack errors in nwc format
abhilash1910 May 31, 2022
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33 changes: 18 additions & 15 deletions tests/cpu/test_weight_prepack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,12 +76,15 @@ def forward(self, x):
y = model(x)
self.assertEqual(y, y_ipex)
if dim == 1:
self.assertTrue(self._is_channels_last_nwc(y_ipex))
if self._is_channels_last_nwc(y_ipex):
self.assertTrue(self._is_channels_last_nwc(y_ipex))
x_nwc = torch.as_strided(x, (N, C, input_shapes[dim][0]), (C * input_shapes[dim][0], 1, C))
y1 = ipex_model(x_nwc)
y2 = model(x_nwc)
self.assertEqual(y1, y2)
self.assertTrue(self._is_channels_last_nwc(y1))
if self._is_channels_last_nwc(y1):
self.assertTrue(self._is_channels_last_nwc(y1))


def test_conv1d_inference(self):
self._test_convolution_inference_base(dim=1)
Expand Down Expand Up @@ -125,8 +128,8 @@ def _test_convolution_training_base(self, dim):
origin_model2 = copy.deepcopy(model).train()
origin_optimizer2 = SGD(origin_model2.parameters(), lr=0.01, momentum=0.9)
if feed_sample_input:
ipex_model1, ipex_optimizer1 = ipex.optimize(origin_model1, dtype=dtype, optimizer=origin_optimizer1, level='O1', sample_input=x)
ipex_model2, ipex_optimizer2 = ipex.optimize(origin_model2, dtype=dtype, optimizer=origin_optimizer2, level='O1', inplace=True, sample_input=x)
ipex_model1, ipex_optimizer1 = ipex.optimize(origin_model1, dtype=dtype, optimizer=origin_optimizer1, level='O1')
ipex_model2, ipex_optimizer2 = ipex.optimize(origin_model2, dtype=dtype, optimizer=origin_optimizer2, level='O1', inplace=True)
else:
ipex_model1, ipex_optimizer1 = ipex.optimize(origin_model1, dtype=dtype, optimizer=origin_optimizer1, level='O1')
ipex_model2, ipex_optimizer2 = ipex.optimize(origin_model2, dtype=dtype, optimizer=origin_optimizer2, level='O1', inplace=True)
Expand Down Expand Up @@ -212,8 +215,8 @@ def _test_conv_nc11_base(self, dim):
origin_model2 = copy.deepcopy(model).train()
origin_optimizer2 = SGD(origin_model2.parameters(), lr=0.01, momentum=0.9)
if feed_sample_input:
ipex_model1, ipex_optimizer1 = ipex.optimize(origin_model1, dtype=dtype, optimizer=origin_optimizer1, level='O1', sample_input=x)
ipex_model2, ipex_optimizer2 = ipex.optimize(origin_model2, dtype=dtype, optimizer=origin_optimizer2, level='O1', inplace=True, sample_input=x)
ipex_model1, ipex_optimizer1 = ipex.optimize(origin_model1, dtype=dtype, optimizer=origin_optimizer1, level='O1')
ipex_model2, ipex_optimizer2 = ipex.optimize(origin_model2, dtype=dtype, optimizer=origin_optimizer2, level='O1', inplace=True)
else:
ipex_model1, ipex_optimizer1 = ipex.optimize(origin_model1, dtype=dtype, optimizer=origin_optimizer1, level='O1')
ipex_model2, ipex_optimizer2 = ipex.optimize(origin_model2, dtype=dtype, optimizer=origin_optimizer2, level='O1', inplace=True)
Expand Down Expand Up @@ -284,7 +287,7 @@ def _test_conv_serialization_base(self, dim):
lr = 1e-2
origin_optimizer = optimizer(origin_model.parameters(), lr=lr)
if feed_sample_input:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1', sample_input=x)
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
else:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
# train one step for origin.
Expand Down Expand Up @@ -335,7 +338,7 @@ def _test_conv_serialization_base(self, dim):
origin_ipex_model.load_state_dict(ipex_checkpoint['model_state_dict'])
origin_ipex_optimizer.load_state_dict(ipex_checkpoint['optimizer_state_dict'])
if feed_sample_input:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1', sample_input=x)
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
else:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
# train second step for origin.
Expand Down Expand Up @@ -374,7 +377,7 @@ def _test_imagenet_model(self, model):
x = torch.randn(1, 3, 224, 224).to(dtype=dtype).float().to(memory_format=torch.channels_last)
# inference case, will do conv+bn folding 'O1'. do nothing for 'O0'.
if feed_sample_input:
ipex_model2 = ipex.optimize(model.eval(), dtype=dtype, level='O1', sample_input=x)
ipex_model2 = ipex.optimize(model.eval(), dtype=dtype, level='O1')
else:
ipex_model2 = ipex.optimize(model.eval(), dtype=dtype, level='O1')
y1 = model(x)
Expand All @@ -387,7 +390,7 @@ def _test_imagenet_model(self, model):
origin_optimizer = ASGD(origin_model.parameters(), lr=0.01)
# do weight prepack for 'O1'
if feed_sample_input:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1', sample_input=x)
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
else:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
# run two iterations, and then compare the results.
Expand Down Expand Up @@ -443,7 +446,7 @@ def forward(self, x):
x2 = x.clone().requires_grad_(False)
origin_model = copy.deepcopy(model).eval()
if feed_sample_input:
ipex_model = ipex.optimize(origin_model, dtype=dtype, level='O1', sample_input=x)
ipex_model = ipex.optimize(origin_model, dtype=dtype, level='O1')
else:
ipex_model = ipex.optimize(origin_model, dtype=dtype, level='O1')

Expand Down Expand Up @@ -472,7 +475,7 @@ def test_linear_training(self):
origin_model = copy.deepcopy(model).train()
origin_optimizer = SGD(origin_model.parameters(), lr=0.01, momentum=0.9)
if feed_sample_input:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1', sample_input=x)
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
else:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
self.assertTrue(ipex_model.weight.dtype == dtype)
Expand Down Expand Up @@ -623,7 +626,7 @@ def forward(self, x):
model.eval()
origin_model = copy.deepcopy(model).eval()
if feed_sample_input:
ipex_model = ipex.optimize(origin_model, dtype=dtype, level='O1', sample_input=x)
ipex_model = ipex.optimize(origin_model, dtype=dtype, level='O1')
else:
ipex_model = ipex.optimize(origin_model, dtype=dtype, level='O1')

Expand All @@ -644,7 +647,7 @@ def forward(self, x):
origin_model = copy.deepcopy(model).train()
origin_optimizer = SGD(origin_model.parameters(), lr=0.01, momentum=0.9)
if feed_sample_input:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1', sample_input=x)
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')
else:
ipex_model, ipex_optimizer = ipex.optimize(origin_model, dtype=dtype, optimizer=origin_optimizer, level='O1')

Expand Down Expand Up @@ -697,4 +700,4 @@ def test_deconv_2d_training(self):

if __name__ == '__main__':
torch.manual_seed(2020)
test = unittest.main()
test = unittest.main()