add generator #255
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,180 @@ | ||
| # Copyright (c) 2023, DeepLink. | ||
| import torch | ||
| import torch_dipu | ||
|
|
||
| from torch_dipu.testing._internal.common_utils import create_common_tensor, TestCase, run_tests | ||
|
|
||
|
|
||
| class TestGenerator(TestCase): | ||
| def test_python_api(self): | ||
| torch.seed() | ||
| torch.cuda.seed_all() | ||
| torch.cuda.random.seed_all() | ||
| torch.cuda.manual_seed_all(1) | ||
| rngs = torch.cuda.get_rng_state_all() | ||
| torch.cuda.set_rng_state_all(rngs) | ||
| torch.manual_seed(1) | ||
| assert torch.cuda.initial_seed() == 1 | ||
| assert torch.initial_seed() == 1 | ||
| for i in range(torch.cuda.device_count()): | ||
| torch.cuda.manual_seed(i) | ||
|
|
||
| state = torch.cuda.get_rng_state(0) | ||
| new_state = torch.ones_like(state) | ||
| torch.cuda.set_rng_state(new_state, 0) | ||
| current_state = torch.cuda.get_rng_state(0) | ||
| assert torch.allclose(current_state, torch.tensor(1, device=current_state.device, dtype=current_state.dtype)) | ||
|
|
||
| def test_torch_generator(self): | ||
| gen = torch.Generator() | ||
| assert gen.device.type == 'cpu' | ||
| gen.manual_seed(1) | ||
| assert gen.initial_seed() == 1 | ||
|
|
||
| gen = torch.Generator("cpu") | ||
| assert gen.device.type == 'cpu' | ||
|
|
||
| gen = torch.Generator("cuda") | ||
| assert gen.device.type == 'xpu' | ||
|
|
||
| gen = torch.Generator("cuda:0") | ||
| assert gen.device == torch.device('xpu:0') | ||
|
|
||
| gen = torch.Generator("dipu") | ||
| assert gen.device.type == 'xpu' | ||
| gen.manual_seed(1) | ||
| assert gen.initial_seed() == 1 | ||
|
|
||
| def test_randn_with_generator(self): | ||
| gen = torch.Generator() | ||
| gen.manual_seed(1) | ||
| data1 = torch.randn(2, 3, generator = gen) | ||
| gen.manual_seed(1) | ||
| data2 = torch.randn(2, 3, generator = gen) | ||
| assert torch.allclose(data1, data2) | ||
| data2 = torch.randn(2, 3, generator = gen) | ||
| assert not torch.allclose(data1, data2) | ||
|
|
||
| gen = torch.Generator('cuda') | ||
| gen.manual_seed(1) | ||
| data1 = torch.randn(2, 3, generator = gen, device = 'cuda') | ||
| gen.manual_seed(1) | ||
| data2 = torch.randn(2, 3, generator = gen, device = 'cuda') | ||
| assert torch.allclose(data1, data2) | ||
| data2 = torch.randn(2, 3, generator = gen, device = 'cuda') | ||
| assert not torch.allclose(data1, data2) | ||
|
|
||
| def test_uniform_(self): | ||
| t1 = torch.arange(0, 100, dtype=torch.float32).cuda() | ||
| t2 = t1.clone() | ||
| torch.manual_seed(1) | ||
| t1.uniform_() | ||
| torch.manual_seed(1) | ||
| t2.uniform_() | ||
| assert torch.allclose(t1, t2) | ||
| t2.uniform_() | ||
| assert not torch.allclose(t1, t2) | ||
| print("uniform_ allclose success") | ||
|
|
||
| def test_normal_(self): | ||
| t1 = torch.arange(0, 100, dtype=torch.float32).cuda() | ||
| t2 = t1.clone() | ||
| torch.manual_seed(1) | ||
| t1.normal_() | ||
| torch.manual_seed(1) | ||
| t2.normal_() | ||
| assert torch.allclose(t1, t2) | ||
| t2.normal_() | ||
| assert not torch.allclose(t1, t2) | ||
| print("normal_ allclose success") | ||
|
|
||
| def test_random_(self): | ||
| t1 = torch.arange(0, 100, dtype=torch.float32).cuda() | ||
| t2 = t1.clone() | ||
| torch.manual_seed(1) | ||
| t1.random_(0, 100) | ||
| torch.manual_seed(1) | ||
| t2.random_(0, 100) | ||
| assert torch.allclose(t1, t2) | ||
| t2.random_(0, 100) | ||
| assert not torch.allclose(t1, t2) | ||
|
|
||
| torch.manual_seed(1) | ||
| t1.random_() | ||
| torch.manual_seed(1) | ||
| t2.random_() | ||
| assert torch.allclose(t1, t2) | ||
| t2.random_() | ||
| assert not torch.allclose(t1, t2) | ||
| print("random_ allclose success") | ||
|
|
||
| def test_multinomial(self): | ||
| data = torch.arange(0, 100, dtype=torch.float).cuda() | ||
| torch.manual_seed(1) | ||
| data1 = torch.multinomial(data, 10) | ||
| torch.manual_seed(1) | ||
| data2 = torch.multinomial(data, 10) | ||
| assert torch.allclose(data1, data2) | ||
| data2 = torch.multinomial(data, 10) | ||
| assert not torch.allclose(data1, data2) | ||
| print("multinomial allclose success") | ||
|
|
||
| def test_randn(self): | ||
| torch.manual_seed(1) | ||
| t1 = torch.randn(100, device='cuda') | ||
| torch.manual_seed(1) | ||
| t2 = torch.randn(100, device='cuda') | ||
| assert torch.allclose(t1, t2) | ||
| t2 = torch.randn(100, device='cuda') | ||
| assert not torch.allclose(t1, t2) | ||
| print("randn allclose success") | ||
|
|
||
| def test_randperm(self): | ||
| if torch_dipu.dipu.vendor_type == "MLU": | ||
| return | ||
|
|
||
| torch.manual_seed(1) | ||
| t1 = torch.randperm(100, device='cuda') | ||
| torch.manual_seed(1) | ||
| t2 = torch.randperm(100, device='cuda') | ||
| assert torch.allclose(t1, t2) | ||
| t2 = torch.randperm(100, device='cuda') | ||
| assert not torch.allclose(t1, t2) | ||
| print("randperm allclose success") | ||
|
|
||
| def test_dropout(self): | ||
| m = torch.nn.Dropout(p=0.2).cuda() | ||
| input = torch.randn(20, 16).cuda() | ||
| torch.manual_seed(1) | ||
| t1 = m(input) | ||
| torch.manual_seed(1) | ||
| t2 = m(input) | ||
| assert torch.allclose(t1, t2) | ||
| t2 = m(input) | ||
| assert not torch.allclose(t1, t2) | ||
| print("dropout allclose success") | ||
|
|
||
| def test_dropout_(self): | ||
| m = torch.nn.Dropout(p=0.2, inplace=True).cuda() | ||
| input = torch.randn(20, 16).cuda() | ||
| p = 0.2 | ||
| torch.manual_seed(1) | ||
| t1 = input.clone() | ||
| m(t1) | ||
| torch.manual_seed(1) | ||
| t2 = input.clone() | ||
| m(t2) | ||
| assert torch.allclose(t1, t2) | ||
| t2 = input.clone() | ||
| m(t2) | ||
| assert not torch.allclose(t1, t2) | ||
| print("dropout_ allclose success") | ||
|
|
||
| def test_default_generators(self): | ||
| assert len(torch.cuda.default_generators) > 0 | ||
| torch.cuda.default_generators[0].manual_seed(1) | ||
| assert torch.cuda.default_generators[0].initial_seed() == 1 | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| run_tests() |
Submodule DIOPI
updated
45 files
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
::diopiGeneratorHandle_t ${arg_name}DiopiGenerator = toDiopiGeneratorHandle((${arg_name}.has_value() && ${arg_name}.value().defined()) ? ${arg_name} : getDefaultDIPUGenerator());