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import pytest |
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from fastNLP.envs.imports import _NEED_IMPORT_JITTOR, _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH |
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from fastNLP.modules.mix_modules.utils import ( |
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paddle2torch, |
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torch2paddle, |
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jittor2torch, |
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torch2jittor, |
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) |
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if _NEED_IMPORT_TORCH: |
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import torch |
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if _NEED_IMPORT_PADDLE: |
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import paddle |
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if _NEED_IMPORT_JITTOR: |
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import jittor |
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############################################################################ |
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# |
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# 测试paddle到torch的转换 |
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# |
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############################################################################ |
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@pytest.mark.torchpaddle |
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class TestPaddle2Torch: |
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def check_torch_tensor(self, tensor, device, requires_grad): |
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""" |
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检查张量设备和梯度情况的工具函数 |
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""" |
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assert isinstance(tensor, torch.Tensor) |
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assert tensor.device == torch.device(device) |
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assert tensor.requires_grad == requires_grad |
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def test_gradient(self): |
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""" |
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测试张量转换后的反向传播是否正确 |
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""" |
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x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0, 5.0], stop_gradient=False) |
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y = paddle2torch(x) |
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z = 3 * (y ** 2) |
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z.sum().backward() |
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assert y.grad.tolist() == [6, 12, 18, 24, 30] |
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def test_tensor_transfer(self): |
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""" |
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测试单个张量的设备和梯度转换是否正确 |
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""" |
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paddle_tensor = paddle.rand((3, 4, 5)).cpu() |
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res = paddle2torch(paddle_tensor) |
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self.check_torch_tensor(res, "cpu", not paddle_tensor.stop_gradient) |
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res = paddle2torch(paddle_tensor, target_device="cuda:2", no_gradient=None) |
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self.check_torch_tensor(res, "cuda:2", not paddle_tensor.stop_gradient) |
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res = paddle2torch(paddle_tensor, target_device="cuda:1", no_gradient=True) |
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self.check_torch_tensor(res, "cuda:1", False) |
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res = paddle2torch(paddle_tensor, target_device="cuda:1", no_gradient=False) |
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self.check_torch_tensor(res, "cuda:1", True) |
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def test_list_transfer(self): |
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""" |
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测试张量列表的转换 |
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""" |
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paddle_list = [paddle.rand((6, 4, 2)).cuda(1) for i in range(10)] |
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res = paddle2torch(paddle_list) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_torch_tensor(t, "cuda:1", False) |
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res = paddle2torch(paddle_list, target_device="cpu", no_gradient=False) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_torch_tensor(t, "cpu", True) |
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def test_tensor_tuple_transfer(self): |
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""" |
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测试张量元组的转换 |
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""" |
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paddle_list = [paddle.rand((6, 4, 2)).cuda(1) for i in range(10)] |
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paddle_tuple = tuple(paddle_list) |
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res = paddle2torch(paddle_tuple) |
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assert isinstance(res, tuple) |
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for t in res: |
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self.check_torch_tensor(t, "cuda:1", False) |
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def test_dict_transfer(self): |
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""" |
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测试包含复杂结构的字典的转换 |
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""" |
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paddle_dict = { |
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"tensor": paddle.rand((3, 4)).cuda(0), |
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"list": [paddle.rand((6, 4, 2)).cuda(0) for i in range(10)], |
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"dict":{ |
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"list": [paddle.rand((6, 4, 2)).cuda(0) for i in range(10)], |
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"tensor": paddle.rand((3, 4)).cuda(0) |
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}, |
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"int": 2, |
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"string": "test string" |
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} |
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res = paddle2torch(paddle_dict) |
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assert isinstance(res, dict) |
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self.check_torch_tensor(res["tensor"], "cuda:0", False) |
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assert isinstance(res["list"], list) |
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for t in res["list"]: |
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self.check_torch_tensor(t, "cuda:0", False) |
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assert isinstance(res["int"], int) |
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assert isinstance(res["string"], str) |
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assert isinstance(res["dict"], dict) |
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assert isinstance(res["dict"]["list"], list) |
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for t in res["dict"]["list"]: |
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self.check_torch_tensor(t, "cuda:0", False) |
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self.check_torch_tensor(res["dict"]["tensor"], "cuda:0", False) |
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############################################################################ |
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# |
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# 测试torch到paddle的转换 |
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# |
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############################################################################ |
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@pytest.mark.torchpaddle |
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class TestTorch2Paddle: |
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def check_paddle_tensor(self, tensor, device, stop_gradient): |
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""" |
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检查得到的paddle张量设备和梯度情况的工具函数 |
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""" |
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assert isinstance(tensor, paddle.Tensor) |
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if device == "cpu": |
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assert tensor.place.is_cpu_place() |
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elif device.startswith("gpu"): |
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paddle_device = paddle.device._convert_to_place(device) |
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assert tensor.place.is_gpu_place() |
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if hasattr(tensor.place, "gpu_device_id"): |
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# paddle中,有两种Place |
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# paddle.fluid.core.Place是创建Tensor时使用的类型 |
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# 有函数gpu_device_id获取设备 |
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assert tensor.place.gpu_device_id() == paddle_device.get_device_id() |
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else: |
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# 通过_convert_to_place得到的是paddle.CUDAPlace |
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# 通过get_device_id获取设备 |
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assert tensor.place.get_device_id() == paddle_device.get_device_id() |
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else: |
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raise NotImplementedError |
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assert tensor.stop_gradient == stop_gradient |
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def test_gradient(self): |
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""" |
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测试转换后梯度的反向传播 |
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""" |
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x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], requires_grad=True) |
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y = torch2paddle(x) |
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z = 3 * (y ** 2) |
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z.sum().backward() |
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assert y.grad.tolist() == [6, 12, 18, 24, 30] |
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def test_tensor_transfer(self): |
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""" |
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测试单个张量的转换 |
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""" |
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torch_tensor = torch.rand((3, 4, 5)) |
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res = torch2paddle(torch_tensor) |
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self.check_paddle_tensor(res, "cpu", True) |
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res = torch2paddle(torch_tensor, target_device="gpu:2", no_gradient=None) |
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self.check_paddle_tensor(res, "gpu:2", True) |
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res = torch2paddle(torch_tensor, target_device="gpu:2", no_gradient=True) |
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self.check_paddle_tensor(res, "gpu:2", True) |
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res = torch2paddle(torch_tensor, target_device="gpu:2", no_gradient=False) |
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self.check_paddle_tensor(res, "gpu:2", False) |
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def test_tensor_list_transfer(self): |
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""" |
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测试张量列表的转换 |
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""" |
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torch_list = [torch.rand(6, 4, 2) for i in range(10)] |
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res = torch2paddle(torch_list) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_paddle_tensor(t, "cpu", True) |
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res = torch2paddle(torch_list, target_device="gpu:1", no_gradient=False) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_paddle_tensor(t, "gpu:1", False) |
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def test_tensor_tuple_transfer(self): |
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""" |
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测试张量元组的转换 |
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""" |
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torch_list = [torch.rand(6, 4, 2) for i in range(10)] |
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torch_tuple = tuple(torch_list) |
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res = torch2paddle(torch_tuple, target_device="cpu") |
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assert isinstance(res, tuple) |
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for t in res: |
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self.check_paddle_tensor(t, "cpu", True) |
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def test_dict_transfer(self): |
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""" |
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测试复杂的字典结构的转换 |
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""" |
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torch_dict = { |
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"tensor": torch.rand((3, 4)), |
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"list": [torch.rand(6, 4, 2) for i in range(10)], |
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"dict":{ |
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"list": [torch.rand(6, 4, 2) for i in range(10)], |
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"tensor": torch.rand((3, 4)) |
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}, |
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"int": 2, |
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"string": "test string" |
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} |
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res = torch2paddle(torch_dict) |
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assert isinstance(res, dict) |
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self.check_paddle_tensor(res["tensor"], "cpu", True) |
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assert isinstance(res["list"], list) |
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for t in res["list"]: |
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self.check_paddle_tensor(t, "cpu", True) |
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assert isinstance(res["int"], int) |
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assert isinstance(res["string"], str) |
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assert isinstance(res["dict"], dict) |
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assert isinstance(res["dict"]["list"], list) |
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for t in res["dict"]["list"]: |
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self.check_paddle_tensor(t, "cpu", True) |
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self.check_paddle_tensor(res["dict"]["tensor"], "cpu", True) |
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############################################################################ |
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# |
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# 测试jittor到torch的转换 |
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# |
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############################################################################ |
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class TestJittor2Torch: |
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def check_torch_tensor(self, tensor, device, requires_grad): |
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""" |
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检查得到的torch张量的工具函数 |
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""" |
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assert isinstance(tensor, torch.Tensor) |
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if device == "cpu": |
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assert not tensor.is_cuda |
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else: |
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assert tensor.device == torch.device(device) |
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assert tensor.requires_grad == requires_grad |
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def test_var_transfer(self): |
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""" |
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测试单个Jittor Var的转换 |
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""" |
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jittor_var = jittor.rand((3, 4, 5)) |
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res = jittor2torch(jittor_var) |
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self.check_torch_tensor(res, "cpu", True) |
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res = jittor2torch(jittor_var, target_device="cuda:2", no_gradient=None) |
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self.check_torch_tensor(res, "cuda:2", True) |
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res = jittor2torch(jittor_var, target_device="cuda:2", no_gradient=True) |
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self.check_torch_tensor(res, "cuda:2", False) |
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res = jittor2torch(jittor_var, target_device="cuda:2", no_gradient=False) |
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self.check_torch_tensor(res, "cuda:2", True) |
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def test_var_list_transfer(self): |
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""" |
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测试Jittor列表的转换 |
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""" |
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jittor_list = [jittor.rand((6, 4, 2)) for i in range(10)] |
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res = jittor2torch(jittor_list) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_torch_tensor(t, "cpu", True) |
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res = jittor2torch(jittor_list, target_device="cuda:1", no_gradient=False) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_torch_tensor(t, "cuda:1", True) |
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def test_var_tuple_transfer(self): |
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""" |
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测试Jittor变量元组的转换 |
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""" |
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jittor_list = [jittor.rand((6, 4, 2)) for i in range(10)] |
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jittor_tuple = tuple(jittor_list) |
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res = jittor2torch(jittor_tuple, target_device="cpu") |
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assert isinstance(res, tuple) |
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for t in res: |
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self.check_torch_tensor(t, "cpu", True) |
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def test_dict_transfer(self): |
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""" |
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测试字典结构的转换 |
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""" |
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jittor_dict = { |
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"tensor": jittor.rand((3, 4)), |
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"list": [jittor.rand(6, 4, 2) for i in range(10)], |
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"dict":{ |
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"list": [jittor.rand(6, 4, 2) for i in range(10)], |
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"tensor": jittor.rand((3, 4)) |
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}, |
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"int": 2, |
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"string": "test string" |
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} |
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res = jittor2torch(jittor_dict) |
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assert isinstance(res, dict) |
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self.check_torch_tensor(res["tensor"], "cpu", True) |
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assert isinstance(res["list"], list) |
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for t in res["list"]: |
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self.check_torch_tensor(t, "cpu", True) |
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assert isinstance(res["int"], int) |
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assert isinstance(res["string"], str) |
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assert isinstance(res["dict"], dict) |
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assert isinstance(res["dict"]["list"], list) |
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for t in res["dict"]["list"]: |
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self.check_torch_tensor(t, "cpu", True) |
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self.check_torch_tensor(res["dict"]["tensor"], "cpu", True) |
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############################################################################ |
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# |
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# 测试torch到jittor的转换 |
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# |
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############################################################################ |
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class TestTorch2Jittor: |
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def check_jittor_var(self, var, requires_grad): |
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""" |
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检查得到的Jittor Var梯度情况的工具函数 |
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""" |
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assert isinstance(var, jittor.Var) |
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assert var.requires_grad == requires_grad |
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def test_gradient(self): |
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""" |
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测试反向传播的梯度 |
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""" |
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x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], requires_grad=True) |
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y = torch2jittor(x) |
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z = 3 * (y ** 2) |
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grad = jittor.grad(z, y) |
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assert grad.tolist() == [6.0, 12.0, 18.0, 24.0, 30.0] |
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def test_tensor_transfer(self): |
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""" |
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测试单个张量转换为Jittor |
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""" |
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torch_tensor = torch.rand((3, 4, 5)) |
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res = torch2jittor(torch_tensor) |
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self.check_jittor_var(res, False) |
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res = torch2jittor(torch_tensor, no_gradient=None) |
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self.check_jittor_var(res, False) |
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res = torch2jittor(torch_tensor, no_gradient=True) |
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self.check_jittor_var(res, False) |
|
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|
|
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res = torch2jittor(torch_tensor, no_gradient=False) |
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self.check_jittor_var(res, True) |
|
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|
|
|
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def test_tensor_list_transfer(self): |
|
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""" |
|
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测试张量列表的转换 |
|
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|
""" |
|
|
|
|
|
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|
torch_list = [torch.rand((6, 4, 2)) for i in range(10)] |
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res = torch2jittor(torch_list) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_jittor_var(t, False) |
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res = torch2jittor(torch_list, no_gradient=False) |
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assert isinstance(res, list) |
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for t in res: |
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self.check_jittor_var(t, True) |
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|
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def test_tensor_tuple_transfer(self): |
|
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|
""" |
|
|
|
测试张量元组的转换 |
|
|
|
""" |
|
|
|
|
|
|
|
torch_list = [torch.rand((6, 4, 2)) for i in range(10)] |
|
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|
torch_tuple = tuple(torch_list) |
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|
|
res = torch2jittor(torch_tuple) |
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|
assert isinstance(res, tuple) |
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|
for t in res: |
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|
self.check_jittor_var(t, False) |
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|
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|
|
def test_dict_transfer(self): |
|
|
|
""" |
|
|
|
测试字典结构的转换 |
|
|
|
""" |
|
|
|
|
|
|
|
torch_dict = { |
|
|
|
"tensor": torch.rand((3, 4)), |
|
|
|
"list": [torch.rand(6, 4, 2) for i in range(10)], |
|
|
|
"dict":{ |
|
|
|
"list": [torch.rand(6, 4, 2) for i in range(10)], |
|
|
|
"tensor": torch.rand((3, 4)) |
|
|
|
}, |
|
|
|
"int": 2, |
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|
|
"string": "test string" |
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|
|
} |
|
|
|
res = torch2jittor(torch_dict) |
|
|
|
assert isinstance(res, dict) |
|
|
|
self.check_jittor_var(res["tensor"], False) |
|
|
|
assert isinstance(res["list"], list) |
|
|
|
for t in res["list"]: |
|
|
|
self.check_jittor_var(t, False) |
|
|
|
assert isinstance(res["int"], int) |
|
|
|
assert isinstance(res["string"], str) |
|
|
|
assert isinstance(res["dict"], dict) |
|
|
|
assert isinstance(res["dict"]["list"], list) |
|
|
|
for t in res["dict"]["list"]: |
|
|
|
self.check_jittor_var(t, False) |
|
|
|
self.check_jittor_var(res["dict"]["tensor"], False) |