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