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Make transpose, trace, kron, reshape, sum op support complex type (#29321)

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* add complex64 and complex128 type; add +-*/@ and slice opreator for complex types

* add test cases for complex elementwise, matmul and getitem unittest

* add test cases for complex types

* add test cases for complex matmul unittest

* kron, reshape, transpose support complex types

* sum and trace op support complex types

* add test case of sum and trace op

* fix the bug of imag part of complex not initialized

* format file

* format code style

* kron support type promotion; modify test cases
revert-31562-mean
chentianyu03 4 years ago committed by GitHub
parent 66fd1c00a0
commit 879e913b6d
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 360 additions and 137 deletions
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32
paddle/fluid/operators/kron_op.cc
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@ -18,6 +18,8 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/operators/kron_op.h"
#include "paddle/fluid/platform/complex128.h"
#include "paddle/fluid/platform/complex64.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
@ -51,8 +53,22 @@ class KronOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
auto data_type =
OperatorWithKernel::IndicateOrPromoteVarDataTypes(ctx, "X", "Y");
return framework::OpKernelType(data_type, ctx.GetPlace());
}
framework::OpKernelType GetKernelTypeForVar(
const std::string& var_name, const framework::Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const {
if (framework::IsComplexType(expected_kernel_type.data_type_)) {
// only promote inputs’s types when contains complex input
return framework::OpKernelType(tensor.type(), tensor.place(),
tensor.layout());
} else {
return framework::OpKernelType(expected_kernel_type.data_type_,
tensor.place(), tensor.layout());
}
}
};
@ -154,7 +170,11 @@ REGISTER_OP_CPU_KERNEL(
ops::KronKernel<paddle::platform::CPUDeviceContext,
paddle::platform::float16>,
ops::KronKernel<paddle::platform::CPUDeviceContext, int>,
ops::KronKernel<paddle::platform::CPUDeviceContext, int64_t>);
ops::KronKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::KronKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::KronKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);
REGISTER_OPERATOR(kron_grad, ops::KronGradOp);
REGISTER_OP_CPU_KERNEL(
@ -163,4 +183,8 @@ REGISTER_OP_CPU_KERNEL(
ops::KronGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::float16>,
ops::KronGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::KronGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
ops::KronGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::KronGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::KronGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);

14
paddle/fluid/operators/kron_op.cu
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@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/kron_op.h"
#include "paddle/fluid/platform/complex128.h"
#include "paddle/fluid/platform/complex64.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
@ -22,7 +24,11 @@ REGISTER_OP_CUDA_KERNEL(
ops::KronKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::KronKernel<paddle::platform::CUDADeviceContext, int>,
ops::KronKernel<paddle::platform::CUDADeviceContext, int64_t>);
ops::KronKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::KronKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex64>,
ops::KronKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CUDA_KERNEL(
kron_grad, ops::KronGradKernel<paddle::platform::CUDADeviceContext, float>,
@ -30,4 +36,8 @@ REGISTER_OP_CUDA_KERNEL(
ops::KronGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::KronGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::KronGradKernel<paddle::platform::CUDADeviceContext, int64_t>);
ops::KronGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::KronGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex64>,
ops::KronGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);

10
paddle/fluid/operators/reduce_ops/reduce_sum_op.cc
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@ -115,6 +115,12 @@ REGISTER_OP_CPU_KERNEL(
ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CPUDeviceContext, int, ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CPUDeviceContext, int64_t,
ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64, ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128,
ops::SumFunctor>);
template <typename T>
@ -125,4 +131,6 @@ using CPUReduceSumGradKernel =
REGISTER_OP_CPU_KERNEL(reduce_sum_grad, CPUReduceSumGradKernel<float>,
CPUReduceSumGradKernel<double>,
CPUReduceSumGradKernel<int>,
CPUReduceSumGradKernel<int64_t>);
CPUReduceSumGradKernel<int64_t>,
CPUReduceSumGradKernel<paddle::platform::complex64>,
CPUReduceSumGradKernel<paddle::platform::complex128>);

4
paddle/fluid/operators/reduce_ops/reduce_sum_op.cu
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@ -72,4 +72,6 @@ class ReduceSumKernel : public framework::OpKernel<T> {
REGISTER_OP_CUDA_KERNEL(reduce_sum, ops::ReduceSumKernel<float>,
ops::ReduceSumKernel<double>, ops::ReduceSumKernel<int>,
ops::ReduceSumKernel<int64_t>);
ops::ReduceSumKernel<int64_t>,
ops::ReduceSumKernel<paddle::platform::complex64>,
ops::ReduceSumKernel<paddle::platform::complex128>);

80
paddle/fluid/operators/reshape_op.cc
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@ -618,26 +618,26 @@ REGISTER_OPERATOR(reshape2_grad_grad, ops::Reshape2DoubleGradOp,
ops::ReshapeDoubleGradInplaceInferer,
ops::ReshapeDoubleGradOpNoNeedBufferVarInferer);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int8_t, ops::ReshapeKernel,
uint8_t, ops::ReshapeKernel, int,
ops::ReshapeKernel, int64_t, ops::ReshapeKernel,
bool, ops::ReshapeKernel,
paddle::platform::bfloat16, ops::ReshapeKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, uint8_t,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel, bool,
ops::ReshapeGradKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad_grad, float,
ops::ReshapeDoubleGradKernel, double,
ops::ReshapeDoubleGradKernel, int,
ops::ReshapeDoubleGradKernel, uint8_t,
ops::ReshapeDoubleGradKernel, int64_t,
ops::ReshapeDoubleGradKernel, bool,
ops::ReshapeDoubleGradKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(
reshape2, float, ops::ReshapeKernel, double, ops::ReshapeKernel, int8_t,
ops::ReshapeKernel, uint8_t, ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel, bool, ops::ReshapeKernel,
paddle::platform::bfloat16, ops::ReshapeKernel, paddle::platform::complex64,
ops::ReshapeKernel, paddle::platform::complex128, ops::ReshapeKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(
reshape2_grad, float, ops::ReshapeGradKernel, double,
ops::ReshapeGradKernel, int, ops::ReshapeGradKernel, uint8_t,
ops::ReshapeGradKernel, int64_t, ops::ReshapeGradKernel, bool,
ops::ReshapeGradKernel, paddle::platform::complex64, ops::ReshapeGradKernel,
paddle::platform::complex128, ops::ReshapeGradKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(
reshape2_grad_grad, float, ops::ReshapeDoubleGradKernel, double,
ops::ReshapeDoubleGradKernel, int, ops::ReshapeDoubleGradKernel, uint8_t,
ops::ReshapeDoubleGradKernel, int64_t, ops::ReshapeDoubleGradKernel, bool,
ops::ReshapeDoubleGradKernel, paddle::platform::complex64,
ops::ReshapeDoubleGradKernel, paddle::platform::complex128,
ops::ReshapeDoubleGradKernel);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
@ -656,34 +656,38 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
uint8_t, ops::ReshapeKernel, int64_t,
ops::ReshapeKernel, plat::float16,
ops::ReshapeKernel, bool, ops::ReshapeKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, uint8_t,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel, plat::float16,
ops::ReshapeGradKernel, bool,
ops::ReshapeGradKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad_grad, float,
ops::ReshapeDoubleGradKernel, double,
ops::ReshapeDoubleGradKernel, int,
ops::ReshapeDoubleGradKernel, uint8_t,
ops::ReshapeDoubleGradKernel, int64_t,
ops::ReshapeDoubleGradKernel, plat::float16,
ops::ReshapeDoubleGradKernel, bool,
ops::ReshapeDoubleGradKernel);
ops::ReshapeKernel, bool, ops::ReshapeKernel,
plat::complex64, ops::ReshapeKernel,
plat::complex128, ops::ReshapeKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(
reshape2_grad, float, ops::ReshapeGradKernel, double,
ops::ReshapeGradKernel, int, ops::ReshapeGradKernel, uint8_t,
ops::ReshapeGradKernel, int64_t, ops::ReshapeGradKernel, plat::float16,
ops::ReshapeGradKernel, bool, ops::ReshapeGradKernel, plat::complex64,
ops::ReshapeGradKernel, plat::complex128, ops::ReshapeGradKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(
reshape2_grad_grad, float, ops::ReshapeDoubleGradKernel, double,
ops::ReshapeDoubleGradKernel, int, ops::ReshapeDoubleGradKernel, uint8_t,
ops::ReshapeDoubleGradKernel, int64_t, ops::ReshapeDoubleGradKernel,
plat::float16, ops::ReshapeDoubleGradKernel, bool,
ops::ReshapeDoubleGradKernel, plat::complex64, ops::ReshapeDoubleGradKernel,
plat::complex128, ops::ReshapeDoubleGradKernel);
#endif
#ifdef PADDLE_WITH_XPU
REGISTER_OP_XPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel, plat::float16,
ops::ReshapeKernel, bool, ops::ReshapeKernel);
ops::ReshapeKernel, bool, ops::ReshapeKernel,
plat::complex64, ops::ReshapeKernel,
plat::complex128, ops::ReshapeKernel);
REGISTER_OP_XPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel, plat::float16,
ops::ReshapeGradKernel, bool,
ops::ReshapeGradKernel, plat::complex64,
ops::ReshapeGradKernel, plat::complex128,
ops::ReshapeGradKernel);
#endif

12
paddle/fluid/operators/trace_op.cc
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@ -163,9 +163,17 @@ REGISTER_OP_CPU_KERNEL(
trace, ops::TraceKernel<paddle::platform::CPUDeviceContext, int>,
ops::TraceKernel<paddle::platform::CPUDeviceContext, float>,
ops::TraceKernel<paddle::platform::CPUDeviceContext, double>,
ops::TraceKernel<paddle::platform::CPUDeviceContext, int64_t>);
ops::TraceKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::TraceKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::TraceKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CPU_KERNEL(
trace_grad, ops::TraceGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::TraceGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TraceGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::TraceGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
ops::TraceGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::TraceGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::TraceGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);

12
paddle/fluid/operators/trace_op.cu
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@ -60,11 +60,19 @@ REGISTER_OP_CUDA_KERNEL(
ops::TraceCUDAKernel<paddle::platform::CUDADeviceContext,
platform::float16>,
ops::TraceCUDAKernel<paddle::platform::CUDADeviceContext, float>,
ops::TraceCUDAKernel<paddle::platform::CUDADeviceContext, double>);
ops::TraceCUDAKernel<paddle::platform::CUDADeviceContext, double>,
ops::TraceCUDAKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex64>,
ops::TraceCUDAKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CUDA_KERNEL(
trace_grad, ops::TraceGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::TraceGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::TraceGradKernel<paddle::platform::CUDADeviceContext,
platform::float16>,
ops::TraceGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::TraceGradKernel<paddle::platform::CUDADeviceContext, double>);
ops::TraceGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::TraceGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex64>,
ops::TraceGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);

24
paddle/fluid/operators/transpose_op.cc
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@ -321,11 +321,19 @@ REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CPU_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);
REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
ops::Transpose2GradMaker<paddle::framework::OpDesc>,
@ -336,10 +344,18 @@ REGISTER_OP_CPU_KERNEL(
transpose2, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, int32_t>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CPU_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, int32_t>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex64>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex128>);

22
paddle/fluid/operators/transpose_op.cu
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@ -730,14 +730,21 @@ REGISTER_OP_CUDA_KERNEL(
transpose,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, plat::float16>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
paddle::platform::complex64>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CUDA_KERNEL(
transpose_grad,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
plat::float16>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex64>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CUDA_KERNEL(
transpose2,
@ -745,8 +752,11 @@ REGISTER_OP_CUDA_KERNEL(
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext, plat::float16>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
paddle::platform::complex64>,
ops::TransposeGPUKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);
REGISTER_OP_CUDA_KERNEL(
transpose2_grad,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext, int32_t>,
@ -754,4 +764,8 @@ REGISTER_OP_CUDA_KERNEL(
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext, double>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
plat::float16>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex64>,
ops::TransposeGradGPUKernel<paddle::platform::CUDADeviceContext,
paddle::platform::complex128>);

1
paddle/fluid/platform/complex64.h
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@ -124,6 +124,7 @@ struct PADDLE_ALIGN(8) complex64 {
HOSTDEVICE inline complex64& operator=(int32_t val) {
real = static_cast<float>(val);
imag = 0;
return *this;
}

74
python/paddle/fluid/tests/unittests/test_complex_kron.py
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@ -27,42 +27,68 @@ class ComplexKronTestCase(unittest.TestCase):
def setUp(self):
self.ref_result = np.kron(self.x, self.y)
self._places = [paddle.CPUPlace()]
if fluid.is_compiled_with_cuda():
self._places.append(paddle.CUDAPlace(0))
def runTest(self):
place = fluid.CPUPlace()
self.test_identity(place)
if fluid.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
self.test_identity(place)
for place in self._places:
self.test_complex_api(place)
self.test_basic_api(place)
def test_identity(self, place):
def test_complex_api(self, place):
with dg.guard(place):
x_var = dg.to_variable(self.x)
y_var = dg.to_variable(self.y)
out_var = paddle.complex.kron(x_var, y_var)
np.testing.assert_allclose(out_var.numpy(), self.ref_result)
self.assertTrue(np.allclose(out_var.numpy(), self.ref_result))
def test_basic_api(self, place):
with dg.guard(place):
x_var = paddle.Tensor(
value=self.x,
place=place,
persistable=False,
zero_copy=None,
stop_gradient=True)
y_var = paddle.Tensor(
value=self.y,
place=place,
persistable=False,
zero_copy=None,
stop_gradient=True)
out_var = tensor.math.kron(x_var, y_var)
self.assertTrue(np.allclose(out_var.numpy(), self.ref_result))
def load_tests(loader, standard_tests, pattern):
suite = unittest.TestSuite()
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2) + 1j * np.random.randn(2, 2),
y=np.random.randn(3, 3) + 1j * np.random.randn(3, 3)))
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2),
y=np.random.randn(3, 3) + 1j * np.random.randn(3, 3)))
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2) + 1j * np.random.randn(2, 2),
y=np.random.randn(3, 3)))
for dtype in ["float32", "float64"]:
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2).astype(dtype) + 1j * np.random.randn(
2, 2).astype(dtype),
y=np.random.randn(3, 3).astype(dtype) + 1j * np.random.randn(
3, 3).astype(dtype)))
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2).astype(dtype),
y=np.random.randn(3, 3).astype(dtype) + 1j * np.random.randn(
3, 3).astype(dtype)))
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2).astype(dtype) + 1j * np.random.randn(
2, 2).astype(dtype),
y=np.random.randn(3, 3).astype(dtype)))
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2).astype(dtype) + 1j * np.random.randn(
2, 2).astype(dtype),
y=np.random.randn(2, 2, 3).astype(dtype)))
suite.addTest(
ComplexKronTestCase(
x=np.random.randn(2, 2) + 1j * np.random.randn(2, 2),
y=np.random.randn(2, 2, 3)))
return suite

84
python/paddle/fluid/tests/unittests/test_complex_reshape.py
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@ -13,6 +13,7 @@
# limitations under the License.
import paddle.fluid as fluid
import paddle
from paddle import complex as cpx
import paddle.fluid.dygraph as dg
import numpy as np
@ -20,31 +21,72 @@ import unittest
class TestComplexReshape(unittest.TestCase):
def setUp(self):
self._dtypes = ["float32", "float64"]
self._places = [paddle.CPUPlace()]
if fluid.core.is_compiled_with_cuda():
self._places.append(paddle.CUDAPlace(0))
def test_case1(self):
x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
shape = (2, -1)
for dtype in self._dtypes:
x_np = np.random.randn(
2, 3, 4).astype(dtype) + 1j * np.random.randn(2, 3,
4).astype(dtype)
shape = (2, -1)
for place in self._places:
with dg.guard(place):
x_var = dg.to_variable(x_np)
y_var = cpx.reshape(x_var, shape)
y_np = y_var.numpy()
np.testing.assert_allclose(np.reshape(x_np, shape), y_np)
def test_case2(self):
for dtype in self._dtypes:
x_np = np.random.randn(
2, 3, 4).astype(dtype) + 1j * np.random.randn(2, 3,
4).astype(dtype)
shape = (0, -1)
shape_ = (2, 12)
for place in self._places:
with dg.guard(place):
x_var = dg.to_variable(x_np)
y_var = cpx.reshape(x_var, shape, inplace=True)
y_np = y_var.numpy()
np.testing.assert_allclose(np.reshape(x_np, shape_), y_np)
place = fluid.CPUPlace()
with dg.guard(place):
x_var = dg.to_variable(x_np)
y_var = cpx.reshape(x_var, shape)
y_np = y_var.numpy()
def test_case3(self):
for dtype in self._dtypes:
x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
shape = (2, -1)
for place in self._places:
with dg.guard(place):
x_var = paddle.Tensor(
value=x_np,
place=fluid.framework._current_expected_place(),
persistable=False,
zero_copy=None,
stop_gradient=True)
y_var = fluid.layers.reshape(x_var, shape)
y_np = y_var.numpy()
np.testing.assert_allclose(np.reshape(x_np, shape), y_np)
np.testing.assert_allclose(np.reshape(x_np, shape), y_np)
def test_case4(self):
for dtype in self._dtypes:
x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
shape = (0, -1)
shape_ = (2, 12)
def test_case2(self):
x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
shape = (0, -1)
shape_ = (2, 12)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
with dg.guard(place):
x_var = dg.to_variable(x_np)
y_var = cpx.reshape(x_var, shape, inplace=True)
y_np = y_var.numpy()
np.testing.assert_allclose(np.reshape(x_np, shape_), y_np)
for place in self._places:
with dg.guard(place):
x_var = paddle.Tensor(
value=x_np,
place=fluid.framework._current_expected_place(),
persistable=False,
zero_copy=None,
stop_gradient=True)
y_var = fluid.layers.reshape(x_var, shape)
y_np = y_var.numpy()
np.testing.assert_allclose(np.reshape(x_np, shape_), y_np)
if __name__ == "__main__":

41
python/paddle/fluid/tests/unittests/test_complex_sum_layer.py
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@ -14,28 +14,47 @@
import unittest
import numpy as np
import paddle
from numpy.random import random as rand
from paddle import complex as cpx
from paddle import tensor
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
class TestComplexSumLayer(unittest.TestCase):
def setUp(self):
self._dtype = "float64"
self._places = [fluid.CPUPlace()]
self._dtypes = ["float32", "float64"]
self._places = [paddle.CPUPlace()]
if fluid.core.is_compiled_with_cuda():
self._places.append(fluid.CUDAPlace(0))
self._places.append(paddle.CUDAPlace(0))
def test_complex_x(self):
input = rand([2, 10, 10]).astype(self._dtype) + 1j * rand(
[2, 10, 10]).astype(self._dtype)
for place in self._places:
with dg.guard(place):
var_x = dg.to_variable(input)
result = cpx.sum(var_x, dim=[1, 2]).numpy()
target = np.sum(input, axis=(1, 2))
self.assertTrue(np.allclose(result, target))
for dtype in self._dtypes:
input = rand([2, 10, 10]).astype(dtype) + 1j * rand(
[2, 10, 10]).astype(dtype)
for place in self._places:
with dg.guard(place):
var_x = dg.to_variable(input)
result = cpx.sum(var_x, dim=[1, 2]).numpy()
target = np.sum(input, axis=(1, 2))
self.assertTrue(np.allclose(result, target))
def test_complex_basic_api(self):
for dtype in self._dtypes:
input = rand([2, 10, 10]).astype(dtype) + 1j * rand(
[2, 10, 10]).astype(dtype)
for place in self._places:
with dg.guard(place):
var_x = paddle.Tensor(
value=input,
place=place,
persistable=False,
zero_copy=None,
stop_gradient=True)
result = tensor.sum(var_x, axis=[1, 2]).numpy()
target = np.sum(input, axis=(1, 2))
self.assertTrue(np.allclose(result, target))
if __name__ == '__main__':

41
python/paddle/fluid/tests/unittests/test_complex_trace_layer.py
Unescape View File

@ -14,28 +14,49 @@
import unittest
import numpy as np
import paddle
from numpy.random import random as rand
from paddle import complex as cpx
from paddle import tensor
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
class TestComplexTraceLayer(unittest.TestCase):
def setUp(self):
self._dtype = "float64"
self._dtypes = ["float32", "float64"]
self._places = [fluid.CPUPlace()]
if fluid.core.is_compiled_with_cuda():
self._places.append(fluid.CUDAPlace(0))
def test_complex_x(self):
input = rand([2, 20, 2, 3]).astype(self._dtype) + 1j * rand(
[2, 20, 2, 3]).astype(self._dtype)
for place in self._places:
with dg.guard(place):
var_x = dg.to_variable(input)
result = cpx.trace(var_x, offset=1, axis1=0, axis2=2).numpy()
target = np.trace(input, offset=1, axis1=0, axis2=2)
self.assertTrue(np.allclose(result, target))
def test_complex_api(self):
for dtype in self._dtypes:
input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
[2, 20, 2, 3]).astype(dtype)
for place in self._places:
with dg.guard(place):
var_x = dg.to_variable(input)
result = cpx.trace(
var_x, offset=1, axis1=0, axis2=2).numpy()
target = np.trace(input, offset=1, axis1=0, axis2=2)
self.assertTrue(np.allclose(result, target))
def test_basic_api(self):
for dtype in self._dtypes:
input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
[2, 20, 2, 3]).astype(dtype)
for place in self._places:
with dg.guard(place):
var_x = paddle.Tensor(
value=input,
place=place,
persistable=False,
zero_copy=None,
stop_gradient=True)
result = tensor.trace(
var_x, offset=1, axis1=0, axis2=2).numpy()
target = np.trace(input, offset=1, axis1=0, axis2=2)
self.assertTrue(np.allclose(result, target))
if __name__ == '__main__':

46
python/paddle/fluid/tests/unittests/test_complex_transpose.py
Unescape View File

@ -21,21 +21,41 @@ import paddle.fluid.dygraph as dg
class TestComplexTransposeLayer(unittest.TestCase):
def setUp(self):
self._places = [fluid.CPUPlace()]
self._dtypes = ["float32", "float64"]
self._places = [paddle.CPUPlace()]
if fluid.core.is_compiled_with_cuda():
self._places.append(fluid.CUDAPlace(0))
self._places.append(paddle.CUDAPlace(0))
def test_identity(self):
data = np.random.random(
(2, 3, 4, 5)).astype("float32") + 1J * np.random.random(
(2, 3, 4, 5)).astype("float32")
perm = [3, 2, 0, 1]
np_trans = np.transpose(data, perm)
for place in self._places:
with dg.guard(place):
var = dg.to_variable(data)
trans = paddle.complex.transpose(var, perm=perm)
self.assertTrue(np.allclose(trans.numpy(), np_trans))
def test_transpose_by_complex_api(self):
for dtype in self._dtypes:
data = np.random.random(
(2, 3, 4, 5)).astype(dtype) + 1J * np.random.random(
(2, 3, 4, 5)).astype(dtype)
perm = [3, 2, 0, 1]
np_trans = np.transpose(data, perm)
for place in self._places:
with dg.guard(place):
var = dg.to_variable(data)
trans = paddle.complex.transpose(var, perm=perm)
self.assertTrue(np.allclose(trans.numpy(), np_trans))
def test_transpose_by_basic_api(self):
for dtype in self._dtypes:
data = np.random.random(
(2, 3, 4, 5)).astype(dtype) + 1J * np.random.random(
(2, 3, 4, 5)).astype(dtype)
perm = [3, 2, 0, 1]
np_trans = np.transpose(data, perm)
for place in self._places:
with dg.guard(place):
var = paddle.Tensor(
value=data,
place=place,
persistable=False,
zero_copy=None,
stop_gradient=True)
trans = paddle.transpose(var, perm=perm)
self.assertTrue(np.allclose(trans.numpy(), np_trans))
if __name__ == '__main__':

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