Nigel
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Gpu Minimum & Maximum kernels support int32

This commit is contained in:
wilfChen 2020-06-19 17:11:04 +08:00
parent b3f91a4f22
commit 480bf4151b
5 changed files with 70 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
mindspore/ccsrc/kernel/gpu/cuda_impl/broadcast_grad_impl.cu
View File

@ -110,7 +110,13 @@ void NoBroadcastGrad(const int &nums, enum BroadcastGradOpType op, const T *x1,
template void NoBroadcastGrad(const int &nums, enum BroadcastGradOpType op, const float *x1, const float *x2,
const float *dy, float *dx1, float *dx2, cudaStream_t stream);
template void NoBroadcastGrad(const int &nums, enum BroadcastGradOpType op, const int *x1, const int *x2,
const int *dy, int *dx1, int *dx2, cudaStream_t stream);
template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
enum BroadcastGradOpType op, const float *x1, const float *x2, const float *dy, float *dx1,
float *dx2, cudaStream_t stream);
template void BroadcastGrad(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
enum BroadcastGradOpType op, const int *x1, const int *x2, const int *dy, int *dx1,
int *dx2, cudaStream_t stream);

6
mindspore/ccsrc/kernel/gpu/math/broadcast_gpu_kernel.cc
View File

@ -90,5 +90,11 @@ MS_REG_GPU_KERNEL_TWO(
MS_REG_GPU_KERNEL_TWO(
TensorAdd, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
BroadcastOpGpuKernel, int, int)
MS_REG_GPU_KERNEL_TWO(
Minimum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
BroadcastOpGpuKernel, int, int)
MS_REG_GPU_KERNEL_TWO(
Maximum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
BroadcastOpGpuKernel, int, int)
} // namespace kernel
} // namespace mindspore

16
mindspore/ccsrc/kernel/gpu/math/broadcast_grad_gpu_kernel.cc
View File

@ -34,5 +34,21 @@ MS_REG_GPU_KERNEL_ONE(MaximumGrad,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
BroadcastOpGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(MinimumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
BroadcastOpGradGpuKernel, int)
MS_REG_GPU_KERNEL_ONE(MaximumGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
BroadcastOpGradGpuKernel, int)
} // namespace kernel
} // namespace mindspore

24
tests/st/ops/gpu/test_maximum_op.py
View File

@ -222,3 +222,27 @@ def test_broadcast_diff_dims():
output_ms = net(Tensor(x1_np), Tensor(x2_np), Tensor(dy_np))
assert np.allclose(output_ms[0].asnumpy(), expect_dx1)
assert np.allclose(output_ms[1].asnumpy(), expect_dx2)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_maximum_int():
x = Tensor(np.array([[1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2]]).astype(np.int32))
expect = [[2, 2, 3]]
error = np.ones(shape=[1, 3]) * 1.0e-5
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
max_op = Net()
output = max_op(x, y)
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
max_op_2 = Net()
output = max_op_2(x, y)
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)

18
tests/st/ops/gpu/test_minimum_op.py
View File

@ -218,3 +218,21 @@ def test_broadcast_diff_dims():
output_ms = net(Tensor(x1_np), Tensor(x2_np), Tensor(dy_np))
assert np.allclose(output_ms[0].asnumpy(), expect_dx1)
assert np.allclose(output_ms[1].asnumpy(), expect_dx2)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_broadcast_int32():
context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target='GPU')
x1_np = np.random.rand(3, 4).astype(np.int32)
x2_np = np.random.rand(3, 4).astype(np.int32)
dy_np = np.random.rand(3, 4).astype(np.int32)
net = Grad(MinimumNet())
output_ms = net(Tensor(x1_np), Tensor(x2_np), Tensor(dy_np))
output0_np = np.where(x1_np < x2_np, dy_np, 0)
output1_np = np.where(x1_np < x2_np, 0, dy_np)
assert np.allclose(output_ms[0].asnumpy(), output0_np)
assert np.allclose(output_ms[1].asnumpy(), output1_np)