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!4298 Execution MaxmimumGrad on demand 

Merge pull request !4298 from chenweifeng/maximumgrad
This commit is contained in:
mindspore-ci-bot 2020-08-12 10:21:09 +08:00 committed by Gitee
parent 85364d59c2 89ce0bdb78
commit 6e3c87be46
3 changed files with 61 additions and 44 deletions
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77
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/broadcast_grad_impl.cu
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@ -19,10 +19,11 @@
template <typename T>
struct MinimumGradFunc {
__device__ __forceinline__ void operator()(const T &x1, const T &x2, const T &dy, T *dx1, T *dx2) {
if (x1 < x2) {
__device__ __forceinline__ void operator()(const T &x1, const T &x2, const bool &grad_x1, const bool &grad_x2,
const T &dy, T *dx1, T *dx2) {
if (grad_x1 && x1 < x2) {
atomicAdd(dx1, dy);
} else {
} else if (grad_x2 && x1 >= x2) {
atomicAdd(dx2, dy);
}
}
@ -30,10 +31,11 @@ struct MinimumGradFunc {
template <typename T>
struct MaximumGradFunc {
__device__ __forceinline__ void operator()(const T &x1, const T &x2, const T &dy, T *dx1, T *dx2) {
if (x1 > x2) {
__device__ __forceinline__ void operator()(const T &x1, const T &x2, const bool &grad_x1, const bool &grad_x2,
const T &dy, T *dx1, T *dx2) {
if (grad_x1 && x1 > x2) {
atomicAdd(dx1, dy);
} else {
} else if (grad_x2 && x1 <= x2) {
atomicAdd(dx2, dy);
}
}
@ -45,7 +47,8 @@ template <typename T, typename Func>
__device__ __forceinline__ void BroadcastGradOperator(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,
const T *x1, const T *x2, const T *dy, T *dx1, T *dx2) {
const bool &grad_x1, const bool &grad_x2, const T *x1,
const T *x2, const T *dy, T *dx1, T *dx2) {
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < d0 * d1 * d2 * d3; pos += blockDim.x * gridDim.x) {
int i = pos / (d1 * d2 * d3) % d0;
int j = pos / (d2 * d3) % d1;
@ -54,69 +57,71 @@ __device__ __forceinline__ void BroadcastGradOperator(const int &l0, const int &
int l_index = Index(i, l0) * l1 * l2 * l3 + Index(j, l1) * l2 * l3 + Index(k, l2) * l3 + Index(l, l3);
int r_index = Index(i, r0) * r1 * r2 * r3 + Index(j, r1) * r2 * r3 + Index(k, r2) * r3 + Index(l, r3);
Func()(x1[l_index], x2[r_index], dy[pos], dx1 + l_index, dx2 + r_index);
Func()(x1[l_index], x2[r_index], grad_x1, grad_x2, dy[pos], dx1 + l_index, dx2 + r_index);
}
}
template <typename T>
__global__ void BroadcastGradKernel(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 T *x1, const T *x2, const T *dy, T *dx1,
T *dx2) {
const bool grad_x1, const bool grad_x2, enum BroadcastGradOpType op, const T *x1,
const T *x2, const T *dy, T *dx1, T *dx2) {
switch (op) {
case BROADCAST_GRAD_TYPE_MINIMUM:
return BroadcastGradOperator<T, MinimumGradFunc<T>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, x1, x2, dy,
dx1, dx2);
return BroadcastGradOperator<T, MinimumGradFunc<T>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, grad_x1,
grad_x2, x1, x2, dy, dx1, dx2);
case BROADCAST_GRAD_TYPE_MAXIMUM:
return BroadcastGradOperator<T, MaximumGradFunc<T>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, x1, x2, dy,
dx1, dx2);
return BroadcastGradOperator<T, MaximumGradFunc<T>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, grad_x1,
grad_x2, x1, x2, dy, dx1, dx2);
}
}
template <typename T>
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 T *x1, const T *x2, const T *dy, T *dx1, T *dx2,
cudaStream_t stream) {
const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const T *x1, const T *x2,
const T *dy, T *dx1, T *dx2, cudaStream_t stream) {
int size = d0 * d1 * d2 * d3;
BroadcastGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, stream>>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, op,
x1, x2, dy, dx1, dx2);
BroadcastGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, stream>>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3,
grad_x1, grad_x2, op, x1, x2, dy, dx1, dx2);
}
template <typename T, typename Func>
__device__ __forceinline__ void NoBroadcastOperator(const int &nums, const T *x1, const T *x2, const T *dy, T *dx1,
T *dx2) {
__device__ __forceinline__ void NoBroadcastOperator(const int &nums, const bool &grad_x1, const bool &grad_x2,
const T *x1, const T *x2, const T *dy, T *dx1, T *dx2) {
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < nums; pos += blockDim.x * gridDim.x) {
Func()(x1[pos], x2[pos], dy[pos], dx1 + pos, dx2 + pos);
Func()(x1[pos], x2[pos], grad_x1, grad_x2, dy[pos], dx1 + pos, dx2 + pos);
}
}
template <typename T>
__global__ void NoBroadcastGradKernel(const int nums, enum BroadcastGradOpType op, const T *x1, const T *x2,
const T *dy, T *dx1, T *dx2) {
__global__ void NoBroadcastGradKernel(const int nums, const bool grad_x1, const bool grad_x2,
enum BroadcastGradOpType op, const T *x1, const T *x2, const T *dy, T *dx1,
T *dx2) {
switch (op) {
case BROADCAST_GRAD_TYPE_MINIMUM:
return NoBroadcastOperator<T, MinimumGradFunc<T>>(nums, x1, x2, dy, dx1, dx2);
return NoBroadcastOperator<T, MinimumGradFunc<T>>(nums, grad_x1, grad_x2, x1, x2, dy, dx1, dx2);
case BROADCAST_GRAD_TYPE_MAXIMUM:
return NoBroadcastOperator<T, MaximumGradFunc<T>>(nums, x1, x2, dy, dx1, dx2);
return NoBroadcastOperator<T, MaximumGradFunc<T>>(nums, grad_x1, grad_x2, x1, x2, dy, dx1, dx2);
}
}
template <typename T>
void NoBroadcastGrad(const int &nums, enum BroadcastGradOpType op, const T *x1, const T *x2, const T *dy, T *dx1,
T *dx2, cudaStream_t stream) {
NoBroadcastGradKernel<<<GET_BLOCKS(nums), GET_THREADS, 0, stream>>>(nums, op, x1, x2, dy, dx1, dx2);
void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op,
const T *x1, const T *x2, const T *dy, T *dx1, T *dx2, cudaStream_t stream) {
NoBroadcastGradKernel<<<GET_BLOCKS(nums), GET_THREADS, 0, stream>>>(nums, grad_x1, grad_x2, op, x1, x2, dy, dx1, dx2);
}
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 NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, 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, const bool &grad_x1, const bool &grad_x2, 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);
const bool &grad_x1, const bool &grad_x2, 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);
const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const int *x1,
const int *x2, const int *dy, int *dx1, int *dx2, cudaStream_t stream);

8
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/broadcast_grad_impl.cuh
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@ -28,11 +28,11 @@ enum BroadcastGradOpType {
template <typename T>
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 T *x1, const T *x2, const T *dy, T *dx1, T *dx2,
cudaStream_t stream);
const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op, const T *x1, const T *x2,
const T *dy, T *dx1, T *dx2, cudaStream_t stream);
template <typename T>
void NoBroadcastGrad(const int &nums, enum BroadcastGradOpType op, const T *x1, const T *x2, const T *dy, T *dx1,
T *dx2, cudaStream_t stream);
void NoBroadcastGrad(const int &nums, const bool &grad_x1, const bool &grad_x2, enum BroadcastGradOpType op,
const T *x1, const T *x2, const T *dy, T *dx1, T *dx2, cudaStream_t stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BROADCAST_GRAD_H_

20
mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_grad_gpu_kernel.h
View File

@ -31,7 +31,13 @@ template <typename T>
class BroadcastOpGradGpuKernel : public GpuKernel {
public:
BroadcastOpGradGpuKernel()
: op_type_(BROADCAST_GRAD_TYPE_INVALID), need_broadcast_(false), input1_num_(1), input2_num_(1), output_num_(1) {}
: op_type_(BROADCAST_GRAD_TYPE_INVALID),
need_broadcast_(false),
input1_num_(1),
input2_num_(1),
output_num_(1),
grad_x_(false),
grad_y_(false) {}
~BroadcastOpGradGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -52,10 +58,11 @@ class BroadcastOpGradGpuKernel : public GpuKernel {
"cudaMemSet Failed");
if (need_broadcast_) {
BroadcastGrad(x1_shape_[0], x1_shape_[1], x1_shape_[2], x1_shape_[3], x2_shape_[0], x2_shape_[1], x2_shape_[2],
x2_shape_[3], dy_shape_[0], dy_shape_[1], dy_shape_[2], dy_shape_[3], op_type_, x1, x2, dy, dx1,
dx2, reinterpret_cast<cudaStream_t>(stream_ptr));
x2_shape_[3], dy_shape_[0], dy_shape_[1], dy_shape_[2], dy_shape_[3], grad_x_, grad_y_, op_type_,
x1, x2, dy, dx1, dx2, reinterpret_cast<cudaStream_t>(stream_ptr));
} else {
NoBroadcastGrad(output_num_, op_type_, x1, x2, dy, dx1, dx2, reinterpret_cast<cudaStream_t>(stream_ptr));
NoBroadcastGrad(output_num_, grad_x_, grad_y_, op_type_, x1, x2, dy, dx1, dx2,
reinterpret_cast<cudaStream_t>(stream_ptr));
}
return true;
@ -85,6 +92,9 @@ class BroadcastOpGradGpuKernel : public GpuKernel {
input2_num_ *= shape2[i];
}
grad_x_ = GetAttr<bool>(kernel_node, "grad_x");
grad_y_ = GetAttr<bool>(kernel_node, "grad_y");
InitSizeLists();
return true;
}
@ -136,6 +146,8 @@ class BroadcastOpGradGpuKernel : public GpuKernel {
int x1_shape_[4] = {1, 1, 1, 1};
int x2_shape_[4] = {1, 1, 1, 1};
int dy_shape_[4] = {1, 1, 1, 1};
bool grad_x_;
bool grad_y_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;