未验证 提交 ea47685f 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #14646 from jczaja/prv-softmax-mkl-sasum

Softmax for inference MKL further changes
......@@ -168,6 +168,9 @@ class Blas {
template <typename T>
void SCAL(int n, const T a, T* x) const;
template <typename T>
T ASUM(int n, T* x, int inc) const;
template <typename T>
void BatchedGEMM(CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N,
int K, T alpha, const T* A, const T* B, T beta, T* C,
......@@ -269,6 +272,11 @@ class BlasT : private Blas<DeviceContext> {
Base()->template SCAL<T>(args...);
}
template <typename... ARGS>
T ASUM(ARGS... args) const {
return Base()->template ASUM<T>(args...);
}
template <typename... ARGS>
void BatchedGEMM(ARGS... args) const {
Base()->template BatchedGEMM<T>(args...);
......
......@@ -84,6 +84,11 @@ struct CBlas<float> {
platform::dynload::cblas_sscal(args...);
}
template <typename... ARGS>
static float ASUM(ARGS... args) {
return platform::dynload::cblas_sasum(args...);
}
template <typename... ARGS>
static void GEMM_BATCH(ARGS... args) {
platform::dynload::cblas_sgemm_batch(args...);
......@@ -174,6 +179,11 @@ struct CBlas<double> {
platform::dynload::cblas_dscal(args...);
}
template <typename... ARGS>
static double ASUM(ARGS... args) {
return platform::dynload::cblas_dasum(args...);
}
template <typename... ARGS>
static void GEMM_BATCH(ARGS... args) {
platform::dynload::cblas_dgemm_batch(args...);
......@@ -268,6 +278,7 @@ struct CBlas<platform::float16> {
static void VPOW(...) { PADDLE_THROW("float16 VPOW not supported on CPU"); }
static void DOT(...) { PADDLE_THROW("float16 DOT not supported on CPU"); };
static void SCAL(...) { PADDLE_THROW("float16 SCAL not supported on CPU"); };
static void ASUM(...) { PADDLE_THROW("float16 ASUM not supported on CPU"); };
#ifdef PADDLE_WITH_MKLML
static void GEMM_BATCH(...) {
PADDLE_THROW("float16 GEMM_BATCH not supported on CPU");
......@@ -476,6 +487,21 @@ void Blas<platform::CPUDeviceContext>::SCAL(int n, const T a, T *x) const {
#endif
}
template <>
template <typename T>
T Blas<platform::CPUDeviceContext>::ASUM(int n, T *x, int inc) const {
auto sum = static_cast<T>(0.0);
#ifdef PADDLE_WITH_MKLML
sum = CBlas<T>::ASUM(n, x, inc);
#else
// TODO(jczaja): check if openblas does provide cblas_sasum/cblas_dasum
for (int c = 0; c < n; ++c) {
sum += x[c];
}
#endif
return sum;
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::GEMV(bool trans_a, int M, int N, T alpha,
......
......@@ -100,11 +100,8 @@ class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
blas.VEXP(num_classes * batch_size, out_data, out_data);
for (int n = 0; n < batch_size; ++n) {
entities[n] = out_data[n * num_classes];
for (int c = 1; c < num_classes; ++c) {
entities[n] += out_data[n * num_classes + c];
}
blas.SCAL(num_classes, 1.0f / entities[n], &out_data[n * num_classes]);
auto sum = blas.ASUM(num_classes, &out_data[n * num_classes], 1);
blas.SCAL(num_classes, 1.0f / sum, &out_data[n * num_classes]);
}
}
};
......
......@@ -36,9 +36,7 @@ class SoftmaxKernel : public framework::OpKernel<T> {
Tensor Out_2d = framework::ReshapeToMatrix(*Out, rank - 1);
#ifdef PADDLE_ON_INFERENCE
math::SoftmaxFunctor<
DeviceContext, T,
std::is_same<DeviceContext, platform::CPUDeviceContext>::value>()(
math::SoftmaxFunctor<DeviceContext, T, true>()(
context.template device_context<DeviceContext>(), &X_2d, &Out_2d);
#else
math::SoftmaxFunctor<DeviceContext, T, false>()(
......
......@@ -68,6 +68,8 @@ extern void* mklml_dso_handle;
__macro(cblas_dgemm_batch); \
__macro(cblas_sdot); \
__macro(cblas_ddot); \
__macro(cblas_sasum); \
__macro(cblas_dasum); \
__macro(cblas_sscal); \
__macro(cblas_dscal); \
__macro(vsAdd); \
......
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