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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/05/20 05:25:02 UTC
[GitHub] [incubator-mxnet] sxjscience commented on a change in pull request
#14893: Integrating the MKL VML functions to MXNET to speed-up the
(element-wised) mathematic computation
sxjscience commented on a change in pull request #14893: Integrating the MKL VML functions to MXNET to speed-up the (element-wised) mathematic computation
URL: https://github.com/apache/incubator-mxnet/pull/14893#discussion_r285431736
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File path: src/operator/mkl_functions-inl.h
##########
@@ -0,0 +1,202 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file mkl_functions-inl.h
+ * \brief Wrapper for MKL VML functions
+ * \author Tao Lv, Shufan Wu
+*/
+#ifndef MXNET_OPERATOR_MKL_FUNCTIONS_INL_H_
+#define MXNET_OPERATOR_MKL_FUNCTIONS_INL_H_
+
+#if MSHADOW_USE_MKL == 1
+#include "mkl_vml.h"
+
+namespace mxnet {
+namespace op {
+namespace mkl_func {
+
+MSHADOW_XINLINE
+static bool check_size(const size_t n) {
+ const size_t MKL_INT_MAX = (sizeof(MKL_INT) == sizeof(int)) ? INT_MAX : LLONG_MAX;
+ return (n <= MKL_INT_MAX);
+}
+
+MSHADOW_XINLINE
+static bool check_type(const int t) {
+ return (t == mshadow::kFloat32 || t == mshadow::kFloat64);
+}
+
+#define MXNET_MKL_UNARY_MATH_FUNC(name, func) \
+struct name { \
+ MSHADOW_XINLINE static void Vectorize(const index_t n, const float *src, float *dst) { \
+ vs##func(static_cast<MKL_INT>(n), src, dst); \
+ } \
+ MSHADOW_XINLINE static void Vectorize(const index_t n, const double *src, double *dst) { \
+ vd##func(static_cast<MKL_INT>(n), src, dst); \
+ } \
+};
+
+#define MXNET_MKL_BINARY_MATH_FUNC(name, func) \
+struct name { \
+ MSHADOW_XINLINE static void Vectorize(const index_t n, \
+ const float *a, \
+ const float *b, \
+ float *c) { \
+ vs##func(static_cast<MKL_INT>(n), a, b, c); \
+ } \
+ MSHADOW_XINLINE static void Vectorize(const index_t n, \
+ const double *a, \
+ const double *b, \
+ double *c) { \
+ vd##func(static_cast<MKL_INT>(n), a, b, c); \
+ } \
+};
+
+MXNET_MKL_UNARY_MATH_FUNC(erf, Erf);
+MXNET_MKL_UNARY_MATH_FUNC(exp, Exp);
+MXNET_MKL_UNARY_MATH_FUNC(exp2, Exp2);
+MXNET_MKL_UNARY_MATH_FUNC(exp10, Exp10);
+MXNET_MKL_UNARY_MATH_FUNC(expm1, Expm1);
+MXNET_MKL_UNARY_MATH_FUNC(log, Ln);
+MXNET_MKL_UNARY_MATH_FUNC(log2, Log2);
+MXNET_MKL_UNARY_MATH_FUNC(log10, Log10);
+MXNET_MKL_UNARY_MATH_FUNC(log1p, Log1p);
+
+MXNET_MKL_UNARY_MATH_FUNC(sin, Sin);
+MXNET_MKL_UNARY_MATH_FUNC(cos, Cos);
+MXNET_MKL_UNARY_MATH_FUNC(tan, Tan);
+MXNET_MKL_UNARY_MATH_FUNC(asin, Asin);
+MXNET_MKL_UNARY_MATH_FUNC(acos, Acos);
+MXNET_MKL_UNARY_MATH_FUNC(atan, Atan);
+
+MXNET_MKL_UNARY_MATH_FUNC(sinh, Sinh);
+MXNET_MKL_UNARY_MATH_FUNC(cosh, Cosh);
+MXNET_MKL_UNARY_MATH_FUNC(tanh, Tanh);
+MXNET_MKL_UNARY_MATH_FUNC(asinh, Asinh);
+MXNET_MKL_UNARY_MATH_FUNC(acosh, Acosh);
+MXNET_MKL_UNARY_MATH_FUNC(atanh, Atanh);
+
+MXNET_MKL_UNARY_MATH_FUNC(sqrt, Sqrt);
+MXNET_MKL_UNARY_MATH_FUNC(abs, Abs);
+MXNET_MKL_UNARY_MATH_FUNC(cbrt, Cbrt);
+MXNET_MKL_UNARY_MATH_FUNC(round, Round);
+MXNET_MKL_UNARY_MATH_FUNC(ceil, Ceil);
+MXNET_MKL_UNARY_MATH_FUNC(floor, Floor);
+MXNET_MKL_UNARY_MATH_FUNC(trunc, Trunc);
+
+MXNET_MKL_UNARY_MATH_FUNC(lgamma, LGamma);
+MXNET_MKL_UNARY_MATH_FUNC(tgamma, TGamma);
+MXNET_MKL_UNARY_MATH_FUNC(square, Sqr);
+
+MXNET_MKL_BINARY_MATH_FUNC(add, Add);
+MXNET_MKL_BINARY_MATH_FUNC(sub, Sub);
+MXNET_MKL_BINARY_MATH_FUNC(mul, Mul);
+MXNET_MKL_BINARY_MATH_FUNC(pow, Pow);
+MXNET_MKL_BINARY_MATH_FUNC(hypot, Hypot);
+
+
+template <typename DType>
+MSHADOW_XINLINE static void sub_(index_t n, DType *in, DType b, DType *dst) {
+ for (index_t i = 0; i < n; i++)
+ dst[i] = in[i] - b;
+}
+
+template <typename DType>
+MSHADOW_XINLINE static void div_(index_t n, DType *in, DType b, DType *dst) {
+ for (index_t i = 0; i < n; i++)
+ dst[i] = in[i] / b;
+}
+
+template <typename DType>
+MSHADOW_XINLINE static void sum_(index_t n, DType *in, DType *dst) {
+ // dst[0] = cblas_sasum(n, in, 1);
+ DType sum = 0.0f;
+ for (index_t i = 0; i < n; i++)
+ sum += in[i];
+
+ dst[0] = sum;
+}
+
+template <typename DType>
+MSHADOW_XINLINE static void max_(int n, DType *in, DType *dst) {
+ dst[0] = in[0];
+ for (int i = 1; i < n; i++)
+ dst[0] = (dst[0] < in[i]) ? in[i] : dst[0];
+}
+
+// LayerNorm on the last dimension
+template <typename DType>
+MSHADOW_XINLINE static void LayerNormLastDim(index_t m,
+ index_t n,
+ DType *a,
+ DType *b,
+ DType *ws,
+ DType *gamma,
+ DType *beta,
+ DType *mean,
+ DType *var,
+ DType eps) {
+ auto nthreads = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+#pragma omp parallel for num_threads(nthreads)
+ for (index_t i = 0; i < m; i++) {
+ DType* in_offset = a + i * n;
+ DType* out_offset = b + i * n;
+ DType* ws_offset = ws + i * n;
+
+ sum_(n, in_offset, &(mean[i]));
+ mean[i] /= n;
+ sub_(n, in_offset, mean[i], out_offset);
+ square::Vectorize(n, out_offset, ws_offset);
+ sum_(n, ws_offset, &(var[i]));
+ var[i] = math::sqrt(var[i] / n + eps);
+
+ mul::Vectorize(n, out_offset, gamma, out_offset);
+ div_(n, out_offset, var[i], out_offset);
+ add::Vectorize(n, out_offset, beta, out_offset);
Review comment:
After reading the code, I think the current implementation, which relies on the vectorized operations should be fast at scaling and shifting the data (data * gamma & data + beta). One possible improvement is to use the Welford's online algorithm to calculate the mean/variance in one pass, the code will look like this:
```C++
template <typename DType>
MSHADOW_XINLINE static void mean_var_(index_t n, DType *in, DType *mean, DType* variance) {
DType sigma2 = 0;
DType mean_v = 0;
DType old_mean_v = 0;
for (index_t i = 0; i < n; i++) {
DType x = in[i];
old_mean_v = mean_v;
mean_v += (x - old_mean_v) / (i + 1);
sigma2 += (x - old_mean_v) * (x - mean_v);
}
mean[0] = mean_v;
variance[0] = sigma2 / n;
}
template <typename DType>
MSHADOW_XINLINE static void LayerNormLastDim(index_t m,
index_t n,
DType *a,
DType *b,
DType *ws,
DType *gamma,
DType *beta,
DType *mean,
DType *var,
DType eps) {
auto nthreads = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
#pragma omp parallel for num_threads(nthreads)
for (index_t i = 0; i < m; i++) {
DType ele_mean, ele_var;
DType* in_offset = a + i * n;
DType* out_offset = b + i * n;
mean_var_(n, in_offset, &ele_mean, &ele_var);
sub_(n, in_offset, ele_mean, out_offset);
ele_var = math::sqrt(ele_var + eps);
mul::Vectorize(n, out_offset, gamma, out_offset);
div_(n, out_offset, ele_var, out_offset);
add::Vectorize(n, out_offset, beta, out_offset);
mean[i] = ele_mean;
var[i] = ele_var;
}
}
```
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