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jit benchmark use tensor #15515

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Jan 25, 2019
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jit benchmark use tensor #15515

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2 changes: 1 addition & 1 deletion paddle/fluid/operators/jit/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -21,5 +21,5 @@ endif()
cc_library(jit_kernel_helper SRCS ${jit_kernel_cc_srcs} DEPS ${JIT_KERNEL_DEPS})
cc_test(jit_kernel_test SRCS test.cc DEPS jit_kernel_helper)
if(NOT WIN32)
cc_binary(jit_kernel_benchmark SRCS benchmark.cc DEPS jit_kernel_helper device_tracer)
cc_binary(jit_kernel_benchmark SRCS benchmark.cc DEPS jit_kernel_helper device_tracer tensor)
endif()
107 changes: 70 additions & 37 deletions paddle/fluid/operators/jit/benchmark.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/fluid/platform/device_tracer.h"
#include "paddle/fluid/platform/place.h"
Expand Down Expand Up @@ -155,34 +156,50 @@ void BenchAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
LOG(INFO) << loginfos.str();
}

using Tensor = paddle::framework::Tensor;

template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void BenchXYZNKernel() {
for (int d : TestSizes()) {
std::vector<T> x(d), y(d), z(d);
RandomVec<T>(d, x.data());
RandomVec<T>(d, y.data());
BenchAllImpls<KT, jit::XYZNTuples<T>, PlaceType>(d, x.data(), y.data(),
z.data(), d);
Tensor x, y, z;
x.Resize({d});
y.Resize({d});
z.Resize({d});
T* x_data = x.mutable_data<T>(PlaceType());
T* y_data = y.mutable_data<T>(PlaceType());
T* z_data = z.mutable_data<T>(PlaceType());
RandomVec<T>(d, x_data);
RandomVec<T>(d, y_data);
BenchAllImpls<KT, jit::XYZNTuples<T>, PlaceType>(d, x.data<T>(),
y.data<T>(), z_data, d);
}
}

template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void BenchAXYNKernel() {
for (int d : TestSizes()) {
const T a = static_cast<T>(3);
std::vector<T> x(d), y(d);
RandomVec<T>(d, x.data());
BenchAllImpls<KT, jit::AXYNTuples<T>, PlaceType>(d, &a, x.data(), y.data(),
Tensor x, y;
x.Resize({d});
y.Resize({d});
T* x_data = x.mutable_data<T>(PlaceType());
T* y_data = y.mutable_data<T>(PlaceType());
RandomVec<T>(d, x_data);
BenchAllImpls<KT, jit::AXYNTuples<T>, PlaceType>(d, &a, x.data<T>(), y_data,
d);
}
}

template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void BenchXYNKernel() {
for (int d : TestSizes()) {
std::vector<T> x(d), y(d);
RandomVec<T>(d, x.data());
BenchAllImpls<KT, jit::XYNTuples<T>, PlaceType>(d, x.data(), y.data(), d);
Tensor x, y;
x.Resize({d});
y.Resize({d});
T* x_data = x.mutable_data<T>(PlaceType());
T* y_data = y.mutable_data<T>(PlaceType());
RandomVec<T>(d, x_data);
BenchAllImpls<KT, jit::XYNTuples<T>, PlaceType>(d, x.data<T>(), y_data, d);
}
}

Expand All @@ -192,16 +209,23 @@ void BenchLSTMKernel() {
for (int d : TestSizes()) {
const jit::lstm_attr_t attr(d, jit::kVSigmoid, jit::kVTanh, jit::kVTanh,
use_peephole);
std::vector<T> x(4 * d), ct_1(d), ct(d), ht(d), wp(3 * d), checked(2 * d);
RandomVec<T>(4 * d, x.data(), -2.f, 2.f);
RandomVec<T>(3 * d, wp.data(), -2.f, 2.f);
RandomVec<T>(d, ct_1.data(), -2.f, 2.f);
const T* ct_1_data = ct_1.data();
const T* wp_data = wp.data();
T* x_data = x.data();
T* checked_data = checked.data();
T* ct_data = ct.data();
T* ht_data = ht.data();
Tensor x, ct_1, ct, ht, wp, checked;
x.Resize({4 * d});
ct_1.Resize({d});
ct.Resize({d});
ht.Resize({d});
wp.Resize({3 * d});
checked.Resize({2 * d});
auto place = PlaceType();
RandomVec<T>(x.numel(), x.mutable_data<T>(place), -2.f, 2.f);
RandomVec<T>(wp.numel(), wp.mutable_data<T>(place), -2.f, 2.f);
RandomVec<T>(ct_1.numel(), ct_1.mutable_data<T>(place), -2.f, 2.f);
const T* ct_1_data = ct_1.data<T>();
const T* wp_data = wp.data<T>();
T* x_data = x.mutable_data<T>(place);
T* checked_data = checked.mutable_data<T>(place);
T* ct_data = ct.mutable_data<T>(place);
T* ht_data = ht.mutable_data<T>(place);
jit::lstm_t step;
step.gates = x_data;
step.ct_1 = ct_1_data;
Expand All @@ -220,12 +244,16 @@ template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void BenchGRUKernel() {
for (int d : TestSizes()) {
const jit::gru_attr_t attr(d, jit::kVSigmoid, jit::kVTanh);
std::vector<T> x(3 * d), ht_1(d), ht(d);
RandomVec<T>(3 * d, x.data(), -2.f, 2.f);
RandomVec<T>(d, ht_1.data(), -2.f, 2.f);
const T* ht_1_data = ht_1.data();
T* x_data = x.data();
T* ht_data = ht.data();
auto place = PlaceType();
Tensor x, ht_1, ht;
x.Resize({3 * d});
ht_1.Resize({d});
ht.Resize({d});
RandomVec<T>(3 * d, x.mutable_data<T>(place), -2.f, 2.f);
RandomVec<T>(d, ht_1.mutable_data<T>(place), -2.f, 2.f);
const T* ht_1_data = ht_1.data<T>();
T* x_data = x.mutable_data<T>(place);
T* ht_data = ht.mutable_data<T>(place);
jit::gru_t step;
step.gates = x_data;
step.ht_1 = ht_1_data;
Expand All @@ -243,10 +271,12 @@ void BenchSeqPoolKernel() {
jit::seq_pool_attr_t attr(w, type);
for (int h : TestSizes()) {
attr.h = h;
std::vector<T> x(h * w), y(w);
RandomVec<T>(h * w, x.data(), -2.f, 2.f);
const T* x_data = x.data();
T* y_data = y.data();
Tensor x, y;
x.Resize({h * w});
y.Resize({w});
RandomVec<T>(h * w, x.mutable_data<T>(PlaceType()), -2.f, 2.f);
const T* x_data = x.data<T>();
T* y_data = y.mutable_data<T>(PlaceType());
BenchAllImpls<KT, jit::SeqPoolTuples<T>, PlaceType>(attr, x_data,
y_data, &attr);
}
Expand All @@ -259,12 +289,15 @@ void BenchMatMulKernel() {
for (int m : {1, 2, 3, 4}) {
for (int n : TestSizes()) {
for (int k : TestSizes()) {
std::vector<T> a(m * k), b(k * n), c(m * n);
RandomVec<T>(m * k, a.data(), -2.f, 2.f);
RandomVec<T>(k * n, b.data(), -2.f, 2.f);
const T* a_data = a.data();
const T* b_data = b.data();
T* c_data = c.data();
Tensor a, b, c;
a.Resize({m * k});
b.Resize({k * n});
c.Resize({m * n});
RandomVec<T>(m * k, a.mutable_data<T>(PlaceType()), -2.f, 2.f);
RandomVec<T>(k * n, b.mutable_data<T>(PlaceType()), -2.f, 2.f);
const T* a_data = a.data<T>();
const T* b_data = b.data<T>();
T* c_data = c.mutable_data<T>(PlaceType());
BenchAllImpls<KT, jit::MatMulTuples<T>, PlaceType>(k, a_data, b_data,
c_data, m, n, k);
}
Expand Down