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Paddle-Lite
提交 52e13225 编写于 作者: Z Zhen Wang
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add int8_t gemm and enable MulOp to support int8_t.

上级 1c893a02
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Showing with 413 addition and 182 deletion
src/framework/operator.cpp
浏览文件 @ 52e13225
......@@ -32,7 +32,7 @@ template <typename Dtype>
vector<string> OperatorBase<Dtype>::GetInputKeys() const {
auto it = op_input_output_key.find(type_);
if (it == op_input_output_key.end()) {
DLOG << type_ << " has no outputs";
DLOG << type_ << " has no inputs";
return {};
}
return it->second.first;
......
src/framework/tensor.h
浏览文件 @ 52e13225
......@@ -338,10 +338,12 @@ inline Print &operator<<(Print &printer, const Tensor &tensor) {
for (int i = 0; i < tensor.numel(); i += stride) {
if (tensor.type() == typeid(float)) {
printer << tensor.data<float>()[i] << " ";
} else if (tensor.type() == typeid(int32_t)) {
printer << tensor.data<int32_t>()[i] << " ";
} else if (tensor.type() == typeid(int64_t)) {
printer << tensor.data<int64_t>()[i] << " ";
} else if (tensor.type() == typeid(int8_t)) {
printer << tensor.data<int8_t>()[i] << " ";
printer << static_cast<int32_t>(tensor.data<int8_t>()[i]) << " ";
}
}
#endif
......
src/operators/kernel/arm/mul_kernel.cpp
浏览文件 @ 52e13225
......@@ -25,12 +25,15 @@ bool MulKernel<CPU, float>::Init(MulParam<CPU> *param) {
return true;
}
template <>
void MulKernel<CPU, float>::Compute(const MulParam<CPU> &param) const {
MulCompute<float>(param);
param.Out()->set_lod(param.InputX()->lod());
}
template class MulKernel<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/kernel/central-arm-func/mul_arm_func.h
浏览文件 @ 52e13225
......@@ -58,7 +58,7 @@ void MulCompute(const MulParam<CPU> &param) {
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *out = param.Out();
out->mutable_data<float>();
const Tensor x_matrix =
input_x->dims().size() > 2
? framework::ReshapeToMatrix(*input_x, param.XNumColDims())
......@@ -71,15 +71,21 @@ void MulCompute(const MulParam<CPU> &param) {
if (out_dim.size() != 2) {
out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
if (param.InputX()->type() == typeid(int8_t)) {
out->mutable_data<int32_t>();
math::matmul<int8_t>(x_matrix, false, y_matrix, false,
static_cast<int8_t>(1), out, static_cast<int8_t>(0));
} else {
out->mutable_data<float>();
math::matmul<float>(x_matrix, false, y_matrix, false, static_cast<float>(1),
out, static_cast<float>(0));
}
if (out_dim.size() != 2) {
out->Resize(out_dim);
}
}
template class MulKernel<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/gemm.h
浏览文件 @ 52e13225
......@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <stdint-gcc.h>
#include <string>
#include "common/log.h"
......
src/operators/math/gemm_int8.cpp
浏览文件 @ 52e13225
......@@ -34,8 +34,10 @@ void Gemm::AddDot6x8(int32_t k, const int8_t *a, const int8_t *b, int32_t *c,
const int8_t *a_ptr, *b_ptr;
a_ptr = a;
b_ptr = b;
int32_t kc1 = k >> 1;
int32_t kc2 = k & 1;
int32_t kc1 = k >> 3;
int32_t kc2 = k & 7;
int32_t kc3 = kc2 >> 1;
int32_t kc4 = kc2 & 1;
int32_t step = sizeof(int32_t) * ldc;
asm volatile(
// q4-q15: save 48 results
......@@ -57,16 +59,20 @@ void Gemm::AddDot6x8(int32_t k, const int8_t *a, const int8_t *b, int32_t *c,
"subs %[kc1], %[kc1], #1 \n\t"
"blt 1f \n\t"
"0: \n\t"
"pld [%[a_ptr], #64] \n\t"
"pld [%[b_ptr], #64] \n\t"
"vld1.s8 {d0}, [%[a_ptr]], r0 \n\t" // A col0
"vld1.s8 {d1}, [%[a_ptr]], r0 \n\t" // A col1, q0 used
"vld1.s8 {d2-d3}, [%[b_ptr]]! \n\t" // B row0, B row1, q1 used
"vld1.s8 {d1}, [%[a_ptr]], r0 \n\t" // A col1, q0
// used
"vld1.s8 {d2-d3}, [%[b_ptr]]! \n\t" // B row0, B
// row1, q1
// used
"vmov.s8 q2, #0 \n\t" // q2 used
"vdup.s8 d6, d0[0] \n\t"
"vdup.s8 d7, d1[0] \n\t" // q3 used
"vmlal.s8 q2, d2, d6 \n\t" // A col00 * B row0
"vmlal.s8 q2, d3, d7 \n\t" // A col10 * B row1, q3 free
"vmlal.s8 q2, d2, d6 \n\t" // A col00 * B
// row0
"vmlal.s8 q2, d3, d7 \n\t" // A col10 * B
// row1, q3
// free
"vaddw.s16 q4, q4, d4 \n\t"
"vaddw.s16 q5, q5, d5 \n\t" // res row 0
"vmov.s8 q2, #0 \n\t"
......@@ -90,7 +96,59 @@ void Gemm::AddDot6x8(int32_t k, const int8_t *a, const int8_t *b, int32_t *c,
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q10, q10, d4 \n\t"
"vaddw.s16 q11, q11, d5 \n\t" // res row 3
"vmov.s8 q2, #0. \n\t"
"vdup.s8 d6, d0[4] \n\t"
"vdup.s8 d7, d1[4] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q12, q12, d4 \n\t"
"vaddw.s16 q13, q13, d5 \n\t" // res row 4
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[5] \n\t"
"vdup.s8 d7, d1[5] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q14, q14, d4 \n\t"
"vaddw.s16 q15, q15, d5 \n\t" // res row 5
"vld1.s8 {d0}, [%[a_ptr]], r0 \n\t" // A col0
"vld1.s8 {d1}, [%[a_ptr]], r0 \n\t" // A col1, q0
// used
"vld1.s8 {d2-d3}, [%[b_ptr]]! \n\t" // B row0, B
// row1, q1
// used
"vmov.s8 q2, #0 \n\t" // q2 used
"vdup.s8 d6, d0[0] \n\t"
"vdup.s8 d7, d1[0] \n\t" // q3 used
"vmlal.s8 q2, d2, d6 \n\t" // A col00 * B
// row0
"vmlal.s8 q2, d3, d7 \n\t" // A col10 * B
// row1, q3
// free
"vaddw.s16 q4, q4, d4 \n\t"
"vaddw.s16 q5, q5, d5 \n\t" // res row 0
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[1] \n\t"
"vdup.s8 d7, d1[1] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q6, q6, d4 \n\t"
"vaddw.s16 q7, q7, d5 \n\t" // res row 1
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[2] \n\t"
"vdup.s8 d7, d1[2] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q8, q8, d4 \n\t"
"vaddw.s16 q9, q9, d5 \n\t" // res row 2
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[3] \n\t"
"vdup.s8 d7, d1[3] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q10, q10, d4 \n\t"
"vaddw.s16 q11, q11, d5 \n\t" // res row 3
"vmov.s8 q2, #0. \n\t"
"vdup.s8 d6, d0[4] \n\t"
"vdup.s8 d7, d1[4] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
......@@ -105,11 +163,175 @@ void Gemm::AddDot6x8(int32_t k, const int8_t *a, const int8_t *b, int32_t *c,
"vaddw.s16 q14, q14, d4 \n\t"
"vaddw.s16 q15, q15, d5 \n\t" // res row 5
"subs %[kc1], %[kc1], #1 \n\t"
"vld1.s8 {d0}, [%[a_ptr]], r0 \n\t" // A col0
"vld1.s8 {d1}, [%[a_ptr]], r0 \n\t" // A col1, q0
// used
"vld1.s8 {d2-d3}, [%[b_ptr]]! \n\t" // B row0, B
// row1, q1
// used
"vmov.s8 q2, #0 \n\t" // q2 used
"vdup.s8 d6, d0[0] \n\t"
"vdup.s8 d7, d1[0] \n\t" // q3 used
"vmlal.s8 q2, d2, d6 \n\t" // A col00 * B
// row0
"vmlal.s8 q2, d3, d7 \n\t" // A col10 * B
// row1, q3
// free
"vaddw.s16 q4, q4, d4 \n\t"
"vaddw.s16 q5, q5, d5 \n\t" // res row 0
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[1] \n\t"
"vdup.s8 d7, d1[1] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q6, q6, d4 \n\t"
"vaddw.s16 q7, q7, d5 \n\t" // res row 1
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[2] \n\t"
"vdup.s8 d7, d1[2] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q8, q8, d4 \n\t"
"vaddw.s16 q9, q9, d5 \n\t" // res row 2
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[3] \n\t"
"vdup.s8 d7, d1[3] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q10, q10, d4 \n\t"
"vaddw.s16 q11, q11, d5 \n\t" // res row 3
"vmov.s8 q2, #0. \n\t"
"vdup.s8 d6, d0[4] \n\t"
"vdup.s8 d7, d1[4] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q12, q12, d4 \n\t"
"vaddw.s16 q13, q13, d5 \n\t" // res row 4
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[5] \n\t"
"vdup.s8 d7, d1[5] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q14, q14, d4 \n\t"
"vaddw.s16 q15, q15, d5 \n\t" // res row 5
"vld1.s8 {d0}, [%[a_ptr]], r0 \n\t" // A col0
"vld1.s8 {d1}, [%[a_ptr]], r0 \n\t" // A col1, q0
// used
"vld1.s8 {d2-d3}, [%[b_ptr]]! \n\t" // B row0, B
// row1, q1
// used
"vmov.s8 q2, #0 \n\t" // q2 used
"vdup.s8 d6, d0[0] \n\t"
"vdup.s8 d7, d1[0] \n\t" // q3 used
"vmlal.s8 q2, d2, d6 \n\t" // A col00 * B
// row0
"vmlal.s8 q2, d3, d7 \n\t" // A col10 * B
// row1, q3
// free
"vaddw.s16 q4, q4, d4 \n\t"
"vaddw.s16 q5, q5, d5 \n\t" // res row 0
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[1] \n\t"
"vdup.s8 d7, d1[1] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q6, q6, d4 \n\t"
"vaddw.s16 q7, q7, d5 \n\t" // res row 1
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[2] \n\t"
"vdup.s8 d7, d1[2] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q8, q8, d4 \n\t"
"vaddw.s16 q9, q9, d5 \n\t" // res row 2
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[3] \n\t"
"vdup.s8 d7, d1[3] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q10, q10, d4 \n\t"
"vaddw.s16 q11, q11, d5 \n\t" // res row 3
"vmov.s8 q2, #0. \n\t"
"vdup.s8 d6, d0[4] \n\t"
"vdup.s8 d7, d1[4] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q12, q12, d4 \n\t"
"vaddw.s16 q13, q13, d5 \n\t" // res row 4
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[5] \n\t"
"vdup.s8 d7, d1[5] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q14, q14, d4 \n\t"
"vaddw.s16 q15, q15, d5 \n\t" // res row 5
"subs %[kc1], %[kc1], #1 \n\t" // last <8 rows
"bge 0b \n\t"
"1: \n\t" // odd, last row
"subs %[kc2], %[kc2], #1 \n\t"
"blt 2f \n\t"
"1: \n\t"
"subs %[kc3], %[kc3], #1 \n\t"
"blt 3f \n\t"
"2: \n\t"
"vld1.s8 {d0}, [%[a_ptr]], r0 \n\t" // A col0
"vld1.s8 {d1}, [%[a_ptr]], r0 \n\t" // A col1, q0
// used
"vld1.s8 {d2-d3}, [%[b_ptr]]! \n\t" // B row0, B
// row1, q1
// used
"vmov.s8 q2, #0 \n\t" // q2 used
"vdup.s8 d6, d0[0] \n\t"
"vdup.s8 d7, d1[0] \n\t" // q3 used
"vmlal.s8 q2, d2, d6 \n\t" // A col00 * B
// row0
"vmlal.s8 q2, d3, d7 \n\t" // A col10 * B
// row1, q3
// free
"vaddw.s16 q4, q4, d4 \n\t"
"vaddw.s16 q5, q5, d5 \n\t" // res row 0
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[1] \n\t"
"vdup.s8 d7, d1[1] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q6, q6, d4 \n\t"
"vaddw.s16 q7, q7, d5 \n\t" // res row 1
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[2] \n\t"
"vdup.s8 d7, d1[2] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q8, q8, d4 \n\t"
"vaddw.s16 q9, q9, d5 \n\t" // res row 2
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[3] \n\t"
"vdup.s8 d7, d1[3] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q10, q10, d4 \n\t"
"vaddw.s16 q11, q11, d5 \n\t" // res row 3
"vmov.s8 q2, #0. \n\t"
"vdup.s8 d6, d0[4] \n\t"
"vdup.s8 d7, d1[4] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q12, q12, d4 \n\t"
"vaddw.s16 q13, q13, d5 \n\t" // res row 4
"vmov.s8 q2, #0 \n\t"
"vdup.s8 d6, d0[5] \n\t"
"vdup.s8 d7, d1[5] \n\t"
"vmlal.s8 q2, d2, d6 \n\t"
"vmlal.s8 q2, d3, d7 \n\t"
"vaddw.s16 q14, q14, d4 \n\t"
"vaddw.s16 q15, q15, d5 \n\t" // res row 5
"subs %[kc3], %[kc3], #1 \n\t"
"bge 2b \n\t"
"3: \n\t" // odd, last
// row
"subs %[kc4], %[kc4], #1 \n\t"
"blt 4f \n\t"
"vld1.s8 {d0}, [%[a_ptr]] \n\t"
"vld1.s8 {d1}, [%[b_ptr]] \n\t"
"vdup.s8 d2, d0[0] \n\t"
......@@ -136,17 +358,16 @@ void Gemm::AddDot6x8(int32_t k, const int8_t *a, const int8_t *b, int32_t *c,
"vmull.s8 q2, d1, d2 \n\t"
"vaddw.s16 q14, q14, d4 \n\t"
"vaddw.s16 q15, q15, d5 \n\t" // res row 4
"2: \n\t"
"4: \n\t"
"vst1.32 {q4, q5}, [%[c]], %[step] \n\t"
"vst1.32 {q6, q7}, [%[c]], %[step] \n\t"
"vst1.32 {q8, q9}, [%[c]], %[step] \n\t"
"vst1.32 {q10, q11}, [%[c]], %[step] \n\t"
"vst1.32 {q12, q13}, [%[c]], %[step] \n\t"
"vst1.32 {q14, q15}, [%[c]] \n\t"
:
: [a_ptr] "r"(a_ptr), [b_ptr] "r"(b_ptr), [c] "r"(c), [kc1] "r"(kc1),
[kc2] "r"(kc2), [step] "r"(step)
[kc3] "r"(kc3), [kc4] "r"(kc4), [step] "r"(step)
: "cc", "memory", "r0", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7",
"q8", "q9", "q10", "q11", "q12", "q13", "q14", "q15");
#endif
......
test/operators/test_mul_op.cpp
浏览文件 @ 52e13225
......@@ -12,80 +12,80 @@ 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. */
#include "../test_helper.h"
#include "../test_include.h"
#include "operators/mul_op.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(g_resnet);
PADDLE_MOBILE_ENFORCE(program.originProgram != nullptr,
"program file read fail");
Executor4Test<paddle_mobile::CPU,
paddle_mobile::operators::MulOp<paddle_mobile::CPU, float>>
executor(program, "mul");
// 1. input_tensors;
vector<Tensor> input_tensors;
Tensor input1;
auto input1_data = CreateInput<float>(&input1, {3, 2, 1, 1}, 0, 1);
input_tensors.push_back(input1);
Tensor input2;
auto input2_data = CreateInput<float>(&input2, {2, 3}, 0, 1);
input_tensors.push_back(input2);
// 2. input_names
vector<string> input_names({
"pool2d_0.tmp_0",
"fc_0.w_0",
});
// 3. output_names
vector<string> output_names({"fc_0.tmp_0"});
// 4. out_dims;
vector<DDim> out_ddims;
auto out_ddim = paddle_mobile::framework::make_ddim({3, 3});
out_ddims.push_back(out_ddim);
auto output = executor.Predict<LoDTensor>(input_tensors, input_names,
output_names, out_ddims);
auto output0_data = output[0]->data<float>();
auto dim_1 = input1.numel() / input1.dims()[0];
DLOG << " input1 : ";
for (int i = 0; i < input1.dims()[0]; ++i) {
for (int j = 0; j < dim_1; ++j) {
DLOGF("%f ", input1_data[i * dim_1 + j]);
}
DLOGF("\n");
#define a(i, j) a[(i)*lda + (j)]
#define b(i, j) b[(i)*ldb + (j)]
#define c(i, j) c[(i)*ldc + (j)]
namespace paddle_mobile {
using framework::AttributeMap;
using framework::DDim;
using framework::Scope;
using framework::make_ddim;
template <typename I, typename O>
int TestMulOP() {
int32_t m = 1024;
int32_t n = 1024;
int32_t k = 1024;
int32_t lda = k;
int32_t ldb = n;
int32_t ldc = n;
DDim inputA_shape = make_ddim({m, k});
DDim inputB_shape = make_ddim({k, n});
VariableNameMap inputs;
VariableNameMap outputs;
auto scope = std::make_shared<Scope>();
inputs["X"] = std::vector<std::string>({"inputA"});
inputs["Y"] = std::vector<std::string>({"inputB"});
outputs["Out"] = std::vector<std::string>({"output"});
auto inputA_var = scope.get()->Var("inputA");
auto inputA = inputA_var->template GetMutable<framework::LoDTensor>();
SetupTensor<I>(inputA, inputA_shape, -127, 127);
auto inputB_var = scope.get()->Var("inputB");
auto inputB = inputB_var->template GetMutable<framework::LoDTensor>();
SetupTensor<I>(inputB, inputB_shape, -127, 127);
auto output_var = scope.get()->Var("output");
AttributeMap attrs;
attrs["x_num_col_dims"].Set<int>(1);
attrs["y_num_col_dims"].Set<int>(1);
auto *op =
new operators::MulOp<CPU, float>("mul", inputs, outputs, attrs, scope);
op->InferShape();
op->Run();
auto output = output_var->template Get<framework::LoDTensor>();
const O *output_data = output->data<O>();
// compare
O *c = static_cast<O *>(memory::Alloc(sizeof(O) * m * n));
I *a = inputA->data<I>();
I *b = inputB->data<I>();
for (int32_t i = 0; i < m; ++i) {
for (int32_t j = 0; j < n; ++j) {
O r = 0;
for (int32_t p = 0; p < k; p++) {
r += static_cast<O>(a(i, p)) * static_cast<O>(b(p, j));
}
auto dim_2 = input2.numel() / input2.dims()[0];
DLOG << " input2 : ";
for (int i = 0; i < input2.dims()[0]; ++i) {
for (int j = 0; j < dim_2; ++j) {
DLOGF("%f ", input2_data[i * dim_2 + j]);
c(i, j) = r;
}
DLOGF("\n");
}
auto dim_output0 = output[0]->numel() / output[0]->dims()[0];
DLOG << " output : ";
for (int i = 0; i < output[0]->dims()[0]; ++i) {
for (int j = 0; j < dim_output0; ++j) {
DLOGF("%f ", output0_data[i * dim_2 + j]);
}
DLOGF("\n");
for (int32_t i = 0; i < m * n; ++i) {
PADDLE_MOBILE_ENFORCE(
output_data[i] == c[i], "output[%d] = %d, output_cmp[%d] = %d", i,
static_cast<int32_t>(output_data[i]), i, static_cast<int32_t>(c[i]));
}
DLOG << "Run MulOp successfully!";
delete op;
return 0;
}
} // namespace paddle_mobile
/// output (3,3)
DLOG << "output memory size : " << output[0]->memory_size();
DLOG << "output numel : " << output[0]->numel();
DLOG << input1_data[0] << " x " << input2_data[0] << " + " << input1_data[1]
<< " x " << input2_data[0 + 3] << " = " << output0_data[0];
int main() {
paddle_mobile::TestMulOP<int8_t, int32_t>();
paddle_mobile::TestMulOP<float, float>();
return 0;
}
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