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提交 e3a8a08e 编写于 作者: L liutuo
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optimize deconv omp

上级 4d9c948a
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Showing with 40 addition and 63 deletion
mace/kernels/arm/deconv_2d_neon.h
浏览文件 @ e3a8a08e
......@@ -26,28 +26,24 @@ namespace kernels {
void Deconv2dNeonK3x3S1(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output);
void Deconv2dNeonK3x3S2(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output);
void Deconv2dNeonK4x4S1(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output);
void Deconv2dNeonK4x4S2(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output);
......
mace/kernels/arm/deconv_2d_neon_3x3.cc
浏览文件 @ e3a8a08e
......@@ -20,7 +20,6 @@ namespace kernels {
void Deconv2dNeonK3x3S1(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output) {
......@@ -40,12 +39,6 @@ void Deconv2dNeonK3x3S1(const float *input,
if (oc + 1 < outch) {
float *out_base0 = output + (b * outch + oc) * out_img_size;
float *out_base1 = out_base0 + out_img_size;
const float bias_value0 = bias ? bias[oc] : 0.f;
const float bias_value1 = bias ? bias[oc + 1] : 0.f;
std::fill_n(out_base0, out_img_size, bias_value0);
std::fill_n(out_base1, out_img_size, bias_value1);
for (index_t ic = 0; ic < inch; ++ic) {
const float *input_base = input + (b * inch + ic) * h * w;
const float *kernel_base0 = filter + (oc * inch + ic) * 9;
......@@ -197,8 +190,6 @@ void Deconv2dNeonK3x3S1(const float *input,
}
} else {
float *out_base0 = output + (b * outch + oc) * outh * outw;
const float bias_value0 = bias ? bias[oc] : 0.f;
std::fill_n(out_base0, outh * outw, bias_value0);
for (index_t ic = 0; ic < inch; ++ic) {
const float *input_base = input + (b * inch + ic) * h * w;
const float *kernel_base0 = filter + (oc * inch + ic) * 9;
......@@ -290,7 +281,6 @@ void Deconv2dNeonK3x3S1(const float *input,
void Deconv2dNeonK3x3S2(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output) {
......@@ -303,15 +293,11 @@ void Deconv2dNeonK3x3S2(const float *input,
const index_t outw = out_shape[3];
const index_t out_img_size = outh * outw;
#pragma omp parallel for collapse(2)
#pragma omp parallel for collapse(3)
for (index_t b = 0; b < out_shape[0]; ++b) {
for (index_t oc = 0; oc < outch; ++oc) {
float *out_base = output + (b * outch + oc) * out_img_size;
const float bias_value = bias ? bias[oc] : 0.f;
std::fill_n(out_base, out_img_size, bias_value);
for (index_t ic = 0; ic < inch; ++ic) {
float *out_base = output + (b * outch + oc) * out_img_size;
const float *input_base = input + (b * inch + ic) * h * w;
const float *kernel_base = filter + (oc * inch + ic) * 9;
const float *in = input_base;
......
mace/kernels/arm/deconv_2d_neon_4x4.cc
浏览文件 @ e3a8a08e
......@@ -20,7 +20,6 @@ namespace kernels {
void Deconv2dNeonK4x4S1(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output) {
......@@ -32,16 +31,12 @@ void Deconv2dNeonK4x4S1(const float *input,
const index_t outw = out_shape[3];
const index_t outch = out_shape[1];
const index_t out_img_size = outh * outw;
#pragma omp parallel for
#pragma omp parallel for collapse(2)
for (int b = 0; b < out_shape[0]; ++b) {
for (int oc = 0; oc < outch; oc += 2) {
if (oc + 1 < outch) {
float *out_base = output + (b * outch + oc) * out_img_size;
float *out_base1 = out_base + out_img_size;
const float bias_value = bias ? bias[oc] : 0.f;
std::fill_n(out_base, out_img_size, bias_value);
const float bias_value1 = bias ? bias[oc + 1] : 0.f;
std::fill_n(out_base1, out_img_size, bias_value1);
for (int q = 0; q < inch; q++) {
const float *input_base = input + (b * inch + q) * h * w;
const float *in = input_base;
......@@ -257,8 +252,6 @@ void Deconv2dNeonK4x4S1(const float *input,
}
} else {
float *out_base = output + (b * outch + oc) * out_img_size;
const float bias_value = bias ? bias[oc] : 0.f;
std::fill_n(out_base, out_img_size, bias_value);
for (int q = 0; q < inch; q++) {
const float *input_base = input + (b * inch + q) * h * w;
const float *kernel_base = filter + (oc * inch + q) * 16;
......@@ -381,7 +374,6 @@ void Deconv2dNeonK4x4S1(const float *input,
void Deconv2dNeonK4x4S2(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output) {
......@@ -394,14 +386,11 @@ void Deconv2dNeonK4x4S2(const float *input,
const index_t outch = out_shape[1];
const index_t out_img_size = outh * outw;
#pragma omp parallel for
#pragma omp parallel for collapse(3)
for (int b = 0; b < out_shape[0]; ++b) {
for (int p = 0; p < outch; p++) {
float *out_base = output + (b * outch + p) * out_img_size;
const float bias_value = bias ? bias[p] : 0.f;
std::fill_n(out_base, outh * outw, bias_value);
for (int q = 0; q < inch; q++) {
float *out_base = output + (b * outch + p) * out_img_size;
const float *input_base = input + (b * inch + q) * h * w;
const float *kernel_base = filter + (p * inch + q) * 16;
const float *in = input_base;
......
mace/kernels/deconv_2d.h
浏览文件 @ e3a8a08e
......@@ -184,7 +184,6 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
void Deconv2dGeneral(const float *input,
const float *filter,
const float *bias,
const index_t kernel_h,
const index_t kernel_w,
const int *strides,
......@@ -206,23 +205,25 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
}
}
#pragma omp parallel for
for (int b = 0; b < in_shape[0]; ++b) {
for (int oc = 0; oc < out_shape[1]; ++oc) {
float *out_base =
output + (b * out_shape[1] + oc) * out_img_size;
const float bias_value = bias ? bias[oc] : 0.f;
std::fill_n(out_base, out_img_size, bias_value);
const index_t batch = in_shape[0];
const index_t out_channels = out_shape[1];
const index_t in_channels = in_shape[1];
#pragma omp parallel for collapse(4)
for (int b = 0; b < batch; ++b) {
for (int oc = 0; oc < out_channels; ++oc) {
for (int i = 0; i < in_height; ++i) {
for (int j = 0; j < in_width; ++j) {
float *out_base =
output + (b * out_channels + oc) * out_img_size;
const index_t out_offset =
i * strides[0] * out_width + j * strides[1];
for (int ic = 0; ic < in_shape[1]; ++ic) {
for (int ic = 0; ic < in_channels; ++ic) {
const index_t input_idx =
(b * in_shape[1] + ic) * in_img_size + i * in_width + j;
(b * in_channels + ic) * in_img_size + i * in_width + j;
const float val = input[input_idx];
const index_t kernel_offset =
(oc * in_shape[1] + ic) * kernel_size;
(oc * in_channels + ic) * kernel_size;
for (int k = 0; k < kernel_size; ++k) {
const index_t out_idx = out_offset + index_map[k];
const index_t kernel_idx = kernel_offset + k;
......@@ -248,7 +249,7 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
const index_t out_height = out_shape[2];
const index_t out_width = out_shape[3];
#pragma omp parallel for
#pragma omp parallel for collapse(3)
for (int i = 0; i < batch; ++i) {
for (int j = 0; j < channel; ++j) {
for (int k = 0; k < out_height; ++k) {
......@@ -324,7 +325,6 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
"Input/Output batch size mismatch");
std::function<void(const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *out_shape,
float *output)> deconv_func;
......@@ -354,6 +354,8 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
scratch->Rewind();
scratch->GrowSize(padded_out_size);
Tensor padded_out(scratch->Scratch(padded_out_size), DT_FLOAT);
padded_out.Reshape(padded_out_shape);
padded_out.Clear();
auto *padded_out_data = padded_out.mutable_data<float>();
bool use_neon_3x3_s1 = kernel_h == kernel_w && kernel_h == 3 &&
......@@ -369,13 +371,11 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
if (use_neon_3x3_s1) {
deconv_func = [=](const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *padded_out_shape,
float *padded_output) {
Deconv2dNeonK3x3S1(input,
filter,
bias,
in_shape,
padded_out_shape,
padded_output);
......@@ -383,13 +383,11 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
} else if (use_neon_3x3_s2) {
deconv_func = [=](const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *padded_out_shape,
float *padded_output) {
Deconv2dNeonK3x3S2(input,
filter,
bias,
in_shape,
padded_out_shape,
padded_output);
......@@ -397,13 +395,11 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
} else if (use_neon_4x4_s1) {
deconv_func = [=](const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *padded_out_shape,
float *padded_output) {
Deconv2dNeonK4x4S1(input,
filter,
bias,
in_shape,
padded_out_shape,
padded_output);
......@@ -411,13 +407,11 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
} else if (use_neon_4x4_s2) {
deconv_func = [=](const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *padded_out_shape,
float *padded_output) {
Deconv2dNeonK4x4S2(input,
filter,
bias,
in_shape,
padded_out_shape,
padded_output);
......@@ -425,13 +419,11 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
} else {
deconv_func = [=](const float *input,
const float *filter,
const float *bias,
const index_t *in_shape,
const index_t *padded_out_shape,
float *padded_output) {
Deconv2dGeneral(input,
filter,
bias,
kernel_h,
kernel_w,
strides_.data(),
......@@ -444,9 +436,24 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
bool no_pad =
padded_out_h == output_shape[2] && padded_out_w == output_shape[3];
float *out_data = no_pad ? output_data : padded_out_data;
if (bias_data != nullptr) {
const index_t batch = output_shape[0];
const index_t channels = output_shape[1];
const index_t img_size = output_shape[2] * output_shape[3];
#pragma omp parallel for collapse(3)
for (index_t b = 0; b < batch; ++b) {
for (index_t c = 0; c < channels; ++c) {
for (index_t i = 0; i < img_size; ++i) {
output_data[(b * channels + c) * img_size + i] +=
bias_data[c];
}
}
}
}
deconv_func(input_data,
filter_data,
bias_data,
in_shape,
padded_out_shape.data(),
out_data);
......@@ -459,6 +466,8 @@ struct Deconv2dFunctor<DeviceType::CPU, float>: Deconv2dFunctorBase {
output_data);
}
DoActivation<float>(output_data,
output_data,
output->size(),
......
mace/ops/deconv_2d_benchmark.cc
浏览文件 @ e3a8a08e
......@@ -120,15 +120,12 @@ static void Deconv2d(int iters,
MACE_BM_DECONV_2D_MACRO(N, C, H, W, KH, KW, S, OH, OW, P, OC, float, GPU); \
MACE_BM_DECONV_2D_MACRO(N, C, H, W, KH, KW, S, OH, OW, P, OC, half, GPU);
MACE_BM_DECONV_2D(1, 128, 15, 15, 1, 1, 1, 15, 15, VALID, 256);
MACE_BM_DECONV_2D(1, 32, 60, 60, 1, 1, 1, 60, 60, VALID, 128);
MACE_BM_DECONV_2D(1, 128, 60, 60, 3, 3, 1, 62, 62, VALID, 128);
MACE_BM_DECONV_2D(1, 32, 60, 60, 3, 3, 1, 60, 60, SAME, 32);
MACE_BM_DECONV_2D(1, 128, 60, 60, 4, 4, 1, 63, 63, VALID, 128);
MACE_BM_DECONV_2D(1, 32, 60, 60, 4, 4, 1, 60, 60, SAME, 32);
MACE_BM_DECONV_2D(1, 3, 224, 224, 4, 4, 2, 448, 448, SAME, 32);
MACE_BM_DECONV_2D(1, 3, 224, 224, 4, 4, 2, 450, 450, VALID, 32);
MACE_BM_DECONV_2D(1, 3, 512, 512, 7, 7, 2, 1023, 1023, SAME, 32);
MACE_BM_DECONV_2D(1, 128, 16, 16, 5, 5, 1, 20, 20, VALID, 32);
......@@ -138,10 +135,10 @@ MACE_BM_DECONV_2D(1, 3, 480, 480, 1, 1, 1, 480, 480, VALID, 3);
MACE_BM_DECONV_2D(1, 64, 32, 32, 1, 1, 1, 32, 32, VALID, 128);
MACE_BM_DECONV_2D(1, 64, 33, 32, 3, 3, 2, 65, 63, SAME, 128);
MACE_BM_DECONV_2D(1, 3, 224, 224, 3, 3, 2, 447, 447, SAME, 32);
MACE_BM_DECONV_2D(1, 3, 224, 224, 3, 3, 2, 449, 449, VALID, 32);
MACE_BM_DECONV_2D(1, 3, 224, 224, 3, 3, 2, 448, 448, SAME, 32);
MACE_BM_DECONV_2D(1, 32, 1014, 762, 9, 9, 2, 2035, 1531, VALID, 1);
} // namespace test
} // namespace ops
} // namespace mace
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