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Merge branch 'master' into fix/pyserial_read_until
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@HeJunchao100813
HeJunchao100813 committed Sep 26, 2023
2 parents 62c398c + 8512637 commit 792fb11
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2 changes: 1 addition & 1 deletion modules/Nncase.Modules.StackVM/CodeGen/StackVM/CodeGenVisitor.g.cs
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
// Copyright (c) Canaan Inc. All rights reserved.
// Licensed under the Apache license. See LICENSE file in the project root for full license information.
/* This file is generated by tools/stackvm_gen/IsaGen at 2023/9/5 19:40:30 +08:00. */
/* This file is generated by tools/stackvm_gen/IsaGen at 2023/9/18 下午5:04:31 +08:00. */

using System;
using System.Collections.Generic;
Expand Down
163 changes: 96 additions & 67 deletions src/Native/src/kernels/stackvm/optimized/convolution.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
* limitations under the License.
*/
#include "../reference/ref_ops.h"
#include "nncase/runtime/util.h"
#include "opt_ops.h"
#include <nncase/kernels/kernel_utils.h>
#include <nncase/runtime/runtime_op_utility.h>
Expand Down Expand Up @@ -55,9 +56,10 @@ using namespace nncase::kernels;
using namespace nncase::kernels::stackvm;
using namespace nncase::kernels::stackvm::optimized;

template <typename T>
result<void>
conv2d_1x1_s1(const float *input, const float *weights, const float *bias,
float *output, gsl::span<const size_t> in_shape,
conv2d_1x1_s1(const T *input, const T *weights, const T *bias, T *output,
gsl::span<const size_t> in_shape,
NNCASE_UNUSED gsl::span<const size_t> in_strides,
NNCASE_UNUSED gsl::span<const size_t> w_shape,
NNCASE_UNUSED gsl::span<const size_t> w_strides,
Expand All @@ -67,8 +69,7 @@ conv2d_1x1_s1(const float *input, const float *weights, const float *bias,
NNCASE_UNUSED const padding &padding_w,
NNCASE_UNUSED int32_t groups, NNCASE_UNUSED int32_t stride_h,
NNCASE_UNUSED int32_t stride_w, NNCASE_UNUSED int32_t dilation_h,
NNCASE_UNUSED int32_t dilation_w,
value_range<float> fused_activation,
NNCASE_UNUSED int32_t dilation_w, value_range<T> fused_activation,
NNCASE_UNUSED kernels::kernel_context &context) noexcept {
const auto widths = in_shape[2] * in_shape[3];
// if oc's type is size_t, openmp will throw error in visual studio
Expand All @@ -82,8 +83,8 @@ conv2d_1x1_s1(const float *input, const float *weights, const float *bias,
#endif
for (int oc = 0; oc < out_channels; oc++) {
const auto out_c = oc;
const float *now_weights = weights + out_c * w_strides[0];
const float *now_img_start = input + batch * in_strides[0];
const T *now_weights = weights + out_c * w_strides[0];
const T *now_img_start = input + batch * in_strides[0];
size_t channel = 0;

auto *now_output_channel_start =
Expand All @@ -94,26 +95,26 @@ conv2d_1x1_s1(const float *input, const float *weights, const float *bias,
bias[oc]);
for (; channel + 4 <= in_shape[1]; channel += 4, now_weights += 4) {
auto *w_output = now_output_channel_start;
const float w0 = now_weights[0];
const float w1 = now_weights[1];
const float w2 = now_weights[2];
const float w3 = now_weights[3];
const T w0 = now_weights[0];
const T w1 = now_weights[1];
const T w2 = now_weights[2];
const T w3 = now_weights[3];

const float *i0 = now_img_start + (channel + 0) * in_strides[1];
const float *i1 = now_img_start + (channel + 1) * in_strides[1];
const float *i2 = now_img_start + (channel + 2) * in_strides[1];
const float *i3 = now_img_start + (channel + 3) * in_strides[1];
const T *i0 = now_img_start + (channel + 0) * in_strides[1];
const T *i1 = now_img_start + (channel + 1) * in_strides[1];
const T *i2 = now_img_start + (channel + 2) * in_strides[1];
const T *i3 = now_img_start + (channel + 3) * in_strides[1];

const float *v0 = i0;
const float *v1 = i1;
const float *v2 = i2;
const float *v3 = i3;
const T *v0 = i0;
const T *v1 = i1;
const T *v2 = i2;
const T *v3 = i3;

for (size_t index = 0; index < widths; ++index) {
float sum0 = *v0 * w0;
float sum1 = *v1 * w1;
float sum2 = *v2 * w2;
float sum3 = *v3 * w3;
T sum0 = *v0 * w0;
T sum1 = *v1 * w1;
T sum2 = *v2 * w2;
T sum3 = *v3 * w3;

*w_output += sum0 + sum1 + sum2 + sum3;

Expand All @@ -127,9 +128,9 @@ conv2d_1x1_s1(const float *input, const float *weights, const float *bias,

for (; channel < in_shape[1]; ++channel) {
auto *w_output = now_output_channel_start;
const float *v = now_img_start + channel * in_strides[1];
const T *v = now_img_start + channel * in_strides[1];
for (size_t index = 0; index < widths; ++index) {
*w_output += (*now_weights) * (*v);
*w_output += (T)(*now_weights) * (T)(*v);
++w_output;
++v;
}
Expand All @@ -146,9 +147,10 @@ conv2d_1x1_s1(const float *input, const float *weights, const float *bias,
return ok();
}

template <typename T>
result<void>
conv2d_1x1_s2(const float *input, const float *weights, const float *bias,
float *output, gsl::span<const size_t> in_shape,
conv2d_1x1_s2(const T *input, const T *weights, const T *bias, T *output,
gsl::span<const size_t> in_shape,
NNCASE_UNUSED gsl::span<const size_t> in_strides,
NNCASE_UNUSED gsl::span<const size_t> w_shape,
NNCASE_UNUSED gsl::span<const size_t> w_strides,
Expand All @@ -158,8 +160,7 @@ conv2d_1x1_s2(const float *input, const float *weights, const float *bias,
NNCASE_UNUSED const padding &padding_w,
NNCASE_UNUSED int32_t groups, NNCASE_UNUSED int32_t stride_h,
NNCASE_UNUSED int32_t stride_w, NNCASE_UNUSED int32_t dilation_h,
NNCASE_UNUSED int32_t dilation_w,
value_range<float> fused_activation,
NNCASE_UNUSED int32_t dilation_w, value_range<T> fused_activation,
NNCASE_UNUSED kernels::kernel_context &context) noexcept {
const auto batch = in_shape[0], in_channels = in_shape[1],
in_h = in_shape[2], in_w = in_shape[3],
Expand All @@ -178,31 +179,30 @@ conv2d_1x1_s2(const float *input, const float *weights, const float *bias,
#pragma omp parallel for num_threads(context.num_threads)
#endif
for (int oc = 0; oc < out_channels; oc++) {
float *out = output + (b * out_strides[0] + oc * out_strides[1]);
T *out = output + (b * out_strides[0] + oc * out_strides[1]);

std::fill(out, out + out_h * out_w, bias[oc]);
size_t ic = 0;
for (; ic + 3 < in_channels; ic += 4) {
float *outptr = out;
const float *img0 =
T *outptr = out;
const T *img0 =
input + (b * in_strides[0]) + (ic * in_strides[1]);
const float *img1 =
const T *img1 =
input + (b * in_strides[0]) + ((ic + 1) * in_strides[1]);
const float *img2 =
const T *img2 =
input + (b * in_strides[0]) + ((ic + 2) * in_strides[1]);
const float *img3 =
const T *img3 =
input + (b * in_strides[0]) + ((ic + 3) * in_strides[1]);

const float *r0 = img0;
const float *r1 = img1;
const float *r2 = img2;
const float *r3 = img3;
const T *r0 = img0;
const T *r1 = img1;
const T *r2 = img2;
const T *r3 = img3;

const float *k0 =
weights + oc * w_strides[0] + ic * w_strides[1];
const float *k1 = k0 + 1;
const float *k2 = k0 + 2;
const float *k3 = k0 + 3;
const T *k0 = weights + oc * w_strides[0] + ic * w_strides[1];
const T *k1 = k0 + 1;
const T *k2 = k0 + 2;
const T *k3 = k0 + 3;
for (size_t i = 0; i < out_h; i++) {
for (size_t remain = 0; remain < out_w; remain++) {
*outptr += r0[0] * k0[0];
Expand All @@ -223,13 +223,13 @@ conv2d_1x1_s2(const float *input, const float *weights, const float *bias,
}

for (; ic < in_channels; ic++) {
float *outptr = out;
const float *img0 =
T *outptr = out;
const T *img0 =
input + (b * in_strides[0]) + (ic * in_strides[1]);
const float *kernel0 =
const T *kernel0 =
weights + oc * w_strides[0] + ic * w_strides[1];
const float *r0 = img0;
const float *k0 = kernel0;
const T *r0 = img0;
const T *k0 = kernel0;
for (size_t i = 0; i < out_h; i++) {
for (size_t remain = 0; remain < out_w; remain++) {
*outptr += r0[0] * k0[0];
Expand All @@ -240,7 +240,7 @@ conv2d_1x1_s2(const float *input, const float *weights, const float *bias,
}
}
for (size_t h = 0; h < out_h; h++) {
float *r_out = out + h * out_strides[2];
T *r_out = out + h * out_strides[2];
for (size_t w = 0; w < out_w; w++) {
*(r_out + w) = kernels::detail::apply_activation(
*(r_out + w), fused_activation);
Expand Down Expand Up @@ -415,10 +415,10 @@ void conv2d_channel(size_t out_h, size_t out_w, std::array<T, Parallel> &sum,
}

template <size_t Parallel, size_t Filter_h, size_t Filter_w, size_t Stride_h,
size_t Stride_w>
size_t Stride_w, typename T>
result<void>
conv2d_nxm(const float *input, const float *weights, const float *bias,
float *output, gsl::span<const size_t> in_shape,
conv2d_nxm(const T *input, const T *weights, const T *bias, float *output,
gsl::span<const size_t> in_shape,
NNCASE_UNUSED gsl::span<const size_t> in_strides,
NNCASE_UNUSED gsl::span<const size_t> w_shape,
NNCASE_UNUSED gsl::span<const size_t> w_strides,
Expand Down Expand Up @@ -485,9 +485,9 @@ conv2d_nxm(const float *input, const float *weights, const float *bias,
}

template <size_t Parallel, size_t Filter_h, size_t Filter_w, size_t Stride_h,
size_t Stride_w>
size_t Stride_w, typename T>
result<void> conv2d_depthwise_nxm(
const float *input, const float *weights, const float *bias, float *output,
const T *input, const T *weights, const T *bias, T *output,
gsl::span<const size_t> in_shape,
NNCASE_UNUSED gsl::span<const size_t> in_strides,
NNCASE_UNUSED gsl::span<const size_t> w_shape,
Expand All @@ -498,7 +498,7 @@ result<void> conv2d_depthwise_nxm(
NNCASE_UNUSED const padding &padding_w, NNCASE_UNUSED int32_t groups,
NNCASE_UNUSED int32_t stride_h, NNCASE_UNUSED int32_t stride_w,
NNCASE_UNUSED int32_t dilation_h, NNCASE_UNUSED int32_t dilation_w,
value_range<float> fused_activation,
value_range<T> fused_activation,
NNCASE_UNUSED kernels::kernel_context &context) noexcept {
const auto batch = in_shape[0], channels = w_shape[0], in_h = in_shape[2],
in_w = in_shape[3];
Expand All @@ -516,14 +516,13 @@ result<void> conv2d_depthwise_nxm(
#endif
for (int c = 0; c < channels; c++) // channel
{
std::array<float *, Parallel> outptr;
std::array<const float *,
compute_rsize<Parallel, Stride_h, Filter_h>()>
std::array<T *, Parallel> outptr;
std::array<const T *, compute_rsize<Parallel, Stride_h, Filter_h>()>
r;
std::array<const float *, Filter_h> k;
std::array<float, Parallel> sum;
std::array<const T *, Filter_h> k;
std::array<T, Parallel> sum;

float *out = output + out_strides[0] * b + out_strides[1] * c;
T *out = output + out_strides[0] * b + out_strides[1] * c;
std::fill_n(out,
out_strides[2]
? out_h * out_strides[2]
Expand All @@ -539,7 +538,7 @@ result<void> conv2d_depthwise_nxm(
out_h, out_w, sum, r, k, outptr, in_strides[2], out_strides[2],
tail_step);
for (size_t h = 0; h < out_h; h++) {
float *r_out = out + h * out_strides[2];
T *r_out = out + h * out_strides[2];
for (size_t w = 0; w < out_w; w++) {
*(r_out + w) = kernels::detail::apply_activation(
*(r_out + w), fused_activation);
Expand Down Expand Up @@ -610,7 +609,8 @@ result<void> conv2d_depthwise_nxm(
#endif

result<void> optimized::conv2d(
const float *input, const float *weights, const float *bias, float *output,
[[maybe_unused]] typecode_t typecode, const gsl::byte *input1,
const gsl::byte *weights1, const gsl::byte *bias1, gsl::byte *output1,
gsl::span<const size_t> in_shape, gsl::span<const size_t> in_strides,
gsl::span<const size_t> w_shape,
NNCASE_UNUSED gsl::span<const size_t> w_strides,
Expand All @@ -620,6 +620,10 @@ result<void> optimized::conv2d(
int32_t stride_w, int32_t dilation_h, int32_t dilation_w,
value_range<float> fused_activation,
NNCASE_UNUSED kernels::kernel_context &context) noexcept {
[[maybe_unused]] auto input = IN_CAST(float, input1);
[[maybe_unused]] auto weights = IN_CAST(float, weights1);
[[maybe_unused]] auto bias = IN_CAST(float, bias1);
[[maybe_unused]] auto output = OUT_CAST(float, output1);
const auto filter_h = w_shape[2];
const auto filter_w = w_shape[3];

Expand Down Expand Up @@ -675,8 +679,33 @@ result<void> optimized::conv2d(
}
#endif
try_(nncase::kernels::stackvm::reference::conv2d(
input, weights, bias, output, in_shape, in_strides, w_shape, w_strides,
bias_strides, out_strides, padding_h, padding_w, groups, stride_h,
stride_w, dilation_h, dilation_w, fused_activation));
typecode, input1, weights1, bias1, output1, in_shape, in_strides,
w_shape, w_strides, bias_strides, out_strides, padding_h, padding_w,
groups, stride_h, stride_w, dilation_h, dilation_w, fused_activation));
return ok();
}
}

// result<void> optimized::conv2d(
// [[maybe_unused]] typecode_t typecode, const gsl::byte *input,
// const gsl::byte *weights, const gsl::byte *bias, gsl::byte *output,
// gsl::span<const size_t> in_shape, gsl::span<const size_t> in_strides,
// gsl::span<const size_t> w_shape,
// NNCASE_UNUSED gsl::span<const size_t> w_strides,
// NNCASE_UNUSED gsl::span<const size_t> bias_strides,
// NNCASE_UNUSED gsl::span<const size_t> out_strides, const padding
// &padding_h, const padding &padding_w, int32_t groups, int32_t stride_h,
// int32_t stride_w, int32_t dilation_h, int32_t dilation_w,
// value_range<float> fused_activation,
// NNCASE_UNUSED kernels::kernel_context &context) noexcept {
// auto a = conv2d_impl(
// IN_CAST(float, input), IN_CAST(float, weights), IN_CAST(float, bias),
// OUT_CAST(float, output), in_shape, in_strides, w_shape, w_strides,
// bias_strides, out_strides, padding_h, padding_w, groups, stride_h,
// stride_w, dilation_h, dilation_w, fused_activation, context);
// try_(nncase::kernels::stackvm::reference::conv2d(
// typecode, input, weights, bias, output, in_shape, in_strides,
// w_shape, w_strides, bias_strides, out_strides, padding_h, padding_w,
// groups, stride_h, stride_w, dilation_h, dilation_w,
// fused_activation));
// return ok();
// }
7 changes: 4 additions & 3 deletions src/Native/src/kernels/stackvm/optimized/layer_norm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,9 @@ using namespace nncase::kernels::stackvm;
using namespace nncase::kernels::stackvm::optimized;

result<void> nncase::kernels::stackvm::optimized::layer_norm(
const float *input, float *output, const float *scale, const float *bias,
typecode_t typecode, const gsl::byte *input, gsl::byte *output,
const gsl::byte *scale, const gsl::byte *bias,
gsl::span<const size_t> in_shape, int32_t axis, float epsilon) {
return reference::layer_norm(input, output, scale, bias, in_shape, axis,
epsilon);
return reference::layer_norm(typecode, input, output, scale, bias, in_shape,
axis, epsilon);
}
14 changes: 7 additions & 7 deletions src/Native/src/kernels/stackvm/optimized/log_softmax.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -26,17 +26,17 @@ using namespace nncase::kernels::stackvm::optimized;

#include <math.h>

template result<void> optimized::log_softmax<float>(
const float *input, float *output, gsl::span<const size_t> in_shape,
gsl::span<const size_t> in_strides, gsl::span<const size_t> out_strides,
int32_t axis) noexcept;
// template result<void> optimized::log_softmax<float>(
// typecode_t typecode, const gsl::byte *input, gsl::byte *output,
// gsl::span<const size_t> in_shape, gsl::span<const size_t> in_strides,
// gsl::span<const size_t> out_strides, int32_t axis) noexcept;

template <typename T>
result<void> optimized::log_softmax(const T *input, T *output,
result<void> optimized::log_softmax(typecode_t typecode, const gsl::byte *input,
gsl::byte *output,
gsl::span<const size_t> in_shape,
gsl::span<const size_t> in_strides,
gsl::span<const size_t> out_strides,
int32_t axis) noexcept {
return reference::log_softmax(input, output, in_shape, in_strides,
return reference::log_softmax(typecode, input, output, in_shape, in_strides,
out_strides, axis);
}
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