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Add grouped binary convolution support (3/3): indirect BGEMM kernel. #551

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merged 6 commits into from
Jun 7, 2021
Merged

Add grouped binary convolution support (3/3): indirect BGEMM kernel. #551

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705dfbf
Add grouped binary convolution support (3/3): indirect BGEMM kernel.
AdamHillier Oct 22, 2020
e83bbe9
Use `unique_ptr` to avoid need for manual clean-up.
AdamHillier Jun 4, 2021
1700bf4
Use c++14 in Makefile.
AdamHillier Jun 4, 2021
c9c1d15
Review suggestions.
AdamHillier Jun 4, 2021
cf25496
Fix some Clang warnings.
AdamHillier Jun 4, 2021
329ab02
Review suggestions.
AdamHillier Jun 7, 2021
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1 change: 0 additions & 1 deletion larq_compute_engine/core/bconv2d/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,6 @@ cc_library(
deps = [
":zero_padding_correction",
"//larq_compute_engine/core/indirect_bgemm:kernels",
"//larq_compute_engine/core/indirect_bgemm:prepare",
"@org_tensorflow//tensorflow/lite/kernels:cpu_backend_context",
"@org_tensorflow//tensorflow/lite/kernels:cpu_backend_gemm",
"@org_tensorflow//tensorflow/lite/kernels:padding",
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41 changes: 20 additions & 21 deletions larq_compute_engine/core/bconv2d/optimized_indirect_bgemm.h
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Expand Up @@ -12,27 +12,25 @@ namespace bconv2d {

template <typename AccumScalar, typename DstScalar>
inline void BConv2DOptimizedIndirectBGEMM(
const indirect_bgemm::IndirectBGEMMKernel<DstScalar> kernel,
const BConv2DParams* bconv2d_params,
const indirect_bgemm::Kernel* kernel, const BConv2DParams* bconv2d_params,
const RuntimeShape& bitpacked_input_shape, const RuntimeShape& output_shape,
const OutputTransform<DstScalar>& output_transform,
const TBitpacked* packed_weights, const TBitpacked** indirection_buffer,
DstScalar* output_data, const float* padding_buffer, const int pad_value) {
TF_LITE_ASSERT_EQ(bitpacked_input_shape.DimensionsCount(), 4);
TF_LITE_ASSERT_EQ(output_shape.DimensionsCount(), 4);

DstScalar* output_ptr, const float* padding_buffer, const int pad_value) {
ruy::profiler::ScopeLabel label("BConv2D (optimized, indirect BGEMM)");

const std::int32_t conv_kernel_size =
bconv2d_params->filter_height * bconv2d_params->filter_width;
const std::int32_t bitpacked_input_channels = bitpacked_input_shape.Dims(3);
const std::int32_t output_size =
output_shape.Dims(0) * output_shape.Dims(1) * output_shape.Dims(2);
const std::int32_t output_channels = bconv2d_params->channels_out;
// If writing bitpacked output with a channel count that isn't a multiple of
// 32 (i.e. where padding bits will be required in the output), fill the
// output tensor with zeroes in advance so that the BGEMM doesn't have to
// worry about doing the padding.
if (std::is_same<DstScalar, TBitpacked>::value &&
(kernel->output_channels % bitpacking_bitwidth != 0)) {
std::fill(
output_ptr,
output_ptr + kernel->num_output_pixels *
bitpacking::GetBitpackedSize(kernel->output_channels),
TBitpacked(0));
}

indirect_bgemm::RunKernel(kernel, conv_kernel_size, bitpacked_input_channels,
output_size, output_channels, output_transform,
packed_weights, indirection_buffer, output_data);
kernel->Dispatch(reinterpret_cast<void*>(output_ptr));

if (std::is_same<DstScalar, float>::value &&
bconv2d_params->padding_type == TfLitePadding::kTfLitePaddingSame &&
Expand All @@ -44,7 +42,8 @@ inline void BConv2DOptimizedIndirectBGEMM(
const int dilation_width_factor = bconv2d_params->dilation_width_factor;
const int dilation_height_factor = bconv2d_params->dilation_height_factor;
const int batches = MatchingDim(bitpacked_input_shape, 0, output_shape, 0);
const int input_depth = bconv2d_params->channels_in;
const int input_depth_per_group =
bconv2d_params->channels_in / bconv2d_params->groups;
const int input_width = bitpacked_input_shape.Dims(2);
const int input_height = bitpacked_input_shape.Dims(1);
const int filter_height = bconv2d_params->filter_height;
Expand All @@ -54,10 +53,10 @@ inline void BConv2DOptimizedIndirectBGEMM(
const int output_height = output_shape.Dims(1);

zero_padding_correction::ApplyCorrection(
batches, input_height, input_width, input_depth, filter_height,
filter_width, output_depth, stride_height, stride_width,
batches, input_height, input_width, input_depth_per_group,
filter_height, filter_width, output_depth, stride_height, stride_width,
dilation_height_factor, dilation_width_factor,
reinterpret_cast<float*>(output_data), output_height, output_width,
reinterpret_cast<float*>(output_ptr), output_height, output_width,
padding_buffer);
}
}
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2 changes: 1 addition & 1 deletion larq_compute_engine/core/bitpacking/bitpack.h
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Expand Up @@ -22,7 +22,7 @@ namespace bitpacking {
// Utility functions

constexpr int GetBitpackedSize(int unpacked_elements) {
return (unpacked_elements + bitpacking_bitwidth - 1) / bitpacking_bitwidth;
return CeilDiv(unpacked_elements, bitpacking_bitwidth);
}

constexpr int GetBitpackedMatrixSize(int rows, int cols) {
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20 changes: 6 additions & 14 deletions larq_compute_engine/core/indirect_bgemm/BUILD
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Expand Up @@ -2,31 +2,23 @@ licenses(["notice"]) # Apache 2.0

package(default_visibility = ["//visibility:public"])

cc_library(
name = "prepare",
hdrs = [
"prepare.h",
],
deps = [
"//larq_compute_engine/core:types",
"//larq_compute_engine/core/bconv2d:params",
"@org_tensorflow//tensorflow/lite/kernels/internal:types",
],
)

cc_library(
name = "kernels",
hdrs = [
"kernel.h",
srcs = [
"kernel_4x2_portable.h",
"kernel_8x4x1_aarch64.h",
"kernel_8x4x2_aarch64.h",
"kernel_8x4x4_aarch64.h",
],
hdrs = [
"kernel.h",
"select_kernel.h",
],
deps = [
"//larq_compute_engine/core:types",
"//larq_compute_engine/core/bconv2d:output_transform",
"//larq_compute_engine/core/bconv2d:params",
"//larq_compute_engine/core/bitpacking:bitpack",
"@org_tensorflow//tensorflow/lite/kernels/internal:types",
"@ruy//ruy/profiler:instrumentation",
],
Expand Down
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