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Commit 134b0ae2 authored by shssf's avatar shssf Committed by Scott Cyphers
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IntelGPU backend: BatchNorm, Dot, Pad operations optimization (#1393)

parent 9c1c5b59
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Showing with 58 additions and 102 deletions
src/ngraph/runtime/intelgpu/intelgpu_op_batchnorm.cpp
View file @ 134b0ae2
......@@ -216,6 +216,7 @@ void runtime::intelgpu::do_batch_norm_operation(cldnn::topology& topology,
const cldnn::layout layout = IntelGPULayout::create_cldnn_layout(output_type, output_shape);
const string entry_point_name = "batch_norm_" + output_name;
codegen::CodeWriter writer;
vector<size_t> gws;
writer << "__kernel void " << entry_point_name << "( const __global float input"
<< array_dims(input_shape) << ", const __global float gamma" << array_dims(gamma_shape)
......@@ -227,45 +228,17 @@ void runtime::intelgpu::do_batch_norm_operation(cldnn::topology& topology,
writer.block_begin();
{ // Main function body
// Loop for Channel axis 1
writer << "for (uint i" << channel_axis << " = 0; i" << channel_axis << " < "
<< output_shape.at(channel_axis) << "; ++i" << channel_axis << ")\n";
writer.block_begin();
{
size_t var_idx = 0;
// Main loops
for (auto const& i : output_shape)
{
if (var_idx != channel_axis)
{
writer << "for (uint i" << var_idx << " = 0; i" << var_idx << " < " << i
<< "; ++i" << var_idx << ")\n";
writer.block_begin();
}
++var_idx;
}
gws = generate_loops(writer, output_shape, true);
writer << "float normalized = (input" << access_dims(input_shape) << " - mean[i"
<< channel_axis << "]) / ("
<< "sqrt(variance[i" << channel_axis << "] + " << eps << ")"
<< ");\n";
writer << "output" << access_dims(output_shape) << " = normalized * gamma[i"
<< channel_axis << "] + beta[i" << channel_axis << "];\n";
var_idx = 0;
// Closing brackets for main loops
for (auto const& i : output_shape)
{
if (var_idx != channel_axis)
{
writer.block_end();
}
++var_idx;
}
writer << "output" << access_dims(output_shape) << " = normalized * gamma[i" << channel_axis
<< "] + beta[i" << channel_axis << "];\n";
} // Closing brackets for Channel axis loop
writer.block_end();
generate_loops(writer, output_shape, false);
} // Main function body
writer.block_end();
......@@ -279,6 +252,6 @@ void runtime::intelgpu::do_batch_norm_operation(cldnn::topology& topology,
get_kernel_args(5, 1),
"",
layout,
{1});
gws);
topology.add(op_batch_norm);
}
src/ngraph/runtime/intelgpu/intelgpu_op_custom_kernels.cpp
View file @ 134b0ae2
......@@ -18,7 +18,6 @@
#include <CPP/custom_gpu_primitive.hpp>
#include <CPP/reshape.hpp>
#include "ngraph/runtime/intelgpu/code_writer.hpp"
#include "ngraph/runtime/intelgpu/intelgpu_layout.hpp"
#include "ngraph/runtime/intelgpu/intelgpu_op_custom_kernels.hpp"
......@@ -88,7 +87,9 @@ string
return buffer;
}
static vector<size_t> generate_loops(codegen::CodeWriter& writer, const Shape& shape, bool is_begin)
vector<size_t> runtime::intelgpu::generate_loops(codegen::CodeWriter& writer,
const Shape& shape,
bool is_begin)
{
const size_t cldnn_gws_lim = 3;
vector<size_t> gws;
......@@ -170,6 +171,7 @@ void runtime::intelgpu::do_pad_operation(cldnn::topology& topology,
{
const string entry_point_name = "op_pad_kernel_" + output_name;
codegen::CodeWriter writer;
vector<size_t> gws;
// The kernel name and parameters
writer << "__kernel void " << entry_point_name << "(const __global float input"
......@@ -179,26 +181,16 @@ void runtime::intelgpu::do_pad_operation(cldnn::topology& topology,
writer.block_begin();
{
// Loop for Broadcast scalar over full output tensor
size_t var_idx = 0;
for (auto const& i : output_shape)
{
writer << "for (uint i" << var_idx << " = 0; i" << var_idx << " < " << i << "; ++i"
<< var_idx << ")\n";
writer.block_begin();
++var_idx;
}
gws = runtime::intelgpu::generate_loops(writer, output_shape, true);
writer << "output" << access_dims(output_shape) << " = scalar[0];\n";
// Closing brackets for Broadcast loop
for (auto const& i : output_shape)
{
writer.block_end();
}
runtime::intelgpu::generate_loops(writer, output_shape, false);
// Loop for Copy input matrix into output matrix with padding.
// Padding include "pad_below" and "pad_interior" according nGraph documentation
var_idx = 0;
size_t var_idx = 0;
for (auto const& i : input_shape)
{
writer << "for (uint i" << var_idx << " = 0; i" << var_idx << " < " << i << "; ++i"
......@@ -220,15 +212,15 @@ void runtime::intelgpu::do_pad_operation(cldnn::topology& topology,
writer.block_end();
const cldnn::layout layout = IntelGPULayout::create_cldnn_layout(output_type, output_shape);
const cldnn::custom_gpu_primitive op_scalar(output_name,
const cldnn::custom_gpu_primitive op_pad(output_name,
{input_name, scalar_name},
{writer.get_code()},
entry_point_name,
get_kernel_args(2, 1),
"",
layout,
{1});
topology.add(op_scalar);
gws);
topology.add(op_pad);
}
static void do_1d_scalar_mul(codegen::CodeWriter& writer,
......@@ -256,7 +248,7 @@ static void do_1d_scalar_mul(codegen::CodeWriter& writer,
writer.block_end();
}
static void do_2d_2d_mul(codegen::CodeWriter& writer,
static vector<size_t> do_2d_2d_mul(codegen::CodeWriter& writer,
string& kernel_name,
const Shape& shapeA,
const Shape& shapeB,
......@@ -264,6 +256,7 @@ static void do_2d_2d_mul(codegen::CodeWriter& writer,
{
const size_t colrow = shapeA.at(1);
kernel_name += "_do_2d_2d_mul";
vector<size_t> gws;
writer << "__kernel void " << kernel_name << "(const __global float inputA"
<< runtime::intelgpu::array_dims(shapeA) << ", const __global float inputB"
......@@ -273,13 +266,7 @@ static void do_2d_2d_mul(codegen::CodeWriter& writer,
{
size_t var_idx = 0;
// Main loops
for (auto const& i : shapeZ)
{
writer << "for (uint i" << var_idx << " = 0; i" << var_idx << " < " << i << "; ++i"
<< var_idx << ")\n";
writer.block_begin();
++var_idx;
}
gws = runtime::intelgpu::generate_loops(writer, shapeZ, true);
// Inner loop
writer << "float sum = 0.0f;\n";
......@@ -292,15 +279,14 @@ static void do_2d_2d_mul(codegen::CodeWriter& writer,
writer << "output[i0][i1] = sum;\n";
// Closing brackets for main loops
for (auto const& i : shapeZ)
{
writer.block_end();
}
runtime::intelgpu::generate_loops(writer, shapeZ, false);
}
writer.block_end();
return gws;
}
static void do_3d_3d_mul(codegen::CodeWriter& writer,
static vector<size_t> do_3d_3d_mul(codegen::CodeWriter& writer,
string& kernel_name,
const Shape& shapeA,
const Shape& shapeB,
......@@ -308,6 +294,7 @@ static void do_3d_3d_mul(codegen::CodeWriter& writer,
{
const size_t colrow = shapeA.back();
kernel_name += "_do_3d_3d_mul";
vector<size_t> gws;
writer << "__kernel void " << kernel_name << "(const __global float inputA"
<< runtime::intelgpu::array_dims(shapeA) << ", const __global float inputB"
......@@ -317,13 +304,7 @@ static void do_3d_3d_mul(codegen::CodeWriter& writer,
{
size_t var_idx = 0;
// Main loops
for (auto const& i : shapeZ)
{
writer << "for (uint i" << var_idx << " = 0; i" << var_idx << " < " << i << "; ++i"
<< var_idx << ")\n";
writer.block_begin();
++var_idx;
}
gws = runtime::intelgpu::generate_loops(writer, shapeZ, true);
// Inner loop
writer << "float sum = 0.0f;\n";
......@@ -336,15 +317,14 @@ static void do_3d_3d_mul(codegen::CodeWriter& writer,
writer << "output[i0][i1][i2][i3] = sum;\n";
// Closing brackets for main loops
for (auto const& i : shapeZ)
{
writer.block_end();
}
runtime::intelgpu::generate_loops(writer, shapeZ, false);
}
writer.block_end();
return gws;
}
static void do_3d_2d_mul(codegen::CodeWriter& writer,
static vector<size_t> do_3d_2d_mul(codegen::CodeWriter& writer,
string& kernel_name,
const Shape& shapeA,
const Shape& shapeB,
......@@ -352,6 +332,7 @@ static void do_3d_2d_mul(codegen::CodeWriter& writer,
{
const size_t colrow = shapeA.back();
kernel_name += "_do_3d_2d_mul";
vector<size_t> gws;
writer << "__kernel void " << kernel_name << "(const __global float inputA"
<< runtime::intelgpu::array_dims(shapeA) << ", const __global float inputB"
......@@ -361,13 +342,7 @@ static void do_3d_2d_mul(codegen::CodeWriter& writer,
{
size_t var_idx = 0;
// Main loops
for (auto const& i : shapeZ)
{
writer << "for (uint i" << var_idx << " = 0; i" << var_idx << " < " << i << "; ++i"
<< var_idx << ")\n";
writer.block_begin();
++var_idx;
}
gws = runtime::intelgpu::generate_loops(writer, shapeZ, true);
// Inner loop
writer << "float sum = 0.0f;\n";
......@@ -380,33 +355,34 @@ static void do_3d_2d_mul(codegen::CodeWriter& writer,
writer << "output[i0][i1][i2] = sum;\n";
// Closing brackets for main loops
for (auto const& i : shapeZ)
{
writer.block_end();
}
runtime::intelgpu::generate_loops(writer, shapeZ, false);
}
writer.block_end();
return gws;
}
static void do_2d_1d_mul(codegen::CodeWriter& writer,
static vector<size_t> do_2d_1d_mul(codegen::CodeWriter& writer,
string& kernel_name,
const Shape& shapeA,
const Shape& shapeB)
const Shape& shapeB,
const Shape& shapeZ)
{
const size_t rows = shapeA.at(0);
const size_t colrow = shapeA.at(1);
kernel_name += "_do_2d_1d_mul";
vector<size_t> gws;
writer << "__kernel void " << kernel_name << "(const __global float inputA"
<< runtime::intelgpu::array_dims(shapeA) << ", const __global float inputB"
<< runtime::intelgpu::array_dims(shapeB) << ", __global float output"
<< runtime::intelgpu::array_dims({rows}) << ")\n";
writer.block_begin();
{
writer << "for (uint i0 = 0; i0 < " << rows << "; ++i0)\n";
<< runtime::intelgpu::array_dims(shapeZ) << ")\n";
writer.block_begin();
{
// Main loops
gws = runtime::intelgpu::generate_loops(writer, shapeZ, true);
writer << "float sum = 0.0f;\n";
// Inner loop
writer << "for (uint i1 = 0; i1 < " << colrow << "; ++i1)\n";
writer.block_begin();
{
......@@ -414,10 +390,13 @@ static void do_2d_1d_mul(codegen::CodeWriter& writer,
}
writer.block_end();
writer << "output[i0] = sum;\n";
// Closing brackets for main loops
runtime::intelgpu::generate_loops(writer, shapeZ, false);
}
writer.block_end();
}
writer.block_end();
return gws;
}
static void do_scalar_scalar_mul(codegen::CodeWriter& writer, string& kernel_name)
......@@ -473,6 +452,7 @@ void runtime::intelgpu::do_dot_operation(cldnn::topology& topology,
const cldnn::layout layout = IntelGPULayout::create_cldnn_layout(output_type, output_shape);
string entry_point_name = "dot_" + output_name;
codegen::CodeWriter writer;
vector<size_t> gws = {1};
const bool A_is_scalar = inputA_shape.empty();
const bool B_is_scalar = inputB_shape.empty();
......@@ -494,19 +474,19 @@ void runtime::intelgpu::do_dot_operation(cldnn::topology& topology,
{
if (inputA_shape.size() == 2 && inputB_shape.size() == 1)
{
do_2d_1d_mul(writer, entry_point_name, inputA_shape, inputB_shape);
gws = do_2d_1d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
}
else if (inputA_shape.size() == 2 && inputB_shape.size() == 2)
{
do_2d_2d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
gws = do_2d_2d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
}
else if (inputA_shape.size() == 3 && inputB_shape.size() == 3)
{
do_3d_3d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
gws = do_3d_3d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
}
else if (inputA_shape.size() == 3 && inputB_shape.size() == 2)
{
do_3d_2d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
gws = do_3d_2d_mul(writer, entry_point_name, inputA_shape, inputB_shape, output_shape);
}
else
{
......@@ -518,7 +498,6 @@ void runtime::intelgpu::do_dot_operation(cldnn::topology& topology,
do_dot_operation_error(inputA_shape, inputB_shape, output_shape);
}
//cout << writer.get_code() << endl;
const cldnn::custom_gpu_primitive op_dot(output_name,
{inputA_name, inputB_name},
{writer.get_code()},
......@@ -526,7 +505,7 @@ void runtime::intelgpu::do_dot_operation(cldnn::topology& topology,
get_kernel_args(2, 1),
"",
layout,
{1});
gws);
topology.add(op_dot);
}
......
src/ngraph/runtime/intelgpu/intelgpu_op_custom_kernels.hpp
View file @ 134b0ae2
......@@ -18,6 +18,8 @@
#include <CPP/topology.hpp>
#include "ngraph/runtime/intelgpu/code_writer.hpp"
#include "ngraph/axis_set.hpp"
#include "ngraph/coordinate.hpp"
#include "ngraph/shape.hpp"
......@@ -96,6 +98,8 @@ namespace ngraph
std::string access_dims(const Shape& dimentions,
const AxisSet& axis = {},
bool is_reversed = false);
std::vector<size_t>
generate_loops(codegen::CodeWriter& writer, const Shape& shape, bool is_begin);
}
}
}
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