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Commit 518bba03 authored by Jayaram Bobba's avatar Jayaram Bobba
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Merge remote-tracking branch 'origin/master' into jbobba/maxpool-layouts

parents eed7b313 e46184a1
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Showing with 318 additions and 27 deletions
src/ngraph/runtime/cpu/cpu_emitter.cpp
View file @ 518bba03
......@@ -272,13 +272,6 @@ namespace ngraph
const char* cbeta = "0.0f";
if (args.size() > 2)
{
writer << "memcpy(" << out[0].get_name() << ", " << args[2].get_name() << ", "
<< out[0].get_size() * out[0].get_element_type().size() << ");\n";
cbeta = "1.0f";
}
writer << "cblas::cblas_sgemm("
<< "cblas::Layout::RowMajor, " << tranpose_a << tranpose_b << m << ", " << n
<< ", " << k << ",\n"
......@@ -287,6 +280,101 @@ namespace ngraph
<< " " << out[0].get_name() << ", " << max(1UL, arg2_shape[1])
<< ");\n";
if (args.size() > 2)
{
auto axes = cg->get_broadcast_axes();
if (axes.size() == 1)
{
if (*(axes.begin()) == 0)
{
writer << "static " << out[0].get_element_type().c_type_string()
<< " ones_row[" << arg2_shape[0] << "]"
<< " = { 1.0f";
for (size_t i = 1; i < arg2_shape[0]; ++i)
{
writer << ", 1.0f";
}
writer << "};\n";
writer << "cblas::cblas_sgemm("
<< "cblas::Layout::RowMajor, " << cnotranspose << cnotranspose
<< arg2_shape[0] << ", " << arg2_shape[1] << ", 1"
<< ",\n"
<< " 1.0f, ones_row, "
<< "1"
<< ", " << args[2].get_name() << ", " << max(1UL, arg2_shape[1])
<< ", "
<< "1.0f"
<< ",\n"
<< " " << out[0].get_name() << ", "
<< max(1UL, arg2_shape[1]) << ");\n";
}
else
{
writer << "static " << out[0].get_element_type().c_type_string()
<< " ones_col[" << arg2_shape[1] << "]"
<< " = { 1.0f";
for (size_t i = 1; i < arg2_shape[1]; ++i)
{
writer << ", 1.0f";
}
writer << "};\n";
writer << "cblas::cblas_sgemm("
<< "cblas::Layout::RowMajor, " << cnotranspose << ctranspose
<< arg2_shape[0] << ", " << arg2_shape[1] << ", 1"
<< ",\n"
<< " 1.0f, ones_col," << max(1UL, arg2_shape[1]) << ", "
<< args[2].get_name() << ", "
<< "1"
<< ", "
<< "1.0f"
<< ",\n"
<< " " << out[0].get_name() << ", "
<< max(1UL, arg2_shape[1]) << ");\n";
}
}
else
{
if (axes.size() != 2)
{
throw ngraph_error("unexpected broadcast rank");
}
writer << out[0].get_element_type().c_type_string() << " bias["
<< arg2_shape[1] << "]"
<< " = { " << args[2].get_name() << "[0]";
for (size_t i = 1; i < arg2_shape[1]; ++i)
{
writer << "," << args[2].get_name() << "[0]";
}
writer << "};\n";
writer << "static " << out[0].get_element_type().c_type_string()
<< " ones_scalar[" << arg2_shape[0] << "]"
<< " = { 1.0f";
for (size_t i = 1; i < arg2_shape[0]; ++i)
{
writer << ", 1.0f";
}
writer << "};\n";
writer << "cblas::cblas_sgemm("
<< "cblas::Layout::RowMajor, " << cnotranspose << cnotranspose
<< arg2_shape[0] << ", " << arg2_shape[1] << ", 1"
<< ",\n"
<< " 1.0f, ones_scalar, "
<< "1"
<< ", "
<< "bias"
<< ", " << max(1UL, arg2_shape[1]) << ", "
<< "1.0f"
<< ",\n"
<< " " << out[0].get_name() << ", " << max(1UL, arg2_shape[1])
<< ");\n";
}
}
writer.indent--;
writer << "}\n";
}
......@@ -3022,6 +3110,19 @@ namespace ngraph
auto output_format =
dynamic_cast<runtime::cpu::LayoutDescriptor&>(*output_tvl).get_mkldnn_format();
// MKLDNN relies on format names for selecting optimized kernel implementations
// Hacky way to deal with this until they move to using canonicalized layouts
if (input_format == mkldnn::memory::format::nchw &&
runtime::cpu::mkldnn_utils::is_mkldnn_filter_format(output_format))
{
input_format = mkldnn::memory::format::oihw;
}
if (output_format == mkldnn::memory::format::nchw &&
runtime::cpu::mkldnn_utils::is_mkldnn_filter_format(input_format))
{
output_format = mkldnn::memory::format::oihw;
}
auto& mkldnn_emitter = external_function->get_mkldnn_emitter();
auto input_desc = mkldnn_emitter->build_memory_descriptor(args[0], input_format);
auto result_desc = mkldnn_emitter->build_memory_descriptor(out[0], output_format);
......
src/ngraph/runtime/cpu/mkldnn_utils.cpp
View file @ 518bba03
......@@ -110,6 +110,23 @@ static const std::map<memory::format, const std::string> s_mkldnn_format_string_
{memory::format::OhIw16o4i, "memory::format::OhIw16o4i"},
};
static const std::set<memory::format> s_filter_formats{
memory::format::oihw,
memory::format::ihwo,
memory::format::hwio,
// memory::format::oIhw8i, // These currently map to nChw8c and nChw16c
// memory::format::oIhw16i,
memory::format::OIhw8i8o,
memory::format::OIhw16i16o,
memory::format::IOhw16o16i,
memory::format::OIhw8o8i,
memory::format::OIhw16o16i,
memory::format::Oihw8o,
memory::format::Oihw16o,
memory::format::Ohwi8o,
memory::format::Ohwi16o,
memory::format::OhIw16o4i};
bool runtime::cpu::mkldnn_utils::IsMKLDNNOp(ngraph::Node& op)
{
return (s_op_registry.find(TI(op)) != s_op_registry.end());
......@@ -157,16 +174,16 @@ const std::string& runtime::cpu::mkldnn_utils::get_mkldnn_format_string(memory::
}
mkldnn::memory::format runtime::cpu::mkldnn_utils::get_input_mkldnn_format(const Node* node,
int index)
size_t index)
{
auto tvl = node->get_inputs()[index].get_output().get_tensor_view()->get_tensor_view_layout();
return dynamic_cast<runtime::cpu::LayoutDescriptor&>(*tvl).get_mkldnn_format();
}
mkldnn::memory::format runtime::cpu::mkldnn_utils::get_output_mkldnn_format(const Node* node,
int index)
size_t index)
{
auto tvl = node->get_output_tensor_view(0)->get_tensor_view_layout();
auto tvl = node->get_output_tensor_view(index)->get_tensor_view_layout();
return dynamic_cast<runtime::cpu::LayoutDescriptor&>(*tvl).get_mkldnn_format();
}
......@@ -181,8 +198,8 @@ bool runtime::cpu::mkldnn_utils::use_mkldnn_kernel(const ngraph::Node* node)
bool runtime::cpu::mkldnn_utils::compare_mkldnn_formats(mkldnn::memory::format fmt1,
mkldnn::memory::format fmt2)
{
set<mkldnn::memory::format> similar_4d_formats{mkldnn::memory::format::nchw,
mkldnn::memory::format::oihw};
std::set<mkldnn::memory::format> similar_4d_formats{mkldnn::memory::format::nchw,
mkldnn::memory::format::oihw};
if ((fmt1 == fmt2) || (similar_4d_formats.find(fmt1) != similar_4d_formats.end() &&
similar_4d_formats.find(fmt2) != similar_4d_formats.end()))
{
......@@ -190,3 +207,12 @@ bool runtime::cpu::mkldnn_utils::compare_mkldnn_formats(mkldnn::memory::format f
}
return false;
}
bool runtime::cpu::mkldnn_utils::is_mkldnn_filter_format(mkldnn::memory::format fmt)
{
if (s_filter_formats.find(fmt) != s_filter_formats.end())
{
return true;
}
return false;
}
src/ngraph/runtime/cpu/mkldnn_utils.hpp
View file @ 518bba03
......@@ -39,11 +39,12 @@ namespace ngraph
mkldnn::memory::data_type get_mkldnn_data_type(const ngraph::element::Type& type);
const std::string& get_mkldnn_format_string(mkldnn::memory::format fmt);
mkldnn::memory::format get_input_mkldnn_format(const Node* node, int index);
mkldnn::memory::format get_output_mkldnn_format(const Node* node, int index);
mkldnn::memory::format get_input_mkldnn_format(const Node* node, size_t index);
mkldnn::memory::format get_output_mkldnn_format(const Node* node, size_t index);
bool use_mkldnn_kernel(const ngraph::Node* node);
bool compare_mkldnn_formats(mkldnn::memory::format fmt1,
mkldnn::memory::format fmt2);
bool is_mkldnn_filter_format(mkldnn::memory::format fmt);
}
}
}
......
src/ngraph/runtime/cpu/ops/matmul_bias.cpp
View file @ 518bba03
......@@ -32,7 +32,8 @@ std::shared_ptr<ngraph::Node>
m_shape_w,
m_shape_x,
m_transpose_w,
m_transpose_x);
m_transpose_x,
m_broadcast_axes);
}
ngraph::op::MatmulBias::MatmulBias(std::shared_ptr<ngraph::Node> W,
......@@ -41,7 +42,8 @@ ngraph::op::MatmulBias::MatmulBias(std::shared_ptr<ngraph::Node> W,
Shape shape_w,
Shape shape_x,
bool transpose_w,
bool transpose_x)
bool transpose_x,
AxisSet axes)
: RequiresTensorViewArgs("MatMulBias",
b == nullptr ? std::vector<std::shared_ptr<Node>>{W, x}
: std::vector<std::shared_ptr<Node>>{W, x, b})
......@@ -49,8 +51,24 @@ ngraph::op::MatmulBias::MatmulBias(std::shared_ptr<ngraph::Node> W,
, m_shape_x(shape_x)
, m_transpose_w(transpose_w)
, m_transpose_x(transpose_x)
, m_broadcast_axes(axes)
{
if (axes.size() == 0 && b != nullptr)
{
throw ngraph_error("Bias but no broadcast axes");
}
if (b == nullptr && axes.size() != 0)
{
throw ngraph_error("Broadcast axes but no bias");
}
if (axes.size() > 2)
{
throw ngraph_error("Broadcasting to > 2D tensor");
}
if (shape_w.size() != 2)
{
NGRAPH_DEBUG << "W shape = " << vector_to_string(shape_w);
......
src/ngraph/runtime/cpu/ops/matmul_bias.hpp
View file @ 518bba03
......@@ -16,6 +16,7 @@
#pragma once
#include "ngraph/axis_set.hpp"
#include "ngraph/ops/util/requires_tensor_view_args.hpp"
namespace ngraph
......@@ -31,12 +32,14 @@ namespace ngraph
Shape shape_w,
Shape shape_x,
bool transpose_w,
bool transpose_x);
bool transpose_x,
AxisSet axes = AxisSet{});
bool get_is_arg0_transposed() const { return m_transpose_w; }
bool get_is_arg1_transposed() const { return m_transpose_x; }
Shape get_arg0_shape() const { return m_shape_w; }
Shape get_arg1_shape() const { return m_shape_x; }
const AxisSet& get_broadcast_axes() const { return m_broadcast_axes; }
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
......@@ -45,6 +48,7 @@ namespace ngraph
Shape m_shape_x;
bool m_transpose_w;
bool m_transpose_x;
AxisSet m_broadcast_axes;
};
}
}
src/ngraph/runtime/cpu/pass/cpu_assignment.cpp
View file @ 518bba03
......@@ -27,6 +27,7 @@
#include "ngraph/descriptor/output.hpp"
#include "ngraph/ops/add.hpp"
#include "ngraph/ops/avg_pool.hpp"
#include "ngraph/ops/batch_norm.hpp"
#include "ngraph/ops/convolution.hpp"
#include "ngraph/ops/max_pool.hpp"
#include "ngraph/ops/relu.hpp"
......@@ -265,6 +266,16 @@ namespace ngraph
relu_bprop->set_op_annotations(op_annotations);
}
}
template <>
void CPUAssignment::ASSIGN_DECL(ngraph::op::BatchNorm)
{
auto batchnorm = static_cast<op::BatchNorm*>(node);
auto op_annotations =
std::make_shared<ngraph::runtime::cpu::CPUOpAnnotations>();
op_annotations->set_mkldnn_op(true);
batchnorm->set_op_annotations(op_annotations);
}
}
}
}
......@@ -277,6 +288,7 @@ static const runtime::cpu::pass::AssignOpMap s_dispatcher{
{TI(ngraph::op::AvgPool), &runtime::cpu::pass::CPUAssignment::assign<ngraph::op::AvgPool>},
{TI(ngraph::op::AvgPoolBackprop),
&runtime::cpu::pass::CPUAssignment::assign<ngraph::op::AvgPoolBackprop>},
{TI(ngraph::op::BatchNorm), &runtime::cpu::pass::CPUAssignment::assign<ngraph::op::BatchNorm>},
{TI(ngraph::op::Convolution),
&runtime::cpu::pass::CPUAssignment::assign<ngraph::op::Convolution>},
{TI(ngraph::op::ConvolutionBackpropData),
......
src/ngraph/runtime/cpu/pass/cpu_fusion.cpp
View file @ 518bba03
......@@ -134,12 +134,21 @@ void ngraph::runtime::cpu::pass::CPUFusion::construct_matmulbias_pattern()
<< m.match_root()->get_name();
auto mpattern = m.match_root(); //add
auto m_matmul = mpattern->get_input_op(0);
auto m_broadcast = mpattern->get_input_op(1);
auto m_matmul = std::dynamic_pointer_cast<op::MatmulBias>(mpattern->get_input_op(0));
auto m_broadcast = std::dynamic_pointer_cast<op::Broadcast>(mpattern->get_input_op(1));
auto m_bias = m_broadcast->get_input_op(0);
auto pattern_map = m.get_pattern_map();
return m_matmul->copy_with_new_args(
NodeVector{pattern_map[W], pattern_map[x], m_broadcast});
auto mmb = std::make_shared<op::MatmulBias>(pattern_map[W],
pattern_map[x],
m_bias,
m_matmul->get_arg0_shape(),
m_matmul->get_arg1_shape(),
m_matmul->get_is_arg0_transposed(),
m_matmul->get_is_arg1_transposed(),
m_broadcast->get_broadcast_axes());
return mmb;
};
auto m = std::make_shared<ngraph::pattern::Matcher>(padd, callback);
......
src/ngraph/runtime/cpu/pass/cpu_layout.cpp
View file @ 518bba03
......@@ -28,8 +28,10 @@
#include "ngraph/log.hpp"
#include "ngraph/ops/add.hpp"
#include "ngraph/ops/avg_pool.hpp"
#include "ngraph/ops/batch_norm.hpp"
#include "ngraph/ops/convolution.hpp"
#include "ngraph/ops/max_pool.hpp"
#include "ngraph/ops/get_output_element.hpp"
#include "ngraph/ops/op.hpp"
#include "ngraph/ops/relu.hpp"
#include "ngraph/ops/result.hpp"
......@@ -796,6 +798,17 @@ namespace ngraph
set_output_layouts(node, prim_output_formats);
}
template <>
void CPULayout::LAYOUT_DECL(ngraph::op::GetOutputElement)
{
auto goe = static_cast<const ngraph::op::GetOutputElement*>(node.get());
auto input_layout = runtime::cpu::mkldnn_utils::get_input_mkldnn_format(
node.get(), goe->get_n());
vector<memory::format> prim_output_formats;
prim_output_formats.push_back(input_layout);
set_output_layouts(node, prim_output_formats);
}
template <>
void CPULayout::LAYOUT_DECL(ngraph::op::Relu)
{
......@@ -836,6 +849,32 @@ namespace ngraph
}
}
template <>
void CPULayout::LAYOUT_DECL(ngraph::op::BatchNorm)
{
if (runtime::cpu::mkldnn_utils::use_mkldnn_kernel(node.get()))
{
auto input_layout =
runtime::cpu::mkldnn_utils::get_input_mkldnn_format(node.get(), 2);
vector<memory::format> prim_input_formats;
vector<memory::format> prim_output_formats;
prim_input_formats.push_back(memory::format::x);
prim_input_formats.push_back(memory::format::x);
prim_input_formats.push_back(input_layout);
prim_output_formats.push_back(input_layout);
prim_output_formats.push_back(memory::format::x);
prim_output_formats.push_back(memory::format::x);
node =
insert_input_conversions(external_function, node, prim_input_formats);
set_output_layouts(node, prim_output_formats);
}
else
{
throw ngraph_error("Batchnorm only supported in MKLDNN for now");
}
}
template <>
void CPULayout::LAYOUT_DECL(ngraph::op::Add)
{
......@@ -878,6 +917,9 @@ static const runtime::cpu::pass::LayoutOpMap s_dispatcher{
{TI(ngraph::op::MaxPool), &runtime::cpu::pass::CPULayout::layout<ngraph::op::MaxPool>},
{TI(ngraph::op::MaxPoolBackprop),
&runtime::cpu::pass::CPULayout::layout<ngraph::op::MaxPoolBackprop>},
{TI(ngraph::op::BatchNorm), &runtime::cpu::pass::CPULayout::layout<ngraph::op::BatchNorm>},
{TI(ngraph::op::GetOutputElement),
&runtime::cpu::pass::CPULayout::layout<ngraph::op::GetOutputElement>},
{TI(ngraph::op::Relu), &runtime::cpu::pass::CPULayout::layout<ngraph::op::Relu>},
{TI(ngraph::op::Result), &runtime::cpu::pass::CPULayout::layout<ngraph::op::Result>},
{TI(ngraph::op::ReluBackprop),
......
test/cpu_fusion.cpp
View file @ 518bba03
......@@ -91,11 +91,89 @@ TEST(cpu_fusion, gemm_pattern)
ASSERT_EQ(n.get_pattern_map()[x], B);
ASSERT_EQ(n.get_pattern_map()[b], C);
auto cg =
make_shared<op::MatmulBias>(W, x, broadcast, W->get_shape(), x->get_shape(), false, false);
auto cg = make_shared<op::MatmulBias>(
W, x, C, W->get_shape(), x->get_shape(), false, false, AxisSet{0});
}
TEST(cpu_fusion, gemm_cpu_broadcast_row)
{
Shape shapeA{3, 2};
Shape shapeB{2, 3};
Shape shapeC{2, 2};
auto A = make_shared<op::Parameter>(element::f32, shapeA);
auto B = make_shared<op::Parameter>(element::f32, shapeB);
auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, Shape{2, 3});
auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, Shape{3, 2});
auto one = op::Constant::create<float>(element::f32, Shape{2}, std::vector<float>{1.0f, 1.0f});
auto broadcast = make_shared<op::Broadcast>(one, shapeC, AxisSet{0});
auto cg = make_shared<op::MatmulBias>(
A, B, one, A->get_shape(), B->get_shape(), true, true, AxisSet{0});
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
auto manager = runtime::Manager::get("CPU");
auto external = manager->compile(f);
auto backend = manager->allocate_backend();
auto cf = backend->make_call_frame(external);
shared_ptr<runtime::TensorView> a = backend->make_primary_tensor_view(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->make_primary_tensor_view(element::f32, shapeB);
shared_ptr<runtime::TensorView> result =
backend->make_primary_tensor_view(element::f32, shapeC);
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
copy_data(a, dataA);
copy_data(b, dataB);
cf->call({a, b}, {result});
vector<float> expected{10, 28, 37, 109};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, gemm_cpu)
TEST(cpu_fusion, gemm_cpu_broadcast_column)
{
Shape shapeA{3, 2};
Shape shapeB{2, 3};
Shape shapeC{2, 2};
auto A = make_shared<op::Parameter>(element::f32, shapeA);
auto B = make_shared<op::Parameter>(element::f32, shapeB);
auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, Shape{2, 3});
auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, Shape{3, 2});
auto one = op::Constant::create<float>(element::f32, Shape{2}, std::vector<float>{1.0f, 1.0f});
auto broadcast = make_shared<op::Broadcast>(one, shapeC, AxisSet{1});
auto cg = make_shared<op::MatmulBias>(
A, B, one, A->get_shape(), B->get_shape(), true, true, AxisSet{1});
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
auto manager = runtime::Manager::get("CPU");
auto external = manager->compile(f);
auto backend = manager->allocate_backend();
auto cf = backend->make_call_frame(external);
shared_ptr<runtime::TensorView> a = backend->make_primary_tensor_view(element::f32, shapeA);
shared_ptr<runtime::TensorView> b = backend->make_primary_tensor_view(element::f32, shapeB);
shared_ptr<runtime::TensorView> result =
backend->make_primary_tensor_view(element::f32, shapeC);
vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
copy_data(a, dataA);
copy_data(b, dataB);
cf->call({a, b}, {result});
vector<float> expected{10, 28, 37, 109};
ASSERT_TRUE(read_vector<float>(result) == expected);
}
TEST(cpu_fusion, gemm_cpu_broadcast_matrix)
{
Shape shapeA{3, 2};
Shape shapeB{2, 3};
......@@ -109,8 +187,8 @@ TEST(cpu_fusion, gemm_cpu)
auto one = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{1.0f});
auto broadcast = make_shared<op::Broadcast>(one, shapeC, AxisSet{0, 1});
auto cg =
make_shared<op::MatmulBias>(A, B, broadcast, A->get_shape(), B->get_shape(), true, true);
auto cg = make_shared<op::MatmulBias>(
A, B, one, A->get_shape(), B->get_shape(), true, true, AxisSet{0, 1});
auto f = make_shared<Function>(cg, op::ParameterVector{A, B});
......@@ -212,7 +290,7 @@ TEST(cpu_fusion, cpu_fusion_pass_matmul_bias)
pass_manager.run_passes(func);
auto gmm = graph->get_input_op(0);
ASSERT_TRUE(std::dynamic_pointer_cast<op::MatmulBias>(gmm));
ASSERT_EQ(gmm->get_input_op(2), broadcast);
ASSERT_EQ(gmm->get_input_op(2), b);
}
TEST(cpu_fusion, cpu_fusion_pass_matmul_no_bias)
......
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