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Commit c527d0b5 authored by wsttiger's avatar wsttiger
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parent f9f4f713
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src/targets/gpu/CMakeLists.txt
View file @ c527d0b5
...@@ -26,6 +26,9 @@ add_library(migraph_gpu ...@@ -26,6 +26,9 @@ add_library(migraph_gpu
hip.cpp hip.cpp
target.cpp target.cpp
lowering.cpp lowering.cpp
gemm.cpp
pooling.cpp
convolution.cpp
write_literals.cpp write_literals.cpp
rocblas.cpp rocblas.cpp
) )
......
src/targets/gpu/convolution.cpp 0 → 100644
View file @ c527d0b5
#include <migraph/gpu/convolution.hpp>
#include <migraph/operators.hpp>
#include <migraph/manage_ptr.hpp>
#include <migraph/gpu/miopen.hpp>
#include <utility>
namespace migraph {
namespace gpu {
shape miopen_convolution::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(4).standard();
return op.compute_shape({inputs.at(0), inputs.at(1)});
}
argument
miopen_convolution::compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
auto x_desc = make_tensor(args[0].get_shape());
auto w_desc = make_tensor(args[1].get_shape());
auto y_desc = make_tensor(output_shape);
float alpha = 1, beta = 0;
miopenConvolutionForward(ctx.handle.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
w_desc.get(),
args[1].implicit(),
cd.get(),
algo,
&beta,
y_desc.get(),
args[3].implicit(),
args[2].implicit(),
args[2].get_shape().bytes());
return args[3];
}
shape miopen_convolution::compile(context& ctx, const shape& output_shape, std::vector<instruction_ref> inputs)
{
shape workspace_shape{};
auto x_desc = make_tensor(inputs[0]->get_shape());
auto w_desc = make_tensor(inputs[1]->get_shape());
auto y_desc = make_tensor(output_shape);
std::size_t workspace_size = 0;
miopenConvolutionForwardGetWorkSpaceSize(
ctx.handle.get(), w_desc.get(), x_desc.get(), cd.get(), y_desc.get(), &workspace_size);
workspace_shape = shape{shape::int8_type, {workspace_size}};
auto x = to_gpu(generate_argument(inputs[0]->get_shape()));
auto w = to_gpu(generate_argument(inputs[1]->get_shape()));
auto y = to_gpu(generate_argument(output_shape));
auto workspace = allocate_gpu(workspace_shape);
int algo_count = 1;
miopenConvAlgoPerf_t perf;
miopenFindConvolutionForwardAlgorithm(ctx.handle.get(),
x_desc.get(),
x.implicit(),
w_desc.get(),
w.implicit(),
cd.get(),
y_desc.get(),
y.implicit(),
1,
&algo_count,
&perf,
workspace.implicit(),
workspace_size,
false);
algo = perf.fwd_algo;
return shape{shape::int8_type, {perf.memory}};
}
} // namespace gpu
} // namespace migraph
src/targets/gpu/gemm.cpp 0 → 100644
View file @ c527d0b5
#include <migraph/gpu/gemm.hpp>
#include <migraph/operators.hpp>
#include <migraph/manage_ptr.hpp>
#include <migraph/gpu/miopen.hpp>
#include <utility>
namespace migraph {
namespace gpu {
shape miopen_gemm::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(3);
return op.compute_shape({inputs.at(0), inputs.at(1)});
}
argument
miopen_gemm::compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
float alpha = 1.0f;
float beta = 0.0f;
bool transa = args[0].get_shape().transposed();
bool transb = args[1].get_shape().transposed();
rocblas_int lda = args[0].get_shape().strides()[transa ? 1 : 0];
rocblas_int ldb = args[1].get_shape().strides()[transb ? 1 : 0];
rocblas_int ldc = args[2].get_shape().strides()[0];
rocblas_int m = output_shape.lens()[0];
rocblas_int n = output_shape.lens()[1];
rocblas_int k = args[0].get_shape().lens()[1];
rocblas_sgemm(ctx.rbhandle.get(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
&alpha,
args[1].implicit(),
ldb,
args[0].implicit(),
lda,
&beta,
args[2].implicit(),
ldc);
return args[2];
}
} // namespace gpu
} // namespace migraph
src/targets/gpu/include/migraph/gpu/convolution.hpp 0 → 100644
View file @ c527d0b5
#ifndef MIGRAPH_GUARD_RTGLIB_CONVOLUTION_HPP
#define MIGRAPH_GUARD_RTGLIB_CONVOLUTION_HPP
#include <migraph/gpu/lowering.hpp>
#include <migraph/manage_ptr.hpp>
#include <migraph/instruction.hpp>
#include <migraph/operators.hpp>
#include <migraph/generate.hpp>
#include <migraph/shape_for_each.hpp>
#include <migraph/gpu/miopen.hpp>
#include <migraph/gpu/hip.hpp>
#include <migraph/dfor.hpp>
#include <migraph/gpu/device/contiguous.hpp>
#include <migraph/gpu/device/add.hpp>
#include <migraph/iterator_for.hpp>
#include <migraph/gpu/rocblas.hpp>
#include <migraph/gpu/context.hpp>
#include <utility>
namespace migraph {
namespace gpu {
struct miopen_convolution
{
convolution op;
shared<convolution_descriptor> cd;
miopenConvFwdAlgorithm_t algo{};
std::string name() const { return "gpu::convolution"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
shape compile(context& ctx, const shape& output_shape, std::vector<instruction_ref> inputs);
friend std::ostream& operator<<(std::ostream& os, const miopen_convolution& self)
{
os << self.name() << "[";
os << self.op << ", ";
os << "algo=" << self.algo;
os << "]";
return os;
}
};
} // namespace gpu
} // namespace migraph
#endif
src/targets/gpu/include/migraph/gpu/gemm.hpp 0 → 100644
View file @ c527d0b5
#ifndef MIGRAPH_GUARD_RTGLIB_GEMM_HPP
#define MIGRAPH_GUARD_RTGLIB_GEMM_HPP
#include <migraph/gpu/lowering.hpp>
#include <migraph/manage_ptr.hpp>
#include <migraph/instruction.hpp>
#include <migraph/operators.hpp>
#include <migraph/generate.hpp>
#include <migraph/shape_for_each.hpp>
#include <migraph/gpu/miopen.hpp>
#include <migraph/gpu/hip.hpp>
#include <migraph/dfor.hpp>
#include <migraph/gpu/device/contiguous.hpp>
#include <migraph/gpu/device/add.hpp>
#include <migraph/iterator_for.hpp>
#include <migraph/gpu/rocblas.hpp>
#include <migraph/gpu/context.hpp>
#include <utility>
namespace migraph {
namespace gpu {
struct miopen_gemm
{
gemm op;
std::string name() const { return "gpu::gemm"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
};
} // namespace gpu
} // namespace migraph
#endif
src/targets/gpu/include/migraph/gpu/pooling.hpp 0 → 100644
View file @ c527d0b5
#ifndef MIGRAPH_GUARD_RTGLIB_POOLING_HPP
#define MIGRAPH_GUARD_RTGLIB_POOLING_HPP
#include <migraph/gpu/lowering.hpp>
#include <migraph/manage_ptr.hpp>
#include <migraph/instruction.hpp>
#include <migraph/operators.hpp>
#include <migraph/generate.hpp>
#include <migraph/shape_for_each.hpp>
#include <migraph/gpu/miopen.hpp>
#include <migraph/gpu/hip.hpp>
#include <migraph/dfor.hpp>
#include <migraph/gpu/device/contiguous.hpp>
#include <migraph/gpu/device/add.hpp>
#include <migraph/iterator_for.hpp>
#include <migraph/gpu/rocblas.hpp>
#include <migraph/gpu/context.hpp>
#include <utility>
namespace migraph {
namespace gpu {
struct miopen_pooling
{
pooling op;
shared<pooling_descriptor> pd;
std::string name() const { return "gpu::pooling"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
};
} // namespace gpu
} // namespace migraph
#endif
src/targets/gpu/lowering.cpp
View file @ c527d0b5
...@@ -13,6 +13,9 @@ ...@@ -13,6 +13,9 @@
#include <migraph/iterator_for.hpp> #include <migraph/iterator_for.hpp>
#include <migraph/gpu/rocblas.hpp> #include <migraph/gpu/rocblas.hpp>
#include <migraph/gpu/context.hpp> #include <migraph/gpu/context.hpp>
#include <migraph/gpu/convolution.hpp>
#include <migraph/gpu/pooling.hpp>
#include <migraph/gpu/gemm.hpp>
#include <utility> #include <utility>
namespace migraph { namespace migraph {
...@@ -59,124 +62,6 @@ struct miopen_batch_norm_inference ...@@ -59,124 +62,6 @@ struct miopen_batch_norm_inference
} }
}; };
struct miopen_convolution
{
convolution op;
shared<convolution_descriptor> cd;
miopenConvFwdAlgorithm_t algo{};
std::string name() const { return "gpu::convolution"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(4).standard();
return op.compute_shape({inputs.at(0), inputs.at(1)});
}
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
auto x_desc = make_tensor(args[0].get_shape());
auto w_desc = make_tensor(args[1].get_shape());
auto y_desc = make_tensor(output_shape);
float alpha = 1, beta = 0;
miopenConvolutionForward(ctx.handle.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
w_desc.get(),
args[1].implicit(),
cd.get(),
algo,
&beta,
y_desc.get(),
args[3].implicit(),
args[2].implicit(),
args[2].get_shape().bytes());
return args[3];
}
shape compile(context& ctx, const shape& output_shape, std::vector<instruction_ref> inputs)
{
shape workspace_shape{};
auto x_desc = make_tensor(inputs[0]->get_shape());
auto w_desc = make_tensor(inputs[1]->get_shape());
auto y_desc = make_tensor(output_shape);
std::size_t workspace_size = 0;
miopenConvolutionForwardGetWorkSpaceSize(
ctx.handle.get(), w_desc.get(), x_desc.get(), cd.get(), y_desc.get(), &workspace_size);
workspace_shape = shape{shape::int8_type, {workspace_size}};
auto x = to_gpu(generate_argument(inputs[0]->get_shape()));
auto w = to_gpu(generate_argument(inputs[1]->get_shape()));
auto y = to_gpu(generate_argument(output_shape));
auto workspace = allocate_gpu(workspace_shape);
int algo_count = 1;
miopenConvAlgoPerf_t perf;
miopenFindConvolutionForwardAlgorithm(ctx.handle.get(),
x_desc.get(),
x.implicit(),
w_desc.get(),
w.implicit(),
cd.get(),
y_desc.get(),
y.implicit(),
1,
&algo_count,
&perf,
workspace.implicit(),
workspace_size,
false);
algo = perf.fwd_algo;
return shape{shape::int8_type, {perf.memory}};
}
friend std::ostream& operator<<(std::ostream& os, const miopen_convolution& self)
{
os << self.name() << "[";
os << self.op << ", ";
os << "algo=" << self.algo;
os << "]";
return os;
}
};
struct miopen_pooling
{
pooling op;
shared<pooling_descriptor> pd;
std::string name() const { return "gpu::pooling"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).standard();
return op.compute_shape({inputs.at(0)});
}
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
auto x_desc = make_tensor(args[0].get_shape());
auto y_desc = make_tensor(output_shape);
float alpha = 1, beta = 0;
miopenPoolingForward(ctx.handle.get(),
pd.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
&beta,
y_desc.get(),
args[1].implicit(),
false,
nullptr,
0);
return args[1];
}
};
struct hip_add struct hip_add
{ {
std::string name() const { return "gpu::add"; } std::string name() const { return "gpu::add"; }
...@@ -225,46 +110,6 @@ struct miopen_add ...@@ -225,46 +110,6 @@ struct miopen_add
} }
}; };
struct miopen_gemm
{
gemm op;
std::string name() const { return "gpu::gemm"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(3);
return op.compute_shape({inputs.at(0), inputs.at(1)});
}
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
float alpha = 1.0f;
float beta = 0.0f;
bool transa = args[0].get_shape().transposed();
bool transb = args[1].get_shape().transposed();
rocblas_int lda = args[0].get_shape().strides()[transa ? 1 : 0];
rocblas_int ldb = args[1].get_shape().strides()[transb ? 1 : 0];
rocblas_int ldc = args[2].get_shape().strides()[0];
rocblas_int m = output_shape.lens()[0];
rocblas_int n = output_shape.lens()[1];
rocblas_int k = args[0].get_shape().lens()[1];
rocblas_sgemm(ctx.rbhandle.get(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
&alpha,
args[1].implicit(),
ldb,
args[0].implicit(),
lda,
&beta,
args[2].implicit(),
ldc);
return args[2];
}
};
struct miopen_contiguous struct miopen_contiguous
{ {
contiguous op; contiguous op;
......
src/targets/gpu/pooling.cpp 0 → 100644
View file @ c527d0b5
#include <migraph/gpu/pooling.hpp>
#include <migraph/operators.hpp>
#include <migraph/manage_ptr.hpp>
#include <migraph/gpu/miopen.hpp>
#include <utility>
namespace migraph {
namespace gpu {
shape miopen_pooling::compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).standard();
return op.compute_shape({inputs.at(0)});
}
argument
miopen_pooling::compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
auto x_desc = make_tensor(args[0].get_shape());
auto y_desc = make_tensor(output_shape);
float alpha = 1, beta = 0;
miopenPoolingForward(ctx.handle.get(),
pd.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
&beta,
y_desc.get(),
args[1].implicit(),
false,
nullptr,
0);
return args[1];
}
} // namespace gpu
} // namespace migraph
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