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Commit 31065c7d authored by charlie's avatar charlie
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Merge branch 'dyn_squeeze' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

parents 6bec381f 6acbd4e4
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src/targets/gpu/include/migraphx/gpu/tanh.hpp deleted 100644 → 0
View file @ 6bec381f
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_TANH_HPP
#define MIGRAPHX_GUARD_RTGLIB_TANH_HPP
#include <migraphx/gpu/oper.hpp>
#include <migraphx/gpu/device/tanh.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct hip_tanh : unary_device<hip_tanh, device::tanh>
{
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/targets/gpu/include/migraphx/gpu/topk.hpp
View file @ 31065c7d
......@@ -27,7 +27,7 @@
#include <migraphx/argument.hpp>
#include <migraphx/reflect.hpp>
#include <migraphx/op/topk.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <migraphx/gpu/context.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......
src/targets/gpu/include/migraphx/gpu/unary_not.hpp deleted 100644 → 0
View file @ 6bec381f
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_UNARY_NOT_HPP
#define MIGRAPHX_GUARD_RTGLIB_UNARY_NOT_HPP
#include <migraphx/gpu/oper.hpp>
#include <migraphx/gpu/device/unary_not.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct hip_unary_not : unary_device<hip_unary_not, device::unary_not>
{
std::string name() const { return "gpu::not"; }
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/targets/gpu/include/migraphx/gpu/softmax.hpp → src/targets/gpu/jit/concat.cpp
View file @ 31065c7d
......@@ -21,54 +21,78 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
#define MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
#include <migraphx/gpu/lowering.hpp>
#include <migraphx/manage_ptr.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/op/softmax.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/config.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <migraphx/gpu/hip.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/gpu/device/contiguous.hpp>
#include <migraphx/gpu/device/add.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/gpu/rocblas.hpp>
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/gpu/context.hpp>
#include <utility>
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/reduce_dims.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct context;
using namespace migraphx::gpu::gen; // NOLINT
// NOLINTNEXTLINE
static const char* const concat_kernel = R"__migraphx__(
#include <migraphx/kernels/concat.hpp>
#include <migraphx/kernels/vectorize.hpp>
#include <args.hpp>
namespace migraphx {
extern "C" {
struct hip_softmax
__global__ void ${kernel}(${params})
{
op::softmax op;
transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto y, auto... xs) {
concat<${axis}>(y, xs...);
});
}
}
} // namespace migraphx
template <class Self, class F>
static auto reflect(Self& self, F f)
)__migraphx__";
struct concat_compiler : compiler<concat_compiler>
{
std::vector<std::string> names() const { return {"concat"}; }
static std::size_t get_concat_elements(const std::vector<shape>& inputs)
{
return migraphx::reflect(self.op, f);
return inputs.back().elements() / (inputs.size() - 1);
}
std::string name() const { return "gpu::softmax"; }
shape compute_shape(const std::vector<shape>& inputs) const;
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
return shapes.size() - 1;
// TODO: Use reduce_dims
hip_compile_options options;
options.inputs = inputs;
options.output = inputs.back();
options.params = "-Wno-float-equal";
auto axis = find_fast_axis(options.inputs);
auto vec = vectorize::elements(ctx, axis, options.inputs);
options.kernel_name = v.get("kernel", "concat_kernel");
options.set_launch_params(
v, compute_global_for(ctx, get_concat_elements(options.inputs) / vec.size, 256));
auto src = interpolate_string(concat_kernel,
{{"kernel", options.kernel_name},
{"params", enum_params(inputs.size(), "void * private_p")},
{"args", enum_params(inputs.size(), "private_p")},
{"transformers", make_transformer_args(vec)},
{"axis", v.at("axis").to<std::string>()}});
return compile_hip_code_object(src, options);
}
compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
{
return replace(compile_op(ctx, to_shapes(ins->inputs()), op.to_value()));
}
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif
src/targets/gpu/jit/gathernd.cpp
View file @ 31065c7d
......@@ -27,13 +27,6 @@
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/module.hpp>
#include <migraphx/pass_manager.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -72,7 +65,7 @@ struct gathernd_compiler : compiler<gathernd_compiler>
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
hip_compile_options options;
auto out_s = inputs.back();
const auto& out_s = inputs.back();
options.set_launch_params(v, compute_global_for(ctx, out_s.elements()));
options.inputs = inputs;
options.output = out_s;
......
src/targets/gpu/jit/layernorm.cpp 0 → 100644
View file @ 31065c7d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
using namespace migraphx::gpu::gen; // NOLINT
static const char* const layernorm_kernel = R"__migraphx__(
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/layernorm.hpp>
#include <migraphx/kernels/vectorize.hpp>
#include <migraphx/kernels/preload.hpp>
#include <args.hpp>
namespace migraphx {
${preamble}
extern "C" {
__global__ void ${kernel}(${params})
{
transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto... xs) {
${layernorm}<${axis}>(${post}, ${eps}, xs...);
});
}
}
} // namespace migraphx
)__migraphx__";
struct layernorm_compiler : compiler<layernorm_compiler>
{
std::vector<std::string> names() const
{
return {"layernorm", "gpu::prelayernorm", "gpu::preadd_layernorm"};
}
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
// TODO: Use reduce_dims
auto axis = inputs.front().lens().size() - 1;
auto faxis = find_fast_axis({inputs.front()});
vectorize vec{};
// Vectorize if the axis is a reduction axis
if(axis == faxis)
{
vec = vectorize::elements(ctx, faxis, inputs);
}
auto relements = inputs[0].lens()[axis] / vec.size;
auto nelements = (inputs.back().elements() / inputs[0].lens()[axis]);
auto block_size = compute_block_size(relements, 256);
hip_compile_options options;
options.set_launch_params(
v, compute_global_for(ctx, nelements * block_size, 256), block_size);
options.output = inputs.back();
options.inputs = inputs;
options.kernel_name = v.get("kernel", "layernorm_kernel");
auto eps = v.get("epsilon", 1e-12f);
auto src = interpolate_string(layernorm_kernel,
{{"kernel", options.kernel_name},
{"params", enum_params(inputs.size(), "void * private_p")},
{"args", enum_params(inputs.size(), "private_p")},
{"transformers", make_transformer_args(vec)},
{"post", v.get("post", std::string{"op::id{}"})},
{"preamble", v.get("preamble", std::string{})},
{"layernorm", v.get("layernorm", std::string{"layernorm"})},
{"axis", to_string(axis)},
{"eps", to_string(eps)}});
return compile_hip_code_object(src, options);
}
compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
{
auto v = op.to_value();
v["layernorm"] = "layernorm";
v["kernel"] = "layernorm_kernel";
if(op.name() == "gpu::preadd_layernorm")
{
v["layernorm"] = "add_layernorm";
v["kernel"] = "add_layernorm_kernel";
}
if(not ins->module_inputs().empty())
{
auto* pm = ins->module_inputs().front();
v["preamble"] = generate_pointwise(*pm, "post_layernorm");
v["post"] = "MIGRAPHX_LIFT(post_layernorm)";
v["kernel"] =
v["layernorm"].to<std::string>() + "_" + generate_name_from_ops(*pm) + "_kernel";
}
return replace(compile_op(ctx, to_shapes(ins->inputs()), v));
}
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/jit/mlir.cpp
View file @ 31065c7d
......@@ -41,7 +41,7 @@ struct mlir_compiler : compiler<mlir_compiler>
{
auto* smod = ins->module_inputs().front();
assert(smod->get_parameter_names().size() == ins->inputs().size() - 1);
return insert(compile_mlir(ctx, *smod));
return insert(compile_mlir(ctx, *smod, ins->inputs()));
}
compiler_replace insert(code_object_op co) const
......
src/targets/gpu/batch_norm_inference.cpp → src/targets/gpu/jit/pad.cpp
View file @ 31065c7d
......@@ -21,65 +21,80 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/gpu/batch_norm_inference.hpp>
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/float_equal.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
shape miopen_batch_norm_inference::compute_shape(const std::vector<shape>& inputs) const
using namespace migraphx::gpu::gen; // NOLINT
static const char* const pointwise_kernel = R"__migraphx__(
#include <migraphx/kernels/pad.hpp>
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/ops.hpp>
#include <args.hpp>
namespace migraphx {
extern "C" {
__global__ void pad_kernel(void* input_p, void* output_p)
{
check_shapes{inputs, *this}.has(6);
check_shapes{inputs.data(), inputs.data() + 1, *this}.same_ndims().max_ndims(5);
return op.compute_shape({inputs.at(0), inputs.at(1), inputs.at(2), inputs.at(3), inputs.at(4)});
auto offsets = index_ints<${offsets}>{};
auto idx = make_index();
make_tensors()(input_p, output_p)([&](auto input, auto output) {
pad(idx, offsets, input, output, ${pad_val});
});
}
}
inline shape reshape_to_2d(const shape& input)
{
auto dims = input.lens();
if(dims.size() >= 4)
return input;
} // namespace migraphx
std::vector<size_t> new_dims(dims.begin(), dims.end());
std::size_t num = 4 - dims.size();
new_dims.insert(new_dims.end(), num, 1);
return {input.type(), new_dims};
}
)__migraphx__";
argument miopen_batch_norm_inference::compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args) const
struct pad_compiler : compiler<pad_compiler>
{
shape x_shape = args[0].get_shape();
shape y_shape = output_shape;
shape bn_shape = args[3].get_shape();
std::vector<std::string> names() const { return {"pad"}; }
auto x_desc = make_tensor(reshape_to_2d(x_shape));
auto y_desc = make_tensor(reshape_to_2d(y_shape));
auto bn_desc = make_tensor(reshape_to_2d(bn_shape));
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
hip_compile_options options;
options.inputs = inputs;
options.output = inputs.back();
options.virtual_inputs = reduce_dims(inputs);
options.kernel_name = "pad_kernel";
options.set_launch_params(v, compute_global_for(ctx, inputs.at(1).elements()));
float alpha = 1.0;
float beta = 0.0f;
auto pad_val = v.get("value", 0.f);
auto pad_val_string = to_string(pad_val);
if(float_equal(pad_val, std::numeric_limits<float>::lowest()))
pad_val_string = "lowest{}";
if(float_equal(pad_val, std::numeric_limits<float>::max()))
pad_val_string = "highest{}";
miopenBatchNormalizationForwardInference(ctx.get_stream().get_miopen(),
miopenBatchNormMode_t(op.bn_mode),
&alpha,
&beta,
x_desc.get(),
args[0].implicit(),
y_desc.get(),
args[5].implicit(),
bn_desc.get(),
args[1].implicit(),
args[2].implicit(),
args[3].implicit(),
args[4].implicit(),
op.epsilon);
auto padding = v.at("pads").to_vector<int64_t>();
auto input_lens = inputs.front().lens();
std::vector<size_t> offsets(input_lens.size());
std::copy(padding.begin(), padding.begin() + offsets.size(), offsets.begin());
return args[5];
}
auto src = interpolate_string(
pointwise_kernel,
{{"pad_val", to_string(pad_val_string)}, {"offsets", to_string_range(offsets)}});
return compile_hip_code_object(src, options);
}
compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
{
return replace(compile_op(ctx, to_shapes(ins->inputs()), op.to_value()));
}
};
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/targets/gpu/jit/pointwise.cpp
View file @ 31065c7d
......@@ -26,16 +26,7 @@
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/cpp_generator.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/permutation.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/module.hpp>
#include <migraphx/pass_manager.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -65,18 +56,6 @@ __global__ void ${kernel}(${params})
)__migraphx__";
static std::vector<std::string> get_op_names(const module& m)
{
std::vector<std::string> result;
for(auto& ins : m)
{
if(starts_with(ins.name(), "@"))
continue;
result.push_back(ins.name());
}
return result;
}
struct pointwise_compiler : compiler<pointwise_compiler>
{
std::vector<std::string> names() const { return {"pointwise", "contiguous"}; }
......@@ -96,20 +75,16 @@ struct pointwise_compiler : compiler<pointwise_compiler>
options.virtual_inputs = reduce_dims(inputs);
options.params = "-Wno-float-equal";
auto axis = find_fast_axis(options.virtual_inputs);
auto vec = vectorize::elements(axis, options.virtual_inputs);
auto preloads = preload::broadcasts(axis, options.virtual_inputs);
auto vec = vectorize::elements(ctx, axis, options.virtual_inputs);
options.kernel_name = v.get("kernel", "kernel");
options.set_launch_params(
v,
compute_global_for(ctx,
options.output.elements() / vec.size,
oversubscribe_if(not preloads.is_preloading())));
v, compute_global_for(ctx, options.output.elements() / vec.size, 256));
auto src = interpolate_string(pointwise_kernel,
{{"kernel", options.kernel_name},
{"params", enum_params(inputs.size(), "void * private_p")},
{"args", enum_params(inputs.size(), "private_p")},
{"lambda", v.at("lambda").to<std::string>()},
{"transformers", make_transformer_args(preloads, vec)},
{"transformers", make_transformer_args(vec)},
{"preamble", v.get("preamble", std::string{})}});
return compile_hip_code_object(src, options);
}
......@@ -126,34 +101,14 @@ struct pointwise_compiler : compiler<pointwise_compiler>
else
{
assert(not ins->module_inputs().empty());
auto* pm = ins->module_inputs().front();
run_passes(*pm, {eliminate_common_subexpression{}, dead_code_elimination{}});
cpp_generator g;
g.fmap([](const std::string& fname) { return "migraphx::" + fname; });
g.add_point_op("where", "${function:where}(${0}, ${1}, ${2})");
g.add_point_op("prelu", "${function:where}(${0} < 0, ${0} * ${1}, ${0})");
g.add_point_op("sign",
"${function:where}(${0} > 0, 1, ${function:where}(${0} < 0, -1, 0))");
g.add_point_op("equal", "migraphx::abs(${0} == ${1})");
g.add_point_op("less", "migraphx::abs(${0} < ${1})");
g.add_point_op("greater", "migraphx::abs(${0} > ${1})");
g.add_point_op("not", "migraphx::abs(not ${0})");
g.add_point_op("mod", "migraphx::mod(${0}, ${1})");
g.add_point_op("fmod", "migraphx::fmod(${0}, ${1})");
// Add explict conversions
g.fresult([](const shape& s) {
return "migraphx::convert<" + shape::cpp_type(s.type()) + ">";
});
auto name = g.create_function(
g.generate_module(*pm).set_attributes({"__device__"}).set_generic_types(*pm));
std::string lambda = "MIGRAPHX_LIFT(" + name + ")";
auto op_names = get_op_names(*pm);
op_names.push_back("kernel");
auto op_name_string = join_strings(op_names, "_");
return replace(compile_op(
ctx,
to_shapes(ins->inputs()),
{{"lambda", lambda}, {"preamble", g.str()}, {"kernel", op_name_string}}));
auto* pm = ins->module_inputs().front();
auto pf = generate_pointwise(*pm, "inner_pointwise");
std::string lambda = "MIGRAPHX_LIFT(inner_pointwise)";
auto kernel_name = generate_name_from_ops(*pm) + "_kernel";
return replace(
compile_op(ctx,
to_shapes(ins->inputs()),
{{"lambda", lambda}, {"preamble", pf}, {"kernel", kernel_name}}));
}
}
};
......
src/targets/gpu/jit/reduce.cpp
View file @ 31065c7d
......@@ -26,15 +26,7 @@
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/cpp_generator.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/module.hpp>
#include <migraphx/pass_manager.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -129,7 +121,7 @@ struct reduce_compiler : compiler<reduce_compiler>
// Vectorize if the axis is a reduction axis
if(options.virtual_inputs.back().lens()[faxis] == 1)
{
vec = vectorize::elements(faxis, options.virtual_inputs);
vec = vectorize::elements(ctx, faxis, options.virtual_inputs);
}
auto relements = get_reduce_elements(options.virtual_inputs) / vec.size;
auto nelements = options.virtual_inputs.back().elements();
......
src/targets/gpu/jit/roialign.cpp
View file @ 31065c7d
......@@ -24,16 +24,7 @@
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/cpp_generator.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/module.hpp>
#include <migraphx/pass_manager.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......
src/targets/gpu/jit/scatternd.cpp
View file @ 31065c7d
......@@ -24,16 +24,8 @@
#include <migraphx/gpu/compiler.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/module.hpp>
#include <migraphx/pass_manager.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -87,9 +79,10 @@ struct scatternd_compiler : compiler<scatternd_compiler>
{
assert(starts_with(op.name(), "scatternd_"));
auto reduction = op.name().substr(10);
return insert(compile_op(ctx,
to_shapes({ins->inputs().begin() + 1, ins->inputs().end()}),
{{"reduction", reduction}}));
return insert(compile_op(
ctx,
to_shapes(std::vector<instruction_ref>{ins->inputs().begin() + 1, ins->inputs().end()}),
{{"reduction", reduction}}));
}
compiler_replace insert(const operation& op) const
......
src/targets/gpu/jit/softmax.cpp
View file @ 31065c7d
......@@ -26,20 +26,14 @@
#include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/cpp_generator.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/module.hpp>
#include <migraphx/pass_manager.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_USE_FAST_SOFTMAX)
using namespace migraphx::gpu::gen; // NOLINT
static const char* const softmax_kernel = R"__migraphx__(
......@@ -77,7 +71,7 @@ struct softmax_compiler : compiler<softmax_compiler>
// Vectorize if the axis is a reduction axis
if(faxis == axis)
{
vec = vectorize::elements(faxis, inputs);
vec = vectorize::elements(ctx, faxis, inputs);
}
auto relements = inputs[0].lens()[axis] / vec.size;
auto nelements = (inputs.back().elements() / inputs[0].lens()[axis]);
......@@ -89,6 +83,9 @@ struct softmax_compiler : compiler<softmax_compiler>
options.inputs = inputs;
options.kernel_name = "softmax_kernel";
if(enabled(MIGRAPHX_USE_FAST_SOFTMAX{}))
options.params = "-DMIGRAPHX_USE_FAST_SOFTMAX";
auto src = interpolate_string(
softmax_kernel,
{{"transformers", make_transformer_args(vec)}, {"axis", to_string(axis)}});
......
src/targets/gpu/kernel.cpp
View file @ 31065c7d
......@@ -80,7 +80,9 @@ void launch_kernel(hipFunction_t fun,
std::size_t global,
std::size_t local,
void* kernargs,
std::size_t size)
std::size_t size,
hipEvent_t start,
hipEvent_t stop)
{
assert(global > 0);
assert(local > 0);
......@@ -97,34 +99,55 @@ void launch_kernel(hipFunction_t fun,
#endif
};
auto status = hipExtModuleLaunchKernel(
fun, global, 1, 1, local, 1, 1, 0, stream, nullptr, reinterpret_cast<void**>(&config));
auto status = hipExtModuleLaunchKernel(fun,
global,
1,
1,
local,
1,
1,
0,
stream,
nullptr,
reinterpret_cast<void**>(&config),
start,
stop);
if(status != hipSuccess)
MIGRAPHX_THROW("Failed to launch kernel: " + hip_error(status));
if(stop != nullptr)
{
status = hipEventSynchronize(stop);
if(status != hipSuccess)
MIGRAPHX_THROW("Failed to sync event: " + hip_error(status));
}
}
void kernel::launch(hipStream_t stream,
std::size_t global,
std::size_t local,
std::vector<void*> args) const
std::vector<void*> args,
hipEvent_t start,
hipEvent_t stop) const
{
assert(impl != nullptr);
void* kernargs = args.data();
std::size_t size = args.size() * sizeof(void*);
launch_kernel(impl->fun, stream, global, local, kernargs, size);
launch_kernel(impl->fun, stream, global, local, kernargs, size, start, stop);
}
void kernel::launch(hipStream_t stream,
std::size_t global,
std::size_t local,
const std::vector<kernel_argument>& args) const
const std::vector<kernel_argument>& args,
hipEvent_t start,
hipEvent_t stop) const
{
assert(impl != nullptr);
std::vector<char> kernargs = pack_args(args);
std::size_t size = kernargs.size();
launch_kernel(impl->fun, stream, global, local, kernargs.data(), size);
launch_kernel(impl->fun, stream, global, local, kernargs.data(), size, start, stop);
}
} // namespace gpu
......
src/targets/gpu/kernels/include/migraphx/kernels/algorithm.hpp
View file @ 31065c7d
......@@ -163,7 +163,7 @@ constexpr Iterator1 search(Iterator1 first, Iterator1 last, Iterator2 s_first, I
{
return last;
}
if(!(*it == *s_it))
if(not(*it == *s_it))
{
break;
}
......
src/targets/gpu/kernels/include/migraphx/kernels/array.hpp
View file @ 31065c7d
......@@ -33,49 +33,95 @@
namespace migraphx {
// NOLINTNEXTLINE
#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op) \
template <class U> \
constexpr array& operator op(const array<U, N>& x) \
{ \
for(index_int i = 0; i < N; i++) \
d[i] op x[i]; \
return *this; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
constexpr array& operator op(const U& x) \
{ \
for(index_int i = 0; i < N; i++) \
d[i] op x; \
return *this; \
} \
template <class U> \
friend constexpr auto operator binary_op(const array& x, const array<U, N>& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
for(index_int i = 0; i < N; i++) \
z[i] = x[i] binary_op y[i]; \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const array& x, const U& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
for(index_int i = 0; i < N; i++) \
z[i] = x[i] binary_op y; \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const U& x, const array& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
for(index_int i = 0; i < N; i++) \
z[i] = x binary_op y[i]; \
return z; \
#define MIGRAPHX_DEVICE_ARRAY_OP(op, binary_op) \
template <class U> \
constexpr array& operator op(const array<U, N>& x) \
{ \
array_detail::array_for_each(*this, x)([](auto& sy, auto sx) { sy op sx; }); \
return *this; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
constexpr array& operator op(const U& x) \
{ \
array_detail::array_for_each (*this)([&](auto& sy) { sy op x; }); \
return *this; \
} \
template <class U> \
friend constexpr auto operator binary_op(const array& x, const array<U, N>& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
array_detail::array_for_each(z, x, y)( \
[&](auto& sz, auto sx, auto sy) { sz = sx binary_op sy; }); \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const array& x, const U& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
array_detail::array_for_each(z, x)([&](auto& sz, auto sx) { sz = sx binary_op y; }); \
return z; \
} \
template <class U, MIGRAPHX_REQUIRES(is_convertible<U, T>{})> \
friend constexpr auto operator binary_op(const U& x, const array& y) \
{ \
array<decltype(T {} binary_op U{}), N> z{}; \
array_detail::array_for_each(z, y)([&](auto& sz, auto sy) { sz = x binary_op sy; }); \
return z; \
}
namespace array_detail {
template <class T>
constexpr auto is_vectorizable()
{
return not is_same<T, bool>{} and (is_fundamental<T>{} or is_same<T, half>{});
}
template <class T>
__device__ auto& array2vec(T& x)
{
using value_type = typename T::value_type;
constexpr auto size = decltype(x.size()){};
using type = vec<value_type, size>;
if constexpr(is_const<T>{})
return reinterpret_cast<const type&>(x);
else
return reinterpret_cast<type&>(x);
}
template <class T, class... Ts>
constexpr auto array_for_each(T& x, Ts&... xs)
{
MIGRAPHX_ASSERT(((x.size() == xs.size()) and ...));
return [&](auto f) {
constexpr auto size = decltype(x.size()){};
if constexpr((is_vectorizable<typename T::value_type>() or
(is_vectorizable<typename Ts::value_type>() or ...)) and
size <= 8 and size > 1 and (size % 2 == 0))
{
if(__builtin_is_constant_evaluated())
{
for(index_int i = 0; i < size; i++)
f(x[i], xs[i]...);
}
else
{
using vec_type = std::remove_reference_t<decltype(array2vec(x))>;
f(array2vec(x), __builtin_convertvector(array2vec(xs), vec_type)...);
}
}
else
{
for(index_int i = 0; i < size; i++)
f(x[i], xs[i]...);
}
};
}
} // namespace array_detail
template <class T, index_int N>
struct array
{
using value_type = T;
T d[N];
constexpr T& operator[](index_int i)
{
......@@ -108,18 +154,13 @@ struct array
constexpr T dot(const array& x) const
{
T result = 0;
for(index_int i = 0; i < N; i++)
result += x[i] * d[i];
return result;
auto r = x * (*this);
return r.reduce([](auto a, auto b) { return a + b; }, 0);
}
constexpr T product() const
{
T result = 1;
for(index_int i = 0; i < N; i++)
result *= d[i];
return result;
return reduce([](auto x, auto y) { return x * y; }, 1);
}
constexpr T single(index_int width = 100) const
......@@ -134,6 +175,24 @@ struct array
return result;
}
template <class F>
constexpr auto apply(F f) const
{
array<decltype(f(d[0])), N> result;
for(index_int i = 0; i < N; i++)
result[i] = f(d[i]);
return result;
}
template <class F>
constexpr auto reduce(F f, T init) const
{
T result = init;
for(index_int i = 0; i < N; i++)
result = f(result, d[i]);
return result;
}
MIGRAPHX_DEVICE_ARRAY_OP(+=, +)
MIGRAPHX_DEVICE_ARRAY_OP(-=, -)
MIGRAPHX_DEVICE_ARRAY_OP(*=, *)
......@@ -153,7 +212,7 @@ struct array
return true;
}
friend constexpr bool operator!=(const array& x, const array& y) { return !(x == y); }
friend constexpr bool operator!=(const array& x, const array& y) { return not(x == y); }
// This uses the product order rather than lexical order
friend constexpr bool operator<(const array& x, const array& y)
{
......@@ -201,6 +260,11 @@ struct array
}
};
template <class T, class... Ts>
constexpr array<T, sizeof...(Ts) + 1> make_array(T x, Ts... xs)
{
return {x, static_cast<T>(xs)...};
}
template <class T, T... Xs>
struct integral_const_array : array<T, sizeof...(Xs)>
{
......
src/targets/gpu/include/migraphx/gpu/max.hpp → src/targets/gpu/kernels/include/migraphx/kernels/concat.hpp
View file @ 31065c7d
......@@ -21,22 +21,46 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#ifndef MIGRAPHX_GUARD_RTGLIB_MAX_HPP
#define MIGRAPHX_GUARD_RTGLIB_MAX_HPP
#include <migraphx/gpu/oper.hpp>
#include <migraphx/gpu/device/max.hpp>
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/functional.hpp>
#include <migraphx/kernels/tensor_view.hpp>
#ifndef MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
#define MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct hip_max : binary_device<hip_max, device::max>
template <index_int Axis, class Output, class Input, class Start>
constexpr auto concat_slice(Output out, Input, Start)
{
};
constexpr auto lens = get_shape_c<Input>{}.lens;
constexpr auto strides = get_shape_c<Output>{}.strides;
constexpr auto offset = return_c([] {
constexpr auto output_shape = get_shape_c<Output>{};
return Start{} * output_shape.strides[Axis];
});
constexpr auto s = make_shape(lens, strides);
return make_tensor_view(&out[offset], s);
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
template <index_int Axis, class Input>
constexpr auto concat_ends(Input)
{
constexpr auto lens = get_shape_c<Input>{}.lens;
return _c<lens[Axis]>;
}
#endif
template <index_int Axis, class Output, class... Inputs>
__device__ void concat(Output output, Inputs... inputs)
{
auto idx = make_index();
fold([&](auto start, auto input) {
auto y = concat_slice<Axis>(output, input, start);
idx.global_stride(input.get_shape().elements(), [&](auto i) { y[i] = input[i]; });
return start + concat_ends<Axis>(input);
})(_c<0>, inputs...);
}
} // namespace migraphx
#endif // MIGRAPHX_GUARD_KERNELS_CONCAT_HPP
src/targets/gpu/kernels/include/migraphx/kernels/functional.hpp
View file @ 31065c7d
......@@ -31,8 +31,9 @@
->decltype(__VA_ARGS__) { return __VA_ARGS__; }
// NOLINTNEXTLINE
#define MIGRAPHX_LIFT(...) \
[](auto&&... xs) MIGRAPHX_RETURNS((__VA_ARGS__)(static_cast<decltype(xs)>(xs)...))
#define MIGRAPHX_LIFT(...) \
[](auto&&... private_lisft_xs) MIGRAPHX_RETURNS( \
(__VA_ARGS__)(static_cast<decltype(private_lisft_xs)>(private_lisft_xs)...))
namespace migraphx {
......
src/targets/gpu/kernels/include/migraphx/kernels/index.hpp
View file @ 31065c7d
......@@ -28,9 +28,60 @@
#include <migraphx/kernels/types.hpp>
#include <migraphx/kernels/integral_constant.hpp>
#include <migraphx/kernels/type_traits.hpp>
#include <migraphx/kernels/debug.hpp>
namespace migraphx {
#if defined(MIGRAPHX_NGLOBAL) && defined(MIGRAPHX_NLOCAL)
#define MIGRAPHX_NGROUP ((MIGRAPHX_NGLOBAL + MIGRAPHX_NLOCAL - 1) / MIGRAPHX_NLOCAL)
#endif
inline __device__ __attribute__((const)) index_int compute_global_size()
{
#ifdef MIGRAPHX_NGLOBAL
return MIGRAPHX_NGLOBAL;
#else
// This actualy works even when global is not divisible by local size.
// This doesnt actually do a multiplicatiosn. Instead it calls a device
// function to get the global size, which is why it works.
return blockDim.x * gridDim.x; // NOLINT
#endif
}
// We cant just use blockDim.x to get the local size since its broken on hip
// when global is not divisible by local size. In this case, we calulate the
// size for the last group.
inline __device__ __attribute__((const)) index_int compute_local_size()
{
#ifdef MIGRAPHX_NLOCAL
const auto nlocal = MIGRAPHX_NLOCAL;
#else
const auto nlocal = blockDim.x; // NOLINT
#endif
#ifdef MIGRAPHX_NGROUP
const auto ngroup = MIGRAPHX_NGROUP;
#else
const auto ngroup = gridDim.x; // NOLINT
#endif
const auto group_id = blockIdx.x; // NOLINT
const auto nglobal = compute_global_size();
if(group_id == ngroup - 1)
{
return 1 + (nglobal - 1) % nlocal;
}
else
{
return nlocal; // NOLINT
}
}
#ifdef MIGRAPHX_NGROUP
// If global is divisible by local then local can be a const
#if(MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL == 0) || (MIGRAPHX_NGROUP == 1)
#define MIGRAPHX_HAS_CONST_LOCAL 1
#endif
#endif
struct index
{
index_int global = 0;
......@@ -38,20 +89,44 @@ struct index
index_int group = 0;
#ifdef MIGRAPHX_NGLOBAL
constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const { return {}; }
constexpr index_constant<MIGRAPHX_NGLOBAL> nglobal() const
{
static_assert(MIGRAPHX_NGLOBAL > 0, "Global size must be greater than 0");
return {};
}
#else
__device__ index_int nglobal() const
{
return blockDim.x * gridDim.x; // NOLINT
MIGRAPHX_ASSERT(compute_global_size() > 0);
return compute_global_size(); // NOLINT
}
#endif
#ifdef MIGRAPHX_NLOCAL
constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const { return {}; }
#ifdef MIGRAPHX_HAS_CONST_LOCAL
constexpr index_constant<MIGRAPHX_NLOCAL> nlocal() const
{
static_assert(MIGRAPHX_NLOCAL > 0, "Local size must be greater than 0");
return {};
}
#else
__device__ index_int nlocal() const
{
return blockDim.x; // NOLINT
#ifdef MIGRAPHX_NGROUP
static_assert((MIGRAPHX_NGLOBAL % MIGRAPHX_NLOCAL != 0) and (MIGRAPHX_NGROUP > 1),
"Local size should be const");
#endif
MIGRAPHX_ASSERT(compute_local_size() > 0);
return compute_local_size(); // NOLINT
}
#endif
#ifdef MIGRAPHX_NLOCAL
constexpr index_constant<MIGRAPHX_NLOCAL> max_nlocal() const { return {}; }
#else
__device__ index_int max_nlocal() const
{
MIGRAPHX_ASSERT(blockDim.x > 0);
return blockDim.x;
}
#endif
template <class N, class Stride>
......@@ -63,6 +138,7 @@ struct index
template <class F, class N, class Stride>
static constexpr void for_stride(index_int start, N n, Stride stride, F f)
{
MIGRAPHX_ASSERT(start < stride);
if constexpr(not is_integral<N>{} and not is_integral<Stride>{} and
max_stride_iterations(n, stride) == 1)
{
......
src/targets/gpu/kernels/include/migraphx/kernels/integral_constant.hpp
View file @ 31065c7d
......@@ -73,10 +73,10 @@ MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(!=)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(&)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(^)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(|)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(&&)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(||)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(and)
MIGRAPHX_INTEGRAL_CONSTANT_BINARY_OP(or)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(!)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(not )
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(~)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(+)
MIGRAPHX_INTEGRAL_CONSTANT_UNARY_OP(-)
......
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