Skip to content

GitLab

Commit c96139f8 authored by Alan Turner's avatar Alan Turner
Browse files

Move common functions to ck.hpp + other cleanup

parent a9b32b71
Hide whitespace changes
Inline Side-by-side
Showing with 196 additions and 466 deletions
src/targets/gpu/fuse_ck.cpp
View file @ c96139f8
......@@ -76,7 +76,7 @@ MIGRAPHX_REGISTER_OP(ck_gemm);
struct ck_gemm_softmax_gemm
{
operation op = make_op("dot");
double scale = 1.0;
float scale = 1.0;
template <class Self, class F>
static auto reflect(Self& self, F f)
......@@ -203,7 +203,7 @@ struct find_ck_gemm
}
};
static auto is_mul_module(module& m)
auto is_mul_module(module& m)
{
auto is_mul =
match::arg(0)(match::name("mul")(match::all_of[match::inputs()](match::name("@param"))));
......@@ -243,7 +243,7 @@ struct find_ck_gemm_softmax_gemm
if(not ck_gemm_softmax_gemm::is_ck_supported_type(gemm1_ins->get_shape().type()))
return;
double scale = 1.0;
float scale = 1.0;
scale_lit->eval().visit([&](const auto s) {
// CK only supports single-valued scale
if(std::all_of(
......
src/targets/gpu/include/migraphx/gpu/ck.hpp 0 → 100644
View file @ c96139f8
/*
* 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_GPU_CK_HPP
#define MIGRAPHX_GUARD_GPU_CK_HPP
#include <migraphx/config.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/compile_src.hpp>
#include "ck/host/device_gemm_multiple_d.hpp"
#include "ck/host/device_batched_gemm_softmax_gemm.hpp"
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_LOG_CK_GEMM);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_DEBUG);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TUNE_CK);
// NOLINTNEXTLINE
const char* const disable_warning_pragma = R"__migraphx__(
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Weverything"
${content}
#pragma clang diagnostic pop
)__migraphx__";
template <class P>
std::string ck_disable_warnings(P p)
{
return interpolate_string(disable_warning_pragma,
{{"content", std::string{p.first, p.second}}});
}
static std::unordered_map<std::string, std::string> create_ck_header_strings()
{
std::unordered_map<std::string, std::string> result;
auto ck_headers = ck::host::GetHeaders();
std::transform(
ck_headers.begin(), ck_headers.end(), std::inserter(result, result.begin()), [&](auto&& p) {
return std::make_pair(p.first, ck_disable_warnings(p.second));
});
return result;
}
static std::vector<src_file> create_ck_headers()
{
static const auto& header_strings = create_ck_header_strings();
std::vector<src_file> srcs;
std::transform(
header_strings.begin(), header_strings.end(), std::back_inserter(srcs), [&](auto&& p) {
return src_file{fs::path{p.first},
{p.second.data(), p.second.data() + p.second.size()}};
});
return srcs;
}
static const std::vector<src_file>& ck_headers()
{
static const auto& headers = create_ck_headers();
return headers;
}
inline bool transposed_matrix(const shape& s) { return s.strides().back() != 1; }
inline float matrix_distance(const shape& x, const shape& y)
{
if(x.type() != y.type())
return std::numeric_limits<float>::max();
if(transposed_matrix(x) != transposed_matrix(y))
return std::numeric_limits<float>::max();
auto sum_squared = std::inner_product(x.lens().rbegin(),
x.lens().rbegin() + 2,
y.lens().rbegin(),
0,
std::plus<>{},
[](auto a, auto b) { return (a - b) * (a - b); });
return std::sqrt(sum_squared);
}
inline std::string get_layout(const shape& s)
{
return transposed_matrix(s) ? "ck::tensor_layout::gemm::ColumnMajor"
: "ck::tensor_layout::gemm::RowMajor";
}
inline ck::host::DataType get_type(const shape& s)
{
if(s.type() == shape::half_type)
return ck::host::DataType::Half;
else if(s.type() == shape::float_type)
return ck::host::DataType::Float;
else if(s.type() == shape::int8_type)
return ck::host::DataType::Int8;
else if(s.type() == shape::int32_type)
return ck::host::DataType::Int32;
MIGRAPHX_THROW("Unsupported ck type");
}
inline std::size_t get_batch_count(const shape& s)
{
return std::accumulate(
s.lens().rbegin() + 2, s.lens().rend(), std::size_t{1}, std::multiplies<std::size_t>());
}
inline void fold_batch_dims(shape& s)
{
auto lens = s.lens();
if(lens.size() <= 2)
return;
auto batch_count = get_batch_count(s);
auto m1 = lens.at(lens.size() - 2);
auto m2 = lens.at(lens.size() - 1);
if(transposed_matrix(s))
s = shape{s.type(), {m1, m2 * batch_count}};
else
s = shape{s.type(), {m1 * batch_count, m2}};
}
inline void remove_batch_dims(shape& s)
{
auto lens = s.lens();
if(lens.size() <= 2)
return;
auto m1 = lens.at(lens.size() - 2);
auto m2 = lens.at(lens.size() - 1);
s = shape{s.type(), {m1, m2}};
}
inline bool standard_batch(const shape& s)
{
if(s.lens().size() < 3)
return true;
std::vector<std::size_t> lens(s.lens().begin(), s.lens().end() - 2);
std::vector<std::size_t> strides(s.strides().begin(), s.strides().end() - 2);
auto base = *(s.lens().end() - 2) * *(s.lens().end() - 1);
std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto stride) {
return stride / base;
});
return shape{s.type(), lens, strides}.standard();
}
inline bool can_fold_batch(const std::vector<shape>& inputs)
{
const auto& b_shape = inputs[1];
if(std::any_of(inputs.begin() + 2, inputs.end() - 1, [](auto input) {
return not standard_batch(input);
}))
return false;
const auto& b_strides = b_shape.strides();
return std::all_of(
b_strides.begin(), b_strides.end() - 2, [](auto stride) { return stride == 0; });
}
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
#endif // MIGRAPHX_GUARD_GPU_CK_HPP
src/targets/gpu/jit/ck_gemm.cpp
View file @ c96139f8
......@@ -27,6 +27,7 @@
#include <migraphx/make_op.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/ck.hpp>
#include <migraphx/env.hpp>
#include <migraphx/file_buffer.hpp>
#include <migraphx/gpu/compile_gen.hpp>
......@@ -37,8 +38,6 @@
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include "ck/host/device_gemm_multiple_d.hpp"
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -46,12 +45,6 @@ namespace gpu {
using namespace migraphx::gpu::gen; // NOLINT
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_LOG_CK_GEMM);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_TUNING);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_TUNING_VALUE);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_DEBUG);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TUNE_CK);
// NOLINTNEXTLINE
static const char* const ck_gemm_kernel = R"__migraphx__(
#include <args.hpp>
......@@ -79,230 +72,18 @@ MIGRAPHX_GLOBAL void ${kernel}(${params})
)__migraphx__";
// NOLINTNEXTLINE
static const char* const disable_warning_pragma = R"__migraphx__(
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Weverything"
${content}
#pragma clang diagnostic pop
)__migraphx__";
template <class P>
static std::string ck_disable_warnings(P p)
{
return interpolate_string(disable_warning_pragma,
{{"content", std::string{p.first, p.second}}});
}
static std::unordered_map<std::string, std::string> create_ck_header_strings()
{
std::unordered_map<std::string, std::string> result;
auto ck_headers = ck::host::GetHeaders();
std::transform(
ck_headers.begin(), ck_headers.end(), std::inserter(result, result.begin()), [&](auto&& p) {
return std::make_pair(p.first, ck_disable_warnings(p.second));
});
return result;
}
static std::vector<src_file> create_ck_headers()
{
static const auto& header_strings = create_ck_header_strings();
std::vector<src_file> srcs;
std::transform(
header_strings.begin(), header_strings.end(), std::back_inserter(srcs), [&](auto&& p) {
return src_file{fs::path{p.first},
{p.second.data(), p.second.data() + p.second.size()}};
});
return srcs;
}
static const std::vector<src_file>& ck_headers()
{
static const auto& headers = create_ck_headers();
return headers;
}
static bool transposed_matrix(const shape& s) { return s.strides().back() != 1; }
using tuning_entry = std::pair<std::vector<shape>, size_t>;
static std::vector<tuning_entry> read_tuning(const std::string& s)
{
if(not fs::exists(s))
return {};
return from_value<std::vector<tuning_entry>>(from_json_string(read_string(s)));
}
static float matrix_distance(const shape& x, const shape& y)
{
if(x.type() != y.type())
return std::numeric_limits<float>::max();
if(transposed_matrix(x) != transposed_matrix(y))
return std::numeric_limits<float>::max();
auto sum_squared = std::inner_product(x.lens().rbegin(),
x.lens().rbegin() + 2,
y.lens().rbegin(),
0,
std::plus<>{},
[](auto a, auto b) { return (a - b) * (a - b); });
return std::sqrt(sum_squared);
}
static std::size_t get_tuning_for(const std::vector<shape>& inputs)
{
static auto tuning = read_tuning(string_value_of(MIGRAPHX_CK_TUNING{}, ""));
if(tuning.empty())
{
std::cout << "*********** Warning: No CK tuning! for config:" << std::endl;
std::cout << " " << inputs[0] << std::endl;
std::cout << " " << inputs[1] << std::endl;
std::cout << " " << inputs[2] << std::endl;
}
auto it = std::find_if(
tuning.begin(), tuning.end(), [&](const auto& p) { return p.first == inputs; });
if(it == tuning.end())
{
std::cout << "*********** Warning: CK tuning missing for config!" << std::endl;
std::cout << " " << inputs[0] << std::endl;
std::cout << " " << inputs[1] << std::endl;
std::cout << " " << inputs[2] << std::endl;
std::vector<std::pair<float, std::size_t>> w;
std::transform(tuning.begin(), tuning.end(), std::back_inserter(w), [&](const auto& p) {
if(inputs.size() < 3 or p.first.size() < 3)
MIGRAPHX_THROW("Invalid CK config");
auto avg_distance = std::inner_product(
p.first.begin(),
p.first.begin() + 3,
inputs.begin(),
0.0f,
std::plus<>{},
[](const auto& x, const auto& y) { return matrix_distance(x, y) / 3.0f; });
return std::make_pair(avg_distance, p.second);
});
std::sort(w.begin(), w.end());
std::size_t default_value = 4;
if(not w.empty())
default_value = w.front().second;
auto tuning_val = value_of(MIGRAPHX_CK_TUNING_VALUE{}, default_value);
std::cout << "*********** Warning: CK try tuning: " << tuning_val << std::endl;
return tuning_val;
}
return it->second;
}
struct ck_gemm_compiler : compiler<ck_gemm_compiler>
{
static std::string get_layout(const shape& s)
{
return transposed_matrix(s) ? "ck::tensor_layout::gemm::ColumnMajor"
: "ck::tensor_layout::gemm::RowMajor";
}
static ck::host::DataType get_type(const shape& s)
{
if(s.type() == shape::half_type)
return ck::host::DataType::Half;
else if(s.type() == shape::float_type)
return ck::host::DataType::Float;
else if(s.type() == shape::int8_type)
return ck::host::DataType::Int8;
else if(s.type() == shape::int32_type)
return ck::host::DataType::Int32;
MIGRAPHX_THROW("Unsupported ck type");
}
template <class Iterator, class F>
static std::string ck_tuple(Iterator start, Iterator last, F f)
{
std::vector<std::string> s;
std::transform(start, last, std::back_inserter(s), f);
return "ck::Tuple<" + join_strings(s, ",") + ">";
}
static std::vector<shape> adjust_inputs(std::vector<shape> inputs, bool& swap_inputs)
{
swap_inputs = false;
auto c_shape = inputs.back();
if(not transposed_matrix(c_shape))
return inputs;
std::vector<int64_t> perm(c_shape.lens().size());
std::iota(perm.begin(), perm.end(), 0);
std::swap(perm[perm.size() - 1], perm[perm.size() - 2]);
std::transform(inputs.begin(), inputs.end(), inputs.begin(), [&](shape s) {
return reorder_shape(s, perm);
});
swap_inputs = true;
return inputs;
}
static std::size_t get_batch_count(const shape& s)
{
return std::accumulate(
s.lens().rbegin() + 2, s.lens().rend(), std::size_t{1}, std::multiplies<std::size_t>());
}
static void fold_batch_dims(shape& s)
{
auto lens = s.lens();
if(lens.size() <= 2)
return;
auto batch_count = get_batch_count(s);
auto m1 = lens.at(lens.size() - 2);
auto m2 = lens.at(lens.size() - 1);
if(transposed_matrix(s))
s = shape{s.type(), {m1, m2 * batch_count}};
else
s = shape{s.type(), {m1 * batch_count, m2}};
}
static void remove_batch_dims(shape& s)
{
auto lens = s.lens();
if(lens.size() <= 2)
return;
auto m1 = lens.at(lens.size() - 2);
auto m2 = lens.at(lens.size() - 1);
s = shape{s.type(), {m1, m2}};
}
std::vector<std::string> names() const { return {"ck_gemm", "gpu::ck_gemm"}; }
static bool standard_batch(const shape& s)
{
if(s.lens().size() < 3)
return true;
std::vector<std::size_t> lens(s.lens().begin(), s.lens().end() - 2);
std::vector<std::size_t> strides(s.strides().begin(), s.strides().end() - 2);
auto base = *(s.lens().end() - 2) * *(s.lens().end() - 1);
std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto stride) {
return stride / base;
});
return shape{s.type(), lens, strides}.standard();
}
bool can_fold_batch(const std::vector<shape>& inputs) const
{
const auto& b_shape = inputs[1];
if(std::any_of(inputs.begin() + 2, inputs.end() - 1, [](auto input) {
return not standard_batch(input);
}))
return false;
const auto& b_strides = b_shape.strides();
return std::all_of(
b_strides.begin(), b_strides.end() - 2, [](auto stride) { return stride == 0; });
}
ck::host::device_gemm_multiple_d::Problem create_problem(const std::vector<shape>& inputs,
const value& v) const
{
const auto& a_shape = inputs[0];
const auto& b_shape = inputs[1];
const auto& c_shape = inputs.back();
// cppcheck-suppress unreadVariable
auto rank = a_shape.ndim();
auto rank = a_shape.ndim();
auto batch_count = get_batch_count(c_shape);
auto m = c_shape.lens()[rank - 2];
m = can_fold_batch(inputs) ? m * batch_count : m;
......@@ -352,12 +133,8 @@ struct ck_gemm_compiler : compiler<ck_gemm_compiler>
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
const auto& a_shape = inputs[0];
const auto& b_shape = inputs[1];
const auto& c_shape = inputs.back();
auto tuning_value = v.get("tuning_value", 4);
if(not v.contains("tuning_value"))
tuning_value = get_tuning_for({a_shape, b_shape, c_shape});
auto tuning_value = v.get("tuning_value", 0);
auto batch_count = get_batch_count(c_shape);
auto problem = create_problem(inputs, v);
......
src/targets/gpu/jit/ck_gemm_softmax_gemm.cpp
View file @ c96139f8
......@@ -29,6 +29,7 @@
#include <migraphx/env.hpp>
#include <migraphx/file_buffer.hpp>
#include <migraphx/gpu/ck.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/gpu/compile_hip_code_object.hpp>
......@@ -37,8 +38,6 @@
#include <migraphx/reduce_dims.hpp>
#include <migraphx/stringutils.hpp>
#include "ck/host/device_batched_gemm_softmax_gemm.hpp"
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -46,12 +45,6 @@ namespace gpu {
using namespace migraphx::gpu::gen; // NOLINT
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_LOG_CK_GEMM);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_TUNING);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_TUNING_VALUE);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_DEBUG);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TUNE_CK);
// NOLINTNEXTLINE
static const char* const ck_gemm_softmax_gemm_kernel = R"__migraphx__(
#include <args.hpp>
......@@ -82,236 +75,22 @@ MIGRAPHX_GLOBAL void ${kernel}(${params})
)__migraphx__";
// NOLINTNEXTLINE
static const char* const disable_warning_pragma = R"__migraphx__(
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Weverything"
${content}
#pragma clang diagnostic pop
)__migraphx__";
template <class P>
static std::string ck_disable_warnings(P p)
{
return interpolate_string(disable_warning_pragma,
{{"content", std::string{p.first, p.second}}});
}
static std::unordered_map<std::string, std::string> create_ck_header_strings()
{
std::unordered_map<std::string, std::string> result;
auto ck_headers = ck::host::GetHeaders();
std::transform(
ck_headers.begin(), ck_headers.end(), std::inserter(result, result.begin()), [&](auto&& p) {
return std::make_pair(p.first, ck_disable_warnings(p.second));
});
return result;
}
static std::vector<src_file> create_ck_headers()
{
static const auto& header_strings = create_ck_header_strings();
std::vector<src_file> srcs;
std::transform(
header_strings.begin(), header_strings.end(), std::back_inserter(srcs), [&](auto&& p) {
return src_file{fs::path{p.first},
{p.second.data(), p.second.data() + p.second.size()}};
});
return srcs;
}
static const std::vector<src_file>& ck_headers()
{
static const auto& headers = create_ck_headers();
return headers;
}
static bool transposed_matrix(const shape& s) { return s.strides().back() != 1; }
using tuning_entry = std::pair<std::vector<shape>, size_t>;
static std::vector<tuning_entry> read_tuning(const std::string& s)
{
if(not fs::exists(s))
return {};
return from_value<std::vector<tuning_entry>>(from_json_string(read_string(s)));
}
static float matrix_distance(const shape& x, const shape& y)
{
if(x.type() != y.type())
return std::numeric_limits<float>::max();
if(transposed_matrix(x) != transposed_matrix(y))
return std::numeric_limits<float>::max();
auto sum_squared = std::inner_product(x.lens().rbegin(),
x.lens().rbegin() + 2,
y.lens().rbegin(),
0,
std::plus<>{},
[](auto a, auto b) { return (a - b) * (a - b); });
return std::sqrt(sum_squared);
}
static std::size_t get_tuning_for(const std::vector<shape>& inputs)
{
static auto tuning = read_tuning(string_value_of(MIGRAPHX_CK_TUNING{}, ""));
if(tuning.empty())
{
std::cout << "*********** Warning: No CK tuning! for config:" << std::endl;
std::cout << " " << inputs[0] << std::endl;
std::cout << " " << inputs[1] << std::endl;
std::cout << " " << inputs[2] << std::endl;
std::cout << " " << inputs[3] << std::endl;
}
auto it = std::find_if(
tuning.begin(), tuning.end(), [&](const auto& p) { return p.first == inputs; });
if(it == tuning.end())
{
std::cout << "*********** Warning: CK tuning missing for config!" << std::endl;
std::cout << " " << inputs[0] << std::endl;
std::cout << " " << inputs[1] << std::endl;
std::cout << " " << inputs[2] << std::endl;
std::cout << " " << inputs[3] << std::endl;
std::vector<std::pair<float, std::size_t>> w;
std::transform(tuning.begin(), tuning.end(), std::back_inserter(w), [&](const auto& p) {
if(inputs.size() < 3 or p.first.size() < 3)
MIGRAPHX_THROW("Invalid CK config");
auto avg_distance = std::inner_product(
p.first.begin(),
p.first.begin() + 3,
inputs.begin(),
0.0f,
std::plus<>{},
[](const auto& x, const auto& y) { return matrix_distance(x, y) / 3.0f; });
return std::make_pair(avg_distance, p.second);
});
std::sort(w.begin(), w.end());
std::size_t default_value = 5;
if(not w.empty())
default_value = w.front().second;
auto tuning_val = value_of(MIGRAPHX_CK_TUNING_VALUE{}, default_value);
std::cout << "*********** Warning: CK try tuning: " << tuning_val << std::endl;
return tuning_val;
}
return it->second;
}
struct ck_gemm_softmax_gemm_compiler : compiler<ck_gemm_softmax_gemm_compiler>
{
static std::string get_layout(const shape& s)
{
return transposed_matrix(s) ? "ck::tensor_layout::gemm::ColumnMajor"
: "ck::tensor_layout::gemm::RowMajor";
}
static ck::host::DataType get_type(const shape& s)
{
if(s.type() == shape::half_type)
return ck::host::DataType::Half;
else if(s.type() == shape::float_type)
return ck::host::DataType::Float;
else if(s.type() == shape::int8_type)
return ck::host::DataType::Int8;
else if(s.type() == shape::int32_type)
return ck::host::DataType::Int32;
MIGRAPHX_THROW("Unsupported ck type");
}
template <class Iterator, class F>
static std::string ck_tuple(Iterator start, Iterator last, F f)
{
std::vector<std::string> s;
std::transform(start, last, std::back_inserter(s), f);
return "ck::Tuple<" + join_strings(s, ",") + ">";
}
static std::vector<shape> adjust_inputs(std::vector<shape> inputs, bool& swap_inputs)
{
swap_inputs = false;
auto c_shape = inputs.back();
if(not transposed_matrix(c_shape))
return inputs;
std::vector<int64_t> perm(c_shape.lens().size());
std::iota(perm.begin(), perm.end(), 0);
std::swap(perm[perm.size() - 1], perm[perm.size() - 2]);
std::transform(inputs.begin(), inputs.end(), inputs.begin(), [&](shape s) {
return reorder_shape(s, perm);
});
swap_inputs = true;
return inputs;
}
static std::size_t get_batch_count(const shape& s)
{
return std::accumulate(
s.lens().rbegin() + 2, s.lens().rend(), std::size_t{1}, std::multiplies<std::size_t>());
}
static void fold_batch_dims(shape& s)
{
auto lens = s.lens();
if(lens.size() <= 2)
return;
auto batch_count = get_batch_count(s);
auto m1 = lens.at(lens.size() - 2);
auto m2 = lens.at(lens.size() - 1);
if(transposed_matrix(s))
s = shape{s.type(), {m1, m2 * batch_count}};
else
s = shape{s.type(), {m1 * batch_count, m2}};
}
static void remove_batch_dims(shape& s)
{
auto lens = s.lens();
if(lens.size() <= 2)
return;
auto m1 = lens.at(lens.size() - 2);
auto m2 = lens.at(lens.size() - 1);
s = shape{s.type(), {m1, m2}};
}
std::vector<std::string> names() const
{
return {"ck_gemm_softmax_gemm", "gpu::ck_gemm_softmax_gemm"};
}
static bool standard_batch(const shape& s)
{
if(s.lens().size() < 3)
return true;
std::vector<std::size_t> lens(s.lens().begin(), s.lens().end() - 2);
std::vector<std::size_t> strides(s.strides().begin(), s.strides().end() - 2);
auto base = *(s.lens().end() - 2) * *(s.lens().end() - 1);
std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto stride) {
return stride / base;
});
return shape{s.type(), lens, strides}.standard();
}
bool can_fold_batch(const std::vector<shape>& inputs) const
{
const auto& b_shape = inputs[1];
if(std::any_of(inputs.begin() + 2, inputs.end() - 1, [](auto input) {
return not standard_batch(input);
}))
return false;
const auto& b_strides = b_shape.strides();
return std::all_of(
b_strides.begin(), b_strides.end() - 2, [](auto stride) { return stride == 0; });
}
ck::host::device_batched_gemm_softmax_gemm::Problem
create_problem(const std::vector<shape>& inputs, const value& v) const
create_problem(const std::vector<shape>& inputs, const value&) const
{
const auto& a_shape = inputs[0];
const auto& b_shape = inputs[1];
const auto& b1_shape = inputs[2];
const auto& c_shape = inputs.back();
// cppcheck-suppress unreadVariable
auto rank = a_shape.ndim();
auto rank = a_shape.ndim();
auto batch_count = get_batch_count(c_shape);
auto m = c_shape.lens()[rank - 2];
m = can_fold_batch(inputs) ? m * batch_count : m;
......@@ -349,13 +128,8 @@ struct ck_gemm_softmax_gemm_compiler : compiler<ck_gemm_softmax_gemm_compiler>
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
const auto& a_shape = inputs[0];
const auto& b_shape = inputs[1];
const auto& b1_shape = inputs[2];
const auto& c_shape = inputs.back();
auto tuning_value = v.get("tuning_value", 4);
if(not v.contains("tuning_value"))
tuning_value = get_tuning_for({a_shape, b_shape, b1_shape, c_shape});
auto tuning_value = v.get("tuning_value", 5);
auto batch_count = get_batch_count(c_shape);
auto problem = create_problem(inputs, v);
......@@ -399,7 +173,7 @@ struct ck_gemm_softmax_gemm_compiler : compiler<ck_gemm_softmax_gemm_compiler>
{"blocks_per_batch", to_string(blocks_per_batch)},
{"preamble", v.get("preamble", std::string{})},
{"kernel", options.kernel_name}});
return compile_hip_code_object(src, options);
}
......
test/verify/ck_gemm_softmax_gemm.cpp
View file @ c96139f8
......@@ -35,17 +35,14 @@ struct ck_gemm_softmax_gemm : verify_program<ck_gemm_softmax_gemm>
auto* mm = p.get_main_module();
migraphx::shape m1_shape{migraphx::shape::half_type, {1, 12, 256, 256}};
migraphx::shape m2_shape{migraphx::shape::half_type, {1, 12, 256, 256}};
auto m2_elements = 1 * 12 * 256 * 256;
auto m2_elements = m2_shape.elements();
auto a = mm->add_parameter("1", m1_shape);
auto b = mm->add_parameter("2", m1_shape);
auto b1 = mm->add_parameter("3", m1_shape);
auto c = mm->add_parameter("4", m1_shape);
std::vector<float> eights(m2_elements, 0.125);
auto eight = mm->add_literal(migraphx::literal{m2_shape, eights});
std::vector<float> zeros(m2_elements, 0);
auto zero = mm->add_literal(migraphx::literal{m2_shape, zeros});
std::vector<float> ones(m2_elements, 1);
auto one = mm->add_literal(migraphx::literal{m2_shape, ones});
b = mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 1, 3, 2}}}), b);
auto gemm1 = mm->add_instruction(migraphx::make_op("dot"), a, b);
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment