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Commit 2751143d authored by Brian Pickrell's avatar Brian Pickrell
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recreated branch by hand merge with develop branch

parent 30af1697
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src/targets/gpu/CMakeLists.txt
View file @ 2751143d
# ####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
# Copyright (c) 2015-2023 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
......@@ -215,10 +215,14 @@ else()
endif()
# Check miopen find mode api
include(CheckLibraryExists)
get_target_property(MIOPEN_LOCATION MIOpen LOCATION)
get_target_property(ROCBLAS_LOCATION roc::rocblas LOCATION)
check_library_exists(MIOpen "miopenHiddenSetConvolutionFindMode" "${MIOPEN_LOCATION}" HAS_FIND_MODE_API)
check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_2_API)
# Beta API for automated GEMM tuning
check_library_exists(roc::rocblas "rocblas_gemm_ex_get_solutions" "${ROCBLAS_LOCATION}" HAS_ROCBLAS_BETA_FEATURES_API)
set(MIGRAPHX_USE_FIND_2_API "${HAS_FIND_2_API}" CACHE BOOL "")
......@@ -236,6 +240,13 @@ else()
message(STATUS "MIOpen does not have find mode api")
endif()
if(HAS_ROCBLAS_BETA_FEATURES_API)
target_compile_definitions(migraphx_gpu PUBLIC -DROCBLAS_BETA_FEATURES_API -DROCBLAS_NO_DEPRECATED_WARNINGS)
message(STATUS "MIGraphx is using Beta API of rocBLAS")
else()
message(STATUS "rocBLAS does not have Beta API")
endif()
target_link_libraries(migraphx_gpu PUBLIC migraphx MIOpen roc::rocblas)
target_link_libraries(migraphx_gpu PRIVATE migraphx_device migraphx_kernels)
......
src/targets/gpu/gemm_impl.cpp
View file @ 2751143d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -21,10 +21,24 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/**
* Contains a templated struct implementation that wraps several rocBLAS API calls
* used by the GEMM operator. These are accessed by methods declared in gemm_impl.hpp
*
*/
#include <rocblas/rocblas.h>
#include <migraphx/gpu/gemm_impl.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/permutation.hpp>
#include <migraphx/time.hpp>
using microseconds = std::chrono::duration<double, std::micro>;
#if ROCBLAS_VERSION_MAJOR > 2 || (ROCBLAS_VERSION_MAJOR == 2 && ROCBLAS_VERSION_MINOR >= 38)
using flag_type = rocblas_gemm_flags;
#else
using flag_type = int;
#endif
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -81,198 +95,515 @@ shape transpose_batch(const shape& s, unsigned trans_batch)
return shape::from_permutation(s.type(), s.lens(), perm);
}
template <class R, class... Ts, class... Us>
R rocblas_invoke(R (*f)(Ts...), Us... xs)
/**
* Returns results of rocblas_status_success, rocblas_status_perf_degraded,
* or rocblas_status_invalid_value. Caller
* is expected to check for invalid index. Any other result causes an exception.
*
*/
template <class F, class Pack, class... Ts>
auto rocblas_invoke(F f, Pack p, Ts... xs)
{
if constexpr(sizeof...(Ts) == sizeof...(Us))
return f(xs...);
else
return f(xs..., nullptr, nullptr);
return p([=](auto... ws) {
auto status = f(ws..., xs...);
if(status != rocblas_status_success and status != rocblas_status_invalid_value)
{
if(status == rocblas_status_perf_degraded)
{
std::cerr << "WARNING: degraded perf. in rocBLAS call" << std::endl;
}
else
MIGRAPHX_THROW("rocblas_invoke: rocBLAS call failed with status " +
std::to_string(status));
}
return status;
});
}
static bool is_transposed(const shape& s)
{
if(not s.transposed())
return false;
return s.strides().back() != 1;
}
static bool is_transposed(const shape& s) { return s.transposed() and s.strides().back() != 1; }
static rocblas_int get_batch_stride(const argument& a)
{
return a.get_shape().strides()[a.get_shape().strides().size() - 3];
// This value is not needed for non-strided inputs
if(a.get_shape().strides().size() < 3)
return 0;
else
return a.get_shape().strides()[a.get_shape().strides().size() - 3];
}
template <class T>
void gemm_impl(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
T alpha,
T beta,
bool int8_x4_format,
bool compute_fp32)
/**
* Wrapper for multiple rocBLAS calls. The constructor creates parameters for
* these calls based on data shapes and other values contained in the associated
* instruction and operation.
*
* The template parameter T is not the type of the input data but of the weighting
* coefficients alpha and beta (these are float in rocBLAS internals)
*/
template <typename T>
struct gemm_impl
{
const bool is_3inputs = (args.size() == 4);
if(not is_3inputs)
gemm_impl(const shape& output_shape,
const std::vector<shape>& input_shapes,
T alpha_param,
T beta_param,
bool int8_x4_format,
bool compute_fp32_flag)
: alpha(alpha_param),
beta(beta_param),
is_3inputs(input_shapes.size() == 4),
compute_fp32(compute_fp32_flag)
{
beta = 0;
}
bool transa = is_transposed(args[0].get_shape());
bool transb = is_transposed(args[1].get_shape());
auto n_dim = output_shape.lens().size();
auto dim_1 = n_dim - 1;
auto dim_0 = n_dim - 2;
rocblas_int lda = args[0].get_shape().strides()[transa ? dim_1 : dim_0];
rocblas_int ldb = args[1].get_shape().strides()[transb ? dim_1 : dim_0];
rocblas_int ldc = args[2].get_shape().strides()[dim_0];
rocblas_int ldd = is_3inputs ? args[3].get_shape().strides()[dim_0] : ldc;
rocblas_datatype arg_type = get_type(args[0].get_shape().type());
auto output_type = arg_type;
if(output_type == rocblas_datatype_i8_r)
{
output_type = rocblas_datatype_i32_r;
}
auto compute_type = output_type;
if(compute_fp32)
{
if(arg_type == rocblas_datatype_f16_r)
compute_type = rocblas_datatype_f32_r;
}
#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
rocblas_gemm_flags flag =
int8_x4_format ? rocblas_gemm_flags_pack_int8x4 : rocblas_gemm_flags_none;
#else
(void)int8_x4_format;
int flag = 0;
#endif
if(not is_3inputs)
{
beta = 0;
}
auto a_lens = args[0].get_shape().lens();
auto b_lens = args[1].get_shape().lens();
output_shape.visit_type([&](auto as) {
auto alpha_r = as(alpha);
auto beta_r = as(beta);
// Create lambdas that will cast alpha, beta to the output shape type
// and retain the values being pointed to
output_shape.visit_type([&](auto as) {
auto alpha_r = as(alpha);
auto beta_r = as(beta);
if(compute_fp32)
{
get_alpha = [=] { return &alpha; };
get_beta = [=] { return &beta; };
}
else
{
get_alpha = [=] { return &alpha_r; };
get_beta = [=] { return &beta_r; };
}
});
// use void pointer to select different data type if using fp32 mode
void* alpha_v = &alpha_r;
void* beta_v = &beta_r;
transa = is_transposed(input_shapes[0]);
transb = is_transposed(input_shapes[1]);
auto n_dim = output_shape.lens().size();
auto dim_0 = n_dim - 2;
auto dim_1 = n_dim - 1;
// Leading dimensions of matrices
lda = input_shapes[0].strides()[transa ? dim_1 : dim_0];
ldb = input_shapes[1].strides()[transb ? dim_1 : dim_0];
ldc = input_shapes[2].strides()[dim_0];
ldd = is_3inputs ? input_shapes[3].strides()[dim_0] : ldc;
arg_type = get_type(input_shapes[0].type());
output_type = arg_type;
if(output_type == rocblas_datatype_i8_r)
{
output_type = rocblas_datatype_i32_r;
}
compute_type = output_type;
if(compute_fp32)
{
alpha_v = &alpha;
beta_v = &beta;
if(arg_type == rocblas_datatype_f16_r)
compute_type = rocblas_datatype_f32_r;
}
auto out_lens = output_shape.lens();
rocblas_int m = out_lens[dim_0];
rocblas_int n = out_lens[dim_1];
rocblas_int k = args[0].get_shape().lens()[dim_1];
auto to_pointer = [&](auto&& arg) { return as.from(arg.data()); };
if(args[0].get_shape().type() == shape::int8_type and (k % 4) != 0 and int8_x4_format)
int8_flag = int8_x4_format ? rocblas_gemm_flags_pack_int8x4 : rocblas_gemm_flags_none;
auto a_lens = input_shapes[0].lens();
auto b_lens = input_shapes[1].lens();
auto out_lens = output_shape.lens();
m = out_lens[dim_0];
n = out_lens[dim_1];
k = input_shapes[0].lens()[dim_1];
if(input_shapes[0].type() == shape::int8_type and (k % 4) != 0 and int8_x4_format)
{
MIGRAPHX_THROW("ROCBLAS_GEMM: k size of int8 type input must be mutlple of 4!");
MIGRAPHX_THROW("ROCBLAS_GEMM: k size of int8 type input must be multiple of 4!");
}
auto num_matrices = std::accumulate(
a_stride = get_batch_stride(input_shapes[0]);
b_stride = get_batch_stride(input_shapes[1]);
c_stride = get_batch_stride(input_shapes[2]);
d_stride = is_3inputs ? get_batch_stride(input_shapes[3]) : c_stride;
num_matrices = std::accumulate(
out_lens.rbegin() + 2, out_lens.rend(), std::size_t{1}, std::multiplies<std::size_t>());
if(num_matrices == 1 or (num_matrices > 1 and get_batch_stride(args[1]) == 0))
if(num_matrices == 1 or (num_matrices > 1 and b_stride == 0))
{
// If the batch dimension of B is broadcasted, then we can
// multiply m by the batch_size and use rocblas_gemm_ex
// instead of rocblas_gemm_strided_batched_ex.
m *= num_matrices;
strided_batched = false;
}
}
// the rocblas_gemm API handles inputs and output matrices as
// column-major format. When doing a C = A * B, we actually do
// C^T = (B^T) * (A^T). That is the reason we input args[1] as
// A and args[0] as B in calling the rocblas_gemm.
rocblas_invoke(&rocblas_gemm_ex,
ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
alpha_v,
to_pointer(args.at(1)),
arg_type,
ldb,
to_pointer(args.at(0)),
arg_type,
lda,
beta_v,
to_pointer(args[2]),
output_type,
ldc,
is_3inputs ? to_pointer(args[3]) : to_pointer(args[2]),
output_type,
ldd,
compute_type,
void run(context& ctx, const std::vector<argument>& input_args, int32_t solution_idx = 0) const
{
if(strided_batched)
{
auto common_args = create_strided_batched_args_common(ctx, input_args);
rocblas_invoke(&rocblas_gemm_strided_batched_ex,
common_args,
rocblas_gemm_algo_standard,
0,
flag);
solution_idx,
int8_flag);
}
else
{
auto a_stride = get_batch_stride(args[0]);
auto b_stride = get_batch_stride(args[1]);
auto c_stride = get_batch_stride(args[2]);
auto d_stride = is_3inputs ? get_batch_stride(args[3]) : c_stride;
rocblas_invoke(&rocblas_gemm_strided_batched_ex,
ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
alpha_v,
to_pointer(args.at(1)),
arg_type,
ldb,
b_stride,
to_pointer(args.at(0)),
arg_type,
lda,
a_stride,
beta_v,
to_pointer(args[2]),
output_type,
ldc,
c_stride,
is_3inputs ? to_pointer(args[3]) : to_pointer(args[2]),
output_type,
ldd,
d_stride,
num_matrices,
compute_type,
rocblas_gemm_algo_standard,
0,
flag);
auto common_args = create_gemm_ex_args_common(ctx, input_args);
rocblas_invoke(
&rocblas_gemm_ex, common_args, rocblas_gemm_algo_standard, solution_idx, int8_flag);
}
});
}
#ifdef ROCBLAS_BETA_FEATURES_API
auto validate(context& ctx, const std::vector<shape>& input_shapes, int32_t solution_idx) const
{
std::vector<argument> input_args;
std::transform(input_shapes.begin(),
input_shapes.end(),
std::back_inserter(input_args),
[](const shape& x) { return to_gpu(generate_argument(x)); });
return validate(ctx, input_args, solution_idx);
}
/**
* Checks a particular solution for validity by running it with the flag
* rocblas_gemm_flags_check_solution_index (could be invalid if this model was
* tuned with a different rocBLAS version)
*
* @return Returns either solution_idx if valid, or else the default value 0
* if not. The default does not mean list index 0, but tells the picker
* to choose a solution.
*/
int32_t
validate(context& ctx, const std::vector<argument>& input_args, int32_t solution_idx) const
{
rocblas_status_ check_valid(rocblas_status_success);
if(strided_batched)
{
auto common_args = create_strided_batched_args_common(ctx, input_args);
check_valid = rocblas_invoke(&rocblas_gemm_strided_batched_ex,
common_args,
rocblas_gemm_algo_solution_index,
solution_idx,
rocblas_gemm_flags_check_solution_index);
}
else
{
auto common_args = create_gemm_ex_args_common(ctx, input_args);
check_valid = rocblas_invoke(&rocblas_gemm_ex,
common_args,
rocblas_gemm_algo_standard,
solution_idx,
rocblas_gemm_flags_check_solution_index);
}
if(check_valid == rocblas_status_invalid_value)
{
std::cerr << "WARNING: tuned solution is invalid; reverting to default" << std::endl;
return 0;
}
return solution_idx;
}
#endif
/**
* Helper method to create that subset of a long rocBLAS argument list that is common
* to multiple "...strided_batched..." calls.
*
* The rocblas_gemm API handles inputs and output matrices as
* column-major format. When doing a C = A * B, we actually do
* C^T = (B^T) * (A^T). That is the reason we input args[1] as
* A and args[0] as B in calling the rocblas_gemm.
*
*/
auto create_strided_batched_args_common(context& ctx, const std::vector<argument>& args) const
{
return pack(ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
get_alpha(),
args[1].data(),
arg_type,
ldb,
b_stride,
args[0].data(),
arg_type,
lda,
a_stride,
get_beta(),
args[2].data(),
output_type,
ldc,
c_stride,
is_3inputs ? args[3].data() : args[2].data(),
output_type,
ldd,
d_stride,
num_matrices,
compute_type);
}
/**
* Helper method to create that subset of a long rocBLAS argument list that is common
* to multiple "gemm_ex..." calls.
*
* The rocblas_gemm API handles inputs and output matrices as
* column-major format. When doing a C = A * B, we actually do
* C^T = (B^T) * (A^T). That is the reason we input args[1] as
* A and args[0] as B in calling the rocblas_gemm.
*
* */
auto create_gemm_ex_args_common(context& ctx, const std::vector<argument>& args) const
{
return pack(ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
get_alpha(),
args[1].data(),
arg_type,
ldb,
args[0].data(),
arg_type,
lda,
get_beta(),
args[2].data(),
output_type,
ldc,
is_3inputs ? args[3].data() : args[2].data(),
output_type,
ldd,
compute_type);
}
#ifdef ROCBLAS_BETA_FEATURES_API
/**
* Find best rocBLAS solution: Get list of solutions and try them all, returning the index
* of the fastest one.
*/
int tune(context& ctx, const std::vector<shape>& input_shapes) const
{
// tuning meta parameters
const int hot_calls = 40;
const int cold_calls = 1;
std::vector<argument> input_args;
std::transform(input_shapes.begin(),
input_shapes.end(),
std::back_inserter(input_args),
[](const shape& x) { return to_gpu(generate_argument(x)); });
// Get the solutions list in 2 rocBLAS steps:
// 1. Find out how many solutions there are and allocate the array
// 2. Get the solutions
//
rocblas_int list_size = 0;
std::vector<rocblas_int> solution_indices;
if(strided_batched)
{
auto common_args = create_strided_batched_args_common(ctx, input_args);
rocblas_invoke(&rocblas_gemm_strided_batched_ex_get_solutions,
common_args,
rocblas_gemm_algo_solution_index,
int8_flag,
nullptr,
&list_size);
solution_indices.resize(list_size);
auto common_sol_args = create_strided_batched_args_common(ctx, input_args);
rocblas_invoke(&rocblas_gemm_strided_batched_ex_get_solutions,
common_sol_args,
rocblas_gemm_algo_solution_index,
int8_flag,
solution_indices.data(),
&list_size);
}
else
{
auto common_args = create_gemm_ex_args_common(ctx, input_args);
rocblas_invoke(&rocblas_gemm_ex_get_solutions,
common_args,
rocblas_gemm_algo_solution_index,
int8_flag,
nullptr,
&list_size);
solution_indices.resize(list_size);
auto common_sol_args = create_gemm_ex_args_common(ctx, input_args);
rocblas_invoke(&rocblas_gemm_ex_get_solutions,
common_sol_args,
rocblas_gemm_algo_solution_index,
int8_flag,
solution_indices.data(),
&list_size);
}
double bestTime = std::numeric_limits<double>::max();
double first_time = -1;
// Initialize to default solution index
rocblas_int bestSol = 0;
for(auto sol : solution_indices)
{
// Define the function to be timed
auto run_func = [&]() {
run(ctx, input_args, sol);
ctx.finish();
};
// Warmup: the first few calls to an op. may not be representative since there is
// more time taken initializing caches, etc. so we won't time them.
for(int cc = 0; cc < cold_calls; ++cc)
{
run_func();
}
double host_time = 0.0;
for(int hc = 0; hc < hot_calls; ++hc)
{
ctx.finish();
host_time += time<microseconds>(run_func);
}
// todo: Measured time dropped from 20 us to about 6.7 us when I raised hot_calls from
// 1 to 11. The higher the hot_calls value, the faster per-call time up to at least 25,
// and increasing cold_calls makes little or no difference. Why?
host_time /= hot_calls;
// debug only: track time for first solution.
if(first_time < 0)
first_time = host_time;
// track current best
if(host_time < bestTime)
{
std::cout << " current best index " << sol << ", time " << host_time << std::endl;
bestSol = sol;
bestTime = host_time;
}
}
std::cout << "Winner: " << bestSol << " in " << bestTime << " us, beats " << first_time
<< std::endl;
return bestSol;
}
#endif
private:
size_t num_matrices;
rocblas_int m, n, k;
bool transa, transb;
T alpha, beta;
std::function<const void*()> get_alpha;
std::function<const void*()> get_beta;
flag_type int8_flag;
rocblas_int lda, ldb, ldc, ldd;
rocblas_int a_stride, b_stride, c_stride, d_stride;
rocblas_datatype compute_type, arg_type, output_type;
bool strided_batched = true, is_3inputs = true, compute_fp32 = true;
}; // gemm_impl
void gemm_compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
float alpha,
float beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx)
{
std::vector<shape> input_shapes;
std::transform(args.begin(),
args.end(),
std::back_inserter(input_shapes),
[](const argument& x) { return x.get_shape(); });
auto gemm_item =
gemm_impl<float>(output_shape, input_shapes, alpha, beta, int8_x4_format, compute_fp32);
gemm_item.run(ctx, args, solution_idx);
}
void gemm_compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
int32_t alpha,
int32_t beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx)
{
std::vector<shape> input_shapes;
std::transform(args.begin(),
args.end(),
std::back_inserter(input_shapes),
[](const argument& x) { return x.get_shape(); });
auto gemm_item =
gemm_impl<int32_t>(output_shape, input_shapes, alpha, beta, int8_x4_format, compute_fp32);
gemm_item.run(ctx, args, solution_idx);
}
void gemm(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
float alpha,
float beta,
bool int8_x4_format,
bool compute_fp32)
int32_t gemm_finalize(context& ctx,
const shape& output_shape,
const std::vector<shape>& input_shapes,
float alpha,
float beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx)
{
gemm_impl(ctx, output_shape, args, alpha, beta, int8_x4_format, compute_fp32);
#ifdef ROCBLAS_BETA_FEATURES_API
if(ctx.get_exhaustive_tune_flag() && solution_idx == 0)
// if((true))
{
auto gemm_item =
gemm_impl<float>(output_shape, input_shapes, alpha, beta, int8_x4_format, compute_fp32);
solution_idx = gemm_item.tune(ctx, input_shapes);
}
else if(solution_idx != 0)
{
// If a tuned solution index is already given, don't tune again but validate
// in case the data was tuned with a different rocBLAS version
auto gemm_item =
gemm_impl<float>(output_shape, input_shapes, alpha, beta, int8_x4_format, compute_fp32);
solution_idx = gemm_item.validate(ctx, input_shapes, solution_idx);
}
#else
// suppress compiler warnings
(void)ctx, (void)output_shape, (void)input_shapes;
(void)alpha, (void)beta, (void)int8_x4_format, (void)compute_fp32;
#endif
return solution_idx;
}
void gemm(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
int32_t alpha,
int32_t beta,
bool int8_x4_format,
bool compute_fp32)
int32_t gemm_finalize(context& ctx,
const shape& output_shape,
const std::vector<shape>& input_shapes,
int32_t alpha,
int32_t beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx)
{
gemm_impl(ctx, output_shape, args, alpha, beta, int8_x4_format, compute_fp32);
#ifdef ROCBLAS_BETA_FEATURES_API
if(ctx.get_exhaustive_tune_flag() && solution_idx == 0)
{
auto gemm_item = gemm_impl<int32_t>(
output_shape, input_shapes, alpha, beta, int8_x4_format, compute_fp32);
solution_idx = gemm_item.tune(ctx, input_shapes);
}
else if(solution_idx != 0)
{
// If a tuned solution index is already given, don't tune again but validate
// in case the data was tuned with a different rocBLAS version
auto gemm_item = gemm_impl<int32_t>(
output_shape, input_shapes, alpha, beta, int8_x4_format, compute_fp32);
solution_idx = gemm_item.validate(ctx, input_shapes, solution_idx);
}
#else
// suppress compiler warnings
(void)ctx, (void)output_shape, (void)input_shapes;
(void)alpha, (void)beta, (void)int8_x4_format, (void)compute_fp32;
#endif
return solution_idx;
}
} // namespace gpu
......
src/targets/gpu/include/migraphx/gpu/convolution.hpp
View file @ 2751143d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -27,6 +27,7 @@
#include <migraphx/shape.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/operation.hpp>
#include <migraphx/register_op.hpp>
#include <migraphx/gpu/miopen.hpp>
#include <migraphx/op/identity.hpp>
#include <migraphx/op/convolution.hpp>
......@@ -35,6 +36,9 @@
#include <unordered_map>
#include <migraphx/reflect.hpp>
#include <migraphx/gpu/context.hpp>
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_ENABLE_CONV_TUNING);
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
......@@ -163,6 +167,20 @@ struct miopen_convolution
auto w_desc = make_tensor(reshape_if_1d(inputs[1]), int8_x4_format);
auto y_desc = make_tensor(reshape_if_1d(output_shape));
std::size_t workspace_size = 0;
// TODO: Using an environment variable to disable convolution tuning while still allowing
// other (i.e. GEMM) tuning with --exhaustive-tune, is a workaround for a problem in which
// convolution tuning takes multiple hours.
const bool convo_tune_enabled = enabled(MIGRAPHX_ENABLE_CONV_TUNING{});
if(ctx.get_exhaustive_tune_flag() and not convo_tune_enabled)
{
std::cerr
<< "WARNING: MIGraphX convolution tuning not enabled. To run tuning, set both the "
"env "
"var MIGRAPHX_ENABLE_CONV_TUNING and the command argument --exhaustive-tune."
<< std::endl;
}
#ifdef MIGRAPHX_HAS_FIND_2_API
{
auto conv_problem = make_obj<miopen_problem>(
......@@ -174,9 +192,10 @@ struct miopen_convolution
auto* miopen_stream_handle = ctx.get_stream().get_miopen();
solution_ptr = find_solution(
miopen_stream_handle, conv_problem.get(), ctx.get_exhaustive_tune_flag());
auto status = miopenGetSolutionWorkspaceSize(solution_ptr.get(), &workspace_size);
solution_ptr = find_solution(miopen_stream_handle,
conv_problem.get(),
ctx.get_exhaustive_tune_flag() and convo_tune_enabled);
auto status = miopenGetSolutionWorkspaceSize(solution_ptr.get(), &workspace_size);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen" + op.name() + " : failed to get solution's workspace size");
......@@ -233,7 +252,8 @@ struct miopen_convolution
&perf,
workspace.implicit(),
workspace_size,
ctx.get_exhaustive_tune_flag());
ctx.get_exhaustive_tune_flag() and
convo_tune_enabled);
if(status != miopenStatusSuccess)
MIGRAPHX_THROW("MIOpen " + op.name() + " : find convolution failed");
algo = perf.fwd_algo;
......
src/targets/gpu/include/migraphx/gpu/gemm.hpp
View file @ 2751143d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -40,9 +40,8 @@ inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
struct context;
void blas_shape(const shape& s);
shape transpose_batch(const shape& s, unsigned trans_batch);
void blas_shape(const shape& s);
template <class Op>
struct rocblas_gemm
......@@ -53,6 +52,7 @@ struct rocblas_gemm
bool int8_x4_format = true;
bool compute_fp32 = false;
unsigned trans_batch = 0;
int32_t solution_idx = 0;
template <class Self, class F>
static auto reflect(Self& self, F f)
......@@ -62,7 +62,8 @@ struct rocblas_gemm
f(self.beta, "beta"),
f(self.int8_x4_format, "int8_x4_format"),
f(self.compute_fp32, "compute_fp32"),
f(self.trans_batch, "trans_batch")));
f(self.trans_batch, "trans_batch"),
f(self.solution_idx, "solution_idx")));
}
std::string name() const
......@@ -113,17 +114,19 @@ struct rocblas_gemm
{
if(this->name() == "gpu::gemm")
{
gemm(ctx, output_shape, args, alpha, beta, int8_x4_format, compute_fp32);
gemm_compute(
ctx, output_shape, args, alpha, beta, int8_x4_format, compute_fp32, solution_idx);
}
else
{
gemm(ctx,
output_shape,
args,
int32_t(alpha),
int32_t(beta),
int8_x4_format,
compute_fp32);
gemm_compute(ctx,
output_shape,
args,
int32_t(alpha),
int32_t(beta),
int8_x4_format,
compute_fp32,
solution_idx);
}
return args.back();
}
......@@ -132,6 +135,40 @@ struct rocblas_gemm
{
return shapes.size() - 1;
}
void finalize(context& ctx, const shape& output_shape, const std::vector<shape>& input_shapes)
{
#ifdef ROCBLAS_BETA_FEATURES_API
if(ctx.get_exhaustive_tune_flag() && solution_idx == 0)
{
if(this->name() == "gpu::gemm")
{
solution_idx = gemm_finalize(ctx,
output_shape,
input_shapes,
alpha,
beta,
int8_x4_format,
compute_fp32,
solution_idx);
}
else
{
solution_idx = gemm_finalize(ctx,
output_shape,
input_shapes,
int32_t(alpha),
int32_t(beta),
int8_x4_format,
compute_fp32,
solution_idx);
}
}
#else
// suppress compiler warnings
(void)ctx, (void)output_shape, (void)input_shapes;
#endif
}
};
} // namespace gpu
......
src/targets/gpu/include/migraphx/gpu/gemm_impl.hpp
View file @ 2751143d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -24,28 +24,75 @@
#ifndef MIGRAPHX_GUARD_RTGLIB_GEMM_IMPL_HPP
#define MIGRAPHX_GUARD_RTGLIB_GEMM_IMPL_HPP
#include <iterator>
#include <migraphx/shape.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/reduce_dims.hpp>
#include <migraphx/gpu/hip.hpp>
#include <migraphx/time.hpp>
using milliseconds = std::chrono::duration<double, std::milli>;
using microseconds = std::chrono::duration<double, std::micro>;
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
void gemm(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
float alpha,
float beta,
bool int8_x4_format,
bool compute_fp32);
void gemm(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
int32_t alpha,
int32_t beta,
bool int8_x4_format,
bool compute_fp32);
#if ROCBLAS_VERSION_MAJOR >= 2 && ROCBLAS_VERSION_MINOR >= 38
using flag_type = rocblas_gemm_flags;
#else
using flag_type = int;
#endif
/**
* @brief Templated implementations of the compute() and finalize() methods of the Gemm operator.
* For each function there are overloads using either float or int32_t for the arguments
* alpha and beta.
*
* @param ctx .
* @param output_shape .
* @param args .
* @param alpha .
* @param beta .
* @param int8_x4_format .
* @param compute_fp32 .
*/
void gemm_compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
float alpha,
float beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx);
void gemm_compute(context& ctx,
const shape& output_shape,
const std::vector<argument>& args,
int32_t alpha,
int32_t beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx);
int32_t gemm_finalize(context& ctx,
const shape& output_shape,
const std::vector<shape>& input_shapes,
float alpha,
float beta,
bool int8_x4_format,
bool compute_fp32);
int32_t gemm_finalize(context& ctx,
const shape& output_shape,
const std::vector<shape>& input_shapes,
int32_t alpha,
int32_t beta,
bool int8_x4_format,
bool compute_fp32,
int32_t solution_idx);
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
......
src/targets/gpu/include/migraphx/gpu/rocblas.hpp
View file @ 2751143d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -25,6 +25,7 @@
#define MIGRAPHX_GUARD_MIGRAPHLIB_ROCBLAS_HPP
#include <migraphx/manage_ptr.hpp>
#include <migraphx/config.hpp>
// ROCBLAS_BETA_FEATURES_API is defined by CMake, if available.
#include <rocblas/rocblas.h>
namespace migraphx {
......
test/gpu/gemm_tune.cpp 0 → 100644
View file @ 2751143d
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2023 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 <iostream>
#include <vector>
#include <migraphx/gpu/gemm.hpp>
#include <hip/hip_runtime_api.h>
#include <migraphx/gpu/target.hpp>
#include <migraphx/verify.hpp>
#include <test.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/iterator_for.hpp>
// includes needed for run_lowering
#include <migraphx/gpu/allocation_model.hpp>
#include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/lowering.hpp>
#include <migraphx/gpu/target.hpp>
#include <migraphx/auto_contiguous.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/pass_manager.hpp>
// Abbreviated lowering; we don't need the usual cleanup passes for this test
void run_lowering(migraphx::program& p, bool offload_copy = false)
{
auto ctx = migraphx::gpu::context{};
migraphx::run_passes(
*p.get_main_module(),
{migraphx::auto_contiguous{}, migraphx::gpu::lowering{&ctx, offload_copy}});
}
/**
* Tests the automatic GEMM tuning feature. In the finalize() method of the gemm op,
* rocBLAS API functions are called to quickly benchmark all the GEMM solutions
* available in the currently installed rocBLAS library and choose the index of the fastest.
*/
TEST_CASE(gemm_tune_with_rocblas)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape sa{migraphx::shape::float_type, {4, 2}};
migraphx::shape sb{migraphx::shape::float_type, {2, 3}};
auto a = mm->add_parameter("a", sa);
auto b = mm->add_parameter("b", sb);
migraphx::operation dot_op = migraphx::make_op("dot");
mm->add_instruction(dot_op, a, b);
// lowering adds gemm implementation for dot operator
run_lowering(p);
migraphx::target gpu_t = migraphx::gpu::target{};
migraphx::compile_options options;
options.exhaustive_tune = true;
p.compile(gpu_t, options);
migraphx::value solution_idx(0);
for(auto ins : iterator_for(*p.get_main_module()))
{
if(ins->name() == "gpu::gemm")
{
auto gemm_op = migraphx::get_operation(ins);
// tuned solution index is not deterministic, but anything other than 0
// (default, invalid, or not available) is good.
// gemm_op.to_value().debug_print();
solution_idx = gemm_op.to_value()["solution_idx"];
break;
}
}
#ifdef ROCBLAS_BETA_FEATURES_API
EXPECT(0 != solution_idx.to<std::size_t>());
#else
EXPECT(0 == solution_idx.to<std::size_t>());
#endif
}
// TODO: make tests that run both strided and not-strided GEMMs. Also, try tuning with an invalid
// start value.
int main(int argc, const char* argv[]) { test::run(argc, argv); }
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