Skip to content

GitLab

Commit 47cc9b7e authored by Astha Rai's avatar Astha Rai
Browse files

added compilation of shared library and multiple instances for gemm, cleaned up code design

parent adbefd90
Hide whitespace changes
Inline Side-by-side
Showing with 1357 additions and 1431 deletions
python/ait_impl/samples/model_container.h deleted 100644 → 0
View file @ adbefd90
#pragma once
#include "model-generated.h"
#include "model_interface.h"
#include "raii_wrapper.h"
#include <condition_variable>
#include <cstring>
#include <future>
#include <mutex>
#include <numeric>
#include <unordered_map>
namespace ait {
// ModelContainer inherits from this class; its implementation is
// generated at compilation time. Most of the ModelContainer
// logic does not need codegen; anything that does should be put
// into this class instead.
class ModelContainerBase {
public:
ModelContainerBase(
size_t num_inputs,
size_t num_outputs,
size_t num_unbound_constants,
size_t params_size,
AITemplateAllocator& allocator);
protected:
// The set of unbound constants/weights/parameters. These are constants which
// have no value at compile time and do not participate in constant folding.
// They must be set via SetConstant prior to inference.
std::unordered_map<std::string, size_t> unbound_constant_name_to_idx_;
// a single piece of memory for all constants
GPUPtr constants_;
// size of the containers below: # inputs + # outputs + # unbound constants.
size_t num_params_;
// These entries correspond to inputs/outputs/unbound constants in order;
// inputs first, then outputs, then constants.
std::vector<const char*> param_names_;
std::vector<std::vector<int64_t>> max_param_shapes_;
std::vector<AITemplateDtype> param_dtypes_;
// NB: technically these could be derived from both the max shape and
// the dytpe, but it's easier to just cache them.
std::vector<size_t> max_param_storage_bytes_;
std::vector<size_t> max_param_numel_;
};
// This creates a new ModelContainer; its implementation is also
// codegened (the parameters passed to the ctor are determined
// at compilation time)
class ModelContainer;
ModelContainer* CreateModelContainer(
size_t num_runtimes,
AITemplateAllocator& allocator);
// Each ModelContainer contains num_models Models. Inference runs
// can be started by invoking Run() with lists of pre-allocated
// input/output tensors. GetOutputMaximumShape() can be used to
// determine how much memory is required for each output.
//
// If there are N tensors marked with is_output=True,
// the user will always be expected to pass N output pointers -
// extra copies will occur if the outputs are views of constants,
// inputs, or other outputs in this case to avoid surprises.
//
// Use stream = nullptr for default stream. ModelContainer/Model does not
// create or own any stream. The user is expected to create and manage streams.
//
// We can support at most num_models concurrent inferences.
// Run() takes a stream to run the inference on. For example,
// to start up two inferences on different streams concurrently,
// we can do this:
//
// model_container.Run(inputs0, num_inputs, outputs0, num_ouputs, stream0, ...);
// model_container.Run(inputs1, num_inputs, outputs1, num_ouputs, stream1, ...);
// StreamSynchronize(stream0);
// StreamSynchronize(stream1);
//
// Note that if there are no models available for inference, Run() will block
// until one becomes available.
//
// ModelContainer optionally takes an allocator argument, which it will use to
// allocate the space for the buffers used for intermediate tensors and
// constants. If it is nullptr, the default allocator will be used (e.g. just
// {cuda/hip}{Malloc/Free}).
// Important: we assume that the allocator lives until the ModelContainer is
// destroyed. The default allocator has a static lifetime.
class ModelContainer : ModelContainerBase {
public:
ModelContainer(
size_t num_models,
size_t blob_size,
size_t workspace_size,
size_t num_inputs,
size_t num_outputs,
size_t num_unbound_constants,
size_t params_size,
AITemplateAllocator& allocator);
void Run(
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
StreamType stream,
bool sync,
bool graph_mode,
int64_t** output_shapes_out);
void RunWithOutputsOnHost(
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
StreamType stream,
bool graph_mode,
int64_t** output_shapes_out);
float Benchmark(
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
StreamType stream,
bool graph_mode,
size_t count,
size_t num_threads,
bool use_unique_stream_per_thread,
int64_t** output_shapes_out);
void SetConstant(const char* name, const AITData& tensor);
size_t NumInputs() const;
size_t NumOutputs() const;
const char* InputName(size_t input_idx) const;
const char* OutputName(size_t output_idx) const;
AITemplateParamShape MaxOutputShape(size_t output_idx) const;
AITemplateDtype OutputDtype(size_t output_idx) const;
size_t MaxOutputStorageBytes(size_t output_idx) const;
size_t GetNumRuntimes() const {
return models_.size();
}
private:
void PrepareForRun(
Model* model,
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs);
Model* GetAvailableModel();
void ReclaimFinishedModels(std::unique_lock<std::mutex>& lk);
void ValidateDtype(AITemplateDtype dtype, size_t idx) const;
float BenchmarkImpl(
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
StreamType stream,
bool graph_mode,
size_t count,
int64_t** output_shapes_out);
AITemplateAllocator& allocator_;
std::vector<Model> models_;
std::vector<Model*> available_models_;
std::deque<Model*> pending_models_;
// Guards accesses to available/pending models.
std::mutex models_mutex_;
// Notified whenever a model is put into pending_models_.
std::condition_variable pending_models_available_;
size_t num_inputs_;
size_t num_outputs_;
};
} // namespace ait
python/ait_impl/samples/model_container_base.cpp deleted 100644 → 0
View file @ adbefd90
#include "model_container.h"
#include "owned_constants.h"
namespace ait {
namespace {
// Contains the metadata for each constant.
constexpr std::array<ConstantInfo, 0> owned_constants = {
};
} // namespace
ModelContainerBase::ModelContainerBase(
size_t num_inputs,
size_t num_outputs,
size_t num_unbound_constants,
size_t params_size,
AITemplateAllocator& allocator)
: constants_(RAII_DeviceMalloc(params_size, allocator)),
num_params_(num_inputs + num_outputs + num_unbound_constants),
param_names_(num_params_),
param_dtypes_(num_params_),
max_param_shapes_(num_params_),
max_param_numel_(num_params_),
max_param_storage_bytes_(num_params_) {
param_names_[0] = "input_0";
param_names_[1] = "input_1";
param_names_[2] = "output_0";
param_dtypes_[0] = AITemplateDtype::kHalf;
param_dtypes_[1] = AITemplateDtype::kHalf;
param_dtypes_[2] = AITemplateDtype::kHalf;
max_param_shapes_[0] = {256, 128};
max_param_shapes_[1] = {128, 32};
max_param_shapes_[2] = {256, 32};
for (size_t i = 0; i < num_params_; ++i) {
max_param_numel_[i] = std::accumulate(
max_param_shapes_[i].begin(),
max_param_shapes_[i].end(),
1,
std::multiplies<int64_t>()
);
max_param_storage_bytes_[i] = max_param_numel_[i] * AITemplateDtypeSizeBytes(param_dtypes_[i]);
}
auto* constants_ptr = static_cast<uint8_t*>(constants_.get());
const auto binary_constants_bin_size = static_cast<size_t>(_binary_constants_bin_end - _binary_constants_bin_start);
for (auto& constant_info : owned_constants) {
auto* dst = constants_ptr + constant_info.internal_offset;
if (constant_info.data_offset + constant_info.num_bytes > binary_constants_bin_size) {
throw std::runtime_error(std::string("Copying constant ") + constant_info.name + " would overflow constant buffer");
}
DEVICE_CHECK(CopyToDevice(dst, _binary_constants_bin_start + constant_info.data_offset, constant_info.num_bytes));
}
}
ModelContainer* CreateModelContainer(size_t num_runtimes, AITemplateAllocator& allocator) {
// num_runtimes, blob_size, workspace_size, num_inputs, num_outputs, num_unbound_constants, param_size, allocator
return new ModelContainer(num_runtimes, 90112, 0, 2, 1, 0, 0, allocator);
}
} // namespace ait
python/ait_impl/samples/model_interface.cpp deleted 100644 → 0
View file @ adbefd90
#include "model_interface.h"
#include <iostream>
#include <unordered_map>
#include "model-generated.h"
#include "model_container.h"
// Important: don't let exceptions escape the functions below.
// They can cause problems when -fvisibility=hidden. But more
// importantly, they can crash the program if they try to cross
// the language boundary into Python.
#define CONVERT_EXCEPTION_TO_ERROR_CODE(...) \
try { \
__VA_ARGS__ \
} catch (const std::exception& e) { \
LOG(ERROR) << "Error: " << e.what(); \
return AITemplateError::AITemplateFailure; \
} catch (...) { \
LOG(ERROR) << "Unknown exception occurred."; \
return AITemplateError::AITemplateFailure; \
} \
return AITemplateError::AITemplateSuccess;
#define RETURN_ERROR_IF_NULL(var) \
if (var == nullptr) { \
LOG(ERROR) << "Variable " << #var << " can't be null"; \
return AITemplateError::AITemplateFailure; \
}
namespace ait {
namespace {
class DefaultAllocator : public AITemplateAllocator {
public:
void* Allocate(size_t n_bytes) override {
void* result;
DEVICE_CHECK(DeviceMalloc(&result, n_bytes));
return result;
}
void Free(void* ptr) override {
DEVICE_CHECK(FreeDeviceMemory(ptr));
}
};
class TrackingAllocator : public DefaultAllocator {
public:
void* Allocate(size_t n_bytes) override {
auto* result = DefaultAllocator::Allocate(n_bytes);
num_bytes_ += n_bytes;
return result;
}
size_t NumBytesAllocated() const {
return num_bytes_;
}
private:
size_t num_bytes_ = 0;
};
DefaultAllocator default_allocator;
} // namespace
} // namespace ait
extern "C" {
AITemplateError AITemplateModelContainerCreate(
AITemplateModelHandle* ret,
size_t num_runtimes,
AITemplateAllocator* allocator) {
if (num_runtimes == 0) {
LOG(ERROR) << "num_runtimes must be positive, but got 0";
return AITemplateError::AITemplateFailure;
}
RETURN_ERROR_IF_NULL(ret)
AITemplateAllocator& allocator_ref =
allocator == nullptr ? ait::default_allocator : *allocator;
CONVERT_EXCEPTION_TO_ERROR_CODE({
auto* m = ait::CreateModelContainer(num_runtimes, allocator_ref);
*ret = reinterpret_cast<AITemplateModelHandle>(m);
})
}
AITemplateError AITemplateModelContainerDelete(AITemplateModelHandle handle) {
RETURN_ERROR_IF_NULL(handle)
CONVERT_EXCEPTION_TO_ERROR_CODE({
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
delete m;
});
}
AITemplateError AITemplateModelContainerSetConstant(
AITemplateModelHandle handle,
const char* name,
const AITData* tensor) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(tensor)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({ m->SetConstant(name, *tensor); })
}
AITemplateError AITemplateModelContainerRun(
AITemplateModelHandle handle,
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
AITemplateStreamHandle stream_handle,
bool sync,
bool graph_mode,
int64_t** output_shapes_out) {
RETURN_ERROR_IF_NULL(handle)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
auto stream = reinterpret_cast<ait::StreamType>(stream_handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({
m->Run(
inputs,
num_inputs,
outputs,
num_outputs,
stream,
sync,
graph_mode,
output_shapes_out);
})
}
AITemplateError AITemplateModelContainerRunWithOutputsOnHost(
AITemplateModelHandle handle,
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
AITemplateStreamHandle stream_handle,
bool graph_mode,
int64_t** output_shapes_out) {
RETURN_ERROR_IF_NULL(handle)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
auto stream = reinterpret_cast<ait::StreamType>(stream_handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({
m->RunWithOutputsOnHost(
inputs,
num_inputs,
outputs,
num_outputs,
stream,
graph_mode,
output_shapes_out);
})
}
AITemplateError AITemplateModelContainerBenchmark(
AITemplateModelHandle handle,
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
AITemplateStreamHandle stream_handle,
bool graph_mode,
size_t count,
size_t num_threads,
bool use_unique_stream_per_thread,
float* runtime_ms,
int64_t** output_shapes_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(runtime_ms)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
auto stream = reinterpret_cast<ait::StreamType>(stream_handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({
*runtime_ms = m->Benchmark(
inputs,
num_inputs,
outputs,
num_outputs,
stream,
graph_mode,
count,
num_threads,
use_unique_stream_per_thread,
output_shapes_out);
})
}
AITemplateError AITemplateModelContainerGetNumInputs(
AITemplateModelHandle handle,
size_t* num_inputs_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(num_inputs_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({ *num_inputs_out = m->NumInputs(); })
}
AITemplateError AITemplateModelContainerGetInputName(
AITemplateModelHandle handle,
size_t input_idx,
const char** input_name_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(input_name_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE(
{ *input_name_out = m->InputName(input_idx); })
}
AITemplateError AITemplateModelContainerGetNumOutputs(
AITemplateModelHandle handle,
size_t* num_outputs_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(num_outputs_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({ *num_outputs_out = m->NumOutputs(); })
}
AITemplateError AITemplateModelContainerGetOutputName(
AITemplateModelHandle handle,
size_t output_idx,
const char** output_name_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(output_name_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE(
{ *output_name_out = m->OutputName(output_idx); })
}
AITemplateError AITemplateModelContainerGetMaximumOutputShape(
AITemplateModelHandle handle,
size_t output_idx,
AITemplateParamShape* shape_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(shape_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE(
{ *shape_out = m->MaxOutputShape(output_idx); })
}
AITemplateError AITemplateModelContainerGetOutputDtype(
AITemplateModelHandle handle,
size_t output_idx,
AITemplateDtype* dtype_out) {
RETURN_ERROR_IF_NULL(handle)
RETURN_ERROR_IF_NULL(dtype_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({ *dtype_out = m->OutputDtype(output_idx); })
}
AITemplateError AITemplateModelContainerGetNumRuntimes(
AITemplateModelHandle handle,
size_t* num_runtimes_out) {
RETURN_ERROR_IF_NULL(num_runtimes_out)
auto* m = reinterpret_cast<ait::ModelContainer*>(handle);
CONVERT_EXCEPTION_TO_ERROR_CODE({ *num_runtimes_out = m->GetNumRuntimes(); })
}
AITemplateError AITemplateAllocatorCreate(
AITemplateAllocator** allocator_out,
AITemplateAllocatorType allocator_type) {
RETURN_ERROR_IF_NULL(allocator_out);
CONVERT_EXCEPTION_TO_ERROR_CODE({
switch (allocator_type) {
case AITemplateAllocatorType::kDefault:
*allocator_out = new ait::DefaultAllocator();
break;
case AITemplateAllocatorType::kTracking:
*allocator_out = new ait::TrackingAllocator();
break;
default:
throw std::runtime_error("Unrecognized allocator type");
}
});
}
AITemplateError AITemplateAllocatorDelete(AITemplateAllocator* allocator) {
RETURN_ERROR_IF_NULL(allocator);
delete allocator;
return AITemplateError::AITemplateSuccess;
}
AITemplateError AITemplateTrackingAllocatorGetNumBytes(
AITemplateAllocator* allocator,
size_t* num_bytes_out) {
RETURN_ERROR_IF_NULL(allocator);
RETURN_ERROR_IF_NULL(num_bytes_out);
CONVERT_EXCEPTION_TO_ERROR_CODE({
auto* tracking_allocator = dynamic_cast<ait::TrackingAllocator*>(allocator);
if (tracking_allocator == nullptr) {
throw std::runtime_error("Allocator was not a tracking allocator!");
}
*num_bytes_out = tracking_allocator->NumBytesAllocated();
});
}
} // extern "C"
\ No newline at end of file
python/ait_impl/samples/model_interface.h deleted 100644 → 0
View file @ adbefd90
#pragma once
#include <stddef.h>
#include <stdint.h>
#include <numeric>
#include <stdexcept>
#include <utility>
#include <vector>
// We compile all models with -fvisibility=hidden. Any symbols that need to be
// exposed in the final shared library must be declared with AIT_EXPORT to make
// them visible.
#ifdef __GNUC__ // Applies to any compiler with GNU extensions (clang and g++)
#define AIT_EXPORT __attribute__((__visibility__("default")))
#else
#ifdef _WIN32
#define AIT_EXPORT __declspec(dllexport)
#else
#define AIT_EXPORT
#endif
#endif
struct AITemplateModelOpaque {};
using AITemplateModelHandle = AITemplateModelOpaque*;
enum class AITemplateError : int {
AITemplateSuccess = 0,
AITemplateFailure = 1,
};
struct AITemplateParamShape {
AITemplateParamShape() : shape_data(nullptr), size(0) {}
AITemplateParamShape(const int64_t* shape_data_in, size_t size_in)
: shape_data(shape_data_in), size(size_in) {}
const int64_t* shape_data;
size_t size;
size_t Numel() const {
return std::accumulate(
shape_data, shape_data + size, 1, std::multiplies<int64_t>());
}
};
enum class AITemplateDtype {
kUnset = 0,
kHalf,
kFloat,
kInt,
kLong,
kBool,
};
struct AITData {
AITData() : ptr(nullptr), dtype(AITemplateDtype::kUnset) {}
AITData(
void* ptr_in,
const AITemplateParamShape& shape_in,
AITemplateDtype dtype_in)
: ptr(ptr_in), shape(shape_in), dtype(dtype_in) {}
void* ptr;
AITemplateParamShape shape;
AITemplateDtype dtype;
};
inline size_t AITemplateDtypeSizeBytes(AITemplateDtype dtype) {
switch (dtype) {
case AITemplateDtype::kHalf:
return 2;
case AITemplateDtype::kFloat:
return 4;
case AITemplateDtype::kInt:
return 4;
case AITemplateDtype::kLong:
return 8;
case AITemplateDtype::kBool:
return 1;
case AITemplateDtype::kUnset:
throw std::runtime_error("Unset dtype has no size!");
}
}
struct AITemplateStreamOpaque {};
using AITemplateStreamHandle = AITemplateStreamOpaque*;
// Allocator to use for GPU mallocs and frees. Allocations will only happen
// when the ModelContainer is created.
class AITemplateAllocator {
public:
virtual void* Allocate(size_t nbytes) = 0;
virtual void Free(void* ptr) = 0;
virtual ~AITemplateAllocator() = default;
};
// Some custom allocators are provided. They can be created by passing
// an enum into the AITemplateAllocatorCreate() function.
enum class AITemplateAllocatorType {
// The default allocator just uses the backend's default malloc/free.
kDefault = 0,
// The tracking allocator is like the default allocator, but it keeps
// track of how many bytes it has allocated. Mainly used for testing.
kTracking,
};
extern "C" {
// Create a ModelContainer. See model_container.h for all the details.
// Some important high-level notes:
// * If allocator is null, a default allocator is used (forwards to
// {cuda/hip}{Malloc/Free}).
// * We assume that the allocator lives at least as long as the ModelContainer.
AIT_EXPORT AITemplateError AITemplateModelContainerCreate(
AITemplateModelHandle* ret,
size_t num_runtimes,
AITemplateAllocator* allocator = nullptr);
AIT_EXPORT AITemplateError
AITemplateModelContainerDelete(AITemplateModelHandle handle);
AIT_EXPORT AITemplateError AITemplateModelContainerSetConstant(
AITemplateModelHandle handle,
const char* name,
const AITData* tensor);
AIT_EXPORT AITemplateError AITemplateModelContainerRun(
AITemplateModelHandle handle,
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
AITemplateStreamHandle stream_handle,
bool sync,
bool graph_mode,
int64_t** output_shapes_out);
// Like AITemplateModelContainerRun, but expects outputs to be allocated on the
// host. Does an extra sync/copy at the end to copy them over. Warning: don't
// use this! It's not optimal with respect to performance. It's here for use by
// internal constant folding passes.
AIT_EXPORT AITemplateError AITemplateModelContainerRunWithOutputsOnHost(
AITemplateModelHandle handle,
const AITData* inputs,
size_t num_inputs,
AITData* outputs,
size_t num_outputs,
AITemplateStreamHandle stream_handle,
bool graph_mode,
int64_t** output_shapes_out);
AIT_EXPORT AITemplateError AITemplateModelContainerBenchmark(
AITemplateModelHandle handle,
const AITData* inputs,
size_t num_inputs,
AITData* ouputs,
size_t num_outputs,
AITemplateStreamHandle stream_handle,
bool graph_mode,
size_t count,
size_t num_threads,
bool use_unique_stream_per_thread,
float* runtime_ms,
int64_t** output_shapes_out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetNumInputs(
AITemplateModelHandle handle,
size_t* num_inputs_out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetInputName(
AITemplateModelHandle handle,
size_t input_idx,
const char** input_name_out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetNumOutputs(
AITemplateModelHandle handle,
size_t* num_outputs_out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetOutputName(
AITemplateModelHandle handle,
size_t output_idx,
const char** output_name_out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetMaximumOutputShape(
AITemplateModelHandle handle,
size_t output_idx,
AITemplateParamShape* shape_out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetOutputDtype(
AITemplateModelHandle handle,
size_t output_idx,
AITemplateDtype* out);
AIT_EXPORT AITemplateError AITemplateModelContainerGetNumRuntimes(
AITemplateModelHandle handle,
size_t* num_runtimes_out);
AIT_EXPORT AITemplateError AITemplateAllocatorCreate(
AITemplateAllocator** allocator_out,
AITemplateAllocatorType allocator_type);
AIT_EXPORT AITemplateError
AITemplateAllocatorDelete(AITemplateAllocator* allocator_out);
// Get the number of bytes allocated; mainly used for testing.
AIT_EXPORT AITemplateError AITemplateTrackingAllocatorGetNumBytes(
AITemplateAllocator* allocator,
size_t* num_bytes_out);
} // extern "C"
\ No newline at end of file
python/ait_impl/samples/norm_common.py deleted 100644 → 0
View file @ adbefd90
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Normalization common codegen for ROCM.
"""
import os
import re
from hashlib import sha1
from typing import Any, Dict, OrderedDict
import jinja2
from ...target import Target
FUNC_CALL_PARAM_TEMPLATE = jinja2.Template("(void *)({{name}})")
INSTANCE_TEMPLATE = jinja2.Template(
"""
{{config}}
using {{name}} = {{ config_name }};
"""
)
ARGS_PARSE_TEMPLATE = jinja2.Template(
"""
{% for idx in range(rank) %}
const int64_t in_{{idx}} = std::stoi(argv[{{ idx + 1 }}]);
{% endfor %}
"""
)
STRUCTS_DEF_TEMPLATE = jinja2.Template(
"""
struct ProfilerMemoryPool {
ProfilerMemoryPool() {
std::random_device rd;
gen = std::mt19937(rd());
uniform_dist = std::uniform_int_distribution<int64_t>(1, 48964896);
offsets.reserve(512);
strides.reserve(512);
copies.reserve(512);
ptrs.reserve(512);
}
~ProfilerMemoryPool() {
for(int i = 0; i < ptrs.size(); i++){
hipFree(ptrs[i]);
}
}
template <typename DType>
DType* AllocateGaussianTensor(int64_t size) {
size_t length = size * sizeof(DType);
DType *d_x;
hipMalloc(&d_x, length);
float mean = 0.0f;
float stddev = 1.0f;
uint64_t seed = uniform_dist(gen);
rocrand_set_seed(generator, seed);
rocrand_generate_normal(generator, reinterpret_cast<float*>(d_x), size, mean, stddev);
return d_x;
}
ck::half_t* AllocateHalfGaussianTensor(int64_t size) {
return reinterpret_cast<ck::half_t*>(
AllocateGaussianTensor<ck::half_t>(size));
}
int AllocateHalfTensor(int64_t size, int64_t copy) {
offsets.push_back(0);
strides.push_back(size);
copies.push_back(copy);
auto ptr = AllocateHalfGaussianTensor(size * copy);
ptrs.push_back(reinterpret_cast<void*>(ptr));
return ptrs.size() - 1;
}
ck::half_t* RequestHalfTensorByIdx(int idx) {
auto copy = copies.at(idx);
auto offset = offsets.at(idx);
auto stride = strides.at(idx);
ck::half_t* ptr = reinterpret_cast<ck::half_t*>(ptrs.at(idx));
ptr += offset;
offset += stride;
if (offset == copy * stride) {
offset = 0;
}
offsets[idx] = offset;
return ptr;
}
std::vector<int64_t> offsets;
std::vector<int64_t> strides;
std::vector<int64_t> copies;
std::vector<void*> ptrs;
std::mt19937 gen;
std::uniform_int_distribution<int64_t> uniform_dist;
rocrand_generator generator;
};
// hack for DeviceMem linking error
// TODO fix this by making CK a header-only lib
// <<< hack begin
DeviceMem::DeviceMem(std::size_t mem_size) : mMemSize(mem_size)
{
hipGetErrorString(hipMalloc(static_cast<void**>(&mpDeviceBuf), mMemSize));
}
void* DeviceMem::GetDeviceBuffer() const { return mpDeviceBuf; }
void DeviceMem::ToDevice(const void* p) const
{
hipGetErrorString(
hipMemcpy(mpDeviceBuf, const_cast<void*>(p), mMemSize, hipMemcpyHostToDevice));
}
void DeviceMem::FromDevice(void* p) const
{
hipGetErrorString(hipMemcpy(p, mpDeviceBuf, mMemSize, hipMemcpyDeviceToHost));
}
DeviceMem::~DeviceMem() { hipGetErrorString(hipFree(mpDeviceBuf)); }
struct KernelTimerImpl
{
KernelTimerImpl() {
hipGetErrorString(hipEventCreate(&mStart));
hipGetErrorString(hipEventCreate(&mEnd));
}
~KernelTimerImpl() {
hipGetErrorString(hipEventDestroy(mStart));
hipGetErrorString(hipEventDestroy(mEnd));
}
void Start() {
hipGetErrorString(hipDeviceSynchronize());
hipGetErrorString(hipEventRecord(mStart, nullptr));
}
void End() {
hipGetErrorString(hipEventRecord(mEnd, nullptr));
hipGetErrorString(hipEventSynchronize(mEnd));
}
float GetElapsedTime() const {
float time;
hipGetErrorString(hipEventElapsedTime(&time, mStart, mEnd));
return time;
}
hipEvent_t mStart, mEnd;
};
// >>> hack end
"""
)
PROFILER_TEMPLATE = jinja2.Template(
"""
size_t GLOBAL_WORKSPACE_SIZE = 0;
{{op_func}}
{{structs_def}}
int main(int argc, char** argv) {
{{args_parse}}
auto memory_pool = std::make_unique<ProfilerMemoryPool>();
hipStream_t stream = nullptr;
{{tensor_decl}}
// warmup
for(int i = 0; i < 3; ++i) {
{{func_call}}
}
// run
KernelTimerImpl timer;
timer.Start();
for(int i = 0; i < 5; ++i) {
{{func_call}}
}
timer.End();
std::cout << "WS:" <<GLOBAL_WORKSPACE_SIZE<<std::endl;
std::cout << "TIME:" << timer.GetElapsedTime() << std::endl;
}
"""
)
FUNC_TEMPLATE = jinja2.Template(
"""
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <random>
#include <rocrand/rocrand.h>
#include "include/ck/utility/print.hpp"
#include "library/include/ck/library/utility/device_memory.hpp"
#include "library/include/ck/library/utility/host_tensor.hpp"
#include "library/include/ck/library/utility/host_tensor_generator.hpp"
#include "include/ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "include/ck/utility/reduction_operator.hpp"
{{extra_headers}}
{{extra_code}}
{{instances_decl}}
{{func_signature}}
{
{{shape_eval}}
{{exec_paths}}
}
"""
)
FUNC_CALL_TEMPLATE = jinja2.Template(
"""
{{indent}}{{func_name}}(
{{indent}} {{input}},
{{indent}} {{output}},
{% for name in input_dim_names %}
{{indent}} const_cast<int64_t *>(&{{name}}),
{% endfor %}
{{indent}} stream
{{indent}});
"""
)
def extract_config(func_attrs):
"""Extract (operation name, operation instance) pair
from all operation candidates.
Parameters
----------
op_kind : ck_lib.library.OperationKind
Operation kind.
extra_kind : ck_lib.library.[AnyKind]
Used to as extra flag to distinguish kernels.
E.g. bias_add_relu vs. add_relu_bias
f_prop_op: function
Used to filter operation.
Returns
-------
Dict
Extracted (operation name, operation instance) pair.
"""
import ck_lib
op_kind = ck_lib.library.OperationKind.Softmax
extra_kind = len(func_attrs["inputs"][0]._attrs["shape"])
extract_ops = list(Target.current()._operators[op_kind][extra_kind].items())
softmax_ops = OrderedDict()
for key, value in extract_ops:
softmax_ops[key] = value[0]
func_attrs["op_instance"] = softmax_ops
def emit_instance(op):
"""Emit instance"""
import ck_lib # noqa: F401
op_def = op.emit()
return op_def
def extract_config_name(config):
"""Extract configuration names.
Parameters
----------
config : str
Configuration as a string in the format of 'using model = xxx'.
Returns
-------
str
Extracted name from the statement, e.g. 'model' for 'using model = xxx'.
Raises
------
RuntimeError
Invalid config.
"""
pattern = re.compile(r"\s*using\s(.*?)\s=")
decl = config.split("\n")[1]
match = pattern.match(decl)
if match is None:
raise RuntimeError("Invalid config: \n" + config)
return match.groups()[0]
def gen_profiler(
func_attrs: Dict[str, Any],
workdir: str,
rank: int,
shape_eval_template: jinja2.Template,
exec_template: jinja2.Template,
tensor_decl_template: jinja2.Template,
extra_header_template: jinja2.Template,
get_func_signature: Any,
extra_code: str = "",
func_call_template: jinja2.Template = FUNC_CALL_TEMPLATE,
indent: str = " ",
) -> str:
"""Generates standalone executables for profiler.
Parameters
----------
func_attrs : Dict
Operation attributes.
workdir : str
Directory to store the generated outputs.
rank: int
Rank of the input tensor. If using [M, N] in exec_key, the rank here
must be 2 because if implies that the inputs are reshaped for profiling.
For code gen, the real shapes are used.
exec_template : jinja2.Template
Execution block template.
tensor_decl_template: jinja2.Template
Tensor declaration template.
extra_header_template : jinja2.Template
Extra header template.
indent : str, optional
Indent for codegen, target dependent e.g. C++, python, etc., by default " ".
"""
op_type = func_attrs["op"]
shape_eval = shape_eval_template.render(rank=rank) if shape_eval_template else ""
eps = func_attrs.get("eps", "1e-5")
op_instance = func_attrs["op_instance"]
file_pairs = []
for op_name, op in op_instance.items():
config = emit_instance(op)
config_name = extract_config_name(config)
instances = INSTANCE_TEMPLATE.render(
name="DeviceInstance", config_name=config_name, config=config
)
exe_path = exec_template.render(
instance="DeviceInstance",
dtype="void",
reduce_dims=rank - 1,
rank=rank,
eps=eps,
)
op_func = FUNC_TEMPLATE.render(
instances_decl=instances,
func_signature=get_func_signature(func_attrs),
shape_eval=shape_eval,
exec_paths=exe_path,
extra_headers=extra_header_template.render(),
extra_code=extra_code,
)
structs_def = STRUCTS_DEF_TEMPLATE.render()
args_parse = ARGS_PARSE_TEMPLATE.render(rank=rank)
tensor_decl = tensor_decl_template.render(rank=rank)
input_dim_names = [f"in_{i}" for i in range(rank)]
func_call = func_call_template.render(
func_name=func_attrs["name"],
input="(void *) memory_pool->RequestHalfTensorByIdx(0)",
gamma="(void *) memory_pool->RequestHalfTensorByIdx(2)",
beta="(void *) memory_pool->RequestHalfTensorByIdx(3)",
output="(void *) memory_pool->RequestHalfTensorByIdx(1)",
input_dim_names=input_dim_names,
indent=indent,
)
code = PROFILER_TEMPLATE.render(
op_func=op_func,
structs_def=structs_def,
args_parse=args_parse,
tensor_decl=tensor_decl,
func_call=func_call,
)
prefix = os.path.join(workdir, "profiler", op_type)
if not os.path.exists(prefix):
os.makedirs(prefix)
src_path = os.path.join(prefix, op_name + ".cpp")
obj_path = os.path.join(prefix, op_name)
if os.path.exists(obj_path):
continue
with open(src_path, "w") as fo:
fo.write(code)
file_pairs.append((src_path, obj_path))
return file_pairs
# no longer used by layernorm
def gen_function(
func_attrs: Dict[str, Any],
exec_template: jinja2.Template,
extra_header_template: jinja2.Template,
get_func_signature: Any,
) -> str:
"""Generate function body.
Parameters
----------
func_attrs : Dict
Operation attributes.
exec_template : jinja2.Template
Execution block template.
extra_header_template : jinja2.Template
Extra header template.
Returns
-------
str
The rendered template of generated function body.
"""
shapes = func_attrs["inputs"][0]._attrs["shape"]
rank = len(shapes)
exec_path = func_attrs["exec_path"]
op_instance = func_attrs["op_instance"]
inst_def_flag = set()
instances = {}
instance_decl = ""
for exec_item in exec_path.values():
fname = "f" + sha1(exec_item.exec_cond.encode()).hexdigest()
algo = exec_item.algo
if algo not in inst_def_flag:
config = emit_instance(op_instance[algo])
inst_def_flag.add(algo)
else:
config = ""
inst = INSTANCE_TEMPLATE.render(
config=config, name=fname, config_name=extract_config_name(config)
)
instances[exec_item.exec_cond] = inst
instance_decl += inst
exec_cond_template = func_attrs["exec_cond_template"]
exec_paths = ""
for key, _ in instances.items():
fname = "f" + sha1(key.encode()).hexdigest()
program = exec_template.render(
instance=fname, dtype="void", reduce_dims=rank - 1, rank=rank
)
cond_vars = re.findall(r"\S+(?= >=)", key)
cond_vars += re.findall(r"\S+(?= ==)", key)
cond = key
for i, var in enumerate(cond_vars):
cond = cond.replace(var + " ", "*in_" + str(i))
exec_inst = exec_cond_template.render(indent=" ", cond=cond, program=program)
exec_paths += exec_inst
return FUNC_TEMPLATE.render(
instances_decl=instance_decl,
func_signature=get_func_signature(func_attrs),
exec_paths=exec_paths,
extra_headers=extra_header_template.render(),
)
def gen_function_call(func_attrs, indent=" "):
"""Generates function call.
Parameters
----------
func_attrs : Dict
Stores the operation attributes.
indent : str, optional
Indent for codegen, target dependent e.g. C++, python, etc., by default " ".
Returns
-------
str
The rendered template of generated function call.
"""
input_name = FUNC_CALL_PARAM_TEMPLATE.render(
name=func_attrs["inputs"][0]._attrs["name"]
)
output_name = FUNC_CALL_PARAM_TEMPLATE.render(
name=func_attrs["outputs"][0]._attrs["name"]
)
shapes = func_attrs["inputs"][0]._attrs["shape"]
input_dim_names = [shape._attrs["name"] for shape in shapes]
return FUNC_CALL_TEMPLATE.render(
func_name=func_attrs["name"],
input=input_name,
output=output_name,
input_dim_names=input_dim_names,
indent=indent,
)
\ No newline at end of file
python/ait_impl/samples/permute_common.py deleted 100644 → 0
View file @ adbefd90
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import jinja2
EXTRA_SHAPE_TEMPLATE = jinja2.Template(
"""
{{indent}}const int64_t stride_a = *a_dim1;
{{indent}}const int64_t stride_b = *b_dim1;
{{indent}}const int64_t stride_c = *c_dim1;
ck::index_t M0 = M / G1 / G2;
ck::index_t M1 = G1;
ck::index_t M2 = G2;
ck::index_t N0 = G3;
ck::index_t N1 = N / G3;
// GEMM shape
//ck::index_t M = M0 * M1 * M2;
//ck::index_t N = N0 * N1;
//ck::index_t K = 128;
//ck::index_t stride_A = K;
//ck::index_t stride_B = K;
// E = [M0, N0, M1, N1, M2]
/* 0, 3, 1, 4, 2
ck::index_t stride_E_M0 = N0 * M1 * N1 * M2;
ck::index_t stride_E_M1 = N1 * M2;
ck::index_t stride_E_M2 = 1;
ck::index_t stride_E_N0 = M1 * N1 * M2;
ck::index_t stride_E_N1 = M2;
*/
// E = [M2, M0, N0, M1, N1] 2, 0, 3, 1, 4
ck::index_t stride_E_M0 = N0* M1* N1;
ck::index_t stride_E_M1 = N1;
ck::index_t stride_E_M2 = M0* N0* M1* N1;
ck::index_t stride_E_N0 = M1 * N1;
ck::index_t stride_E_N1 = 1;
// D = [0, N0, 0, N1, 0]
ck::index_t stride_D_M0 = 0;
ck::index_t stride_D_M1 = 0;
ck::index_t stride_D_M2 = 0;
ck::index_t stride_D_N0 = N1;
ck::index_t stride_D_N1 = 1;
"""
)
EXTRA_SHAPE_TEMPLATE_M2N3 = jinja2.Template(
"""
const int64_t G1 = p_dim0; // G1
const int64_t G2 = p_dim1; // G2
const int64_t G3 = p_dim2; // G3
ck::index_t M0 = M / G1;
ck::index_t M1 = G1;
ck::index_t N0 = G2;
ck::index_t N1 = G3;
ck::index_t N2 = N / G2 / G3;
ck::index_t K0 = K;
ck::index_t G = 1;
// A[G, M0, M1, M2, K0]
std::vector<ck::index_t> a_ms_ks_lengths{G, M0, M1, K0};
std::vector<ck::index_t> a_ms_ks_strides{M0*M1*K0, M1 * K0, K0, 1};
// B[G, N0, N1, K0]
std::vector<ck::index_t> b_ns_ks_lengths{G, N0, N1, N2, K0};
std::vector<ck::index_t> b_ns_ks_strides{N0*N1*N2*K0, N1 * N2 * K0, N2 * K0, K0, 1};
// D[G, N0, M0, N1, M1, N2]
std::vector<ck::index_t> d_ms_ns_lengths{G, M0, M1, N0, N1, N2};
std::vector<ck::index_t> d_ms_ns_strides{N0 * N1 * N2, 0, 0, N1 * N2, N2, 1};
// E[G, N0, M0, N1, M1, N2] 2, 0, 3, 1, 4
std::vector<ck::index_t> e_ms_ns_lengths{G, M0, M1, N0, N1, N2};
std::vector<ck::index_t> e_ms_ns_strides{M0* M1* N0* N1* N2,
N1 * M1 * N2,
N2,
M0 * N1 * M1 * N2,
M1 * N2,
1};
"""
)
EXTRA_SHAPE_TEMPLATE_M3N2 = jinja2.Template(
"""
const int64_t G1 = p_dim0; // G1
const int64_t G2 = p_dim1; // G2
const int64_t G3 = p_dim2; // G3
ck::index_t M0 = M / G1 / G2;
ck::index_t M1 = G1;
ck::index_t M2 = G2;
ck::index_t N0 = G3;
ck::index_t N1 = N / G3;
ck::index_t K0 = K;
ck::index_t G = 1;
// A[M0, M1, M2, K0]
std::vector<ck::index_t> a_ms_ks_lengths{G, M0, M1, M2, K0};
std::vector<ck::index_t> a_ms_ks_strides{M0 * M1 * M2 * K0, M1 * M2 * K0, M2 * K0, K0, 1};
// B[N0, N1, K0]
std::vector<ck::index_t> b_ns_ks_lengths{G, N0, N1, K0};
std::vector<ck::index_t> b_ns_ks_strides{N0 * N1 * K0, N1 * K0, K0, 1};
// D[M0, N0, M1, N1, M2]
std::vector<ck::index_t> d_ms_ns_lengths{G, M0, M1, M2, N0, N1};
std::vector<ck::index_t> d_ms_ns_strides{N0*N1, 0, 0, 0, N1, 1};
// E[M0, N0, M1, N1, M2]
std::vector<ck::index_t> e_ms_ns_lengths{G, M0, M1, M2, N0, N1};
std::vector<ck::index_t> e_ms_ns_strides{M0 * M1* M2 * N1* N0, N0* M1* N1, N1, M0* N0* M1* N1, M1 * N1, 1};
"""
)
\ No newline at end of file
python/ait_impl/generation/ex/Makefile → python/normal/Makefile
View file @ 47cc9b7e
...@@ -5,12 +5,12 @@ CXXFLAGS = -std=c++17 ...@@ -5,12 +5,12 @@ CXXFLAGS = -std=c++17
gemm: ex.o host_tensor.o device_memory.o gemm: ex.o host_tensor.o device_memory.o
hipcc $(CXXFLAGS) $(CFLAGS) ex.o host_tensor.o device_memory.o -o gemm hipcc $(CXXFLAGS) $(CFLAGS) ex.o host_tensor.o device_memory.o -o gemm
device_memory.o: ../../../../library/src/utility/device_memory.cpp device_memory.o: ../../library/src/utility/device_memory.cpp
hipcc $(CXXFLAGS) $(CFLAGS) -c ../../../../library/src/utility/device_memory.cpp hipcc $(CXXFLAGS) $(CFLAGS) -c ../../library/src/utility/device_memory.cpp
host_tensor.o: ../../../../library/src/utility/host_tensor.cpp host_tensor.o: ../../library/src/utility/host_tensor.cpp
hipcc $(CXXFLAGS) $(CFLAGS) -c ../../../../library/src/utility/host_tensor.cpp hipcc $(CXXFLAGS) $(CFLAGS) -c ../../library/src/utility/host_tensor.cpp
ex.o: ex.o:
hipcc -fPIC -fvisibility=hidden $(CXXFLAGS) -w /opt/rocm-5.3.0/amdgcn/bitcode/oclc_abi_version_400.bc $(CFLAGS) -L/opt/rocm-5.3.0/rocrand -lrocrand -x hip -c ex.cpp hipcc -fPIC -fvisibility=hidden $(CXXFLAGS) -w $(CFLAGS) -L/opt/rocm-5.3.0/rocrand -lrocrand -x hip -c ex.cpp
\ No newline at end of file
python/normal/ex.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
256,
64,
128,
8,
2,
2,
4,
1,
S<8, 2>,
S<8, 2>,
S<1, 1, 2, 2>,
S<8, 1, 32, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 2, 1>,
S<0, 3, 1, 2>,
S<1, 1, 2, 2>,
S<1, 1, 4, 2>,
S<8, 1, 32, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
4>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_256_64_128_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool run_gemm_example_256_64_128_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_256_64_128_8_2(problem_size, config);
}
int main(int argc, char* argv[]) { return !run_gemm_example_256_64_128_8_2(argc, argv); }
python/ait_impl/generation/ex/gemm_ex.py → python/normal/gemm_ex.py
View file @ 47cc9b7e
...@@ -6,6 +6,8 @@ import operator ...@@ -6,6 +6,8 @@ import operator
import collections import collections
import subprocess import subprocess
import re import re
import gemm_op
from gemm_op import *
def SubstituteTemplate(template, values): def SubstituteTemplate(template, values):
text = template text = template
...@@ -30,14 +32,14 @@ CXXFLAGS = -std=c++17 ...@@ -30,14 +32,14 @@ CXXFLAGS = -std=c++17
gemm: ex.o host_tensor.o device_memory.o gemm: ex.o host_tensor.o device_memory.o
hipcc $(CXXFLAGS) $(CFLAGS) ex.o host_tensor.o device_memory.o -o gemm hipcc $(CXXFLAGS) $(CFLAGS) ex.o host_tensor.o device_memory.o -o gemm
device_memory.o: ../../../../library/src/utility/device_memory.cpp device_memory.o: ../../library/src/utility/device_memory.cpp
hipcc $(CXXFLAGS) $(CFLAGS) -c ../../../../library/src/utility/device_memory.cpp hipcc $(CXXFLAGS) $(CFLAGS) -c ../../library/src/utility/device_memory.cpp
host_tensor.o: ../../../../library/src/utility/host_tensor.cpp host_tensor.o: ../../library/src/utility/host_tensor.cpp
hipcc $(CXXFLAGS) $(CFLAGS) -c ../../../../library/src/utility/host_tensor.cpp hipcc $(CXXFLAGS) $(CFLAGS) -c ../../library/src/utility/host_tensor.cpp
ex.o: ex.o:
hipcc -fPIC -fvisibility=hidden $(CXXFLAGS) -w /opt/rocm-5.3.0/amdgcn/bitcode/oclc_abi_version_400.bc $(CFLAGS) -L/opt/rocm-5.3.0/rocrand -lrocrand -x hip -c ex.cpp hipcc -fPIC -fvisibility=hidden $(CXXFLAGS) -w $(CFLAGS) -L/opt/rocm-5.3.0/rocrand -lrocrand -x hip -c ex.cpp
""" """
self.gemm_devop_template = """ self.gemm_devop_template = """
#pragma once #pragma once
...@@ -105,7 +107,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl< ...@@ -105,7 +107,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>; ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm(const ProblemSize& problem_size, const ExecutionConfig& config) bool run_gemm_${name}(const ProblemSize& problem_size, const ExecutionConfig& config)
{ {
using namespace ck::literals; using namespace ck::literals;
...@@ -213,66 +215,69 @@ bool run_gemm(const ProblemSize& problem_size, const ExecutionConfig& config) ...@@ -213,66 +215,69 @@ bool run_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
return true; return true;
} }
bool run_gemm_example(int argc, char* argv[]) bool run_gemm_example_${name}(int argc, char* argv[])
{ {
ProblemSize problem_size; ProblemSize problem_size;
ExecutionConfig config; ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config); return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_${name}(problem_size, config);
} }
int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); } int main(int argc, char* argv[]) { return !run_gemm_example_${name}(argc, argv); }
""" """
def emit(self): def emit(self,operation):
values = { values = {
'type_a' : 'ck::half_t', 'name' : (str(operation.tile_desc.block_size) + "_" + str(operation.tile_desc.m_per_block) + "_" + str(operation.tile_desc.n_per_block)+ "_" + str(operation.tile_desc.k_per_block) + "_" + str(operation.tile_desc.k1)),
'type_b' : 'ck::half_t', 'type_a' : operation.A.element,
'type_c' : 'ck::half_t', 'type_b' : operation.B.element,
'type_c' : operation.C.element,
'type_acc' : 'float', 'type_acc' : 'float',
'layout_a' : 'ck::tensor_layout::gemm::ColumnMajor', 'layout_a' : operation.A.layout,
'layout_b' : 'ck::tensor_layout::gemm::RowMajor', 'layout_b' : operation.B.layout,
'layout_c' : 'ck::tensor_layout::gemm::RowMajor', 'layout_c' : operation.C.layout,
'elementwise_op_a' : 'ck::tensor_operation::element_wise::PassThrough', 'elementwise_op_a' : operation.a_elem_op,
'elementwise_op_b' : 'ck::tensor_operation::element_wise::PassThrough', 'elementwise_op_b' : operation.b_elem_op,
'elementwise_op_c' : 'ck::tensor_operation::element_wise::PassThrough', 'elementwise_op_c' : operation.epilogue_functor,
'Gemm_spec' : 'ck::tensor_operation::device::GemmSpecialization::Default', 'Gemm_spec' : operation.gemm_specialization,
'block_size' : '256', 'block_size' : str(operation.tile_desc.block_size),
'mperblock' : '128', 'mperblock' : str(operation.tile_desc.m_per_block),
'nperblock' : '128', 'nperblock' : str(operation.tile_desc.n_per_block),
'k0perblock' : '16', 'k0perblock' : str(operation.tile_desc.k_per_block),
'k1' : '2', 'k1' : str(operation.tile_desc.k1),
'm1perthread' : '4', 'm1perthread' : str(operation.tile_desc.m_per_thread),
'n1perthread' : '4', 'n1perthread' : str(operation.tile_desc.n_per_thread),
'kperthread' : '1', 'kperthread' : str(operation.tile_desc.k_per_thread),
'm1n1_thcluster_m1xs' : 'S<8, 2>', 'm1n1_thcluster_m1xs' : operation.tile_desc.m1n1_thcluster_m1xs,
'm1n1_thcluster_n1xs' : 'S<8, 2>', 'm1n1_thcluster_n1xs' : operation.tile_desc.m1n1_thcluster_n1xs,
'ABT_thread_slice_lengths_K0_M0_M1_K1' : 'S<2, 1, 4, 2>', 'ABT_thread_slice_lengths_K0_M0_M1_K1' : operation.a_block_transfer.thread_slice_length,
'ABT_thread_cluster_lengths_K0_M0_M1_K1' : 'S<8, 1, 32, 1>', 'ABT_thread_cluster_lengths_K0_M0_M1_K1' : operation.a_block_transfer.thread_cluster_length,
'ABT_thread_cluster_arrange_order' : 'S<0, 3, 1, 2>', 'ABT_thread_cluster_arrange_order' : operation.a_block_transfer.thread_cluster_arrange_order,
'ABT_src_access_order' : 'S<0, 3, 1, 2>', 'ABT_src_access_order' : operation.a_block_transfer.src_access_order,
'ABT_src_vec_tensor_lengths_K0_M0_M1_K1' : 'S<1, 1, 4, 1>', 'ABT_src_vec_tensor_lengths_K0_M0_M1_K1' : operation.a_block_transfer.src_vec_tensor_lengths,
'ABT_src_vec_tensor_cont_dim_order' : 'S<0, 3, 1, 2>', 'ABT_src_vec_tensor_cont_dim_order' : operation.a_block_transfer.src_vec_tensor_cont_dim_order,
'ABT_dst_vec_tensor_lengths_K0_M0_M1_K1' : 'S<1, 1, 4, 2>', 'ABT_dst_vec_tensor_lengths_K0_M0_M1_K1' : operation.a_block_transfer.dst_vec_tensor_lengths,
'BBT_thread_slice_lengths_K0_N0_N1_K1' : 'S<2, 1, 4, 2>', 'BBT_thread_slice_lengths_K0_N0_N1_K1' : operation.b_block_transfer.thread_slice_length,
'BBT_thread_cluster_lengths_K0_N0_N1_K1' : 'S<8, 1, 32, 1>', 'BBT_thread_cluster_lengths_K0_N0_N1_K1' : operation.b_block_transfer.thread_cluster_length,
'BBT_thread_cluster_arrange_order' : 'S<0, 3, 1, 2>', 'BBT_thread_cluster_arrange_order' : operation.b_block_transfer.thread_cluster_arrange_order,
'BBT_src_access_order' : 'S<0, 3, 1, 2>', 'BBT_src_access_order' : operation.b_block_transfer.src_access_order,
'BBT_src_vec_tensor_lengths_K0_N0_N1_K1' : 'S<1, 1, 4, 1>', 'BBT_src_vec_tensor_lengths_K0_N0_N1_K1' : operation.b_block_transfer.src_vec_tensor_lengths,
'BBT_src_vec_tensor_cont_dim_order' : 'S<0, 3, 1, 2>', 'BBT_src_vec_tensor_cont_dim_order' : operation.b_block_transfer.src_vec_tensor_cont_dim_order,
'BBT_dst_vec_tensor_lengths_K0_N0_N1_K1': 'S<1, 1, 4, 2>', 'BBT_dst_vec_tensor_lengths_K0_N0_N1_K1': operation.b_block_transfer.dst_vec_tensor_lengths,
'CTT_src_dst_access_order' : 'S<0, 1, 2, 3, 4, 5>', 'CTT_src_dst_access_order' : operation.c_block_transfer.src_dst_access_order,
'CTT_src_dst_vec_dim' : '5', 'CTT_src_dst_vec_dim' : str(operation.c_block_transfer.src_dst_vec_dim),
'CTT_dst_scalar_per_vector' : '4' 'CTT_dst_scalar_per_vector' : str(operation.c_block_transfer.dst_scalar_per_vector),
} }
template = self.gemm_devop_template
name = (str(operation.tile_desc.block_size) + "_" + str(operation.tile_desc.m_per_block) + "_" + str(operation.tile_desc.n_per_block)
+ "_" + str(operation.tile_desc.k_per_block) + "_" + str(operation.tile_desc.k1))
template = self.gemm_devop_template template = self.gemm_devop_template
cf = open("ex.cpp", 'w') cf = open("ex.cpp", 'w')
print(SubstituteTemplate(template, values))
cf.write(SubstituteTemplate(template, values)) cf.write(SubstituteTemplate(template, values))
cf.close() cf.close()
m_template = self.make_template m_template = self.make_template
cf = open("Makefile", 'w') cf = open("Makefile", 'w')
print(SubstituteTemplate(m_template, values))
cf.write(SubstituteTemplate(m_template, values)) cf.write(SubstituteTemplate(m_template, values))
cf.close() cf.close()
...@@ -288,6 +293,27 @@ int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); } ...@@ -288,6 +293,27 @@ int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }
) )
out, err = proc.communicate() out, err = proc.communicate()
# defining an operation's parameters as input
A = TensorDesc(DataType.f16, Layout.ColumnMajor)
B = TensorDesc(DataType.f16, Layout.RowMajor)
C = TensorDesc(DataType.f16, Layout.RowMajor)
gemm = gemm_op.GemmOperation(
A=A,
B=B,
C=C,
a_elem_op=TensorOperation.PassThrough,
b_elem_op=TensorOperation.PassThrough,
epilogue_functor=TensorOperation.PassThrough,
gemm_specialization=GemmType.GemmDefault,
tile_desc=TileDesc(256, 64, 128, 8, 2, 2, 4, 1, "S<8, 2>", "S<8, 2>"),
a_block_transfer=BlockTransferDesc(
"S<1, 1, 2, 2>", "S<8, 1, 32, 1>", "S<0, 3, 1, 2>", "S<0, 3, 1, 2>", "S<1, 1, 2, 1>", "S<0, 3, 1, 2>", "S<1, 1, 2, 2>"
),
b_block_transfer=BlockTransferDesc(
"S<1, 1, 4, 2>", "S<8, 1, 32, 1>", "S<0, 3, 1, 2>", "S<0, 3, 1, 2>", "S<1, 1, 4, 1>", "S<0, 3, 1, 2>", "S<1, 1, 4, 2>"
),
c_block_transfer=CBlockTransferDesc("S<0, 1, 2, 3, 4, 5>", 5, 4),
)
a = EmitGemmInstance() a = EmitGemmInstance()
a.emit() a.emit(gemm)
python/shared/128_128_32_8_2.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
128,
128,
32,
8,
2,
4,
2,
1,
S<8, 2>,
S<4, 2>,
S<1, 1, 8, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<1, 1, 2, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 2, 1>,
S<0, 3, 1, 2>,
S<1, 1, 2, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
2>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_128_128_32_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool __attribute__((visibility("default"))) run_gemm_128_128_32_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_128_128_32_8_2(problem_size, config);
}
python/shared/128_128_64_8_2.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
128,
128,
64,
8,
2,
4,
4,
1,
S<8, 2>,
S<4, 2>,
S<1, 1, 8, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<1, 1, 4, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
4>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_128_128_64_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool __attribute__((visibility("default"))) run_gemm_128_128_64_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_128_128_64_8_2(problem_size, config);
}
python/shared/128_32_128_8_2.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
128,
32,
128,
8,
2,
2,
4,
1,
S<4, 2>,
S<8, 2>,
S<1, 1, 2, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 2, 1>,
S<0, 3, 1, 2>,
S<1, 1, 2, 2>,
S<1, 1, 8, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
4>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_128_32_128_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool __attribute__((visibility("default"))) run_gemm_128_32_128_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_128_32_128_8_2(problem_size, config);
}
python/shared/128_32_64_8_2.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
128,
32,
64,
8,
2,
2,
2,
1,
S<4, 2>,
S<8, 2>,
S<1, 1, 2, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 2, 1>,
S<0, 3, 1, 2>,
S<1, 1, 2, 2>,
S<1, 1, 4, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
4>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_128_32_64_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool __attribute__((visibility("default"))) run_gemm_128_32_64_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_128_32_64_8_2(problem_size, config);
}
python/shared/128_64_128_8_2.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
128,
64,
128,
8,
2,
4,
4,
1,
S<4, 2>,
S<8, 2>,
S<1, 1, 4, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<1, 1, 8, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
4>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_128_64_128_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool __attribute__((visibility("default"))) run_gemm_128_64_128_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_128_64_128_8_2(problem_size, config);
}
python/shared/128_64_32_8_2.cpp 0 → 100644
View file @ 47cc9b7e
#pragma once
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp"
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using ALayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmDl<
ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::ColumnMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::device::GemmSpecialization::Default,
128,
64,
32,
8,
2,
2,
2,
1,
S<8, 2>,
S<4, 2>,
S<1, 1, 4, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 4, 1>,
S<0, 3, 1, 2>,
S<1, 1, 4, 2>,
S<1, 1, 2, 2>,
S<8, 1, 16, 1>,
S<0, 3, 1, 2>,
S<0, 3, 1, 2>,
S<1, 1, 2, 1>,
S<0, 3, 1, 2>,
S<1, 1, 2, 2>,
S<0, 1, 2, 3, 4, 5>,
5,
2>;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
bool run_gemm_128_64_32_8_2(const ProblemSize& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<ck::half_t> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<ck::half_t> b_k_n(f_host_tensor_descriptor(K, N, StrideB, ck::tensor_layout::gemm::RowMajor{}));
switch(config.init_method)
{
case 0: break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<ck::half_t>{-5.f, 5.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<ck::half_t>{-1.f, 1.f}(b_k_n);
}
Tensor<ck::half_t> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<ck::half_t> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ck::half_t) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(ck::half_t) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(ck::half_t) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
auto a_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto b_element_op = ck::tensor_operation::element_wise::PassThrough{};
auto c_element_op = ck::tensor_operation::element_wise::PassThrough{};
// do GEMM
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
auto argument = gemm.MakeArgument(
static_cast<ck::half_t*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<ck::half_t*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ck::half_t) * M * K + sizeof(ck::half_t) * K * N + sizeof(ck::half_t) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
}
return true;
}
bool __attribute__((visibility("default"))) run_gemm_128_64_32_8_2(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm_128_64_32_8_2(problem_size, config);
}
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