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executor.cpp
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executor.cpp
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#include <torch/csrc/jit/codegen/cuda/codegen.h>
#include <torch/csrc/jit/codegen/cuda/executor_kernel_arg.h>
#include <torch/csrc/jit/codegen/cuda/instrumentation.h>
#include <torch/csrc/jit/codegen/cuda/ir_all_nodes.h>
#include <torch/csrc/jit/codegen/cuda/iter_visitor.h>
#include <torch/csrc/jit/codegen/cuda/kernel_ir.h>
#include <torch/csrc/jit/codegen/cuda/executor.h>
#include <ATen/core/LegacyTypeDispatch.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/Exceptions.h>
#include <ATen/cuda/nvrtc_stub/ATenNVRTC.h>
#include <c10/core/DeviceGuard.h>
#include <c10/cuda/CUDAFunctions.h>
#include <c10/cuda/CUDAStream.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
int FusionExecutor::fusion_id_counter_ = 0;
std::string FusionExecutor::getStructuredCode(const std::string& kernel) {
// generating cuda code;
std::string code = "";
#ifdef __HIP_PLATFORM_HCC__
code += std::string("#include <hip/hip_runtime.h>\n") +
std::string("#include <hip/hip_fp16.h>\n");
#endif
code += std::string("namespace ") + FusionExecutor::kernelNamespace() +
" {\n" + executor_utils::kernelPreamble() + kernel + "}\n";
const char* debug_env = getenv("PYTORCH_CUDA_FUSER_DEBUG");
if (debug_env && atoi(debug_env)) {
std::cout << "\n==== codegen output for kernel: " << kernelName()
<< " ====" << std::endl
<< code << std::endl
<< "======================================\n"
<< std::endl;
}
return code;
}
void FusionExecutor::debugCompileFusionFromStr(
Fusion* fusion,
const std::string& code,
const std::string& name,
int id,
CompileOptions options) {
fusion_ = *fusion;
FusionGuard fg(&fusion_);
options_ = options;
const char* debug_env = getenv("PYTORCH_CUDA_FUSER_DEBUG");
if (debug_env && atoi(debug_env)) {
std::cout << "\n==== codegen output for kernel: " << kernelName()
<< " ====" << std::endl
<< code << std::endl
<< "======================================\n"
<< std::endl;
}
fusion_id_ = id;
lowered_ = GpuLower(&fusion_);
compiled_kernel_ = executor_utils::nvrtcCompile(code, name, fusion_id_);
TORCH_INTERNAL_ASSERT(
fusion_id_ > 0, "assign a fusion_id_ <= 0 is not accepted.");
}
void FusionExecutor::compileFusion(Fusion* fusion, CompileOptions options) {
FUSER_PERF_SCOPE("compileFusion");
TORCH_INTERNAL_ASSERT(
!fusion->outputs().empty(), "No output found for this kernel, aborting.");
for (auto out : fusion->outputs()) {
TORCH_INTERNAL_ASSERT(
out->getValType() == ValType::TensorView,
"Output types from fusions that are not tensors are not supported at this point.");
}
// Clone the fusion so we can store it
fusion_ = *fusion;
FusionGuard fg(&fusion_);
options_ = options;
TORCH_INTERNAL_ASSERT(
options.device.is_cuda(), "Provided device to CUDA fuser is the CPU.");
max_device_smem =
at::cuda::getDeviceProperties(options.device.index())->sharedMemPerBlock;
setUsedTVs();
fusion_id_ = ++fusion_id_counter_;
lowered_ = GpuLower(&fusion_);
const auto kernel = lowered_.kernel();
const auto kernel_code = codegen::generateCudaKernel(kernel, kernelName());
const auto structured_code = getStructuredCode(kernel_code);
const auto& kernel_summary = kernel->summary();
has_block_reductions = kernel_summary.has_block_reductions;
has_grid_reductions = kernel_summary.has_grid_reductions;
has_block_broadcasts = kernel_summary.has_block_broadcasts;
if (!kernel_summary.static_smem_allocations.empty()) {
StatefulExpressionEvaluator static_evaluator(&fusion_);
unsigned static_smem_size = computeSharedMemory(
static_evaluator, kernel_summary.static_smem_allocations);
TORCH_INTERNAL_ASSERT(
static_smem_size < max_device_smem,
"The static shared memory allocation is larger than available memory.");
}
compiled_kernel_ = executor_utils::nvrtcCompile(
structured_code,
(kernelNamespace() + "::" + kernelName()).c_str(),
fusion_id_);
TORCH_INTERNAL_ASSERT(
fusion_id_ > 0, "failed to assign a fusion_id_ after compilation.");
}
namespace {
at::Tensor inferAndAlloc(
const TensorView* tv,
StatefulExpressionEvaluator& see,
const CompileOptions& options,
bool zero_init = false) {
FUSER_PERF_SCOPE("inferAndAlloc");
std::vector<int64_t> sizes;
for (auto id : TensorDomain::noReductions(tv->getMaybeRFactorDomain())) {
auto inferred_val = see.inferValue(id->rawExtent());
TORCH_INTERNAL_ASSERT(
inferred_val.has_value(),
"Could not launch kernel as program could not infer ",
id->rawExtent(),
" for the buffer ",
tv);
sizes.push_back(inferred_val.value());
}
auto at_type = data_type_to_aten(tv->getDataType().value());
auto tensor_options =
at::TensorOptions().dtype(at_type).device(options.device);
if (zero_init) {
c10::IntArrayRef isizes(sizes);
return at::zeros(isizes, tensor_options);
} else {
c10::IntArrayRef isizes(sizes);
// Non Variable type guard for empty_cuda call
at::AutoNonVariableTypeMode non_variable_type_mode;
return at::native::empty_cuda(isizes, tensor_options);
}
}
} // namespace
uint64_t FusionExecutor::computeSharedMemory(
StatefulExpressionEvaluator& see,
const std::vector<kir::Allocate*>& buffers,
bool align_padding,
uint64_t total) {
FUSER_PERF_SCOPE("computeSharedMemory");
for (auto smem_alloc : buffers) {
auto inferred_val = see.inferValue(smem_alloc->size());
if (inferred_val.has_value()) {
const uint64_t data_size = dataTypeSize(smem_alloc->buffer_type());
// Add padding to align dynamic shared memory
if (align_padding) {
total = ceilDiv(total, data_size) * data_size;
}
total += inferred_val.value() * data_size;
} else {
TORCH_INTERNAL_ASSERT(
false,
"Failed to evaluate the size ",
smem_alloc->size(),
" of shared memory buffer - T",
smem_alloc->buffer()->name());
}
}
return total;
}
LaunchParams FusionExecutor::computeLaunchParams(
const LaunchParams& launch_constraints,
StatefulExpressionEvaluator& see) {
FUSER_PERF_SCOPE("computeLaunchParams");
LaunchParams launch_params;
// Lets collect all IterDomains that are bound to a thread binding
std::unordered_map<ParallelType, std::vector<IterDomain*>, TypeHash>
parallel_iter_domains;
for (auto tv : getUsedTVs()) {
for (auto id : tv->domain()->domain()) {
if (id->isThread() && !id->isBroadcast()) {
if (parallel_iter_domains.find(id->getParallelType()) !=
parallel_iter_domains.end()) {
parallel_iter_domains.at(id->getParallelType()).push_back(id);
} else {
parallel_iter_domains[id->getParallelType()] =
std::vector<IterDomain*>({id});
}
}
}
}
// If any dimension was set in launch constraints we need to run through
// IterDomains that have been parallelized, and bind those values. Or make
// sure if they could be inferred the inference matches what was set.
if (launch_constraints.nBlocks() * launch_constraints.nThreads() != -1) {
for (auto& entry : parallel_iter_domains) {
auto p_type = entry.first;
if (launch_constraints.hasDim(p_type)) {
auto parallel_ids = entry.second;
for (auto parallel_id : parallel_ids) {
auto inferred_val = see.inferValue(parallel_id->rawExtent());
if (inferred_val.has_value()) {
// This value could have been inferred, make sure it was set right.
TORCH_CHECK(
inferred_val.value() == launch_constraints.getDim(p_type) ||
launch_constraints.getRawVal(p_type) == -1,
"inferred that ",
p_type,
" should be set to ",
inferred_val.value(),
" but launch constraints specified ",
launch_constraints.getDim(p_type));
} else {
// Bind the launch constraint into our evaluation context
see.safeBind(
parallel_id->rawExtent(),
launch_constraints.getDim(entry.first),
&lowered_);
launch_params.bind(launch_constraints.getDim(p_type), p_type);
}
}
}
}
}
// Run through the rest of the parallel IterDomains and infer their size
for (auto& entry : parallel_iter_domains) {
auto p_type = entry.first;
auto parallel_ids = entry.second;
for (auto parallel_id : parallel_ids) {
auto val = see.inferValue(parallel_id->rawExtent());
TORCH_INTERNAL_ASSERT(
val,
"Tried to evaluate the extent of ",
parallel_id,
" to set launch bounds but could not.");
launch_params.bind(val.value(), p_type);
}
}
const auto kernel = lowered_.kernel();
const auto& kernel_summary = kernel->summary();
// Calculate Dynamic Shared Memory Size
// Add workspace for reduction and broadcast
uint64_t reduction_broadcast_workspace = 0;
if (has_block_reductions || has_grid_reductions || has_block_broadcasts) {
// Not using nThreads here since it does not handle uninitialized value
reduction_broadcast_workspace =
dataTypeSize(kernel_summary.largest_smem_data_type) *
launch_params.bdimx() * launch_params.bdimy() * launch_params.bdimz();
}
const uint64_t dynamic_smem_size = computeSharedMemory(
see,
kernel_summary.dynamic_smem_allocations,
true,
reduction_broadcast_workspace);
const uint64_t static_smem_size =
computeSharedMemory(see, kernel_summary.static_smem_allocations);
TORCH_INTERNAL_ASSERT(
(dynamic_smem_size + static_smem_size) < max_device_smem,
"The total shared memory allocation is larger than available memory.");
launch_params.setSmem(dynamic_smem_size);
return launch_params;
}
FusionExecutor::GlobalBuffers FusionExecutor::allocGlobalVals(
StatefulExpressionEvaluator& see) {
FUSER_PERF_SCOPE("allocGlobalVals");
GlobalBuffers global_buffers;
const auto& kernel_summary = lowered_.kernel()->summary();
for (auto alloc : kernel_summary.global_allocations) {
TORCH_INTERNAL_ASSERT(
alloc->buffer()->getValType() == ValType::KirTensorView,
"Cannot allocate global buffers that are not tensors.");
if (!alloc->zeroInit()) {
global_buffers.empty_buffers.push_back(inferAndAlloc(
alloc->buffer()->as<kir::TensorView>()->fuserTv(),
see,
options_,
false));
} else {
global_buffers.zero_buffers.push_back(inferAndAlloc(
alloc->buffer()->as<kir::TensorView>()->fuserTv(),
see,
options_,
true));
}
}
return global_buffers;
}
std::vector<at::Tensor> FusionExecutor::allocOutputs(
StatefulExpressionEvaluator& see) {
FUSER_PERF_SCOPE("allocOutputs");
std::vector<at::Tensor> outputs;
for (auto output : fusion_.outputs()) {
TORCH_INTERNAL_ASSERT(
output->getValType() == ValType::TensorView,
"Cannot allocate outputs that are not tensors.");
outputs.push_back(
inferAndAlloc(output->as<TensorView>(), see, options_, false));
}
return outputs;
}
void FusionExecutor::setUsedTVs() {
used_tvs_.clear();
auto used_vals = DependencyCheck::getAllValsBetween(
{fusion_.inputs().begin(), fusion_.inputs().end()}, fusion_.outputs());
for (auto val : used_vals) {
if (val->getValType().value() == ValType::TensorView) {
used_tvs_.push_back(val->as<TensorView>());
}
}
}
std::vector<at::Tensor> FusionExecutor::runFusion(
const at::ArrayRef<IValue>& inputs,
const std::vector<at::Tensor>& outputs,
const LaunchParams& launch_constraints,
const c10::optional<size_t>& opt_code) {
FUSER_PERF_SCOPE("runFusion");
TORCH_INTERNAL_ASSERT(
fusion_id_ > 0, "Cannot run fusion, it was not compiled.");
TORCH_INTERNAL_ASSERT(
!opt_code.has_value() || outputs.empty(),
"short cut input cache is not compatible with pre-allocated output");
ExecutorEntry* executor_entry = nullptr;
if (opt_code.has_value()) {
executor_entry = &executor_entry_lookup_[*opt_code];
}
FusionGuard fg(&fusion_);
c10::DeviceGuard dg(options_.device);
auto stream = at::cuda::getCurrentCUDAStream();
LaunchParams launch_params;
std::vector<at::Tensor> alloced_outputs = outputs;
GlobalBuffers global_buffers;
uint64_t rand_offset = 0;
if (executor_entry && executor_entry->init) {
{
// context manager to disable auto grad for `empty_cuda` calls later;
at::AutoNonVariableTypeMode non_variable_type_mode;
// take the short-cut for launch if we see a recorded input set again;
launch_params = executor_entry->launch_params;
for (size_t i = 0; i < executor_entry->output_sizes.size(); i++) {
auto tensor_options = at::TensorOptions()
.dtype(executor_entry->output_types[i])
.device(options_.device);
alloced_outputs.push_back(at::native::empty_cuda(
executor_entry->output_sizes[i], tensor_options));
}
for (size_t i = 0; i < executor_entry->empty_buffer_sizes.size(); i++) {
auto tensor_options = at::TensorOptions()
.dtype(executor_entry->empty_buffer_types[i])
.device(options_.device);
global_buffers.empty_buffers.push_back(at::native::empty_cuda(
executor_entry->empty_buffer_sizes[i], tensor_options));
}
}
for (size_t i = 0; i < executor_entry->zero_buffer_sizes.size(); i++) {
auto tensor_options = at::TensorOptions()
.dtype(executor_entry->zero_buffer_types[i])
.device(options_.device);
global_buffers.zero_buffers.push_back(
at::zeros(executor_entry->zero_buffer_sizes[i], tensor_options));
}
rand_offset = executor_entry->rand_offset;
} else {
// code path to take when either:
// 1. no opt_code is provided or;
// 2. `executor_entry` is not initialized
executor_utils::validateKernelInputs(&fusion_, inputs, options_.device);
StatefulExpressionEvaluator evaluator =
executor_utils::statefulBindInputs(inputs, &fusion_, &lowered_);
launch_params = computeLaunchParams(launch_constraints, evaluator);
if (outputs.empty() || outputs.size() != fusion_.outputs().size()) {
alloced_outputs = allocOutputs(evaluator);
} else {
executor_utils::validateKernelOutputs(
&fusion_, alloced_outputs, options_.device);
}
global_buffers = allocGlobalVals(evaluator);
if (lowered_.kernel()->summary().is_stochastic) {
// NOTE: this is how we map offset to PW kernels in order to have
// identical random number generator to match native PyTorch results.
// But it doesn't really work as it takes assumption how threads are
// binded but is not generally how we handle that in scheduler.
// Refer to `Philox` in generated kernel to understand how the mapping
// works.
rand_offset = 4 *
(std::ceil(
alloced_outputs[0].numel() /
(4.0 * 128 * launch_params.gdimx())) + // NOLINT
1);
}
// This is the entry when we have provided `opt_code` but the entry has not
// been initialized yet.
if (executor_entry) {
// record the the short-cut executor entry for the given input set;
executor_entry->launch_params = launch_params;
for (const auto& output : alloced_outputs) {
executor_entry->output_sizes.push_back(output.sizes().vec());
executor_entry->output_types.push_back(output.scalar_type());
}
for (const auto& buffer : global_buffers.empty_buffers) {
executor_entry->empty_buffer_sizes.push_back(buffer.sizes().vec());
executor_entry->empty_buffer_types.push_back(buffer.scalar_type());
}
for (const auto& buffer : global_buffers.zero_buffers) {
executor_entry->zero_buffer_sizes.push_back(buffer.sizes().vec());
executor_entry->zero_buffer_types.push_back(buffer.scalar_type());
}
executor_entry->rand_offset = rand_offset;
executor_entry->init = true;
}
}
KernelArgumentHolder kernel_arguments;
kernel_arguments.push(inputs);
kernel_arguments.push(alloced_outputs);
kernel_arguments.push(global_buffers.empty_buffers);
kernel_arguments.push(global_buffers.zero_buffers);
if (lowered_.kernel()->summary().is_stochastic) {
kernel_arguments.appendPhiloxRNGSeed(rand_offset);
}
{
FUSER_PERF_SCOPE("cuLaunchKernel");
AT_CUDA_DRIVER_CHECK(at::globalContext().getNVRTC().cuLaunchKernel(
compiled_kernel_.function,
launch_params.gdimx(),
launch_params.gdimy(),
launch_params.gdimz(),
launch_params.bdimx(),
launch_params.bdimy(),
launch_params.bdimz(),
launch_params.smem(),
stream,
kernel_arguments.getBuffer(),
nullptr));
AT_CUDA_CHECK(cudaStreamSynchronize(stream));
}
return alloced_outputs;
}
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch