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non-xla support #2731

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non-xla support #2731

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3f7e238
gpu_blas_lt_gemm_runner
ScXfjiang Oct 25, 2024
ddfd0d5
remove autotuner from gpu blaslt gemm runner --> compile successfully
ScXfjiang Oct 29, 2024
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1 change: 1 addition & 0 deletions tensorflow/compiler/xla/stream_executor/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -450,6 +450,7 @@ tsl_gpu_library(
":temporary_memory_manager",
":timer",
"//tensorflow/compiler/xla/stream_executor/platform",
"//tensorflow/compiler/xla/stream_executor/gpu:gpu_blas_lt_gemm_runner",
"//tensorflow/tsl/platform:env",
"//tensorflow/tsl/platform:errors",
"//tensorflow/tsl/platform:logging",
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16 changes: 9 additions & 7 deletions tensorflow/compiler/xla/stream_executor/gpu/BUILD
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Expand Up @@ -87,13 +87,15 @@ cc_library(
srcs = if_gpu_is_configured(["gpu_blas_lt_gemm_runner.cc"]),
hdrs = if_gpu_is_configured(["gpu_blas_lt_gemm_runner.h"]),
deps = if_gpu_is_configured([
"//tensorflow/core:autotuning_proto_cc",
"//tensorflow/core:autotune_results_proto_cc",
"//tensorflow/compiler/xla:xla_proto",
"//tensorflow/compiler/xla/stream_executor:scratch_allocator",
"//tensorflow/compiler/xla/service/gpu:autotuner_util",
"//tensorflow/compiler/xla:debug_options_flags",
":gpu_blas_lt",
"//tensorflow/core/protobuf:autotuning_proto_cc",
"//tensorflow/compiler/xla:autotune_results_proto_cc",
# "//tensorflow/compiler/xla:xla_proto",
"//tensorflow/compiler/xla/stream_executor:scratch_allocator",
# "//tensorflow/compiler/xla/service/gpu:autotuner_util",
"//tensorflow/compiler/xla:debug_options_flags",
"//tensorflow/core/lib/gtl:array_slice",
"//tensorflow/core/util:env_var",
":gpu_blas_lt",
]),
)

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341 changes: 341 additions & 0 deletions tensorflow/compiler/xla/stream_executor/gpu/gpu_blas_lt_gemm_runner.cc
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@@ -0,0 +1,341 @@
/* Copyright 2023 The OpenXLA Authors.
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.
==============================================================================*/

#include <algorithm>
#include <cstdint>
#include <utility>
#include <sstream>

#include "tensorflow/core/util/env_var.h"
#include "tensorflow/compiler/xla/util.h"
// #include "tensorflow/compiler/xla/service/gpu/autotuner_util.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/compiler/xla/debug_options_flags.h"
#include "tensorflow/compiler/xla/stream_executor/gpu/gpu_blas_lt_gemm_runner.h"
#include "tensorflow/compiler/xla/stream_executor/stream.h"
#include "tensorflow/compiler/xla/stream_executor/stream_executor_pimpl.h"

namespace stream_executor {
namespace gpu {

bool BlasLtGemmRunner::autotune_enabled_ = false;

bool operator ==(const GroupedGemmConfig& rhs, const GroupedGemmConfig& lhs) {
return AsTuple(rhs) == AsTuple(lhs);
}

bool operator ==(const StridedGemmConfig& rhs, const StridedGemmConfig& lhs) {
return AsTuple(rhs) == AsTuple(lhs);
}

std::ostream& operator <<(std::ostream& os, const StridedGemmConfig& cfg) {
return os << "trans_a/b: " << (int)cfg.trans_a << "/" << (int)cfg.trans_b <<
" m: " << cfg.m << " n: " << cfg.n << " k: " << cfg.k <<
" batch_count: " << cfg.batch_count <<
" lda: " << cfg.lda << " ldb: " << cfg.ldb << " ldc: " << cfg.ldc <<
" stride_a: " << cfg.stride_a << " stride_b: " << cfg.stride_b <<
" stride_c: " << cfg.stride_c <<
" type_a: " << (int)cfg.type_a << " type_b: " << (int)cfg.type_b <<
" type_c: " << (int)cfg.type_c <<
" alpha: " << cfg.alpha << " beta: " << cfg.beta;
}

BlasLtGemmRunner::BlasLtGemmRunner(StreamExecutor *parent) :
mutex_(std::make_unique< absl::Mutex >())
// autotune_config_(std::make_unique< xla::gpu::AutotuneConfig >(
// xla::gpu::DeviceConfig{parent, nullptr},
// xla::GetDebugOptionsFromFlags()))
{ }

BlasLtGemmRunner::~BlasLtGemmRunner() { }


/*static*/ BlasLtGemmRunner& BlasLtGemmRunner::i(const Stream *stream) {
static absl::Mutex m(absl::kConstInit);
// Each GPU gets a different cache instance
static std::vector<std::unique_ptr< BlasLtGemmRunner >> meta(8);
absl::MutexLock lock(&m);
size_t dev_id = stream->parent()->device_ordinal();
if (dev_id >= meta.size()) meta.resize(dev_id + 1);
auto& res = meta[dev_id];
// if (!res) {
// autotune_enabled_ = xla::GetDebugOptionsFromFlags().xla_gpu_autotune_level() > 0;
// res.reset(new BlasLtGemmRunner(stream->parent()));
// xla::gpu::AutotunerUtil::LoadAutotuneResultsFromFileOnce(*res->autotune_config_);
// }
return *res;
}

// template < class TuneFunc >
// xla::StatusOr< gpu::BlasLt::MatmulAlgorithm > BlasLtGemmRunner::Autotune(
// const std::vector< gpu::BlasLt::MatmulAlgorithm >& algorithms,
// TuneFunc&& benchmark_func) {
// gpu::BlasLt::MatmulAlgorithm best_algo;
// float best_ms = std::numeric_limits< float >::max(), total_ms = 0;
// uint32_t n_warmups = 1, n_iters = 5, n_total = n_warmups + n_iters, i = 0;

// for (uint32_t j = 0; j < algorithms.size(); j++) {
// const auto& algo = algorithms[j];
// if (!benchmark_func(algo, nullptr).ok()) continue;

// blas::ProfileResult profile;
// for (i = 0, total_ms = 0; i < n_total; i++) {
// auto res = benchmark_func(algo, &profile);
// if (!res.ok() || !profile.is_valid()) {
// VLOG(1) << j << ": gemm algorithm is not valid: " /* << res.error_message() */;
// break;
// }
// if (i >= n_warmups) total_ms += profile.elapsed_time_in_ms();
// }
// if (i < n_total) continue; // invalid algorithm
// total_ms /= n_iters;
// VLOG(2) << j << ": gemm algorithm " << profile.algorithm() << " took "
// << total_ms << "ms, workspace: " << algo.workspace_size;
// if (total_ms < best_ms) {
// best_ms = total_ms, best_algo = algo;
// }
// } // for algorithms
// if (!best_algo.opaque_algo.has_value()) {
// return xla::InternalError("No valid gemm algorithms found!");
// }
// return best_algo;
// }

xla::StatusOr< std::array< uint64_t, 3 >> BlasLtGemmRunner::ContiguousStrides(
const ArraySlice<DeviceMemoryBase *>& a,
const ArraySlice<DeviceMemoryBase *>& b,
const ArraySlice<DeviceMemoryBase *>& c, int64 batch_count) {

uint64_t bsa = 0, bsb = 0, bsc = 0;
using CT = const uint8_t;
for(int64 i = 0; i < batch_count-1; i++) {
uint64_t da = (CT *)a[i + 1]->opaque() - (CT *)a[i]->opaque(),
db = (CT *)b[i + 1]->opaque() - (CT *)b[i]->opaque(),
dc = (CT *)c[i + 1]->opaque() - (CT *)c[i]->opaque();
if(i == 0) {
bsa = da, bsb = db, bsc = dc;
} else if(!(bsa == da && bsb == db && bsc == dc)) { // strides mismatch
return xla::InternalError("Strides are not consistent!");
}
}
return std::array< uint64_t, 3 >{ bsa, bsb, bsc };
}

xla::Status BlasLtGemmRunner::RunBatchedImpl(Stream& stream,
blas::Transpose trans_a, blas::Transpose trans_b, int64 m, int64 n, int64 k,
const void *alpha, blas::DataType type_a, const void** a, int64 lda,
blas::DataType type_b, const void** b, int64 ldb, const void *beta,
blas::DataType type_c, void** c, int64 ldc, int64 batch_count,
ScratchAllocator* allocator)
{

TF_ASSIGN_OR_RETURN(auto compute_type,
gpu::GetBlasComputationType(type_a, type_c, 0));

GroupedGemmConfig cfg{
.m = (int64)m,
.n = (int64)n,
.k = (int64)k,
.batch_count = (int64)batch_count,
.trans_a = trans_a,
.trans_b = trans_b,
.alpha = alpha,
.beta = beta,
.type_a = type_a,
.type_b = type_b,
.type_c = type_c,
.type_d = type_c,
.lda = (int64)lda,
.ldb = (int64)ldb,
.ldc = (int64)ldc,
.ldd = (int64)ldc,
.compute_type = compute_type,
.a = a,
.b = b,
.c = const_cast< const void **>(c),
.d = c,
};

absl::MutexLock lock(mutex_.get());

auto res = grouped_gemm_map_.find(cfg);
if (res == grouped_gemm_map_.end()) {
// NOTE: we assume that pointers a,b,c come from the device mem
// hence we need to block stream here
TF_ASSIGN_OR_RETURN(auto plan_res,
gpu::BlasLt::CreateGroupedMatmulPlan(&stream, cfg));
res = grouped_gemm_map_.emplace(cfg, std::move(plan_res)).first;

size_t num_solutions = autotune_enabled_ ? gpu::BlasLt::kMaxAlgorithms : 1;
// discard solutions with non-zero workspace if allocator is not given
TF_ASSIGN_OR_RETURN(auto algorithms, res->second->GetAlgorithms(
num_solutions, allocator == nullptr ? 0 : 1ull << 32));

VLOG(1) << stream.parent() << ": new GGemm config: " <<
grouped_gemm_map_.size() << " #valid algorithms: " << algorithms.size();

BlasLt::MatmulAlgorithm best_algo;
if (!autotune_enabled_) {
if (algorithms.empty()) return xla::InternalError("No GG algorithms found!");
best_algo = algorithms[0]; // otherwise use default algorithm
} else {
// TF_ASSIGN_OR_RETURN(auto best_algo, Autotune(algorithms,
// [&](const gpu::BlasLt::MatmulAlgorithm& algo, blas::ProfileResult *profile){
// if (profile == nullptr) {
// return res->second->SetAlgorithm(algo, allocator);
// }
// return res->second->ExecuteOnStream(&stream, cfg, profile);
// }));
}
TF_RETURN_IF_ERROR(res->second->SetAlgorithm(best_algo, allocator));
}
return res->second->ExecuteOnStream(&stream, cfg);
}

xla::Status BlasLtGemmRunner::RunStridedBatchedImpl(Stream& stream,
blas::Transpose trans_a, blas::Transpose trans_b, int64 m, int64 n, int64 k,
xla::complex128 alpha,
blas::DataType type_a, const DeviceMemoryBase& a, int64 lda, int64 stride_a,
blas::DataType type_b, const DeviceMemoryBase& b, int64 ldb, int64 stride_b,
double beta,
blas::DataType type_c, DeviceMemoryBase *c, int64 ldc, int64 stride_c,
int64 batch_count, ScratchAllocator* allocator)
{
StridedGemmConfig scfg{
.m = m,
.n = n,
.k = k,
.batch_count = (int64)batch_count,
.trans_a = trans_a,
.trans_b = trans_b,
.alpha = alpha,
.beta = beta,
.type_a = type_a,
.type_b = type_b,
.type_c = type_c,
.lda = lda,
.ldb = ldb,
.ldc = ldc,
.stride_a = stride_a,
.stride_b = stride_b,
.stride_c = stride_c,
};

absl::MutexLock lock(mutex_.get());

auto res = strided_gemm_map_.find(scfg);
while (res == strided_gemm_map_.end()) {
int64 row_a = m, col_a = k, row_b = k, col_b = n;
if (trans_a == blas::Transpose::kTranspose) std::swap(row_a, col_a);
if (trans_b == blas::Transpose::kTranspose) std::swap(row_b, col_b);

auto order = MatrixLayout::Order::kColumnMajor;
GemmConfig cfg = {
.lhs_layout = MatrixLayout(type_a, row_a, col_a, order, batch_count,
lda, stride_a, trans_a),

.rhs_layout = MatrixLayout(type_b, row_b, col_b, order, batch_count,
ldb, stride_b, trans_b),

.c_layout = MatrixLayout(type_c, m, n, order, batch_count,
ldc, stride_c),
.output_layout = MatrixLayout(type_c, m, n, order, batch_count,
ldc, stride_c),
.alpha = alpha,
.beta = beta,
.compute_precision = -1,
.epilogue = gpu::BlasLt::Epilogue::kDefault,
};

TF_ASSIGN_OR_RETURN(auto plan_res,
gpu::BlasLt::GetMatmulPlan(&stream, cfg));
res = strided_gemm_map_.emplace(scfg, std::move(plan_res)).first;

size_t num_solutions = autotune_enabled_ ? gpu::BlasLt::kMaxAlgorithms : 1;
// discard solutions with non-zero workspace if allocator is not given
TF_ASSIGN_OR_RETURN(auto algorithms, res->second->GetAlgorithms(
num_solutions, allocator == nullptr ? 0 : 1ull << 32));

VLOG(1) << &stream << " dev " << stream.parent() << '(' <<
stream.parent()->device_ordinal() << "): new StridedBatched config: "
<< strided_gemm_map_.size() << " #algorithms: " << algorithms.size();

if (!autotune_enabled_) {
if (algorithms.empty()) return xla::InternalError("No algorithms found!");
res->second->SetAlgorithm(algorithms[0]);
break;
}

// BlasLt::MatmulAlgorithm best_algo{ .id = blas::kNoAlgorithm };
// xla::gpu::AutotuneCacheKey key(ToCSVString(cfg, /*full_string*/false));
// auto opt_res = xla::gpu::AutotunerUtil::TryToFindInInMemoryCache(key);
// if (opt_res.has_value()) {
// auto id = *opt_res;
// for (const auto& algo : algorithms) {
// if (algo.id == id) best_algo = algo;
// }
// if (best_algo.id == blas::kNoAlgorithm) {
// LOG(WARNING) << "Best algorithm not valid: need to autotune..";
// }
// }

// if (best_algo.id == blas::kNoAlgorithm) {
// TF_ASSIGN_OR_RETURN(best_algo, Autotune(algorithms,
// [&](const gpu::BlasLt::MatmulAlgorithm& algo, blas::ProfileResult *profile){
// if (profile == nullptr) {
// return res->second->SetAlgorithm(algo);
// }
// return res->second->ExecuteOnStream(
// &stream, a, b, *c, *c,
// DeviceMemoryBase{}, // bias
// DeviceMemoryBase{}, // aux
// DeviceMemoryBase{}, // a_scale
// DeviceMemoryBase{}, // b_scale
// DeviceMemoryBase{}, // c_scale
// DeviceMemoryBase{}, // d_scale
// DeviceMemoryBase{}, // d_amax
// absl::nullopt, // workspace
// allocator, // allocator
// profile);
// }));
// xla::gpu::AutotunerUtil::CacheValue ares = best_algo.id;
// // reread algorithm ID from cache again (in case some other thread has
// // already added this config to the cache to be sure we use the same ID)
// auto new_id = xla::gpu::AutotunerUtil::AddResultToInMemoryCache(key, ares,
// *autotune_config_);

// if (new_id != best_algo.id) {
// for (const auto& algo : algorithms) {
// if (algo.id == new_id) best_algo = algo;
// }
// }
// } // best_algo.id == blas::kNoAlgorithm

// res->second->SetAlgorithm(best_algo);
// break;
} // while
return res->second->ExecuteOnStream(
&stream, a, b, *c, *c,
DeviceMemoryBase{}, // bias
DeviceMemoryBase{}, // aux
DeviceMemoryBase{}, // a_scale
DeviceMemoryBase{}, // b_scale
DeviceMemoryBase{}, // c_scale
DeviceMemoryBase{}, // d_scale
DeviceMemoryBase{}, // d_amax
absl::nullopt, // workspace
allocator); // allocator
}

} // namespace gpu

} // namespace stream_executor
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