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tensorflow/compiler/xla/stream_executor/gpu/gpu_blas_lt_gemm_runner.cc
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/* 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. | ||
==============================================================================*/ | ||
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#include <algorithm> | ||
#include <cstdint> | ||
#include <utility> | ||
#include <sstream> | ||
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#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" | ||
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namespace stream_executor { | ||
namespace gpu { | ||
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bool BlasLtGemmRunner::autotune_enabled_ = true; | ||
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bool operator ==(const GroupedGemmConfig& rhs, const GroupedGemmConfig& lhs) { | ||
return AsTuple(rhs) == AsTuple(lhs); | ||
} | ||
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bool operator ==(const StridedGemmConfig& rhs, const StridedGemmConfig& lhs) { | ||
return AsTuple(rhs) == AsTuple(lhs); | ||
} | ||
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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; | ||
} | ||
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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())) | ||
{ } | ||
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BlasLtGemmRunner::~BlasLtGemmRunner() { } | ||
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/*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; | ||
} | ||
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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; | ||
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for (uint32_t j = 0; j < algorithms.size(); j++) { | ||
const auto& algo = algorithms[j]; | ||
if (!benchmark_func(algo, nullptr).ok()) continue; | ||
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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; | ||
} | ||
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xla::StatusOr< std::array< uint64_t, 3 >> BlasLtGemmRunner::ContiguousStrides( | ||
const ArraySlice<DeviceMemoryBase *>& a, | ||
const ArraySlice<DeviceMemoryBase *>& b, | ||
const ArraySlice<DeviceMemoryBase *>& c, int64 batch_count) { | ||
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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 }; | ||
} | ||
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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) | ||
{ | ||
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TF_ASSIGN_OR_RETURN(auto compute_type, | ||
gpu::GetBlasComputationType(type_a, type_c, 0)); | ||
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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, | ||
}; | ||
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absl::MutexLock lock(mutex_.get()); | ||
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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; | ||
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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)); | ||
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VLOG(1) << stream.parent() << ": new GGemm config: " << | ||
grouped_gemm_map_.size() << " #valid algorithms: " << algorithms.size(); | ||
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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); | ||
} | ||
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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, | ||
}; | ||
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absl::MutexLock lock(mutex_.get()); | ||
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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); | ||
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auto order = MatrixLayout::Order::kColumnMajor; | ||
GemmConfig cfg = { | ||
.lhs_layout = MatrixLayout(type_a, row_a, col_a, order, batch_count, | ||
lda, stride_a, trans_a), | ||
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.rhs_layout = MatrixLayout(type_b, row_b, col_b, order, batch_count, | ||
ldb, stride_b, trans_b), | ||
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.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, | ||
}; | ||
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TF_ASSIGN_OR_RETURN(auto plan_res, | ||
gpu::BlasLt::GetMatmulPlan(&stream, cfg)); | ||
res = strided_gemm_map_.emplace(scfg, std::move(plan_res)).first; | ||
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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)); | ||
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VLOG(1) << &stream << " dev " << stream.parent() << '(' << | ||
stream.parent()->device_ordinal() << "): new StridedBatched config: " | ||
<< strided_gemm_map_.size() << " #algorithms: " << algorithms.size(); | ||
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if (!autotune_enabled_) { | ||
if (algorithms.empty()) return xla::InternalError("No algorithms found!"); | ||
res->second->SetAlgorithm(algorithms[0]); | ||
break; | ||
} | ||
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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.."; | ||
} | ||
} | ||
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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_); | ||
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if (new_id != best_algo.id) { | ||
for (const auto& algo : algorithms) { | ||
if (algo.id == new_id) best_algo = algo; | ||
} | ||
} | ||
} // best_algo.id == blas::kNoAlgorithm | ||
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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 | ||
} | ||
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} // namespace gpu | ||
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} // namespace stream_executor |
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