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machine-learning/0001-fix-rocm-conv-thread-safety.patch
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,149 @@ | ||
| From 60e2603045f220d560ed92e03c8804a806f5a325 Mon Sep 17 00:00:00 2001 | ||
| From: mertalev <[email protected]> | ||
| Date: Fri, 20 Dec 2024 00:59:21 -0500 | ||
| Subject: [PATCH] fix: avoid race condition for rocm conv algo caching | ||
|
|
||
| --- | ||
| onnxruntime/core/providers/rocm/nn/conv.cc | 8 ++++---- | ||
| onnxruntime/core/providers/rocm/nn/conv.h | 13 +++++++++++-- | ||
| .../core/providers/rocm/nn/conv_transpose.cc | 8 ++++---- | ||
| 3 files changed, 19 insertions(+), 10 deletions(-) | ||
|
|
||
| diff --git a/onnxruntime/core/providers/rocm/nn/conv.cc b/onnxruntime/core/providers/rocm/nn/conv.cc | ||
| index d7f47d07a8..98b6b69212 100644 | ||
| --- a/onnxruntime/core/providers/rocm/nn/conv.cc | ||
| +++ b/onnxruntime/core/providers/rocm/nn/conv.cc | ||
| @@ -127,7 +127,6 @@ Status Conv<T, NHWC>::UpdateState(OpKernelContext* context, bool bias_expected) | ||
|
|
||
| if (w_dims_changed) { | ||
| s_.last_w_dims = gsl::make_span(w_dims); | ||
| - s_.cached_benchmark_fwd_results.clear(); | ||
| } | ||
|
|
||
| ORT_RETURN_IF_ERROR(conv_attrs_.ValidateInputShape(X->Shape(), W->Shape(), channels_last, channels_last)); | ||
| @@ -278,7 +277,8 @@ Status Conv<T, NHWC>::UpdateState(OpKernelContext* context, bool bias_expected) | ||
| HIP_CALL_THROW(hipMemsetAsync(s_.b_zero, 0, malloc_size, Stream(context))); | ||
| } | ||
|
|
||
| - if (!s_.cached_benchmark_fwd_results.contains(x_dims_miopen)) { | ||
| + const std::size_t algo_key = HashConvAlgoKey(x_dims_miopen, w_dims); | ||
| + if (!s_.cached_benchmark_fwd_results.contains(algo_key)) { | ||
| miopenConvAlgoPerf_t perf; | ||
| int algo_count = 1; | ||
| const ROCMExecutionProvider* rocm_ep = static_cast<const ROCMExecutionProvider*>(this->Info().GetExecutionProvider()); | ||
| @@ -301,9 +301,9 @@ Status Conv<T, NHWC>::UpdateState(OpKernelContext* context, bool bias_expected) | ||
| algo_search_workspace.get(), | ||
| max_ws_size, | ||
| false)); // Do not do exhaustive algo search. | ||
| - s_.cached_benchmark_fwd_results.insert(x_dims_miopen, {perf.fwd_algo, perf.memory}); | ||
| + s_.cached_benchmark_fwd_results.insert(algo_key, {perf.fwd_algo, perf.memory}); | ||
| } | ||
| - const auto& perf = s_.cached_benchmark_fwd_results.at(x_dims_miopen); | ||
| + const auto& perf = s_.cached_benchmark_fwd_results.at(algo_key); | ||
| s_.fwd_algo = perf.fwd_algo; | ||
| s_.workspace_bytes = perf.memory; | ||
| } else { | ||
| diff --git a/onnxruntime/core/providers/rocm/nn/conv.h b/onnxruntime/core/providers/rocm/nn/conv.h | ||
| index bc9846203e..0b07ee3f08 100644 | ||
| --- a/onnxruntime/core/providers/rocm/nn/conv.h | ||
| +++ b/onnxruntime/core/providers/rocm/nn/conv.h | ||
| @@ -43,6 +43,10 @@ struct vector_hash { | ||
| } | ||
| }; | ||
|
|
||
| +inline std::size_t HashConvAlgoKey(const TensorShapeVector& x_dims, const TensorShapeVector& w_dims) { | ||
| + return std::hash<vector_hash>()(values.x_dims) ^ std::hash<vector_hash>()(values.w_dims); | ||
| +} | ||
| + | ||
| template <typename Key, typename T, | ||
| typename Hash = std::hash<Key>, | ||
| typename KeyEqual = std::equal_to<Key>, | ||
| @@ -52,6 +56,7 @@ class lru_unordered_map { | ||
| lru_unordered_map(size_t max_size) : max_size_(max_size) {} | ||
|
|
||
| void insert(const Key& key, const T& value) { | ||
| + std::lock_guard<std::mutex> guard(mutex_); | ||
| auto it = items_.find(key); | ||
| if (it != items_.end()) { | ||
| it->second.value = value; | ||
| @@ -69,6 +74,7 @@ class lru_unordered_map { | ||
| } | ||
|
|
||
| T& at(const Key& key) { | ||
| + std::lock_guard<std::mutex> guard(mutex_); | ||
| auto it = items_.find(key); | ||
| if (it == items_.end()) { | ||
| throw std::out_of_range("There is no such key in cache"); | ||
| @@ -78,6 +84,7 @@ class lru_unordered_map { | ||
| } | ||
|
|
||
| bool contains(const Key& key) const { | ||
| + std::lock_guard<std::mutex> guard(mutex_); | ||
| return items_.find(key) != items_.end(); | ||
| } | ||
|
|
||
| @@ -86,6 +93,7 @@ class lru_unordered_map { | ||
| } | ||
|
|
||
| void clear() { | ||
| + std::lock_guard<std::mutex> guard(mutex_); | ||
| items_.clear(); | ||
| lru_list_.clear(); | ||
| } | ||
| @@ -106,6 +114,7 @@ class lru_unordered_map { | ||
| size_t max_size_; | ||
| std::unordered_map<Key, value_type, Hash, KeyEqual, MapAllocator> items_; | ||
| list_type lru_list_; | ||
| + mutable std::mutex mutex_; | ||
| }; | ||
|
|
||
| // cached miopen descriptors | ||
| @@ -148,8 +157,8 @@ struct MiopenConvState { | ||
| decltype(AlgoPerfType().memory) memory; | ||
| }; | ||
|
|
||
| - lru_unordered_map<TensorShapeVector, PerfFwdResultParams, vector_hash> cached_benchmark_fwd_results{MAX_CACHED_ALGO_PERF_RESULTS}; | ||
| - lru_unordered_map<TensorShapeVector, PerfBwdResultParams, vector_hash> cached_benchmark_bwd_results{MAX_CACHED_ALGO_PERF_RESULTS}; | ||
| + lru_unordered_map<std::size_t, PerfFwdResultParams> cached_benchmark_fwd_results{MAX_CACHED_ALGO_PERF_RESULTS}; | ||
| + lru_unordered_map<std::size_t, PerfBwdResultParams> cached_benchmark_bwd_results{MAX_CACHED_ALGO_PERF_RESULTS}; | ||
|
|
||
| // Some properties needed to support asymmetric padded Conv nodes | ||
| bool post_slicing_required; | ||
| diff --git a/onnxruntime/core/providers/rocm/nn/conv_transpose.cc b/onnxruntime/core/providers/rocm/nn/conv_transpose.cc | ||
| index 7447113fdf..495aafa200 100644 | ||
| --- a/onnxruntime/core/providers/rocm/nn/conv_transpose.cc | ||
| +++ b/onnxruntime/core/providers/rocm/nn/conv_transpose.cc | ||
| @@ -76,7 +76,6 @@ Status ConvTranspose<T, NHWC>::DoConvTranspose(OpKernelContext* context, bool dy | ||
|
|
||
| if (w_dims_changed) { | ||
| s_.last_w_dims = gsl::make_span(w_dims); | ||
| - s_.cached_benchmark_bwd_results.clear(); | ||
| } | ||
|
|
||
| ConvTransposeAttributes::Prepare p; | ||
| @@ -127,7 +126,8 @@ Status ConvTranspose<T, NHWC>::DoConvTranspose(OpKernelContext* context, bool dy | ||
|
|
||
| y_data = reinterpret_cast<HipT*>(p.Y->MutableData<T>()); | ||
|
|
||
| - if (!s_.cached_benchmark_bwd_results.contains(x_dims)) { | ||
| + const std::size_t algo_key = HashConvAlgoKey(x_dims_miopen, w_dims); | ||
| + if (!s_.cached_benchmark_bwd_results.contains(algo_key)) { | ||
| IAllocatorUniquePtr<void> algo_search_workspace = GetScratchBuffer<void>(AlgoSearchWorkspaceSize, context->GetComputeStream()); | ||
|
|
||
| miopenConvAlgoPerf_t perf; | ||
| @@ -147,10 +147,10 @@ Status ConvTranspose<T, NHWC>::DoConvTranspose(OpKernelContext* context, bool dy | ||
| algo_search_workspace.get(), | ||
| AlgoSearchWorkspaceSize, | ||
| false)); | ||
| - s_.cached_benchmark_bwd_results.insert(x_dims, {perf.bwd_data_algo, perf.memory}); | ||
| + s_.cached_benchmark_bwd_results.insert(algo_key, {perf.bwd_data_algo, perf.memory}); | ||
| } | ||
|
|
||
| - const auto& perf = s_.cached_benchmark_bwd_results.at(x_dims); | ||
| + const auto& perf = s_.cached_benchmark_bwd_results.at(algo_key); | ||
| s_.bwd_data_algo = perf.bwd_data_algo; | ||
| s_.workspace_bytes = perf.memory; | ||
| } | ||
| -- | ||
| 2.43.0 | ||
|
|
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