commit 16839b58d9b3c3162a67ce5d776b36d4d24e801f
Author: mertalev <101130780+mertalev@users.noreply.github.com>
Date: Wed Mar 5 11:25:38 2025 -0500
disable algo caching (attributed to @dmnieto in https://github.com/microsoft/onnxruntime/pull/19567)
diff --git a/onnxruntime/core/providers/rocm/nn/conv.cc b/onnxruntime/core/providers/rocm/nn/conv.cc
index d7f47d07a8..4060a2af52 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));
@@ -277,35 +276,6 @@ Status Conv<T, NHWC>::UpdateState(OpKernelContext* context, bool bias_expected)
HIP_CALL_THROW(hipMalloc(&s_.b_zero, malloc_size));
HIP_CALL_THROW(hipMemsetAsync(s_.b_zero, 0, malloc_size, Stream(context)));
}
-
- if (!s_.cached_benchmark_fwd_results.contains(x_dims_miopen)) {
- miopenConvAlgoPerf_t perf;
- int algo_count = 1;
- const ROCMExecutionProvider* rocm_ep = static_cast<const ROCMExecutionProvider*>(this->Info().GetExecutionProvider());
- static constexpr int num_algos = MIOPEN_CONVOLUTION_FWD_ALGO_COUNT;
- size_t max_ws_size = rocm_ep->GetMiopenConvUseMaxWorkspace() ? GetMaxWorkspaceSize(GetMiopenHandle(context), s_, kAllAlgos, num_algos, rocm_ep->GetDeviceId())
- : AlgoSearchWorkspaceSize;
- IAllocatorUniquePtr<void> algo_search_workspace = GetTransientScratchBuffer<void>(max_ws_size);
- MIOPEN_RETURN_IF_ERROR(miopenFindConvolutionForwardAlgorithm(
- GetMiopenHandle(context),
- s_.x_tensor,
- s_.x_data,
- s_.w_desc,
- s_.w_data,
- s_.conv_desc,
- s_.y_tensor,
- s_.y_data,
- 1, // requestedAlgoCount
- &algo_count, // returnedAlgoCount
- &perf,
- 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});
- }
- const auto& perf = s_.cached_benchmark_fwd_results.at(x_dims_miopen);
- s_.fwd_algo = perf.fwd_algo;
- s_.workspace_bytes = perf.memory;
} else {
// set Y
s_.Y = context->Output(0, TensorShape(s_.y_dims));
@@ -319,6 +289,31 @@ Status Conv<T, NHWC>::UpdateState(OpKernelContext* context, bool bias_expected)
s_.y_data = reinterpret_cast<HipT*>(s_.Y->MutableData<T>());
}
}
+
+ miopenConvAlgoPerf_t perf;
+ int algo_count = 1;
+ const ROCMExecutionProvider* rocm_ep = static_cast<const ROCMExecutionProvider*>(this->Info().GetExecutionProvider());
+ static constexpr int num_algos = MIOPEN_CONVOLUTION_FWD_ALGO_COUNT;
+ size_t max_ws_size = rocm_ep->GetMiopenConvUseMaxWorkspace() ? GetMaxWorkspaceSize(GetMiopenHandle(context), s_, kAllAlgos, num_algos, rocm_ep->GetDeviceId())
+ : AlgoSearchWorkspaceSize;
+ IAllocatorUniquePtr<void> algo_search_workspace = GetTransientScratchBuffer<void>(max_ws_size);
+ MIOPEN_RETURN_IF_ERROR(miopenFindConvolutionForwardAlgorithm(
+ GetMiopenHandle(context),
+ s_.x_tensor,
+ s_.x_data,
+ s_.w_desc,
+ s_.w_data,
+ s_.conv_desc,
+ s_.y_tensor,
+ s_.y_data,
+ 1, // requestedAlgoCount
+ &algo_count, // returnedAlgoCount
+ &perf,
+ algo_search_workspace.get(),
+ max_ws_size,
+ false)); // Do not do exhaustive algo search.
+ s_.fwd_algo = perf.fwd_algo;
+ s_.workspace_bytes = perf.memory;
return Status::OK();
}
diff --git a/onnxruntime/core/providers/rocm/nn/conv.h b/onnxruntime/core/providers/rocm/nn/conv.h
index bc9846203e..d54218f258 100644
--- a/onnxruntime/core/providers/rocm/nn/conv.h
+++ b/onnxruntime/core/providers/rocm/nn/conv.h
@@ -108,9 +108,6 @@ class lru_unordered_map {
list_type lru_list_;
};
-// cached miopen descriptors
-constexpr size_t MAX_CACHED_ALGO_PERF_RESULTS = 10000;
-
template <typename AlgoPerfType>
struct MiopenConvState {
// if x/w dims changed, update algo and miopenTensors
@@ -148,9 +145,6 @@ 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};
-
// Some properties needed to support asymmetric padded Conv nodes
bool post_slicing_required;
TensorShapeVector slice_starts;
diff --git a/onnxruntime/core/providers/rocm/nn/conv_transpose.cc b/onnxruntime/core/providers/rocm/nn/conv_transpose.cc
index 7447113fdf..a662e35b2e 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;
@@ -126,35 +125,29 @@ 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)) {
- IAllocatorUniquePtr<void> algo_search_workspace = GetScratchBuffer<void>(AlgoSearchWorkspaceSize, context->GetComputeStream());
-
- miopenConvAlgoPerf_t perf;
- int algo_count = 1;
- MIOPEN_RETURN_IF_ERROR(miopenFindConvolutionBackwardDataAlgorithm(
- GetMiopenHandle(context),
- s_.x_tensor,
- x_data,
- s_.w_desc,
- w_data,
- s_.conv_desc,
- s_.y_tensor,
- y_data,
- 1,
- &algo_count,
- &perf,
- algo_search_workspace.get(),
- AlgoSearchWorkspaceSize,
- false));
- s_.cached_benchmark_bwd_results.insert(x_dims, {perf.bwd_data_algo, perf.memory});
- }
-
- const auto& perf = s_.cached_benchmark_bwd_results.at(x_dims);
- s_.bwd_data_algo = perf.bwd_data_algo;
- s_.workspace_bytes = perf.memory;
}
+ IAllocatorUniquePtr<void> algo_search_workspace = GetScratchBuffer<void>(AlgoSearchWorkspaceSize, context->GetComputeStream());
+ miopenConvAlgoPerf_t perf;
+ int algo_count = 1;
+ MIOPEN_RETURN_IF_ERROR(miopenFindConvolutionBackwardDataAlgorithm(
+ GetMiopenHandle(context),
+ s_.x_tensor,
+ x_data,
+ s_.w_desc,
+ w_data,
+ s_.conv_desc,
+ s_.y_tensor,
+ y_data,
+ 1,
+ &algo_count,
+ &perf,
+ algo_search_workspace.get(),
+ AlgoSearchWorkspaceSize,
+ false));
+ s_.bwd_data_algo = perf.bwd_data_algo;
+ s_.workspace_bytes = perf.memory;
+
// The following block will be executed in case there has been no change in the shapes of the
// input and the filter compared to the previous run
if (!y_data) {