zhangyiss/mlx
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx.git synced 2025-12-16 01:49:05 +08:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Turn off tf32

Browse Source
This commit is contained in:
Cheng
2025-07-19 19:49:29 -07:00
parent 6444b29651
commit 0430a6a74a
2 changed files with 22 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
mlx/backend/cuda/conv.cpp
View File

@@ -80,7 +80,8 @@ inline cudnn_frontend::Tensor build_tensor(int64_t id, const array& x) {
} }
cudnn_frontend::EngineConfigList get_engine_configs( cudnn_frontend::EngineConfigList get_engine_configs(
const cudnnBackendDescriptorType_t& backend_type, cudnnBackendDescriptorType_t backend_type,
Dtype dtype,
cudnn_frontend::OperationGraph& op_graph, cudnn_frontend::OperationGraph& op_graph,
bool use_fallback = false) { bool use_fallback = false) {
cudnn_frontend::GeneratorSource source; cudnn_frontend::GeneratorSource source;
@@ -103,7 +104,24 @@ cudnn_frontend::EngineConfigList get_engine_configs(
} }
cudnn_frontend::EngineConfigGenerator generator(1, &source); cudnn_frontend::EngineConfigGenerator generator(1, &source);
return generator.generate_engine_config(op_graph); auto configs = generator.generate_engine_config(op_graph);
cudnn_frontend::EngineConfigList filtered_configs;
cudnn_frontend::filter(configs, filtered_configs, [dtype](auto c) {
if (cudnn_frontend::hasNumericalNote<
CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(c)) {
return true;
}
// In PyTorch, tf32 seems to be always turned off for convolution even with
// "torch.backends.cudnn.allow_tf32 = True", so we are disabling tf32 too to
// keep results same.
if (dtype == float32 &&
cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_TENSOR_CORE>(c)) {
return true;
}
return false;
});
return filtered_configs;
} }
bool execute_plan( bool execute_plan(
@@ -238,12 +256,12 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
.build(); .build();
// Try to run plans based on heuristics. // Try to run plans based on heuristics.
auto configs = get_engine_configs(backend_type, op_graph); auto configs = get_engine_configs(backend_type, in.dtype(), op_graph);
if (execute_plans(encoder, configs, op_graph.getTag(), in, wt, out)) { if (execute_plans(encoder, configs, op_graph.getTag(), in, wt, out)) {
return; return;
} }
// Then try fallback plans. // Then try fallback plans.
configs = get_engine_configs(backend_type, op_graph, /* use_fallback */ true); configs = get_engine_configs(backend_type, in.dtype(), op_graph);
if (execute_plans(encoder, configs, op_graph.getTag(), in, wt, out)) { if (execute_plans(encoder, configs, op_graph.getTag(), in, wt, out)) {
return; return;
} }

5
python/tests/cuda_skip.py
View File

@@ -15,16 +15,11 @@ cuda_skip = {
"TestOps.test_hadamard_grad_vmap", "TestOps.test_hadamard_grad_vmap",
# Convolutions NYI # Convolutions NYI
"TestConv.test_1d_conv_with_2d", "TestConv.test_1d_conv_with_2d",
"TestConv.test_asymmetric_padding",
"TestConv.test_conv_1d_groups_flipped", "TestConv.test_conv_1d_groups_flipped",
"TestConv.test_conv_general_flip_grad", "TestConv.test_conv_general_flip_grad",
"TestConv.test_conv_groups_grad", "TestConv.test_conv_groups_grad",
"TestConv.test_numpy_conv",
"TestConv.test_torch_conv_1D",
"TestConv.test_torch_conv_1D_grad", "TestConv.test_torch_conv_1D_grad",
"TestConv.test_torch_conv_2D",
"TestConv.test_torch_conv_2D_grad", "TestConv.test_torch_conv_2D_grad",
"TestConv.test_torch_conv_3D",
"TestConv.test_torch_conv_3D_grad", "TestConv.test_torch_conv_3D_grad",
"TestConv.test_torch_conv_depthwise", "TestConv.test_torch_conv_depthwise",
"TestConv.test_torch_conv_general", "TestConv.test_torch_conv_general",