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[pyTorch] Fix the compilation warnings #2663

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[pyTorch] Fix the compilation warnings #2663

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43e7179
Fix the compilation warnings for the PyTorch extension
ptrendx Feb 9, 2026
5bd16dc
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ptrendx Feb 9, 2026
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7 changes: 4 additions & 3 deletions transformer_engine/common/common.h
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Original file line number Diff line number Diff line change
Expand Up @@ -313,6 +313,9 @@ struct GroupedTensor {
SimpleTensor columnwise_amax;
SimpleTensor scale; // for FP8-DS only

NVTEScalingMode scaling_mode;
size_t num_tensors;

// Shape information (OPTIONAL - empty if dimension is uniform across all tensors)
// first_dims[i] = first dimension of tensor i (empty if all tensors have same first dim)
// last_dims[i] = last dimension of tensor i (empty if all tensors have same last dim)
Expand All @@ -330,8 +333,6 @@ struct GroupedTensor {
// Always 2D with positive dimensions
NVTEShape logical_shape;

NVTEScalingMode scaling_mode;
size_t num_tensors;
NVTEGroupedTensor nvte_tensor;

GroupedTensor(NVTEScalingMode scaling_mode, size_t num_tensors)
Expand All @@ -342,12 +343,12 @@ struct GroupedTensor {
amax(),
columnwise_amax(),
scale(),
scaling_mode(scaling_mode),
num_tensors(num_tensors),
first_dims(nullptr, std::vector<size_t>{0}, DType::kInt64),
last_dims(nullptr, std::vector<size_t>{0}, DType::kInt64),
tensor_offsets(nullptr, std::vector<size_t>{0}, DType::kInt64),
logical_shape(nvte_make_shape(nullptr, 1)),
scaling_mode(scaling_mode),
nvte_tensor(0) {}

explicit operator NVTEGroupedTensor() const noexcept { return nvte_tensor; }
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1 change: 0 additions & 1 deletion transformer_engine/common/fused_attn/fused_attn_f16_arbitrary_seqlen.cu
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Expand Up @@ -250,7 +250,6 @@ void fused_attn_arbitrary_seqlen_fwd_impl(
fe::graph::SDPA_attributes sdpa_options;
sdpa_options = fe::graph::SDPA_attributes()
.set_name("flash_attention")
.set_is_inference(false)
.set_generate_stats(generate_stats)
.set_causal_mask(is_causal)
.set_causal_mask_bottom_right(is_bottom_right)
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2 changes: 1 addition & 1 deletion transformer_engine/common/fused_attn/fused_attn_fp8.cu
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Expand Up @@ -1810,7 +1810,7 @@ void fused_attn_fp8_fwd_impl_v1(
fe::graph::SDPA_fp8_attributes sdpa_options;
sdpa_options = fe::graph::SDPA_fp8_attributes()
.set_name("sdpa_fp8")
.set_is_inference(false)
.set_generate_stats(true)
.set_causal_mask(is_causal)
.set_attn_scale(attn_scale);

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2 changes: 2 additions & 0 deletions transformer_engine/pytorch/csrc/extensions.h
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Expand Up @@ -548,6 +548,7 @@ class CommOverlap : torch::CustomClassHolder, public transformer_engine::CommOve

~CommOverlap() {}

using transformer_engine::CommOverlapCore::copy_into_buffer;
void copy_into_buffer(const at::Tensor &input, bool local_chunk = false);

at::Tensor get_buffer(bool local_chunk = false,
Expand All @@ -569,6 +570,7 @@ class CommOverlapP2P : torch::CustomClassHolder, public transformer_engine::Comm

~CommOverlapP2P() {}

using transformer_engine::CommOverlapP2PBase::copy_into_buffer;
void copy_into_buffer(const at::Tensor &input, bool local_chunk = false);

at::Tensor get_buffer(bool local_chunk = false,
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12 changes: 8 additions & 4 deletions transformer_engine/pytorch/csrc/extensions/pybind.cpp
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Expand Up @@ -492,8 +492,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
py::arg("comm_cga_size") = 2, py::arg("gemm_priority") = 0, py::arg("comm_priority") = 0,
py::arg("num_comm_sm") = 16, py::arg("set_sm_margin") = true,
py::arg("atomic_gemm") = false, py::arg("rs_overlap_first_gemm") = false)
.def("copy_into_buffer", &CommOverlap::copy_into_buffer, py::arg("input"),
py::arg("local_chunk") = false)
.def("copy_into_buffer",
static_cast<void (CommOverlap::*)(const at::Tensor &, bool)>(
&CommOverlap::copy_into_buffer),
py::arg("input"), py::arg("local_chunk") = false)
.def("get_buffer", &CommOverlap::get_buffer, py::arg("local_chunk") = false,
py::arg("shape") = std::nullopt)
.def("get_communication_stream", &CommOverlap::get_communication_stream);
Expand All @@ -510,8 +512,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
py::arg("gemm_priority") = 0, py::arg("comm_priority") = 0, py::arg("num_comm_sm") = 1,
py::arg("set_sm_margin") = false, py::arg("atomic_gemm") = false,
py::arg("use_ce") = true, py::arg("aggregate") = false)
.def("copy_into_buffer", &CommOverlapP2P::copy_into_buffer, py::arg("input"),
py::arg("local_chunk") = false)
.def("copy_into_buffer",
static_cast<void (CommOverlapP2P::*)(const at::Tensor &, bool)>(
&CommOverlapP2P::copy_into_buffer),
py::arg("input"), py::arg("local_chunk") = false)
.def("get_buffer", &CommOverlapP2P::get_buffer, py::arg("local_chunk") = false,
py::arg("shape") = std::nullopt)
.def("get_communication_stream", &CommOverlapP2P::get_communication_stream);
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