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Cpu optimizations v2 #2514

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d32b7ab
avoiding shape copy, torch dynamo and torch autograd overheads
vthumbe1503 Dec 12, 2025
e724815
minor additional change
vthumbe1503 Dec 12, 2025
b725f5b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 12, 2025
7b031d0
changes done to remove the additional nvte_make_shape calls
vthumbe1503 Dec 23, 2025
1141d72
Merge branch 'main' into cpu_optimizations_v2
vthumbe1503 Dec 24, 2025
d6ac3f1
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 24, 2025
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69 changes: 63 additions & 6 deletions transformer_engine/pytorch/csrc/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -26,15 +26,17 @@ std::vector<size_t> convert_shape_back_from_fp4(const std::vector<size_t>& shape
return ret;
}

std::vector<size_t> getTensorShape(const at::Tensor& t) {
NVTEShape getTensorShape(const at::Tensor& t) { return convertTorchShape(t.sizes()); }

std::vector<size_t> getTensorShapeVector(const at::Tensor& t) {
std::vector<size_t> shape;
for (auto s : t.sizes()) {
shape.push_back(s);
}
return shape;
}

NVTEShape convertTorchShape(const c10::IntArrayRef torch_shape) {
NVTEShape convertTorchShape(const c10::IntArrayRef& torch_shape) {
NVTEShape ret;
ret.ndim = torch_shape.size();
constexpr int max_dimensions = sizeof(ret.data) / sizeof(size_t);
Expand Down Expand Up @@ -119,10 +121,7 @@ transformer_engine::TensorWrapper makeTransformerEngineTensor(

transformer_engine::TensorWrapper makeTransformerEngineTensor(at::Tensor tensor) {
transformer_engine::DType dtype = GetTransformerEngineDType(tensor.scalar_type());
std::vector<size_t> shape;
for (auto s : tensor.sizes()) {
shape.push_back(s);
}
NVTEShape shape = getTensorShape(tensor);
return makeTransformerEngineTensor(tensor.data_ptr(), shape, dtype);
}

Expand Down Expand Up @@ -178,6 +177,41 @@ transformer_engine::TensorWrapper makeTransformerEngineTensor(
return ret;
}

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, const NVTEShape& shape, const transformer_engine::DType type, void* amax_ptr,
void* scale_ptr, void* scale_inv_ptr, const NVTEShape& scale_inv_shape,
NVTEScalingMode scaling_mode) {
TensorWrapper ret(scaling_mode);
ret.set_rowwise_data(data_ptr, type, shape);
const size_t meta_shape_data[1] = {1};
NVTEShape meta_shape;
meta_shape.ndim = 1;
meta_shape.data[0] = 1;
ret.set_amax(amax_ptr, DType::kFloat32, meta_shape);
ret.set_scale(scale_ptr, DType::kFloat32, meta_shape);
auto scale_inv_dtype =
(scaling_mode == NVTE_MXFP8_1D_SCALING) ? DType::kFloat8E8M0 : DType::kFloat32;
ret.set_rowwise_scale_inv(scale_inv_ptr, scale_inv_dtype, scale_inv_shape);
return ret;
}

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, const std::vector<size_t>& shape, const transformer_engine::DType type,
void* amax_ptr, void* scale_ptr, void* scale_inv_ptr, const NVTEShape& scale_inv_shape,
NVTEScalingMode scaling_mode) {
TensorWrapper ret(scaling_mode);
ret.set_rowwise_data(data_ptr, type, shape);
NVTEShape meta_shape;
meta_shape.ndim = 1;
meta_shape.data[0] = 1;
ret.set_amax(amax_ptr, DType::kFloat32, meta_shape);
ret.set_scale(scale_ptr, DType::kFloat32, meta_shape);
auto scale_inv_dtype =
(scaling_mode == NVTE_MXFP8_1D_SCALING) ? DType::kFloat8E8M0 : DType::kFloat32;
ret.set_rowwise_scale_inv(scale_inv_ptr, scale_inv_dtype, scale_inv_shape);
return ret;
}

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, void* columnwise_data_ptr, const std::vector<size_t>& shape,
const std::vector<size_t>& columnwise_shape, const transformer_engine::DType type,
Expand All @@ -199,6 +233,29 @@ transformer_engine::TensorWrapper makeTransformerEngineTensor(
return ret;
}

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, void* columnwise_data_ptr, const NVTEShape& shape,
const NVTEShape& columnwise_shape, const transformer_engine::DType type, void* amax_ptr,
void* scale_ptr, void* scale_inv_ptr, void* columnwise_scale_inv_ptr,
const NVTEShape& scale_inv_shape, const NVTEShape& columnwise_scale_inv_shape,
NVTEScalingMode scaling_mode) {
TensorWrapper ret(scaling_mode);
ret.set_rowwise_data(data_ptr, type, shape);
ret.set_columnwise_data(columnwise_data_ptr, type, columnwise_shape);
NVTEShape meta_shape;
meta_shape.ndim = 1;
meta_shape.data[0] = 1;
ret.set_amax(amax_ptr, DType::kFloat32, meta_shape);
ret.set_scale(scale_ptr, DType::kFloat32, meta_shape);
auto scale_inv_dtype = (scaling_mode == NVTE_MXFP8_1D_SCALING) ? DType::kFloat8E8M0
: (scaling_mode == NVTE_NVFP4_1D_SCALING) ? DType::kFloat8E4M3
: DType::kFloat32;
ret.set_rowwise_scale_inv(scale_inv_ptr, scale_inv_dtype, scale_inv_shape);
ret.set_columnwise_scale_inv(columnwise_scale_inv_ptr, scale_inv_dtype,
columnwise_scale_inv_shape);
return ret;
}

transformer_engine::TensorWrapper makeTransformerEngineTensor(at::Tensor tensor, at::Tensor amax,
const at::Tensor scale,
at::Tensor scale_inv,
Expand Down
29 changes: 27 additions & 2 deletions transformer_engine/pytorch/csrc/common.h
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,12 @@ class NoneQuantizer : public Quantizer {
std::pair<TensorWrapper, py::object> create_tensor(const std::vector<size_t>& shape, DType dtype,
at::Tensor data) const;

std::pair<TensorWrapper, py::object> create_tensor(const NVTEShape& shape, DType dtype) const;

/*! @brief Construct a tensor with pre-initialized data */
std::pair<TensorWrapper, py::object> create_tensor(const NVTEShape& shape, DType dtype,
at::Tensor data) const;

std::pair<TensorWrapper, py::object> convert_and_update_tensor(py::object tensor) const override;

void quantize(const TensorWrapper& input, TensorWrapper& out,
Expand Down Expand Up @@ -339,7 +345,9 @@ class NVFP4Quantizer : public Quantizer {

std::unique_ptr<Quantizer> convert_quantizer(py::handle quantizer);

std::vector<size_t> getTensorShape(const at::Tensor& t);
NVTEShape getTensorShape(const at::Tensor& t);

std::vector<size_t> getTensorShapeVector(const at::Tensor& t);

transformer_engine::DType getTransformerEngineFP8Type(bool e4m3_if_hybrid,
const std::string& fp8_recipe);
Expand Down Expand Up @@ -432,6 +440,16 @@ transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* amax_ptr, void* scale_ptr, void* scale_inv_ptr, std::vector<size_t> scale_inv_shape = {1},
NVTEScalingMode scaling_mode = NVTE_DELAYED_TENSOR_SCALING);

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, const NVTEShape& shape, const transformer_engine::DType type, void* amax_ptr,
void* scale_ptr, void* scale_inv_ptr, const NVTEShape& scale_inv_shape,
NVTEScalingMode scaling_mode = NVTE_DELAYED_TENSOR_SCALING);

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, const std::vector<size_t>& shape, const transformer_engine::DType type,
void* amax_ptr, void* scale_ptr, void* scale_inv_ptr, const NVTEShape& scale_inv_shape,
NVTEScalingMode scaling_mode = NVTE_DELAYED_TENSOR_SCALING);

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, void* columnwise_data_ptr, const std::vector<size_t>& shape,
const std::vector<size_t>& columnwise_shape, const transformer_engine::DType type,
Expand All @@ -440,6 +458,13 @@ transformer_engine::TensorWrapper makeTransformerEngineTensor(
const std::vector<size_t>& columnwise_scale_inv_shape = {1},
NVTEScalingMode scaling_mode = NVTE_DELAYED_TENSOR_SCALING);

transformer_engine::TensorWrapper makeTransformerEngineTensor(
void* data_ptr, void* columnwise_data_ptr, const NVTEShape& shape,
const NVTEShape& columnwise_shape, const transformer_engine::DType type, void* amax_ptr,
void* scale_ptr, void* scale_inv_ptr, void* columnwise_scale_inv_ptr,
const NVTEShape& scale_inv_shape, const NVTEShape& columnwise_scale_inv_shape,
NVTEScalingMode scaling_mode = NVTE_DELAYED_TENSOR_SCALING);

transformer_engine::TensorWrapper makeTransformerEngineTensor(void* data_ptr,
const NVTEShape& shape,
const transformer_engine::DType type);
Expand Down Expand Up @@ -479,7 +504,7 @@ std::vector<size_t> convertShape(const NVTEShape& shape);

size_t roundup(const size_t value, const size_t multiple);

NVTEShape convertTorchShape(const c10::IntArrayRef torch_shape);
NVTEShape convertTorchShape(const c10::IntArrayRef& torch_shape);

std::vector<size_t> convert_shape_back_from_fp4(const std::vector<size_t>& shape, bool transpose);

Expand Down
10 changes: 7 additions & 3 deletions transformer_engine/pytorch/csrc/extensions/attention.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -478,10 +478,14 @@ std::vector<py::object> fused_attn_bwd(
auto cu_seqlens_kv_padded_sizes = cu_seqlens_kv_padded.value().sizes().vec();
std::vector<size_t> cu_seqlens_kv_padded_shape{cu_seqlens_kv_padded_sizes.begin(),
cu_seqlens_kv_padded_sizes.end()};
te_cu_seqlens_q_padded = makeTransformerEngineTensor(cu_seqlens_q_padded.value().data_ptr(),
cu_seqlens_q_padded_shape, DType::kInt32);
te_cu_seqlens_q_padded = makeTransformerEngineTensor(
cu_seqlens_q_padded.value().data_ptr(),
nvte_make_shape(cu_seqlens_q_padded_shape.data(), cu_seqlens_q_padded_shape.size()),
DType::kInt32);
te_cu_seqlens_kv_padded = makeTransformerEngineTensor(
cu_seqlens_kv_padded.value().data_ptr(), cu_seqlens_kv_padded_shape, DType::kInt32);
cu_seqlens_kv_padded.value().data_ptr(),
nvte_make_shape(cu_seqlens_kv_padded_shape.data(), cu_seqlens_kv_padded_shape.size()),
DType::kInt32);
}

// convert auxiliary tensors from forward to NVTETensors
Expand Down
6 changes: 3 additions & 3 deletions transformer_engine/pytorch/csrc/extensions/bias.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ std::vector<py::object> bgrad_quantize(const at::Tensor &grad_output, py::handle
// Grad output tensor
auto grad_output_torch = grad_output.contiguous();
const TensorWrapper &grad_output_nvte = makeTransformerEngineTensor(grad_output_torch);
const auto shape = getTensorShape(grad_output_torch);
const auto shape = getTensorShapeVector(grad_output_torch);
auto grad_output_dtype = GetTransformerEngineDType(grad_output_torch.scalar_type());

// Construct grad bias tensor
Expand Down Expand Up @@ -116,11 +116,11 @@ std::vector<py::object> dact_dbias(
// Grad output and activation input tensors
grad_output_torch = grad_output_torch.contiguous();
const TensorWrapper &grad_output_nvte = makeTransformerEngineTensor(grad_output_torch);
const auto output_shape = getTensorShape(grad_output_torch);
const auto output_shape = getTensorShapeVector(grad_output_torch);
auto grad_output_dtype = GetTransformerEngineDType(grad_output_torch.scalar_type());
act_input_torch = act_input_torch.contiguous();
const TensorWrapper &act_input_nvte = makeTransformerEngineTensor(act_input_torch);
const auto input_shape = getTensorShape(act_input_torch);
const auto input_shape = getTensorShapeVector(act_input_torch);

// Construct tensors
auto quantizer_cpp = convert_quantizer(quantizer_py);
Expand Down
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