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mxfp8: use nvte_multi_tensor_quantize #588

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mxfp8: use nvte_multi_tensor_quantize #588

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22 changes: 22 additions & 0 deletions transformer_engine/pytorch/csrc/extensions/cast.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -132,6 +132,14 @@ void multi_tensor_quantize_impl(const std::vector<TensorWrapper> &input_list,
}
}

// Check if all quantizers are MXFP8 (for multi-stream quantize)
bool with_mxfp8_multi_stream = false;
if (!with_fused_kernel) {
with_mxfp8_multi_stream = std::all_of(
quantizer_py_list.begin(), quantizer_py_list.end(),
[](const py::handle &q) { return detail::IsMXFP8Quantizers(q.ptr()); });
}

// Launch TE kernel
if (with_fused_kernel) {
// Fused kernel for multi-tensor quantize
Expand All @@ -145,6 +153,20 @@ void multi_tensor_quantize_impl(const std::vector<TensorWrapper> &input_list,
nvte_multi_cast_transpose(nvte_tensor_input_list.size(), nvte_tensor_input_list.data(),
nvte_tensor_output_list.data(), at::cuda::getCurrentCUDAStream());
});
} else if (with_mxfp8_multi_stream) {
// Multi-stream quantize for MXFP8
std::vector<NVTETensor> nvte_tensor_input_list;
std::vector<NVTETensor> nvte_tensor_output_list;
for (size_t i = 0; i < num_tensors; ++i) {
nvte_tensor_input_list.push_back(input_list[i].data());
nvte_tensor_output_list.push_back(output_list[i].data());
}
QuantizationConfigWrapper quant_config;
NVTE_SCOPED_GIL_RELEASE({
nvte_multi_tensor_quantize(nvte_tensor_input_list.data(), nvte_tensor_output_list.data(),
quant_config, num_tensors,
at::cuda::getCurrentCUDAStream());
});
} else {
// Quantize kernels individually
for (size_t i = 0; i < num_tensors; ++i) {
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