Making large AI models cheaper, faster and more accessible
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/*This code from NVIDIA Megatron:
* with minor changes. */
#include <cuda_fp16.h>
#include <torch/extension.h>
#include <vector>
torch::Tensor fwd_cuda(torch::Tensor const& input, float scale_factor);
torch::Tensor bwd_cuda(torch::Tensor const& output_grads,
torch::Tensor const& softmax_results,
float scale_factor);
torch::Tensor fwd(torch::Tensor const& input, float scale_factor) {
AT_ASSERTM(input.dim() == 3, "expected 3D tensor");
AT_ASSERTM((input.scalar_type() == at::ScalarType::Half) ||
(input.scalar_type() == at::ScalarType::BFloat16),
"Only fp16 and bf16 are supported");
return fwd_cuda(input, scale_factor);
}
torch::Tensor bwd(torch::Tensor const& output_grads,
torch::Tensor const& softmax_results, float scale_factor) {
AT_ASSERTM(output_grads.dim() == 3, "expected 3D tensor");
AT_ASSERTM(softmax_results.dim() == 3, "expected 3D tensor");
AT_ASSERTM((output_grads.scalar_type() == at::ScalarType::Half) ||
(output_grads.scalar_type() == at::ScalarType::BFloat16),
"Only fp16 and bf16 are supported");
AT_ASSERTM((softmax_results.scalar_type() == at::ScalarType::Half) ||
(softmax_results.scalar_type() == at::ScalarType::BFloat16),
"Only fp16 and bf16 are supported");
return bwd_cuda(output_grads, softmax_results, scale_factor);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &fwd,
"Self Multihead Attention scaled, time masked softmax -- Forward.");
m.def("backward", &bwd,
"Self Multihead Attention scaled, time masked softmax -- Backward.");
}