WildfireDetection/apex/csrc/megatron/scaled_masked_softmax.cpp

/* coding=utf-8
 * Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include <cuda_fp16.h>
#include <torch/extension.h>
#include <vector>

namespace multihead_attn {
namespace fused_softmax {
namespace scaled_masked_softmax {

torch::Tensor fwd_cuda(
    torch::Tensor const& input,
    torch::Tensor const& mask,
    float scale_factor);

torch::Tensor bwd_cuda(
    torch::Tensor const& output_grads,
    torch::Tensor const& softmax_results,
    float scale_factor);

int get_batch_per_block_cuda(
    int query_seq_len,
    int key_seq_len,
    int batches,
    int attn_heads);

torch::Tensor fwd(
    torch::Tensor & input,
    torch::Tensor & mask,
    float scale_factor) {
  TORCH_CHECK(input.dim() == 4, "expected 4D tensor");
  TORCH_CHECK((input.scalar_type() == at::ScalarType::Half) ||
	     (input.scalar_type() == at::ScalarType::BFloat16),
      "Only fp16 and bf16 are supported");
  TORCH_CHECK(mask.dim() == 4, "expected 4D tensor");
  if (!input.is_contiguous())
	  input = input.contiguous();
  if (!mask.is_contiguous())
	  mask = mask.contiguous();

  return fwd_cuda(input, mask, scale_factor);
}

torch::Tensor bwd(
    torch::Tensor & output_grads,
    torch::Tensor & softmax_results,
    float scale_factor) {

  TORCH_CHECK(output_grads.dim() == 4, "expected 3D tensor");
  TORCH_CHECK(softmax_results.dim() == 4, "expected 3D tensor");

  TORCH_CHECK((output_grads.scalar_type() == at::ScalarType::Half) ||
	     (output_grads.scalar_type() == at::ScalarType::BFloat16),
      "Only fp16 and bf16 are supported");
  TORCH_CHECK((softmax_results.scalar_type() == at::ScalarType::Half) ||
	     (softmax_results.scalar_type() == at::ScalarType::BFloat16),
      "Only fp16 and bf16 are supported");
  if (!output_grads.is_contiguous())
	  output_grads = output_grads.contiguous();
  if (!softmax_results.is_contiguous())
	  softmax_results = softmax_results.contiguous();

  return bwd_cuda(output_grads, softmax_results, scale_factor);
}

int get_batch_per_block(
    int query_seq_len,
    int key_seq_len,
    int batches,
    int attn_heads) {
    return get_batch_per_block_cuda(query_seq_len, key_seq_len, batches, attn_heads);
}

} // end namespace scaled_masked_softmax
} // end namespace fused_softmax
} // end namespace multihead_attn

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward",
        &multihead_attn::fused_softmax::scaled_masked_softmax::fwd,
	"Self Multihead Attention scaled, time masked softmax -- Forward.");

  m.def("backward",
        &multihead_attn::fused_softmax::scaled_masked_softmax::bwd,
	"Self Multihead Attention scaled, time masked softmax -- Backward.");

  m.def("get_batch_per_block",
        &multihead_attn::fused_softmax::scaled_masked_softmax::get_batch_per_block,
        "Return Batch per block size."
  );
}