text: /* coding=utf-8
   * Copyright 2021 TUNiB Inc.
   * Copyright (c) 2020, 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 <torch/torch.h>
  #include <vector>
  #include <cassert>
  #include <stdio.h>
  #include <compat.h>
  
  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 const& input,
      torch::Tensor const& mask,
      float scale_factor) {
      AT_ASSERTM(input.dim() == 4, "expected 4D tensor");
      AT_ASSERTM((input.scalar_type() == at::ScalarType::Half) ||
                 (input.scalar_type() == at::ScalarType::BFloat16),
                 "Only fp16 and bf16 are supported");
      AT_ASSERTM(mask.dim() == 4, "expected 4D tensor");
  
      return fwd_cuda(input, mask, scale_factor);
  }
  
  torch::Tensor bwd(
      torch::Tensor const& output_grads,
      torch::Tensor const& softmax_results,
      float scale_factor) {
  
      AT_ASSERTM(output_grads.dim() == 4, "expected 3D tensor");
      AT_ASSERTM(softmax_results.dim() == 4, "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);
  }
  
  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
  
  namespace scaled_upper_triang_masked_softmax {
  
  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);
  }
  
  } // end namespace scaled_upper_triang_masked_softmax
  } // end namespace fused_softmax
  } // end namespace multihead_attn
  
  torch::Tensor ngram_repeat_block_cuda_forward(torch::Tensor tokens,
          torch::Tensor lprobs, int bsz,
          int step, int beam_size,
          int no_repeat_ngram_size);
  
  #define CHECK_CUDA(x) \
    TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
  #define CHECK_CONTIGUOUS(x) \
    TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
  #define CHECK_INPUT(x) \
    CHECK_CUDA(x);       \
    CHECK_CONTIGUOUS(x)
  
  // Input check and call to CUDA OP
  // Backward method not required
  torch::Tensor ngram_repeat_block_forward(torch::Tensor tokens,
          torch::Tensor lprobs, int bsz,
          int step, int beam_size,
          int no_repeat_ngram_size) {
      CHECK_INPUT(tokens);
      CHECK_INPUT(lprobs);
      assert(bsz > 0);
      assert(step >= 0);
      assert(beam_size > 0);
      assert(no_repeat_ngram_size > 0);
  
      return ngram_repeat_block_cuda_forward(tokens, lprobs, bsz, step, beam_size,
                                             no_repeat_ngram_size);
  }
  
  PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
      m.def("scaled_masked_softmax_forward",
            &multihead_attn::fused_softmax::scaled_masked_softmax::fwd,
            "Self Multihead Attention scaled, time masked softmax -- Forward.");
      m.def("scaled_masked_softmax_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.");
      m.def("scaled_upper_triang_masked_softmax_forward",
            &multihead_attn::fused_softmax::scaled_upper_triang_masked_softmax::fwd,
            "Self Multihead Attention scaled, time masked softmax -- Forward.");
      m.def("scaled_upper_triang_masked_softmax_backward",
            &multihead_attn::fused_softmax::scaled_upper_triang_masked_softmax::bwd,
            "Self Multihead Attention scaled, time masked softmax -- Backward.");
      m.def("ngram_repeat_block_forward", &ngram_repeat_block_forward,
            "No Repeat Ngram Block forward (CUDA)");
  }