/* coding=utf-8
 * Copyright (c) 2024, 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 <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_fp16.h>
#ifndef __HIP_PLATFORM_HCC__
#include <cuda_profiler_api.h>
#endif
#include <cuda_runtime.h>
#include <torch/extension.h>
#include "scaled_masked_softmax.h"
#include "type_shim.h"

namespace multihead_attn {
namespace fused_softmax {
namespace scaled_masked_softmax {

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

torch::Tensor fwd_cuda(torch::Tensor const& input, torch::Tensor const& mask, float scale_factor)
{
    // input is a 4d tensor with dimensions [batches, attn_heads, seq_len, seq_len]
    const int batches = input.size(0);
    const int pad_batches = mask.size(0);
    const int attn_heads = input.size(1);
    const int query_seq_len = input.size(2);
    const int key_seq_len = input.size(3);
    TORCH_INTERNAL_ASSERT(key_seq_len <= 2048);
    TORCH_INTERNAL_ASSERT(query_seq_len > 1);
    TORCH_INTERNAL_ASSERT(pad_batches == 1 || pad_batches == batches);
    TORCH_INTERNAL_ASSERT(mask.size(1) == 1);
    TORCH_INTERNAL_ASSERT(mask.size(2) == query_seq_len);
    TORCH_INTERNAL_ASSERT(mask.size(3) == key_seq_len);

    // Output
    auto act_options = input.options().requires_grad(false);
    torch::Tensor softmax_results =
        torch::empty({batches, attn_heads, query_seq_len, key_seq_len}, act_options);

    // Softmax Intermediate Result Ptr
    void* input_ptr = static_cast<void*>(input.data_ptr());
    void* mask_ptr = static_cast<void*>(mask.data_ptr());
    void* softmax_results_ptr = static_cast<void*>(softmax_results.data_ptr());

    DISPATCH_HALF_AND_BFLOAT(input.scalar_type(),
                             "dispatch_scaled_masked_softmax_forward",
                             dispatch_scaled_masked_softmax_forward<scalar_t, scalar_t, float>(
                                 reinterpret_cast<scalar_t*>(softmax_results_ptr),
                                 reinterpret_cast<const scalar_t*>(input_ptr),
                                 reinterpret_cast<const uint8_t*>(mask_ptr),
                                 scale_factor,
                                 query_seq_len,
                                 key_seq_len,
                                 batches,
                                 attn_heads,
                                 pad_batches););
    return softmax_results;
}

torch::Tensor bwd_cuda(torch::Tensor const& output_grads_,
                       torch::Tensor const& softmax_results_,
                       float scale_factor)
{
    auto output_grads = output_grads_.contiguous();
    auto softmax_results = softmax_results_.contiguous();

    // output grads is a 4d tensor with dimensions [batches, attn_heads, seq_len, seq_len]
    const int batches = output_grads.size(0);
    const int attn_heads = output_grads.size(1);
    const int query_seq_len = output_grads.size(2);
    const int key_seq_len = output_grads.size(3);

    void* output_grads_ptr = static_cast<void*>(output_grads.data_ptr());

    // Softmax Grad
    DISPATCH_HALF_AND_BFLOAT(output_grads_.scalar_type(),
                             "dispatch_scaled_masked_softmax_backward",
                             dispatch_scaled_masked_softmax_backward<scalar_t, scalar_t, float>(
                                 reinterpret_cast<scalar_t*>(output_grads_ptr),
                                 reinterpret_cast<scalar_t*>(output_grads_ptr),
                                 reinterpret_cast<scalar_t const*>(softmax_results.data_ptr()),
                                 scale_factor,
                                 query_seq_len,
                                 key_seq_len,
                                 batches,
                                 attn_heads););

    // backward pass is completely in-place
    return output_grads;
}
}  // namespace scaled_masked_softmax
}  // namespace fused_softmax
}  // namespace multihead_attn