/* 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 <ATen/ATen.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cuda_profiler_api.h>
#include <ATen/cuda/CUDAContext.h>
#include <torch/extension.h>
#include "fused_triang_softmax.h"
#include "type_shim.h"

namespace multihead_attn {
namespace fused_softmax {
namespace scaled_upper_triang_masked_softmax {

torch::Tensor fwd_cuda(
    torch::Tensor const& input,
    float scale_factor)
{
    // input is a 3d tensor with dimensions [attn_batches, seq_len, seq_len]
    const int attn_batches = input.size(0);
    const int seq_len = input.size(1);
    TORCH_INTERNAL_ASSERT(seq_len <= 2048);

    // Output
    auto act_options = input.options().requires_grad(false);
    torch::Tensor softmax_results =
        torch::empty({attn_batches, seq_len, seq_len}, act_options);

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

    DISPATCH_HALF_AND_BFLOAT(
        input.scalar_type(),
        "dispatch_scaled_upper_triang_masked_softmax_forward",
        dispatch_scaled_upper_triang_masked_softmax_forward<scalar_t, scalar_t, float>(
            reinterpret_cast<scalar_t*>(softmax_results_ptr),
            reinterpret_cast<const scalar_t*>(input_ptr),
            scale_factor,
            seq_len,
            seq_len,
            attn_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 3d tensor with dimensions [attn_batches, seq_len, seq_len]
    const int attn_batches = output_grads.size(0);
    const int seq_len = output_grads.size(1);
    TORCH_INTERNAL_ASSERT(output_grads.size(1) == output_grads.size(2));

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

    //Softmax Grad
    DISPATCH_HALF_AND_BFLOAT(
        output_grads_.scalar_type(),
        "dispatch_scaled_upper_triang_masked_softmax_backward",
        dispatch_scaled_upper_triang_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,
            seq_len,
            seq_len,
            attn_batches);
    );

    //backward pass is completely in-place
    return output_grads;
}
}
}
}