/*************************************************************************
 * Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 *
 * See LICENSE for license information.
 ************************************************************************/

#include "jax/csrc/extensions.h"
#include "transformer_engine/transpose.h"

namespace transformer_engine {
namespace jax {

void TransposeImpl(void *input, size_t rows, size_t cols, DType dtype, cudaStream_t stream,
                   void *output) {
    auto input_shape = std::vector<size_t>{rows, cols};
    auto output_shape = std::vector<size_t>{cols, rows};

    auto input_tensor = TensorWrapper(input, input_shape, dtype);
    auto transposed_tensor = TensorWrapper(output, output_shape, dtype);

    nvte_transpose(input_tensor.data(), transposed_tensor.data(), stream);
}

void Transpose(cudaStream_t stream, void **buffers, const char *opaque, size_t opaque_len) {
    void *input = buffers[0];
    void *output = buffers[1];

    const auto &desc = *UnpackOpaque<CustomCallCommonDescriptor>(opaque, opaque_len);
    auto rows = desc.shape.dims[0];
    auto cols = desc.shape.dims[1];
    assert(desc.in_dtype == desc.out_dtype);
    auto dtype = desc.out_dtype;

    TransposeImpl(input, rows, cols, dtype, stream, output);
}

void CastTranspose(cudaStream_t stream, void **buffers, const char *opaque, size_t opaque_len) {
    auto *input = buffers[0];
    float *amax = reinterpret_cast<float *>(buffers[1]);
    float *scale = reinterpret_cast<float *>(buffers[2]);
    float *scale_inv = reinterpret_cast<float *>(buffers[3]);
    auto *input_cast = buffers[4];
    auto *input_cast_trans = buffers[5];
    float *amax_out = reinterpret_cast<float *>(buffers[6]);
    assert(amax == amax_out);

    const auto &desc = *UnpackOpaque<CustomCallCommonDescriptor>(opaque, opaque_len);
    if (!use_fp8(desc.out_dtype)) {
        scale = nullptr;
        scale_inv = nullptr;
        amax_out = nullptr;
    }
    auto m = desc.shape.dims[0];
    auto n = desc.shape.dims[1];
    auto input_shape = std::vector<size_t>{m, n};
    auto input_trans_shape = std::vector<size_t>{n, m};

    auto input_tensor = TensorWrapper(input, input_shape, desc.in_dtype);
    auto input_cast_tensor =
        TensorWrapper(input_cast, input_shape, desc.out_dtype, amax_out, scale, scale_inv);
    auto input_cast_trans_tensor = TensorWrapper(input_cast_trans, input_trans_shape,
                                                 desc.out_dtype, amax_out, scale, scale_inv);

    nvte_cast_transpose(input_tensor.data(), input_cast_tensor.data(),
                        input_cast_trans_tensor.data(), stream);
}

pybind11::tuple GetDBiasCastTransposeWorkspaceSizes(size_t batch_size, size_t hidden_size,
                                                         DType in_dtype, DType out_dtype) {
    auto input_shape = std::vector<size_t>{batch_size, hidden_size};
    auto output_shape = std::vector<size_t>{batch_size, hidden_size};
    auto output_trans_shape = std::vector<size_t>{hidden_size, batch_size};
    auto dbias_shape = std::vector<size_t>{hidden_size};

    auto input_tensor = TensorWrapper(nullptr, input_shape, in_dtype);
    auto output_tensor = TensorWrapper(nullptr, output_shape, out_dtype);
    auto output_trans_tensor = TensorWrapper(nullptr, output_trans_shape, out_dtype);
    auto dbias_tensor = TensorWrapper(nullptr, dbias_shape, in_dtype);

    TensorWrapper dummy_workspace;

    nvte_cast_transpose_dbias(input_tensor.data(), output_tensor.data(),
                              output_trans_tensor.data(), dbias_tensor.data(),
                              dummy_workspace.data(), nullptr);

    auto work_shape = MakeShapeVector(dummy_workspace.shape());
    return pybind11::make_tuple(std::make_pair(work_shape, dummy_workspace.dtype()));
}

void DBiasCastTranspose(cudaStream_t stream, void **buffers, const char *opaque,
                             size_t opaque_len) {
    auto *input = buffers[0];
    float *amax = reinterpret_cast<float *>(buffers[1]);
    float *scale = reinterpret_cast<float *>(buffers[2]);
    float *scale_inv = reinterpret_cast<float *>(buffers[3]);
    auto *output = buffers[4];
    auto *output_trans = buffers[5];
    auto *dbias = buffers[6];
    float *amax_out = reinterpret_cast<float *>(buffers[7]);
    void *workspace_ptr = buffers[8];

    const auto &desc = *UnpackOpaque<CustomCallCommonWkDescriptor>(opaque, opaque_len);
    assert(amax == amax_out);
    if (!use_fp8(desc.out_dtype)) {
        scale = nullptr;
        scale_inv = nullptr;
        amax_out = nullptr;
    }
    auto m = desc.shape.dims[0];
    auto n = desc.shape.dims[1];
    auto input_shape = std::vector<size_t>{m, n};
    auto output_shape = std::vector<size_t>{m, n};
    auto output_trans_shape = std::vector<size_t>{n, m};
    auto dbias_shape = std::vector<size_t>{n};

    auto input_tensor = TensorWrapper(input, input_shape, desc.in_dtype);
    auto output_tensor =
        TensorWrapper(output, output_shape, desc.out_dtype, amax_out, scale, scale_inv);
    auto output_trans_tensor =
        TensorWrapper(output_trans, output_trans_shape, desc.out_dtype, amax_out, scale, scale_inv);
    auto dbias_tensor = TensorWrapper(dbias, dbias_shape, desc.in_dtype);

    auto workspace = TensorWrapper(workspace_ptr, desc.wkshape.to_vector(), desc.wk_dtype);

    nvte_cast_transpose_dbias(input_tensor.data(), output_tensor.data(),
                              output_trans_tensor.data(), dbias_tensor.data(),
                              workspace.data(), stream);
}

}  // namespace jax
}  // namespace transformer_engine
