/*
 * Copyright (c) 2022-2024, 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 "tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.h"
#include "tensorrt_llm/runtime/torchUtils.h"
#include "tensorrt_llm/thop/thUtils.h"
#include <ATen/cuda/EmptyTensor.h>

namespace torch_ext
{

namespace btg = batchedGemm::trtllm::gen;
using tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::Routing::RoutingMethodType;

torch::Tensor fp8_per_tensor_scale_moe_runner(torch::Tensor const& routing_logits,
    torch::optional<torch::Tensor> const& routing_bias, torch::Tensor const& hidden_states,
    torch::Tensor const& gemm1_weights, torch::Tensor const& output1_scales_scalar,
    torch::Tensor const& output1_scales_gate_scalar, torch::Tensor const& gemm2_weights,
    torch::Tensor const& output2_scales_scalar, int64_t const num_experts, int64_t const top_k,
    std::optional<int64_t> const n_group, std::optional<int64_t> const topk_group, int64_t const intermediate_size,
    int64_t const local_expert_offset, int64_t const local_num_experts,
    std::optional<double> const routed_scaling_factor, bool const use_routing_scales_on_input,
    int64_t const tile_tokens_dim, int64_t const routing_method_type)
{

    auto const sm = tensorrt_llm::common::getSMVersion();
    TORCH_CHECK(sm == 100 || sm == 103, "Only SM100f is supported by FP8 block scale MOE");
    if (use_routing_scales_on_input)
    {
        TORCH_CHECK(routing_logits.scalar_type() == at::ScalarType::BFloat16, "routing_logits must be bfloat16.");
    }
    else
    {
        TORCH_CHECK(routing_logits.scalar_type() == at::ScalarType::Float, "routing_logits must be float.");
    }
    TORCH_CHECK(routing_logits.dim() == 2, "routing_logits must be 2D.");
    TORCH_CHECK(routing_logits.sizes()[1] == num_experts, "routing_logits has incorrect shape.");

    if (routing_bias.has_value())
    {
        TORCH_CHECK(routing_bias.value().scalar_type() == at::ScalarType::BFloat16, "routing_bias must be bfloat16.");
        TORCH_CHECK(routing_bias.value().dim() == 1, "routing_bias must be 1D.");
        TORCH_CHECK(routing_bias.value().sizes()[0] == num_experts, "routing_bias has incorrect shape.");
    }

    if (n_group.has_value() && n_group.value() != 0)
    {
        TORCH_CHECK(static_cast<RoutingMethodType>(routing_method_type) == RoutingMethodType::DeepSeekV3,
            "Routing kernel with groups implies DeepSeekV3 routing method.");
        TORCH_CHECK(topk_group.has_value(), "if n_group is given, topk_group must be given");
        TORCH_CHECK(num_experts % n_group.value() == 0, "num_experts must be divisible by n_group");
        TORCH_CHECK(top_k <= 8 && top_k > 0, "Current routing kernel (with groups) only supports top_k<=8 && top_k>0.");
        TORCH_CHECK(topk_group.value() <= 4 && topk_group.value() > 0,
            "Current routing kernel only (with groups) supports topk_group<=4 && topk_group > 0.");
        TORCH_CHECK(topk_group.value() <= n_group.value(), "n_group must not be smaller than topk_group.");
        // This check ensures we have enough experts in the selected groups to handle the top_k routing
        TORCH_CHECK(top_k < (topk_group.value() * num_experts / n_group.value()),
            "top_k must be less than total number of experts in selected groups");
    }
    else if (static_cast<RoutingMethodType>(routing_method_type) == RoutingMethodType::Renormalize
        || static_cast<RoutingMethodType>(routing_method_type) == RoutingMethodType::RenormalizeNaive)
    {
        TORCH_CHECK(false, "Don't support routing method type Renormalize(Naive).");
    }
    else if (static_cast<RoutingMethodType>(routing_method_type) == RoutingMethodType::Llama4)
    {
        TORCH_CHECK(top_k == 1, "Current routing kernel (no groups, Llama4) only supports top_k=1.");
    }

    TORCH_CHECK(num_experts % 4 == 0, "Routing kernel expects that num_experts must be divisible by 4");
    TORCH_CHECK(num_experts > top_k, "num_experts must be greater than top_k");

    tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::MoE::MoERunnerArgs args;
    tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::MoE::MoEWorkspace workspace;

    // setup args
    args.mDtypeElt = btg::Dtype::E4m3;
    args.routing_logits = routing_logits.data_ptr();
    args.routing_bias = routing_bias.has_value() ? routing_bias.value().data_ptr() : nullptr;
    args.hidden_states = hidden_states.data_ptr();
    args.gemm1_weights = gemm1_weights.data_ptr();
    args.output1_scales_scalar = output1_scales_scalar.data_ptr<float>();
    args.output1_scales_gate_scalar = output1_scales_gate_scalar.data_ptr<float>();
    args.gemm2_weights = gemm2_weights.data_ptr();
    args.output2_scales_scalar = output2_scales_scalar.data_ptr<float>();
    args.num_tokens = hidden_states.sizes()[0];
    args.num_experts = num_experts;
    args.hidden_size = hidden_states.sizes()[1];
    args.top_k = top_k;
    args.n_group = n_group.value_or(0);
    args.topk_group = topk_group.value_or(0);
    args.local_expert_offset = local_expert_offset;
    args.local_num_experts = local_num_experts;
    args.routed_scaling_factor = routed_scaling_factor.value_or(1.0);
    args.intermediate_size = intermediate_size;
    args.mUseRoutingScalesOnInput = use_routing_scales_on_input;

    // allocate workspace for routing kernel
    at::Tensor num_tokens_per_expert
        = at::detail::empty_cuda({num_experts}, at::ScalarType::Int, routing_logits.device(), std::nullopt);
    int32_t max_num_padded_tokens
        = tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::Routing::getMaxPermutedPaddedCount(
            args.num_tokens, top_k, num_experts, tile_tokens_dim);
    at::Tensor total_num_padded_tokens
        = at::empty({}, at::TensorOptions().device(routing_logits.device()).dtype(at::ScalarType::Int));
    at::Tensor expanded_idx_to_permuted_idx = at::detail::empty_cuda(
        {args.num_tokens * args.top_k}, at::ScalarType::Int, routing_logits.device(), std::nullopt);
    at::Tensor permuted_idx_to_token_idx
        = at::detail::empty_cuda({max_num_padded_tokens}, at::ScalarType::Int, routing_logits.device(), std::nullopt);
    at::Tensor expert_weights = at::detail::empty_cuda(
        {args.num_tokens, args.top_k}, at::ScalarType::BFloat16, routing_logits.device(), std::nullopt);
    at::Tensor expert_indexes = at::detail::empty_cuda(
        {args.num_tokens, args.top_k}, at::ScalarType::Int, routing_logits.device(), std::nullopt);
    at::Tensor expert_count_histogram = at::detail::empty_cuda({2 * 256},
        at::ScalarType::Int, // 256 is the max number of threads per block and max number of experts
        routing_logits.device(), std::nullopt);

    // allocate workspace for activation/gemm/finalize kernels
    at::Tensor gemm1_output = at::detail::empty_cuda({max_num_padded_tokens, 2 * intermediate_size},
        at::ScalarType::Float8_e4m3fn, hidden_states.device(), std::nullopt);
    at::Tensor gemm1_output_scale = at::detail::empty_cuda({2 * intermediate_size / 128, max_num_padded_tokens},
        at::ScalarType::Float, hidden_states.device(), std::nullopt);
    at::Tensor activation_output = at::detail::empty_cuda({max_num_padded_tokens, intermediate_size},
        at::ScalarType::Float8_e4m3fn, hidden_states.device(), std::nullopt);
    at::Tensor activation_output_scale = at::detail::empty_cuda(
        {intermediate_size / 128, max_num_padded_tokens}, at::ScalarType::Float, hidden_states.device(), std::nullopt);
    at::Tensor gemm2_output = at::detail::empty_cuda(
        {max_num_padded_tokens, args.hidden_size}, at::ScalarType::BFloat16, hidden_states.device(), std::nullopt);

    int32_t max_num_ctas = tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::Routing::getMaxNumCtasInBatchDim(
        args.num_tokens, args.top_k, args.num_experts, tile_tokens_dim);
    at::Tensor cta_idx_xy_to_batch_idx
        = at::detail::empty_cuda({max_num_ctas}, at::ScalarType::Int, routing_logits.device(), std::nullopt);
    at::Tensor cta_idx_xy_to_mn_limit
        = at::detail::empty_cuda({max_num_ctas}, at::ScalarType::Int, routing_logits.device(), std::nullopt);
    at::Tensor num_non_exiting_ctas
        = at::empty({}, at::TensorOptions().device(routing_logits.device()).dtype(at::ScalarType::Int));

    tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::Routing::Runner routing_runner(tile_tokens_dim);
    auto const& stream = at::cuda::getCurrentCUDAStream(routing_logits.get_device());
    routing_runner.run(routing_logits.data_ptr(), args.routing_bias, args.num_tokens, args.num_experts, args.top_k,
        args.n_group, args.topk_group, args.local_expert_offset, args.local_num_experts, args.routed_scaling_factor,
        expert_indexes.data_ptr<int>(), expert_count_histogram.data_ptr<int>(), total_num_padded_tokens.data_ptr<int>(),
        expanded_idx_to_permuted_idx.data_ptr<int>(), nullptr /*permuted_idx_to_expanded_idx.data_ptr<int>()*/,
        permuted_idx_to_token_idx.data_ptr<int>(), expert_weights.data_ptr(), num_tokens_per_expert.data_ptr<int>(),
        cta_idx_xy_to_batch_idx.data_ptr<int>(), cta_idx_xy_to_mn_limit.data_ptr<int>(),
        num_non_exiting_ctas.data_ptr<int>(), args.mDtypeElt, use_routing_scales_on_input,
        false /* use_deep_seek_fp8 */, static_cast<RoutingMethodType>(routing_method_type), stream);

    // MoE kernel except routing
    TORCH_CHECK(hidden_states.scalar_type() == at::ScalarType::Float8_e4m3fn, "hidden_states must be fp8.");
    TORCH_CHECK(gemm1_weights.scalar_type() == at::ScalarType::Float8_e4m3fn, "gemm1_weights must be fp8.");
    TORCH_CHECK(gemm1_weights.dim() == 3, "gemm1_weights must be 3D.");
    TORCH_CHECK(gemm1_weights.sizes()[1] % 2 == 0, "the second dimension of weights must be even.");
    TORCH_CHECK(intermediate_size == gemm1_weights.sizes()[1] / 2, "intermediate_size has incorrect shape.");
    TORCH_CHECK(gemm1_weights.sizes()[2] == hidden_states.sizes()[1],
        "the third dimension of weights must be equal to hidden_size.");
    TORCH_CHECK(intermediate_size % 128 == 0, "the second dimension of weights must be a multiple of 128.");

    TORCH_CHECK(output1_scales_scalar.scalar_type() == at::ScalarType::Float, "output1_scales_scalar must be float.");
    TORCH_CHECK(output1_scales_scalar.dim() == 1, "output1_scales_scalar must be 1D.");
    TORCH_CHECK(output1_scales_scalar.sizes()[0] == local_num_experts, "output1_scales_scalar has incorrect dim 0.");
    TORCH_CHECK(
        output1_scales_gate_scalar.scalar_type() == at::ScalarType::Float, "output1_scales_gate_scalar must be float.");
    TORCH_CHECK(output1_scales_gate_scalar.dim() == 1, "output1_scales_gate_scalar must be 1D.");
    TORCH_CHECK(
        output1_scales_gate_scalar.sizes()[0] == local_num_experts, "output1_scales_gate_scalar has incorrect dim 0.");

    TORCH_CHECK(gemm2_weights.scalar_type() == at::ScalarType::Float8_e4m3fn, "gemm2_weights must be fp8.");
    TORCH_CHECK(gemm2_weights.dim() == 3, "gemm2_weights must be 3D.");
    TORCH_CHECK(gemm2_weights.sizes()[2] == intermediate_size,
        "the third dimension of weights must be equal to intermediate_size.");

    TORCH_CHECK(output2_scales_scalar.scalar_type() == at::ScalarType::Float, "output2_scales_scalar must be float.");
    TORCH_CHECK(output2_scales_scalar.dim() == 1, "output2_scales_scalar must be 1D.");
    TORCH_CHECK(output2_scales_scalar.sizes()[0] == local_num_experts, "output2_scales_scalar has incorrect dim 0.");

    // allocate output
    at::Tensor output = at::detail::empty_cuda(
        {args.num_tokens, args.hidden_size}, at::ScalarType::BFloat16, hidden_states.device(), std::nullopt);

    // setup workspace
    workspace.total_num_padded_tokens = total_num_padded_tokens.data_ptr<int>();
    workspace.total_max_padded_tokens = max_num_padded_tokens;
    workspace.ProjUpTileN = tile_tokens_dim;
    workspace.routing_expert_indexes = expert_indexes.data_ptr<int>();
    workspace.permuted_idx_size = total_num_padded_tokens.data_ptr<int>();
    workspace.expanded_idx_to_permuted_idx
        = expanded_idx_to_permuted_idx.data_ptr<int>(); // Needed by activation/finalize kernels
    workspace.permuted_idx_to_token_idx = permuted_idx_to_token_idx.data_ptr<int>(); // Needed by permuteGemm1 kernel
    workspace.expert_weights = expert_weights.data_ptr();                            // Consumed by finalize kernel

    workspace.cta_idx_xy_to_batch_idx = cta_idx_xy_to_batch_idx.data_ptr<int>();
    workspace.cta_idx_xy_to_mn_limit = cta_idx_xy_to_mn_limit.data_ptr<int>();
    workspace.num_non_exiting_ctas = num_non_exiting_ctas.data_ptr<int>();

    // gemm1 intermediate ws
    workspace.gemm1_output = gemm1_output.data_ptr();
    workspace.gemm1_output_scale = gemm1_output_scale.data_ptr<float>();
    // activation intermediate ws
    workspace.activation_output = activation_output.data_ptr();
    workspace.activation_output_scale = activation_output_scale.data_ptr<float>();
    // gemm2 intermediate ws
    workspace.gemm2_output = gemm2_output.data_ptr();
    workspace.gemm2_output_scale = nullptr;
    args.output = output.data_ptr();
    args.output_scale = nullptr;

    tensorrt_llm::kernels::trtllmGenFp8BlockScaleMoe::MoE::Runner moe_runner(
        args.mDtypeElt, args.mUseDeepSeekFp8, tile_tokens_dim);

    auto const moeConfigIndex = moe_runner.getDefaultValidConfigIndex(
        args.top_k, args.hidden_size, args.intermediate_size, args.local_num_experts, args.num_tokens);

    auto workspace_sizes = moe_runner.getWorkspaceSizeInBytes(args, moeConfigIndex);
    at::Tensor workspace_fc1 = at::detail::empty_cuda(
        {std::get<0>(workspace_sizes)}, at::ScalarType::Char, hidden_states.device(), std::nullopt);
    at::Tensor workspace_fc2 = at::detail::empty_cuda(
        {std::get<1>(workspace_sizes)}, at::ScalarType::Char, hidden_states.device(), std::nullopt);
    workspace.bmm1_workspace = workspace_fc1.data_ptr();
    workspace.bmm2_workspace = workspace_fc2.data_ptr();
    auto const& moe_stream = at::cuda::getCurrentCUDAStream(hidden_states.get_device());
    moe_runner.run(args, workspace, hidden_states.get_device(), moe_stream, moeConfigIndex);
    return output;
}
} // namespace torch_ext

TORCH_LIBRARY_FRAGMENT(trtllm, m)
{
    m.def(
        "fp8_per_tensor_scale_moe_runner("
        "Tensor routing_logits,"
        "Tensor? routing_bias,"
        "Tensor hidden_states,"
        "Tensor gemm1_weights,"
        "Tensor output1_scales_scalar,"
        "Tensor output1_scales_gate_scalar,"
        "Tensor gemm2_weights,"
        "Tensor output2_scales_scalar,"
        "int num_experts,"
        "int top_k,"
        "int? n_group,"
        "int? topk_group,"
        "int intermediate_size,"
        "int local_expert_offset,"
        "int local_num_experts,"
        "float? routed_scaling_factor,"
        "bool use_routing_scales_on_input,"
        "int tile_tokens_dim,"
        "int routing_method_type) -> Tensor");
}

TORCH_LIBRARY_IMPL(trtllm, CUDA, m)
{
    m.impl("fp8_per_tensor_scale_moe_runner", &torch_ext::fp8_per_tensor_scale_moe_runner);
}