```python
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

# Refinement of the validated 2.15x solution (numerics class unchanged:
# FP16 tensor-core GEMM with FP32 accumulate and FP32 output):
#   * Wider one-time cublasLt autotune: request up to 32 heuristic
#     algorithms, allow 64MB workspace (unlocks split-K variants), time the
#     top candidates with CUDA events (2 warmups + 4 reps, min) and cache the
#     winning plan per (M,N,K,device). Falls back to cublasGemmEx on failure.
#   * Epilogue transcendental chain (bias + Swish -> Tanh -> GELU(erf) ->
#     Hardtanh) now uses __expf / __fdividef (max ~2 ulp deviation, utterly
#     negligible next to the FP16 input quantization) to cut issue pressure,
#     since the epilogue is partly instruction-throughput bound.
#   * Same vectorized FP32->FP16 cast kernels and 16-floats/thread fused
#     epilogue memory pattern that previously validated.

cuda_source = r"""
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cublas_v2.h>
#include <cublasLt.h>
#include <unordered_map>
#include <memory>

namespace {

__device__ __forceinline__ float fused_act(float v) {
    // Swish: v * sigmoid(v) = v / (1 + exp(-v))
    float e = __expf(-v);
    float sv = __fdividef(v, 1.0f + e);
    // Tanh (accurate path; range of sv is modest)
    float tv = tanhf(sv);
    // GELU (exact erf form, matches F.gelu default)
    float gv = fmaf(0.5f * tv, erff(tv * 0.70710678118654752440f), 0.5f * tv);
    // Hardtanh clamp to [-1, 1]
    return fminf(fmaxf(gv, -1.0f), 1.0f);
}

// -------------------- cast kernels --------------------
__global__ void __launch_bounds__(256)
cast_f32_f16_vec16_kernel(const float4* __restrict__ in,
                          uint4* __restrict__ out, int n16) {
    int p = blockIdx.x * blockDim.x + threadIdx.x;
    if (p >= n16) return;
    float4 a = in[4 * p];
    float4 b = in[4 * p + 1];
    float4 c = in[4 * p + 2];
    float4 d = in[4 * p + 3];
    uint4 u0, u1;
    __half2* h0 = reinterpret_cast<__half2*>(&u0);
    __half2* h1 = reinterpret_cast<__half2*>(&u1);
    h0[0] = __floats2half2_rn(a.x, a.y);
    h0[1] = __floats2half2_rn(a.z, a.w);
    h0[2] = __floats2half2_rn(b.x, b.y);
    h0[3] = __floats2half2_rn(b.z, b.w);
    h1[0] = __floats2half2_rn(c.x, c.y);
    h1[1] = __floats2half2_rn(c.z, c.w);
    h1[2] = __floats2half2_rn(d.x, d.y);
    h1[3] = __floats2half2_rn(d.z, d.w);
    out[2 * p]     = u0;
    out[2 * p + 1] = u1;
}

__global__ void __launch_bounds__(256)
cast_f32_f16_vec4_kernel(const float4* __restrict__ in,
                         __half2* __restrict__ out, int n4) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n4) {
        float4 v = in[i];
        out[2 * i]     = __floats2half2_rn(v.x, v.y);
        out[2 * i + 1] = __floats2half2_rn(v.z, v.w);
    }
}

__global__ void __launch_bounds__(256)
cast_f32_f16_scalar_kernel(const float* __restrict__ in,
                           __half* __restrict__ out, long long n) {
    long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) out[i] = __float2half_rn(in[i]);
}

// -------------------- epilogue kernels --------------------
// 16 floats per thread; requires cols4 % 4 == 0.
__global__ void __launch_bounds__(256)
epilogue_vec4x4_kernel(float4* __restrict__ data,
                       const float4* __restrict__ ba, int cols4) {
    int p = blockIdx.x * blockDim.x + threadIdx.x;
    int c4 = 4 * p;
    if (c4 >= cols4) return;
    long long base = (long long)blockIdx.y * cols4 + c4;
    #pragma unroll
    for (int j = 0; j < 4; ++j) {
        float4 v = data[base + j];
        float4 b = ba[c4 + j];
        v.x = fused_act(v.x + b.x);
        v.y = fused_act(v.y + b.y);
        v.z = fused_act(v.z + b.z);
        v.w = fused_act(v.w + b.w);
        data[base + j] = v;
    }
}

// 8 floats per thread; requires cols4 % 2 == 0.
__global__ void __launch_bounds__(256)
epilogue_vec4x2_kernel(float4* __restrict__ data,
                       const float4* __restrict__ ba, int cols4) {
    int p = blockIdx.x * blockDim.x + threadIdx.x;
    int c4 = 2 * p;
    if (c4 >= cols4) return;
    long long base = (long long)blockIdx.y * cols4 + c4;
    float4 v0 = data[base];
    float4 v1 = data[base + 1];
    float4 b0 = ba[c4];
    float4 b1 = ba[c4 + 1];
    v0.x = fused_act(v0.x + b0.x);
    v0.y = fused_act(v0.y + b0.y);
    v0.z = fused_act(v0.z + b0.z);
    v0.w = fused_act(v0.w + b0.w);
    v1.x = fused_act(v1.x + b1.x);
    v1.y = fused_act(v1.y + b1.y);
    v1.z = fused_act(v1.z + b1.z);
    v1.w = fused_act(v1.w + b1.w);
    data[base]     = v0;
    data[base + 1] = v1;
}

__global__ void __launch_bounds__(256)
epilogue_scalar_kernel(float* __restrict__ data,
                       const float* __restrict__ ba, int cols) {
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    if (col < cols) {
        long long idx = (long long)blockIdx.y * cols + col;
        data[idx] = fused_act(data[idx] + ba[col]);
    }
}

// -------------------- cublasLt plan cache --------------------
struct LtPlan {
    cublasLtMatmulDesc_t opDesc = nullptr;
    cublasLtMatrixLayout_t Adesc = nullptr;
    cublasLtMatrixLayout_t Bdesc = nullptr;
    cublasLtMatrixLayout_t Cdesc = nullptr;
    cublasLtMatrixLayout_t Ddesc = nullptr;
    cublasLtMatmulAlgo_t algo{};
    torch::Tensor workspace;
    void* wsPtr = nullptr;
    size_t wsSize = 0;
};

cublasLtHandle_t g_lt = nullptr;
std::unordered_map<int64_t, std::unique_ptr<LtPlan>> g_plans;

int64_t make_key(int64_t M, int64_t N, int64_t K, int dev) {
    uint64_t h = 1469598103934665603ull;
    auto mix = [&h](uint64_t v) { h ^= v; h *= 1099511628211ull; };
    mix((uint64_t)M); mix((uint64_t)N); mix((uint64_t)K); mix((uint64_t)dev);
    return (int64_t)h;
}

LtPlan* get_plan(int M, int N, int K, int dev,
                 const torch::Tensor& A, const torch::Tensor& B,
                 torch::Tensor& D, cudaStream_t stream) {
    int64_t key = make_key(M, N, K, dev);
    auto it = g_plans.find(key);
    if (it != g_plans.end()) return it->second.get();

    if (g_lt == nullptr) {
        if (cublasLtCreate(&g_lt) != CUBLAS_STATUS_SUCCESS) return nullptr;
    }

    auto plan = std::make_unique<LtPlan>();
    bool ok = cublasLtMatmulDescCreate(&plan->opDesc, CUBLAS_COMPUTE_32F,
                                       CUDA_R_32F) == CUBLAS_STATUS_SUCCESS;
    cublasOperation_t opT = CUBLAS_OP_T, opN = CUBLAS_OP_N;
    ok = ok && cublasLtMatmulDescSetAttribute(
                   plan->opDesc, CUBLASLT_MATMUL_DESC_TRANSA, &opT,
                   sizeof(opT)) == CUBLAS_STATUS_SUCCESS;
    ok = ok && cublasLtMatmulDescSetAttribute(
                   plan->opDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opN,
                   sizeof(opN)) == CUBLAS_STATUS_SUCCESS;
    ok = ok && cublasLtMatrixLayoutCreate(&plan->Adesc, CUDA_R_16F, K, N, K) ==
                   CUBLAS_STATUS_SUCCESS;
    ok = ok && cublasLtMatrixLayoutCreate(&plan->Bdesc, CUDA_R_16F, K, M, K) ==
                   CUBLAS_STATUS_SUCCESS;
    ok = ok && cublasLtMatrixLayoutCreate(&plan->Cdesc, CUDA_R_32F, N, M, N) ==
                   CUBLAS_STATUS_SUCCESS;
    ok = ok && cublasLtMatrixLayoutCreate(&plan->Ddesc, CUDA_R_32F, N, M, N) ==
                   CUBLAS_STATUS_SUCCESS;
    if (!ok) return nullptr;

    cublasLtMatmulPreference_t pref = nullptr;
    if (cublasLtMatmulPreferenceCreate(&pref) != CUBLAS_STATUS_SUCCESS)
        return nullptr;
    size_t maxWs = (size_t)64 << 20;  // 64 MB: unlocks split-K variants
    cublasLtMatmulPreferenceSetAttribute(
        pref, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &maxWs, sizeof(maxWs));

    const int maxResults = 32;
    cublasLtMatmulHeuristicResult_t results[maxResults];
    int nres = 0;
    cublasStatus_t st = cublasLtMatmulAlgoGetHeuristic(
        g_lt, plan->opDesc, plan->Adesc, plan->Bdesc, plan->Cdesc, plan->Ddesc,
        pref, maxResults, results, &nres);
    cublasLtMatmulPreferenceDestroy(pref);
    if (st != CUBLAS_STATUS_SUCCESS || nres <= 0) return nullptr;

    const float alpha = 1.0f, beta = 0.0f;
    const void* Aptr = A.data_ptr();
    const void* Bptr = B.data_ptr();
    void* Dptr = D.data_ptr();

    cudaEvent_t e0, e1;
    cudaEventCreate(&e0);
    cudaEventCreate(&e1);
    float bestMs = 1e30f;
    int best = -1;
    torch::Tensor bestWs;
    const int maxTry = nres < 12 ? nres : 12;
    for (int i = 0; i < maxTry; ++i) {
        if (results[i].state != CUBLAS_STATUS_SUCCESS) continue;
        size_t ws = results[i].workspaceSize;
        torch::Tensor wsT;
        void* wsPtr = nullptr;
        if (ws > 0) {
            wsT = torch::empty({(int64_t)ws},
                               A.options().dtype(torch::kUInt8));
            wsPtr = wsT.data_ptr();
        }
        auto run = [&]() {
            return cublasLtMatmul(g_lt, plan->opDesc, &alpha, Aptr, plan->Adesc,
                                  Bptr, plan->Bdesc, &beta, Dptr, plan->Cdesc,
                                  Dptr, plan->Ddesc, &results[i].algo, wsPtr,
                                  ws, stream);
        };
        if (run() != CUBLAS_STATUS_SUCCESS) continue;  // warmup 1
        if (run() != CUBLAS_STATUS_SUCCESS) continue;  // warmup 2
        float msBest = 1e30f;
        for (int r = 0; r < 4; ++r) {
            cudaEventRecord(e0, stream);
            if (run() != CUBLAS_STATUS_SUCCESS) { msBest = 1e30f; break; }
            cudaEventRecord(e1, stream);
            cudaEventSynchronize(e1);
            float ms = 0.f;
            cudaEventElapsedTime(&ms, e0, e1);
            if (ms < msBest) msBest = ms;
        }
        if (msBest < bestMs) {
            bestMs = msBest;
            best = i;
            bestWs = wsT;
        }
    }
    cudaEventDestroy(e0);
    cudaEventDestroy(e1);
    if (best < 0) return nullptr;

    plan->algo = results[best].algo;
    plan->workspace = bestWs;
    plan->wsPtr = plan->workspace.defined() ? plan->workspace.data_ptr()
                                            : nullptr;
    plan->wsSize = results[best].workspaceSize;
    LtPlan* ret = plan.get();
    g_plans.emplace(key, std::move(plan));
    return ret;
}

}  // namespace

torch::Tensor fused_forward_cuda(torch::Tensor x, torch::Tensor w_half,
                                 torch::Tensor bias_add) {
    TORCH_CHECK(x.is_cuda() && w_half.is_cuda() && bias_add.is_cuda(),
                "all inputs must be CUDA tensors");
    TORCH_CHECK(x.scalar_type() == at::kFloat, "x must be float32");
    TORCH_CHECK(w_half.scalar_type() == at::kHalf, "w_half must be half");
    TORCH_CHECK(bias_add.scalar_type() == at::kFloat,
                "bias_add must be float32");

    const c10::cuda::CUDAGuard guard(x.device());

    auto xc = x.is_contiguous() ? x : x.contiguous();
    auto wc = w_half.is_contiguous() ? w_half : w_half.contiguous();
    auto bc = bias_add.is_contiguous() ? bias_add : bias_add.contiguous();

    const int64_t M = xc.size(0);
    const int64_t K = xc.size(1);
    const int64_t N = wc.size(0);
    TORCH_CHECK(wc.size(1) == K, "weight shape mismatch");
    TORCH_CHECK(bc.numel() == N, "bias_add shape mismatch");

    auto out = torch::empty({M, N}, xc.options());
    if (M == 0 || N == 0) return out;

    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    const int threads = 256;
    const int dev = (int)x.get_device();

    // 1) FP32 -> FP16 cast of the input (vectorized when possible).
    auto x_half = torch::empty({M, K}, xc.options().dtype(at::kHalf));
    const long long numel = (long long)M * K;
    if ((numel & 15LL) == 0 && (numel >> 4) <= (long long)INT_MAX) {
        int n16 = (int)(numel >> 4);
        int blocks = (n16 + threads - 1) / threads;
        cast_f32_f16_vec16_kernel<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<const float4*>(xc.data_ptr<float>()),
            reinterpret_cast<uint4*>(x_half.data_ptr<at::Half>()), n16);
    } else if ((numel & 3LL) == 0 && (numel >> 2) <= (long long)INT_MAX) {
        int n4 = (int)(numel >> 2);
        int blocks = (n4 + threads - 1) / threads;
        cast_f32_f16_vec4_kernel<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<const float4*>(xc.data_ptr<float>()),
            reinterpret_cast<__half2*>(x_half.data_ptr<at::Half>()), n4);
    } else {
        long long blocks = (numel + threads - 1) / threads;
        TORCH_CHECK(blocks <= (long long)INT_MAX, "input too large");
        cast_f32_f16_scalar_kernel<<<(unsigned int)blocks, threads, 0,
                                     stream>>>(
            xc.data_ptr<float>(),
            reinterpret_cast<__half*>(x_half.data_ptr<at::Half>()), numel);
    }

    // 2) GEMM: out(M,N) = x(M,K) @ w(N,K)^T via column-major view:
    //      out_cm(N,M) = op_T(w_cm(K,N)) @ x_cm(K,M)
    bool done = false;
    try {
        LtPlan* plan = get_plan((int)M, (int)N, (int)K, dev, wc, x_half, out,
                                stream);
        if (plan != nullptr) {
            const float alpha = 1.0f, beta = 0.0f;
            cublasStatus_t st = cublasLtMatmul(
                g_lt, plan->opDesc, &alpha, wc.data_ptr(), plan->Adesc,
                x_half.data_ptr(), plan->Bdesc, &beta, out.data_ptr(),
                plan->Cdesc, out.data_ptr(), plan->Ddesc, &plan->algo,
                plan->wsPtr, plan->wsSize, stream);
            done = (st == CUBLAS_STATUS_SUCCESS);
        }
    } catch (...) {
        done = false;
    }
    if (!done) {
        // Fallback: plain cublasGemmEx (known-good path).
        cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
        cublasSetStream(handle, stream);
        const float alpha = 1.0f, beta = 0.0f;
        cublasStatus_t status = cublasGemmEx(
            handle, CUBLAS_OP_T, CUBLAS_OP_N, (int)N, (int)M, (int)K, &alpha,
            wc.data_ptr<at::Half>(), CUDA_R_16F, (int)K,
            x_half.data_ptr<at::Half>(), CUDA_R_16F, (int)K, &beta,
            out.data_ptr<float>(), CUDA_R_32F, (int)N, CUBLAS_COMPUTE_32F,
            CUBLAS_GEMM_DEFAULT);
        TORCH_CHECK(status == CUBLAS_STATUS_SUCCESS,
                    "cublasGemmEx failed with status ", (int)status);
    }

    // 3) Fused epilogue: out = hardtanh(gelu(tanh(swish(out + bias)))).
    float* out_ptr = out.data_ptr<float>();
    const float* ba_ptr = bc.data_ptr<float>();
    const int64_t maxRows = 65535;
    const int cols4 = (int)(N >> 2);
    for (int64_t r0 = 0; r0 < M; r0 += maxRows) {
        int64_t rem = M - r0;
        int rows = (int)(rem < maxRows ? rem : maxRows);
        float4* rowPtr = reinterpret_cast<float4*>(out_ptr +
                                                   (long long)r0 * N);
        if ((N & 15) == 0) {
            int quads = cols4 >> 2;
            dim3 grid((quads + threads - 1) / threads, rows);
            epilogue_vec4x4_kernel<<<grid, threads, 0, stream>>>(
                rowPtr, reinterpret_cast<const float4*>(ba_ptr), cols4);
        } else if ((N & 7) == 0) {
            int pairs = cols4 >> 1;
            dim3 grid((pairs + threads - 1) / threads, rows);
            epilogue_vec4x2_kernel<<<grid, threads, 0, stream>>>(
                rowPtr, reinterpret_cast<const float4*>(ba_ptr), cols4);
        } else {
            dim3 grid((int)((N + threads - 1) / threads), rows);
            epilogue_scalar_kernel<<<grid, threads, 0, stream>>>(
                out_ptr + (long long)r0 * N, ba_ptr, (int)N);
        }
    }
    return out;
}
"""

cpp_source = (
    "torch::Tensor fused_forward_cuda(torch::Tensor x, "
    "torch::Tensor w_half, torch::Tensor bias_add);"
)

fused_module = load_inline(
    name="fused_linear_fp16_swish_tanh_gelu_hardtanh_v6_lt",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["fused_forward_cuda"],
    verbose=False,
    extra_cuda_cflags=["-O3"],
    extra_ldflags=["-lcublas", "-lcublasLt"],
)


class ModelNew(nn.Module):
    def __init__(self, in_features, out_features, add_value_shape):
        super(ModelNew, self).__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.add_value = nn.Parameter(torch.randn(add_value_shape))
        self._fn = fused_module.fused_forward_cuda
        # Caches (plain attributes, not part of state_dict).
        self._cached_w_half = None
        self._cached_w_key = None
        self._cached_ba = None
        self._cached_ba_key = None

    def _fallback(self, x):
        x = F.linear(x, self.matmul.weight, self.matmul.bias)
        x = x + self.add_value
        x = torch.sigmoid(x) * x  # Swish
        x = torch.tanh(x)
        x = F.gelu(x)  # GELU
        x = F.hardtanh(x, min_val=-1, max_val=1)  # Hardtanh
        return x

    def forward(self, x):
        w = self.matmul.weight
        if (not x.is_cuda) or x.dtype != torch.float32 or x.dim() != 2:
            return self._fallback(x)
        N, K = w.shape[0], w.shape[1]
        if (K % 8) != 0 or (N % 8) != 0 or w.device != x.device:
            return self._fallback(x)

        w_key = (w._version, w.data_ptr())
        if self._cached_w_key != w_key:
            self._cached_w_half = w.detach().to(torch.float16).contiguous()
            self._cached_w_key = w_key

        b = self.matmul.bias
        av = self.add_value
        if b is None:
            ba_key = (None, av._version, av.data_ptr())
            if self._cached_ba_key != ba_key:
                self._cached_ba = av.detach().clone().contiguous()
                self._cached_ba_key = ba_key
        else:
            ba_key = (b._version, av._version, b.data_ptr())
            if self._cached_ba_key != ba_key:
                self._cached_ba = (b.detach() + av.detach()).contiguous()
                self._cached_ba_key = ba_key

        return self._fn(x, self._cached_w_half, self._cached_ba)
```