import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl_math.abs(tmp0)
    tmp2 = 1e-07
    tmp3 = tmp1 + tmp2
    tmp4 = libdevice.log1p(tmp3)
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_expm1_relu_threshold_backward_1(in_ptr0,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = libdevice.expm1(tmp0)
    tmp2 = tl.full([1, 1], 0, tl.int32)
    tmp3 = triton_helpers.maximum(tmp2, tmp1)
    tmp4 = 0.0
    tmp5 = tmp3 <= tmp4
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp3, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 4 * y3), tmp5, xmask & ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_1, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_clone_expm1_relu_threshold_backward_1[grid(64, 4)](
            buf1, buf2, buf3, 64, 4, XBLOCK=4, YBLOCK=32, num_warps=4,
            num_stages=1)
    return reinterpret_tensor(buf2, (16, 16), (16, 1), 0), reinterpret_tensor(
        buf0, (64, 4), (4, 1), 0), buf1, buf3


class LNNNew(torch.nn.Module):
    """
    A pytorch implementation of LNN layer
    Input shape
        - A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
    Output shape
        - 2D tensor with shape:``(batch_size,LNN_dim*embedding_size)``.
    Arguments
        - **in_features** : Embedding of feature.
        - **num_fields**: int.The field size of feature.
        - **LNN_dim**: int.The number of Logarithmic neuron.
        - **bias**: bool.Whether or not use bias in LNN.
    """

    def __init__(self, num_fields, embed_dim, LNN_dim, bias=False):
        super(LNNNew, self).__init__()
        self.num_fields = num_fields
        self.embed_dim = embed_dim
        self.LNN_dim = LNN_dim
        self.lnn_output_dim = LNN_dim * embed_dim
        self.weight = torch.nn.Parameter(torch.Tensor(LNN_dim, num_fields))
        if bias:
            self.bias = torch.nn.Parameter(torch.Tensor(LNN_dim, embed_dim))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input_0):
        primals_2 = self.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.014085