import torch
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
import torch.nn as nn
import torch.onnx
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 448
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 7 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + x0 + 7 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + x0 + 7 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + x0 + 7 * x1), xmask)
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


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, 7), (112, 28, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(448)](primals_1, buf0, 448,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_1[grid(256)](buf0, primals_2, buf1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
    return buf1, reinterpret_tensor(buf0, (4, 4, 4, 4, 4), (112, 28, 7, 1, 
        1), 0)


class LocallyConnectedLayer1dNew(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
        True, bias=False):
        """
      Defines one locally connected layer for one dimensional vector input.
      NOTE: This model only takes one-dimensional inputs. Batched inputs are also fine.

      input_dim: column size of weight matrix
      output_dim: row size of weight matrix
      kernel_size: number of local connections per parameter
      stride: number of strides of local connections, CANNOT BE ZERO
      padding: whether or not to zero pad
      bias: whether or not to have a bias term
      """
        super(LocallyConnectedLayer1dNew, self).__init__()
        initrange = 0.1
        self.weight = nn.Parameter(torch.empty(output_dim, kernel_size))
        torch.nn.init.uniform_(self.weight, -initrange, initrange)
        if bias:
            self.bias = nn.Parameter(torch.zeros(output_dim))
        else:
            self.register_parameter('bias', None)
        self.kernel_size = kernel_size
        self.stride = stride
        if padding is True:
            pad_size = stride * (output_dim - 1) + kernel_size - input_dim
            self.input_dim = input_dim + pad_size
            self.pad = True
        else:
            resulting_dim = (input_dim - kernel_size) / stride + 1
            self.extra_dim = output_dim - resulting_dim
            self.pad = False

    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: 0.028102