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.nn import Module
import torch.nn as nn
from torch.nn import init
from torch.nn.modules import Module
from torch.nn.modules.utils import _pair
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_mul_0(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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_mul_0[grid(256)](primals_1, primals_2, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(primals_3, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 1), (4, 1, 1, 1))
    return buf1, primals_2, primals_3, buf0


class MaskedConvNew(Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=False):
        super(MaskedConvNew, self).__init__()
        self.kernel_size = _pair(kernel_size)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.use_bias = bias
        self.floatTensor = torch.FloatTensor if not torch.cuda.is_available(
            ) else torch.FloatTensor
        self.weight = nn.Parameter(self.floatTensor(out_channels,
            in_channels, *self.kernel_size), requires_grad=True)
        if bias:
            self.bias = nn.Parameter(self.floatTensor(out_channels),
                requires_grad=True)
        else:
            self.bias = None
        self.mask = nn.Parameter(self.floatTensor(out_channels, in_channels,
            *self.kernel_size), requires_grad=False)
        self.reset_parameters()

    def reset_parameters(self):
        init.xavier_uniform_(self.weight)
        init.ones_(self.mask)
        if self.use_bias:
            init.zeros_(self.bias)

    def forward(self, input_0):
        primals_1 = self.weight
        primals_2 = self.mask
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.024247