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
import torch.nn as nn
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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 3
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 3 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 12 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_relu_sigmoid_threshold_backward_1(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x2, xmask)
    tmp9 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp2)
    tmp7 = tmp5 + tmp6
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 + tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = 0.0
    tmp15 = tmp13 <= tmp14
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp13, xmask)
    tl.store(out_ptr0 + x2, tmp15, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 3), (12, 3, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 3), (12, 1, 12, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 3)](primals_2, buf0, 16, 3,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            4, 4, 4), (64, 1, 16, 4), 0), 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, 4, 2), (32, 1, 8, 4))
        buf2 = buf0
        del buf0
        triton_poi_fused_convolution_0[grid(16, 3)](primals_4, buf2, 16, 3,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            4, 4, 4), (64, 1, 16, 4), 0), buf2, stride=(1, 1), padding=(0, 
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 2), (32, 1, 8, 4))
        buf5 = buf2
        del buf2
        triton_poi_fused_convolution_0[grid(16, 3)](primals_6, buf5, 16, 3,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            4, 4, 4), (64, 1, 16, 4), 0), buf5, stride=(1, 1), padding=(0, 
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 2), (32, 1, 8, 4))
        del buf5
        buf4 = buf3
        del buf3
        buf7 = buf1
        del buf1
        buf8 = empty_strided_cuda((4, 4, 4, 2), (32, 1, 8, 4), torch.bool)
        triton_poi_fused_add_convolution_relu_sigmoid_threshold_backward_1[grid
            (128)](buf4, buf7, primals_5, primals_3, buf6, primals_7, buf8,
            128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
        del primals_3
        del primals_5
        del primals_7
    return reinterpret_tensor(buf7, (4, 4, 2, 4), (32, 8, 4, 1), 0
        ), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (
        4, 4, 4, 4), (64, 1, 16, 4), 0), buf4, buf8


class TimeBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3):
        """
        :param in_channels: Number of input features at each node in each time
        step.
        :param out_channels: Desired number of output channels at each node in
        each time step.
        :param kernel_size: Size of the 1D temporal kernel.
        """
        super(TimeBlockNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, (1, kernel_size))
        self.conv2 = nn.Conv2d(in_channels, out_channels, (1, kernel_size))
        self.conv3 = nn.Conv2d(in_channels, out_channels, (1, kernel_size))

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.085992