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
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, out_ptr1, out_ptr2,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 4 * y3), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_mul_sigmoid_1(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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_out_ptr2 + x2, xmask)
    tmp9 = tl.load(in_ptr2 + 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
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(in_out_ptr1 + x2, tmp5, xmask)
    tl.store(in_out_ptr2 + x2, tmp11, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, buf3,
            buf6, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1), (4, 1, 1))
        del buf0
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 1), (4, 1, 1))
        del buf3
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 1), (4, 1, 1))
        del buf6
        buf2 = buf1
        del buf1
        buf5 = buf4
        del buf4
        buf8 = buf7
        del buf7
        triton_poi_fused_add_convolution_mul_sigmoid_1[grid(16)](buf2, buf5,
            buf8, primals_3, primals_5, primals_7, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        del primals_3
        del primals_5
        del primals_7
    return reinterpret_tensor(buf8, (4, 1, 4), (4, 1, 1), 0
        ), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (
        4, 4, 4), (16, 1, 4), 0), buf2, buf5


class Temporal_Gated_convNew(nn.Module):
    """
    时序卷积模块，通过一位卷积提取时序关系
    """

    def __init__(self, in_channels, out_channels, kernel_size, padding=0,
        stride=1):
        super(Temporal_Gated_convNew, self).__init__()
        self.conv_1 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)
        self.conv_2 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)
        self.conv_3 = nn.Conv1d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding, stride=
            stride)

    def forward(self, input_0):
        primals_1 = self.conv_1.weight
        primals_3 = self.conv_1.bias
        primals_2 = self.conv_2.weight
        primals_5 = self.conv_2.bias
        primals_4 = self.conv_3.weight
        primals_7 = self.conv_3.bias
        primals_6 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.061906