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
import torch.utils.data
import torch.cuda
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 = 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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_glu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 32 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 32 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, 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, (8, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1), (4, 1, 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)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, 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, 8, 4), (32, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(128)](buf2, primals_3, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = buf0
        del buf0
        triton_poi_fused_glu_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=4, bias=None)
        assert_size_stride(buf4, (4, 4, 4), (16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_3[grid(64)](buf5, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4), (16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_4[grid(64)](buf7, primals_7, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_7
    return reinterpret_tensor(buf7, (4, 4, 4), (1, 16, 4), 0
        ), primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (
        4, 4, 4), (4, 1, 16), 0), buf2, buf3, buf5


class ConformerConvBlockNew(nn.Module):

    def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
        super(ConformerConvBlockNew, self).__init__()
        assert (kernel_size - 1) % 2 == 0
        self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels,
            kernel_size=1, stride=1, padding=0, bias=bias)
        self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size,
            stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias
            =bias)
        self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.activation = activation
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_normal_(self.pointwise_conv1.weight, nonlinearity=
            'relu')
        nn.init.kaiming_normal_(self.depthwise_conv.weight, nonlinearity='relu'
            )
        nn.init.kaiming_normal_(self.pointwise_conv2.weight, nonlinearity=
            'relu')
        nn.init.constant_(self.pointwise_conv1.bias, 0)
        nn.init.constant_(self.pointwise_conv2.bias, 0)
        nn.init.constant_(self.depthwise_conv.bias, 0)

    def forward(self, input_0):
        primals_2 = self.pointwise_conv1.weight
        primals_3 = self.pointwise_conv1.bias
        primals_4 = self.depthwise_conv.weight
        primals_5 = self.depthwise_conv.bias
        primals_6 = self.pointwise_conv2.weight
        primals_7 = self.pointwise_conv2.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: 0.092038