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.utils.data.distributed
import torch.utils.data
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 3
    tmp0 = tl.load(in_out_ptr0 + x2, 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4), (16, 4, 1), 0), primals_2, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 3), (12, 3, 1))
        buf1 = reinterpret_tensor(buf0, (4, 3), (3, 1), 0)
        del buf0
        buf4 = empty_strided_cuda((4, 3), (3, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(12)](buf1,
            primals_3, buf4, 12, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 3
            ), (0, 3, 1), 0), primals_4, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf2, (1, 4, 2), (8, 2, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(8)](buf3, primals_5, 8, XBLOCK=
            8, num_warps=1, num_stages=1)
        del primals_5
    return reinterpret_tensor(buf3, (4, 2), (2, 1), 0
        ), primals_2, primals_4, reinterpret_tensor(primals_1, (1, 4, 4), (
        16, 4, 1), 0), reinterpret_tensor(buf1, (1, 4, 3), (12, 3, 1), 0), buf4


class MultiLayeredConv1dNew(torch.nn.Module):
    """Multi-layered conv1d for Transformer block.

    This is a module of multi-leyered conv1d designed
    to replace positionwise feed-forward network
    in Transforner block, which is introduced in
    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.

    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
        https://arxiv.org/pdf/1905.09263.pdf

    """

    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
        """Initialize MultiLayeredConv1d module.

        Args:
            in_chans (int): Number of input channels.
            hidden_chans (int): Number of hidden channels.
            kernel_size (int): Kernel size of conv1d.
            dropout_rate (float): Dropout rate.

        """
        super(MultiLayeredConv1dNew, self).__init__()
        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size,
            stride=1, padding=(kernel_size - 1) // 2)
        self.w_2 = torch.nn.Conv1d(hidden_chans, in_chans, kernel_size,
            stride=1, padding=(kernel_size - 1) // 2)
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, input_0):
        primals_2 = self.w_1.weight
        primals_3 = self.w_1.bias
        primals_4 = self.w_2.weight
        primals_5 = self.w_2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.04971