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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from time import *
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_clone_transpose_0(in_out_ptr0, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 8
    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
    y2 = yindex % 4
    y3 = yindex // 4
    tmp0 = tl.load(in_out_ptr0 + (x1 + 8 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x1 + 8 * y0), tmp0, xmask & ymask)
    tl.store(out_ptr0 + (y2 + 4 * x1 + 32 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 8
    x1 = xindex // 8
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 8
    x2 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 8 * x2), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr1 + (x0 + 8 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x3, tmp5, xmask)


@triton.jit
def triton_poi_fused_clone_view_3(in_out_ptr0, in_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 8
    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 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x2 + 8 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_4(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4, 2, 1, 1), (2, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 4, 4), (32, 16, 4, 1))
        buf2 = extern_kernels.convolution(primals_1, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 2, 4, 4), (32, 16, 4, 1))
        buf3 = reinterpret_tensor(buf1, (4, 8, 4), (32, 1, 8), 0)
        del buf1
        buf15 = empty_strided_cuda((4, 4, 8), (32, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_transpose_0[grid(16, 8)](buf3, buf15, 16, 8,
            XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 8, 8), (64, 8, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf0, (4, 4, 8), (32, 8,
            1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 1, 8), (8, 32, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 1, 8), (8, 32, 1), torch.float32)
        triton_per_fused__softmax_1[grid(32)](buf4, buf5, buf6, 32, 8,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf7, buf5, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf5
        del buf6
        buf8 = reinterpret_tensor(buf2, (4, 8, 4), (32, 1, 8), 0)
        del buf2
        buf14 = reinterpret_tensor(buf3, (4, 4, 8), (32, 1, 4), 0)
        del buf3
        triton_poi_fused_clone_transpose_0[grid(16, 8)](buf8, buf14, 16, 8,
            XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        extern_kernels.bmm(buf7, buf8, out=buf9)
        buf10 = reinterpret_tensor(buf8, (4, 4, 8), (32, 8, 1), 0)
        del buf8
        buf11 = reinterpret_tensor(buf10, (4, 2, 4, 4), (32, 16, 4, 1), 0)
        del buf10
        triton_poi_fused_clone_view_3[grid(16, 8)](buf11, buf9, 16, 8,
            XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
        del buf9
        buf12 = extern_kernels.convolution(buf11, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_add_4[grid(256)](buf13, primals_1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return (buf13, primals_1, primals_2, primals_3, primals_4, primals_5,
        buf7, buf11, buf14, buf15, reinterpret_tensor(buf0, (4, 8, 4), (32,
        1, 8), 0))


class NonLocalBlockNew(nn.Module):

    def __init__(self, channel):
        super(NonLocalBlockNew, self).__init__()
        self.inter_channel = channel // 2
        self.conv_phi = nn.Conv2d(in_channels=channel, out_channels=self.
            inter_channel, kernel_size=1, stride=1, padding=0, bias=False)
        self.conv_theta = nn.Conv2d(in_channels=channel, out_channels=self.
            inter_channel, kernel_size=1, stride=1, padding=0, bias=False)
        self.conv_g = nn.Conv2d(in_channels=channel, out_channels=self.
            inter_channel, kernel_size=1, stride=1, padding=0, bias=False)
        self.softmax = nn.Softmax(dim=1)
        self.conv_mask = nn.Conv2d(in_channels=self.inter_channel,
            out_channels=channel, kernel_size=1, stride=1, padding=0, bias=
            False)

    def forward(self, input_0):
        primals_2 = self.conv_phi.weight
        primals_3 = self.conv_theta.weight
        primals_4 = self.conv_g.weight
        primals_5 = self.conv_mask.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.137952