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.triton_helpers import math as tl_math
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_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_per_fused_div_exp_sum_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = tmp1 / tmp5
    tl.store(out_ptr1 + (r1 + 16 * x0), tmp6, xmask)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_2(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
    x3 = xindex
    x1 = xindex // 16 % 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, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (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, 1, 4, 4), (16, 16, 4, 1))
        buf1 = 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(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        buf2 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](buf2, primals_3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf3 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf1
        triton_poi_fused_convolution_0[grid(64)](buf3, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf3, (4, 1, 16), (16, 0, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        triton_per_fused_div_exp_sum_1[grid(64)](buf4, buf5, buf6, 64, 16,
            XBLOCK=32, num_warps=4, num_stages=1)
        buf7 = extern_kernels.convolution(primals_1, primals_6, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_2[grid(256)](buf8, primals_7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf9 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf8, (4, 4, 16), (64, 16, 1),
            0), buf6, out=buf9)
        buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 4, 
            4, 4), (64, 16, 4, 1), 0), primals_8, stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_2[grid(256)](buf11, primals_9, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
    return (buf11, primals_1, primals_2, primals_4, primals_6, primals_8,
        buf4, buf5, buf6, reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4,
        1), 0), reinterpret_tensor(buf8, (4, 16, 4), (64, 1, 16), 0),
        reinterpret_tensor(buf2, (4, 1, 16), (16, 16, 1), 0),
        reinterpret_tensor(buf3, (4, 16, 1), (16, 1, 16), 0))


class NormalAttention_embedded_gaussianNew(nn.Module):

    def __init__(self, input_channel_num, k=4):
        super(NormalAttention_embedded_gaussianNew, self).__init__()
        self.c_in = input_channel_num
        self.query_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
            self.c_in // k, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=self.c_in, out_channels=self.
            c_in // k, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=self.c_in, out_channels=
            self.c_in, kernel_size=1)
        self.gamma = nn.Conv2d(in_channels=self.c_in, out_channels=self.
            c_in, kernel_size=1)

    def forward(self, input_0):
        primals_2 = self.query_conv.weight
        primals_3 = self.query_conv.bias
        primals_4 = self.key_conv.weight
        primals_5 = self.key_conv.bias
        primals_6 = self.value_conv.weight
        primals_7 = self.value_conv.bias
        primals_8 = self.gamma.weight
        primals_9 = self.gamma.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.016163