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
from torch import 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_per_fused__softmax_0(in_ptr0, out_ptr2, 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.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]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, out_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_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, 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, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (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, 4, 4, 4), (64, 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, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 16, 4), (64, 1, 16),
            0), reinterpret_tensor(buf1, (4, 4, 16), (64, 16, 1), 0), out=buf2)
        buf5 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(64)](buf2, buf5, 64, 16, XBLOCK=32,
            num_warps=4, num_stages=1)
        del buf2
        buf6 = 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(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 16), (64, 16, 1),
            0), reinterpret_tensor(buf5, (4, 16, 16), (256, 1, 16), 0), out
            =buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_1[grid(256)](primals_5, buf7, primals_1,
            buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return (buf8, buf5, primals_1, primals_2, primals_3, primals_4,
        primals_5, buf5, buf7, reinterpret_tensor(buf6, (4, 16, 4), (64, 1,
        16), 0), reinterpret_tensor(buf0, (4, 4, 16), (64, 16, 1), 0),
        reinterpret_tensor(buf1, (4, 16, 4), (64, 1, 16), 0))


class Self_AttnNew(nn.Module):
    """ Self attention Layer"""

    def __init__(self, in_dim, activation):
        super(Self_AttnNew, self).__init__()
        self.chanel_in = in_dim
        self.activation = activation
        if in_dim >= 8:
            self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=
                in_dim // 8, kernel_size=1, bias=False)
            self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=
                in_dim // 8, kernel_size=1, bias=False)
        else:
            self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=
                in_dim, kernel_size=1, bias=False)
            self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=
                in_dim, kernel_size=1, bias=False)
        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim,
            kernel_size=1, bias=False)
        self.gamma = nn.Parameter(torch.zeros(1))
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, input_0):
        primals_5 = self.gamma
        primals_2 = self.query_conv.weight
        primals_3 = self.key_conv.weight
        primals_4 = self.value_conv.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]


Measured latency_ms: 0.014684