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 libdevice, 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__softmax_0(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 4 * x2), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 4 * x2), tmp14 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 4 * x2), tmp16 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x3, tmp22, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_add_native_group_norm_4(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr5 + x3, xmask)
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x3, tmp10, 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, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = extern_kernels.convolution(primals_1, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 1), (16, 1, 4), 
            0), reinterpret_tensor(buf2, (4, 1, 4), (16, 4, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 1, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf5, (4, 4, 4), (16, 1, 4), 0), out=buf6)
        buf7 = buf4
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 1), (16, 1, 4), 
            4), reinterpret_tensor(buf2, (4, 1, 4), (16, 4, 1), 4), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = buf7
        del buf7
        triton_poi_fused__softmax_1[grid(64)](buf8, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 1, 4), (16, 4, 1), 
            4), reinterpret_tensor(buf9, (4, 4, 4), (16, 1, 4), 0), out=buf10)
        buf11 = buf8
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 1), (16, 1, 4), 
            8), reinterpret_tensor(buf2, (4, 1, 4), (16, 4, 1), 8), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf11, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = buf11
        del buf11
        triton_poi_fused__softmax_1[grid(64)](buf12, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf14 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 1, 4), (16, 4, 1), 
            8), reinterpret_tensor(buf13, (4, 4, 4), (16, 1, 4), 0), out=buf14)
        buf15 = buf12
        del buf12
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 1), (16, 1, 4), 
            12), reinterpret_tensor(buf2, (4, 1, 4), (16, 4, 1), 12), out=buf15
            )
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf15, buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf17 = buf15
        del buf15
        triton_poi_fused__softmax_1[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 1, 4), (16, 4, 1), 
            12), reinterpret_tensor(buf17, (4, 4, 4), (16, 1, 4), 0), out=buf18
            )
        buf19 = buf16
        del buf16
        triton_poi_fused_cat_2[grid(64)](buf6, buf10, buf14, buf18, buf19, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf10
        del buf14
        buf20 = extern_kernels.convolution(buf19, primals_5, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=4, bias=None)
        assert_size_stride(buf20, (4, 4, 4), (16, 4, 1))
        buf21 = reinterpret_tensor(buf6, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf6
        buf22 = reinterpret_tensor(buf18, (4, 4, 1, 1), (4, 1, 16, 16), 0)
        del buf18
        triton_poi_fused_native_group_norm_3[grid(16)](buf20, buf21, buf22,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_group_norm_4[grid(64)](buf20, buf21,
            buf22, primals_6, primals_7, primals_1, buf23, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf21
        del buf22
        del primals_7
    return (buf23, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, buf5, buf9, buf13, buf17, buf19, buf20,
        reinterpret_tensor(buf0, (4, 4, 1), (16, 1, 4), 12),
        reinterpret_tensor(buf1, (4, 1, 4), (16, 4, 1), 12),
        reinterpret_tensor(buf2, (4, 4, 1), (16, 1, 4), 12),
        reinterpret_tensor(buf0, (4, 4, 1), (16, 1, 4), 8),
        reinterpret_tensor(buf1, (4, 1, 4), (16, 4, 1), 8),
        reinterpret_tensor(buf2, (4, 4, 1), (16, 1, 4), 8),
        reinterpret_tensor(buf0, (4, 4, 1), (16, 1, 4), 4),
        reinterpret_tensor(buf1, (4, 1, 4), (16, 4, 1), 4),
        reinterpret_tensor(buf2, (4, 4, 1), (16, 1, 4), 4),
        reinterpret_tensor(buf0, (4, 4, 1), (16, 1, 4), 0),
        reinterpret_tensor(buf1, (4, 1, 4), (16, 4, 1), 0),
        reinterpret_tensor(buf2, (4, 4, 1), (16, 1, 4), 0))


class SA_blockNew(nn.Module):
    """Self-Attention block with dot product for point/voxel/pillar context.
    A part of the code is from MLCVNet (CVPR 2020).
    """

    def __init__(self, inplanes, planes, groups=4):
        super().__init__()
        self.groups = groups
        self.t = nn.Conv1d(inplanes, planes, kernel_size=1, stride=1, bias=
            False)
        self.p = nn.Conv1d(inplanes, planes, kernel_size=1, stride=1, bias=
            False)
        self.g = nn.Conv1d(inplanes, planes, kernel_size=1, stride=1, bias=
            False)
        self.z = nn.Conv1d(planes, inplanes, kernel_size=1, stride=1,
            groups=self.groups, bias=False)
        self.gn = nn.GroupNorm(num_groups=self.groups, num_channels=inplanes)
        self.softmax = nn.Softmax(dim=-1)

    def kernel(self, t, p, g, b, c, h):
        """Return the output after dot product per head
        Args:
            t: output of linear value
            p: output of linear query
            g: output of linear keys
            b: batch size
            c: no of channels
            h: spatial breadth of feature maps
        """
        proj_query = p.view(b, c, h).permute(0, 2, 1)
        proj_key = g
        energy = torch.bmm(proj_query, proj_key)
        total_energy = energy
        attention = self.softmax(total_energy)
        proj_value = t
        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(b, c, h)
        return out

    def forward(self, input_0):
        primals_2 = self.t.weight
        primals_3 = self.p.weight
        primals_4 = self.g.weight
        primals_5 = self.z.weight
        primals_6 = self.gn.weight
        primals_7 = self.gn.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: 0.249699