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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 1024 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 256 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_tanh_threshold_backward_4(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = libdevice.tanh(tmp4)
    tmp6 = 0.0
    tmp7 = tmp4 <= tmp6
    tl.store(out_ptr0 + x3, tmp5, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_relu_5(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
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__log_softmax_7(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_8(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (8, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (32, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_13, (64,), (1,))
    assert_size_stride(primals_14, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_17, (64,), (1,))
    assert_size_stride(primals_18, (64, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_19, (64,), (1,))
    assert_size_stride(primals_20, (4, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_21, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(131072)](buf1, primals_3, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(65536)](buf3, primals_5, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 16, 16), (8192, 256, 16, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(32768)](buf5, primals_7, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(16384)](buf7, primals_9, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_3[grid(16384)](buf9, primals_11, 
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_3[grid(16384)](buf11, primals_13,
            16384, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 64, 8, 8), (4096, 64, 8, 1))
        buf13 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.
            float32)
        buf24 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool)
        triton_poi_fused_convolution_relu_tanh_threshold_backward_4[grid(16384)
            ](buf12, primals_15, buf13, buf24, 16384, XBLOCK=256, num_warps
            =4, num_stages=1)
        del buf12
        del primals_15
        buf14 = torch.ops.aten.avg_pool2d.default(buf13, [8, 8], [8, 8], [0,
            0], False, True, None)
        buf15 = buf14
        del buf14
        buf16 = extern_kernels.convolution(buf15, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 64, 1, 1), (64, 1, 1, 1))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_5[grid(256)](buf17, primals_17, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf18 = extern_kernels.convolution(buf17, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 1, 1), (64, 1, 1, 1))
        buf19 = buf18
        del buf18
        triton_poi_fused_convolution_relu_5[grid(256)](buf19, primals_19, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_19
        buf20 = extern_kernels.convolution(buf19, primals_20, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 4, 1, 1), (4, 1, 1, 1))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_6[grid(16)](buf21, primals_21, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_21
        buf22 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        triton_poi_fused__log_softmax_7[grid(16)](buf21, buf22, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf23 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        triton_poi_fused__log_softmax_8[grid(16)](buf22, buf23, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del buf22
    return (reinterpret_tensor(buf23, (4, 4), (4, 1), 0), primals_1,
        primals_2, primals_4, primals_6, primals_8, primals_10, primals_12,
        primals_14, primals_16, primals_18, primals_20, buf1, buf3, buf5,
        buf7, buf9, buf11, buf13, buf15, buf17, buf19, buf21, buf24)


class GatingNew(nn.Module):
    """
	FCN architecture for large scale scene coordiante regression.

	"""

    def __init__(self, num_experts, capacity=1):
        """
		Constructor.
		"""
        super(GatingNew, self).__init__()
        self.capacity = capacity
        self.conv1 = nn.Conv2d(3, 8, 3, 1, 1)
        self.conv2 = nn.Conv2d(8, 16, 3, 2, 1)
        self.conv3 = nn.Conv2d(16, 32, 3, 2, 1)
        self.conv4 = nn.Conv2d(32, 64 * capacity, 3, 2, 1)
        self.res1_conv1 = nn.Conv2d(64 * capacity, 64 * capacity, 3, 1, 1)
        self.res1_conv2 = nn.Conv2d(64 * capacity, 64 * capacity, 1, 1, 0)
        self.res1_conv3 = nn.Conv2d(64 * capacity, 64 * capacity, 3, 1, 1)
        self.fc1 = nn.Conv2d(64 * capacity, 64 * capacity ** 2, 1, 1, 0)
        self.fc2 = nn.Conv2d(64 * capacity ** 2, 64 * capacity ** 2, 1, 1, 0)
        self.fc3 = nn.Conv2d(64 * capacity ** 2, num_experts, 1, 1, 0)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.res1_conv1.weight
        primals_11 = self.res1_conv1.bias
        primals_12 = self.res1_conv2.weight
        primals_13 = self.res1_conv2.bias
        primals_14 = self.res1_conv3.weight
        primals_15 = self.res1_conv3.bias
        primals_16 = self.fc1.weight
        primals_17 = self.fc1.bias
        primals_18 = self.fc2.weight
        primals_19 = self.fc2.bias
        primals_20 = self.fc3.weight
        primals_21 = self.fc3.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,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21])
        return output[0]


Measured latency_ms: 0.280317