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 libdevice
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 81
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 81 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 20736 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_convolution_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)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_red_fused_pow_sum_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 6272
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 8
    x1 = xindex // 8 % 196
    x2 = xindex // 1568
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp0 = tl.load(in_ptr0 + (32 * x0 + 256 * ((r3 + 128 * x1) % 784) +
            200704 * x2 + (r3 + 128 * x1) // 784), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp3, xmask)


@triton.jit
def triton_red_fused_pow_sum_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 32
    rnumel = 196
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 8
    x1 = xindex // 8
    _tmp2 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    x3 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + 8 * r2 + 1568 * x1), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp3 = _tmp2 + tmp1
        _tmp2 = tl.where(rmask & xmask, tmp3, _tmp2)
    tmp2 = tl.sum(_tmp2, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sqrt_5(in_ptr0, in_ptr1, out_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 // 25088
    x0 = xindex % 25088
    x1 = xindex // 25088 % 8
    x2 = xindex // 200704
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (32 * x1 + 256 * (x0 % 784) + 200704 * x2 + x0 //
        784), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.sqrt(tmp0)
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 / tmp6
    tl.store(out_ptr0 + x4, tmp7, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (256, 256, 9, 9), (20736, 81, 9, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 256, 9, 9), (20736, 1, 2304, 256),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(65536, 81)](primals_1, buf0, 65536, 81,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        triton_poi_fused_1[grid(1024, 4096)](primals_3, buf1, 1024, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 28, 28), (200704, 1, 7168, 256))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_2[grid(802816)](buf3, primals_2, 
            802816, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf4 = empty_strided_cuda((4, 8, 1, 196), (1568, 1, 6272, 8), torch
            .float32)
        triton_red_fused_pow_sum_3[grid(6272)](buf3, buf4, 6272, 128,
            XBLOCK=64, RBLOCK=8, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 8, 1), (8, 1, 1), torch.float32)
        triton_red_fused_pow_sum_4[grid(32)](buf4, buf5, 32, 196, XBLOCK=2,
            RBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        buf6 = empty_strided_cuda((4, 8, 25088), (200704, 25088, 1), torch.
            float32)
        triton_poi_fused_add_div_mul_sqrt_5[grid(802816)](buf5, buf3, buf6,
            802816, XBLOCK=1024, num_warps=4, num_stages=1)
    return reinterpret_tensor(buf6, (4, 25088, 8), (200704, 1, 25088), 0
        ), buf0, buf1, buf3, buf5


class PrimaryCapsNew(nn.Module):
    """
        输入:(B,C,H,W)=(B,256,20,20)
        输出：(B,C_N,C_L)=(B,32*6*6, 8)=(B,1152,8)
        C_N:capsule_num，胶囊的个数
        C_L:capsule_length，每个胶囊的长度
    """

    def __init__(self, capsule_length=8, in_channels=256, out_channels=32,
        capsule_num=32 * 6 * 6, kernel_size=9, stride=2, padding=0):
        super(PrimaryCapsNew, self).__init__()
        self.capsule_length = capsule_length
        self.capsule_num = capsule_num
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels * capsule_length, kernel_size=kernel_size, stride=
            stride, padding=padding)

    def toCapsules(self, x):
        B = x.size(0)
        x.size(1)
        H = x.size(2)
        W = x.size(3)
        x = x.reshape(B, self.capsule_length, -1, H, W)
        x = x.reshape(B, self.capsule_length, -1)
        x = self.squash(x)
        x = x.permute(0, 2, 1)
        return x

    def squash(self, input_tensor):
        """
        input_tensor: (B, 1, 10, 16)
        return: output_tensor: (B, 1, 10, 16)
        """
        squared_norm = (input_tensor ** 2).sum(-1, keepdim=True)
        output_tensor = squared_norm * input_tensor / ((1.0 + squared_norm) *
            torch.sqrt(squared_norm))
        return output_tensor

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_2 = self.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.991596