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
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_repeat_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, 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 % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_2(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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_native_layer_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_native_layer_norm_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, 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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    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 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1), tmp8, 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, 4), (16, 4, 1))
    assert_size_stride(primals_3, (1, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 1, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_repeat_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(primals_3, (4, 4, 4), (0, 4, 1), 0), out
            =buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](buf1, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0),
            out=buf3)
        del buf2
        buf4 = reinterpret_tensor(buf1, (4, 4, 4), (4, 16, 1), 0)
        del buf1
        triton_poi_fused_relu_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf3
        buf5 = empty_strided_cuda((4, 4, 1), (1, 4, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 1), (1, 4, 16), torch.float32)
        triton_poi_fused_native_layer_norm_3[grid(16)](buf4, buf5, buf6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_4[grid(64)](buf4, buf5, buf6,
            primals_4, primals_5, buf7, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf5
        del buf6
        del primals_5
    return buf7, primals_4, buf4, reinterpret_tensor(primals_1, (16, 4, 4),
        (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0)


class PEGCNLayerNew(nn.Module):

    def __init__(self, input_dim, output_dim, prop_depth, act=torch.relu,
        dropout=0.0, layer_i=0):
        super(PEGCNLayerNew, self).__init__()
        self.prop_depth = prop_depth
        self.act = act
        self.weight = nn.Parameter(torch.empty(1, prop_depth, input_dim,
            output_dim, dtype=torch.float), requires_grad=True)
        nn.init.uniform_(self.weight.data)
        self.dropout = nn.Dropout(p=dropout)
        self.layer_norm = nn.LayerNorm(output_dim)
        self.layer_i = layer_i
        self.last_layer_flag = False

    def layer(self, x, adj_batch):
        if adj_batch.dim() < 4:
            adj_batch = adj_batch.unsqueeze(0)
        x = x.transpose(0, 1).unsqueeze(dim=1).repeat(1, self.prop_depth, 1, 1)
        x = torch.matmul(x, self.weight)
        x = torch.matmul(adj_batch, x)
        x = x.sum(dim=1)
        x = x.transpose(0, 1)
        return x

    def forward(self, input_0, input_1):
        primals_3 = self.weight
        primals_4 = self.layer_norm.weight
        primals_5 = self.layer_norm.bias
        primals_2 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.054349