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 math
import torch.nn as nn
import torch.utils.data
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_eq_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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_1(in_ptr0, in_ptr1, 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').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -9.999999843067494e+17
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tmp12 * tmp2
    tmp14 = tl.where(tmp11, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tmp17 * tmp2
    tmp19 = tl.where(tmp16, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + x0, tmp20, xmask)
    tl.store(out_ptr1 + x0, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_2(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -9.999999843067494e+17
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tmp11 = 0.0
    tmp12 = tl.where(tmp0, tmp11, tmp10)
    tl.store(in_out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, 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_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_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
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = 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(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(64)](primals_6, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_1[grid(16)](buf3, buf2,
            buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = buf2
        del buf2
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_2[grid(64)](buf6, buf3,
            buf4, buf5, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf7, primals_3, out=buf8)
        buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf9)
        buf10 = buf5
        del buf5
        buf11 = buf4
        del buf4
        triton_poi_fused_add_native_layer_norm_3[grid(16)](primals_3, buf9,
            buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_4[grid(64)](primals_3, buf9,
            buf10, buf11, primals_8, primals_9, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf10
        del buf11
        del primals_9
    return buf12, primals_3, primals_8, buf3, buf6, reinterpret_tensor(buf8,
        (16, 4), (4, 1), 0), buf9, primals_7, reinterpret_tensor(buf0, (4, 
        4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 
        1), 0)


class GCNLayer(nn.Module):

    def __init__(self, embed_size, dropout=0.0):
        super().__init__()
        self.embed_size = embed_size
        self.ctx_layer = nn.Linear(self.embed_size, self.embed_size, bias=False
            )
        self.layernorm = nn.LayerNorm(embed_size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, node_fts, rel_edges):
        """Args:
      node_fts: (batch_size, num_nodes, embed_size)
      rel_edges: (batch_size, num_nodes, num_nodes)
    """
        ctx_embeds = self.ctx_layer(torch.bmm(rel_edges, node_fts))
        node_embeds = node_fts + self.dropout(ctx_embeds)
        node_embeds = self.layernorm(node_embeds)
        return node_embeds


class AttnGCNLayerNew(GCNLayer):

    def __init__(self, embed_size, d_ff, dropout=0.0):
        super().__init__(embed_size, dropout=dropout)
        self.edge_attn_query = nn.Linear(embed_size, d_ff)
        self.edge_attn_key = nn.Linear(embed_size, d_ff)
        self.attn_denominator = math.sqrt(d_ff)

    def forward(self, input_0, input_1):
        primals_1 = self.ctx_layer.weight
        primals_2 = self.layernorm.weight
        primals_5 = self.layernorm.bias
        primals_4 = self.edge_attn_query.weight
        primals_8 = self.edge_attn_query.bias
        primals_7 = self.edge_attn_key.weight
        primals_9 = self.edge_attn_key.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.060428