# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/ex/cexco2jwic5z7azgywysopaagdfks6vfj2d5siyxxphq6tzqdb2c.py
# Topologically Sorted Source Nodes: [mul, sum_1, mul_1, sum_2, alpha, alpha_1], Original ATen: [aten.mul, aten.sum, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   alpha => add
#   alpha_1 => gt, mul_2, where
#   mul => mul
#   mul_1 => mul_1
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, %primals_3), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add, %mul_2), kwargs = {})
triton_poi_fused_add_leaky_relu_mul_sum_0 = async_compile.triton('triton_poi_fused_add_leaky_relu_mul_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_leaky_relu_mul_sum_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x2)), 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*x2)), 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*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (4*x2), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + (4*x0), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr2 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr3 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr2 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr3 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr3 + (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
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 + tmp29
    tmp31 = 0.0
    tmp32 = tmp30 > tmp31
    tmp33 = 0.2
    tmp34 = tmp30 * tmp33
    tmp35 = tl.where(tmp32, tmp30, tmp34)
    tl.store(out_ptr1 + (x2), tmp32, xmask)
    tl.store(out_ptr2 + (x2), tmp35, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sum_1, mul_1, sum_2, alpha, alpha_1], Original ATen: [aten.mul, aten.sum, aten.add, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_mul_sum_0.run(primals_2, primals_1, primals_4, primals_3, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_3
    return (buf2, primals_2, primals_4, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((1, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter


class ConstAttention(nn.Module):

    def __init__(self, **kwargs):
        super(ConstAttention, self).__init__()

    def forward(self, neighbor_vecs, self_vecs):
        return 1


class GatAttention(ConstAttention):

    def __init__(self, num_heads, out_channels):
        super(GatAttention, self).__init__()
        self.num_heads = num_heads
        self.out_channels = out_channels
        self.att_self_weight = Parameter(torch.Tensor(1, self.num_heads,
            self.out_channels))
        self.att_neighbor_weight = Parameter(torch.Tensor(1, self.num_heads,
            self.out_channels))
        self.reset_parameters()

    def reset_parameters(self):
        pass

    def forward(self, neighbor_vecs, self_vecs):
        alpha = (self_vecs * self.att_self_weight).sum(dim=-1) + (neighbor_vecs
             * self.att_neighbor_weight).sum(dim=-1)
        alpha = F.leaky_relu(alpha, negative_slope=0.2)
        return alpha


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'num_heads': 4, 'out_channels': 4}]

import torch
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
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_leaky_relu_mul_sum_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr2 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr3 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr3 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp26 = tl.load(in_ptr2 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr3 + (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
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 + tmp29
    tmp31 = 0.0
    tmp32 = tmp30 > tmp31
    tmp33 = 0.2
    tmp34 = tmp30 * tmp33
    tmp35 = tl.where(tmp32, tmp30, tmp34)
    tl.store(out_ptr1 + x2, tmp32, xmask)
    tl.store(out_ptr2 + x2, tmp35, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_mul_sum_0[grid(64)](primals_2,
            primals_1, primals_4, primals_3, buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        del primals_3
    return buf2, primals_2, primals_4, buf1


class ConstAttention(nn.Module):

    def __init__(self, **kwargs):
        super(ConstAttention, self).__init__()

    def forward(self, neighbor_vecs, self_vecs):
        return 1


class GatAttentionNew(ConstAttention):

    def __init__(self, num_heads, out_channels):
        super(GatAttentionNew, self).__init__()
        self.num_heads = num_heads
        self.out_channels = out_channels
        self.att_self_weight = Parameter(torch.Tensor(1, self.num_heads,
            self.out_channels))
        self.att_neighbor_weight = Parameter(torch.Tensor(1, self.num_heads,
            self.out_channels))
        self.reset_parameters()

    def reset_parameters(self):
        pass

    def forward(self, input_0, input_1):
        primals_1 = self.att_self_weight
        primals_3 = self.att_neighbor_weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
