# 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_7/inductor_cache/fd/cfdzxywc6gfatrb2cciasco4amlaqiff3eq75s6456ncgemwqr2f.py
# Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
# Source node to ATen node mapping:
#   gt => gt
# Graph fragment:
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_4, 0), kwargs = {})
triton_poi_fused_gt_0 = async_compile.triton('triton_poi_fused_gt_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gt_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_gt_0(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qj/cqj2yzzoizkpi7j34ba4c5hjk7zwldtz2asgnlpps6ynwyjfj7av.py
# Topologically Sorted Source Nodes: [zero_vec, _attention, attention], Original ATen: [aten.mul, aten.where, aten._softmax]
# Source node to ATen node mapping:
#   _attention => where
#   attention => amax, exp, sub, sum_1
#   zero_vec => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], -8999999815811072.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_6, %full_default), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_mul_where_1 = async_compile.triton('triton_poi_fused__softmax_mul_where_1', '''
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: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mul_where_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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__softmax_mul_where_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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 + (4*x0), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (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')
    tmp8 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp13 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp19 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = -8999999815811072.0
    tmp5 = tl.where(tmp0, tmp3, tmp4)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.where(tmp6, tmp9, tmp4)
    tmp11 = triton_helpers.maximum(tmp5, tmp10)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.where(tmp12, tmp15, tmp4)
    tmp17 = triton_helpers.maximum(tmp11, tmp16)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.where(tmp18, tmp21, tmp4)
    tmp23 = triton_helpers.maximum(tmp17, tmp22)
    tmp24 = tmp5 - tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp10 - tmp23
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp16 - tmp23
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp22 - tmp23
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tl.store(out_ptr0 + (x0), tmp23, xmask)
    tl.store(out_ptr1 + (x0), tmp34, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ah/cah2hs3rlo3lnpwmpsbts72lgns4kmf25uryjatmojnyggb2i7pl.py
# Topologically Sorted Source Nodes: [zero_vec, _attention, attention], Original ATen: [aten.mul, aten.where, aten._softmax]
# Source node to ATen node mapping:
#   _attention => where
#   attention => div, exp, sub
#   zero_vec => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], -8999999815811072.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_6, %full_default), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_mul_where_2 = async_compile.triton('triton_poi_fused__softmax_mul_where_2', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mul_where_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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__softmax_mul_where_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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)
    tmp2 = tl.load(in_ptr1 + (x2), xmask)
    tmp6 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = -8999999815811072.0
    tmp5 = tl.where(tmp0, tmp3, tmp4)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sz/cszljhyxbwdpeaoht5rks2td5aannynp6sjwinyp7s3qrxmtq4bj.py
# Topologically Sorted Source Nodes: [x_new], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_new => expm1, gt_1, mul_1, mul_3, where_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_10, 0), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_10, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_1, %mul_3), kwargs = {})
triton_poi_fused_elu_3 = async_compile.triton('triton_poi_fused_elu_3', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_elu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_elu_3(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0), tmp7, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 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)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [xW], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [score], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
        stream0 = get_raw_stream(0)
        triton_poi_fused_gt_0.run(primals_4, buf2, 256, grid=grid(256), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [zero_vec, _attention, attention], Original ATen: [aten.mul, aten.where, aten._softmax]
        triton_poi_fused__softmax_mul_where_1.run(buf2, buf1, primals_3, buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [zero_vec, _attention, attention], Original ATen: [aten.mul, aten.where, aten._softmax]
        triton_poi_fused__softmax_mul_where_2.run(buf5, buf2, primals_3, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del buf3
        del buf4
        del primals_3
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_new], Original ATen: [aten.elu]
        triton_poi_fused_elu_3.run(buf6, buf7, 256, grid=grid(256), stream=stream0)
    return (buf7, buf0, buf2, buf5, buf6, reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), )


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((4, 4), (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((4, 4, 4, 4), (64, 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 as nn
import torch.nn.functional as F


def module_test_print(var_input, var_inmed, var_ouput):
    for var in (var_input, var_inmed, var_ouput):
        None
        for key, value in var.items():
            None
            None


class GraphAttentionLayer(nn.Module):

    def __init__(self, dim_input, dim_output, dropout=0.0,
        negative_slope_LeakyRelu=0.01, module_test=False):
        super(GraphAttentionLayer, self).__init__()
        self.dim_input = dim_input
        self.dim_output = dim_output
        self.dropout = dropout
        self.W = nn.Parameter(torch.empty(size=(dim_input, dim_output)))
        nn.init.xavier_uniform_(self.W.data, gain=nn.init.calculate_gain(
            'leaky_relu', negative_slope_LeakyRelu))
        self.leakyrelu = nn.LeakyReLU(negative_slope_LeakyRelu)
        self.module_test = module_test

    def _attention_score(self, x, y=None):
        return torch.matmul(x, x.transpose(-2, -1))

    def forward(self, x, adj, A):
        """
        input:
            x   : (batch_size, n_node, dim_node)
            adj : (batch_size, n_node, n_node)
        """
        xW = torch.matmul(x, self.W)
        score = self._attention_score(xW)
        score += A
        zero_vec = -9000000000000000.0 * torch.ones_like(score)
        _attention = torch.where(adj > 0, score, zero_vec)
        attention = F.softmax(_attention, dim=-1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        x_new = F.elu(torch.matmul(attention, xW))
        if self.module_test:
            var_input = ['x', 'adj']
            var_inmed = ['xW', 'score', 'zero_vec', '_attention', 'attention']
            var_ouput = ['x_new']
            locals_cap = locals()
            module_test_print(dict(zip(var_input, [eval(v, locals_cap) for
                v in var_input])), dict(zip(var_inmed, [eval(v, locals_cap) for
                v in var_inmed])), dict(zip(var_ouput, [eval(v, locals_cap) for
                v in var_ouput])))
        return x_new

    def __repr__(self):
        return '{}({}->{})'.format(self.__class__.__name__, self.dim_input,
            self.dim_output)


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


def get_init_inputs():
    return [[], {'dim_input': 4, 'dim_output': 4}]

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_gt_0(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
    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_mul_where_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 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')
    tmp8 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp1 + tmp2
    tmp4 = -8999999815811072.0
    tmp5 = tl.where(tmp0, tmp3, tmp4)
    tmp9 = tmp7 + tmp8
    tmp10 = tl.where(tmp6, tmp9, tmp4)
    tmp11 = triton_helpers.maximum(tmp5, tmp10)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.where(tmp12, tmp15, tmp4)
    tmp17 = triton_helpers.maximum(tmp11, tmp16)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.where(tmp18, tmp21, tmp4)
    tmp23 = triton_helpers.maximum(tmp17, tmp22)
    tmp24 = tmp5 - tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp10 - tmp23
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp16 - tmp23
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp22 - tmp23
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tl.store(out_ptr0 + x0, tmp23, xmask)
    tl.store(out_ptr1 + x0, tmp34, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_where_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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)
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = -8999999815811072.0
    tmp5 = tl.where(tmp0, tmp3, tmp4)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_elu_3(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 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)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_gt_0[grid(256)](primals_4, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_mul_where_1[grid(64)](buf2, buf1,
            primals_3, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused__softmax_mul_where_2[grid(256)](buf5, buf2,
            primals_3, buf3, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del buf4
        del primals_3
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_elu_3[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf7, buf0, buf2, buf5, buf6, reinterpret_tensor(primals_2, (4, 
        64), (1, 4), 0)


def module_test_print(var_input, var_inmed, var_ouput):
    for var in (var_input, var_inmed, var_ouput):
        None
        for key, value in var.items():
            None
            None


class GraphAttentionLayerNew(nn.Module):

    def __init__(self, dim_input, dim_output, dropout=0.0,
        negative_slope_LeakyRelu=0.01, module_test=False):
        super(GraphAttentionLayerNew, self).__init__()
        self.dim_input = dim_input
        self.dim_output = dim_output
        self.dropout = dropout
        self.W = nn.Parameter(torch.empty(size=(dim_input, dim_output)))
        nn.init.xavier_uniform_(self.W.data, gain=nn.init.calculate_gain(
            'leaky_relu', negative_slope_LeakyRelu))
        self.leakyrelu = nn.LeakyReLU(negative_slope_LeakyRelu)
        self.module_test = module_test

    def _attention_score(self, x, y=None):
        return torch.matmul(x, x.transpose(-2, -1))

    def __repr__(self):
        return '{}({}->{})'.format(self.__class__.__name__, self.dim_input,
            self.dim_output)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.W
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
