# 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_4/inductor_cache/gd/cgdkwbgll3qv33h27jvqqssey37x43ic2aqfujlpcwlrzderhg35.py
# Topologically Sorted Source Nodes: [output, x], Original ATen: [aten.add, aten.clamp, aten.ge]
# Source node to ATen node mapping:
#   output => add_1
#   x => clamp_min
# Graph fragment:
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_plus_mm_1, %primals_5), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_1, 0), kwargs = {})
#   %ge_1 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add_1, 0), kwargs = {})
triton_poi_fused_add_clamp_ge_0 = async_compile.triton('triton_poi_fused_add_clamp_ge_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_ge_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_clamp_ge_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp2 >= tmp3
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qf/cqfba74b3yhmr4smax2cvjl7whxhynb3lorxdgir7eilewsighvu.py
# Topologically Sorted Source Nodes: [add_2, output_1, x_1], Original ATen: [aten.add, aten.clamp, aten.ge]
# Source node to ATen node mapping:
#   add_2 => mm_plus_mm
#   output_1 => add_3
#   x_1 => clamp_min_1
# Graph fragment:
#   %mm_plus_mm : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.mm_plus_mm](args = (), kwargs = {mat1: %primals_3, mat2: %mm_3, mat3: %clamp_min, mat4: %primals_7})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_plus_mm, %primals_8), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_3, 0), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add_3, 0), kwargs = {})
triton_poi_fused_add_clamp_ge_1 = async_compile.triton('triton_poi_fused_add_clamp_ge_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 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_add_clamp_ge_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_clamp_ge_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tmp4 >= tmp5
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/w4/cw4mbfaglppoilurhnlij4aivvpd6du5dgvm56bnk7ylgctpb4hw.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm_1, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm_1, %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 = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[8], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 2)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 - tmp3
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp5 / tmp10
    tl.store(out_ptr0 + (x2), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (4, 32), (32, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 32), (32, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (32, 32), (32, 1))
    assert_size_stride(primals_7, (32, 32), (32, 1))
    assert_size_stride(primals_8, (32, ), (1, ))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (16, 32), (32, 1))
    assert_size_stride(primals_11, (16, ), (1, ))
    assert_size_stride(primals_12, (2, 16), (16, 1))
    assert_size_stride(primals_13, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [potential_msgs], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
        extern_kernels._mm_plus_mm(primals_3, buf0, primals_2, primals_4, out=buf1)
        del primals_4
        buf2 = buf0; del buf0  # reuse
        buf12 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [output, x], Original ATen: [aten.add, aten.clamp, aten.ge]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clamp_ge_0.run(buf1, primals_5, buf2, buf12, 128, grid=grid(128), stream=stream0)
        del primals_5
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [potential_msgs_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, primals_6, out=buf3)
        buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_2], Original ATen: [aten.add]
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [add_2], Original ATen: [aten.add]
        extern_kernels.mm(buf2, primals_7, out=buf5)
        buf6 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add_2, output_1, x_1], Original ATen: [aten.add, aten.clamp, aten.ge]
        triton_poi_fused_add_clamp_ge_1.run(buf4, buf5, primals_8, buf6, buf11, 128, grid=grid(128), stream=stream0)
        del buf4
        del primals_8
        buf7 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [add_2, output_1, x_1, output_2], Original ATen: [aten.add, aten.clamp, aten.mm]
        extern_kernels.mm(primals_9, buf6, out=buf7)
        del buf6
        buf8 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(primals_10, (32, 16), (1, 32), 0), alpha=1, beta=1, out=buf8)
        del primals_11
        buf9 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(primals_12, (16, 2), (1, 16), 0), alpha=1, beta=1, out=buf9)
        del primals_13
        buf10 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf9, buf10, 8, grid=grid(8), stream=stream0)
        del buf9
    return (buf10, buf7, buf8, buf10, primals_12, primals_10, reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), buf11, reinterpret_tensor(buf2, (32, 4), (1, 32), 0), reinterpret_tensor(primals_7, (32, 32), (1, 32), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), reinterpret_tensor(primals_6, (32, 32), (1, 32), 0), buf12, reinterpret_tensor(primals_2, (4, 4), (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, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((2, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    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
from torch.nn.parameter import Parameter


def global_sum_pool(X, batch_mat):
    if batch_mat is None or batch_mat.dim() == 1:
        return torch.sum(X, dim=0).unsqueeze(0)
    else:
        return torch.mm(batch_mat, X)


class BasicGraphConvolutionLayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.W2 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.W1 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.bias = Parameter(torch.zeros(out_channels, dtype=torch.float32))

    def forward(self, X, A):
        potential_msgs = torch.mm(X, self.W2)
        propagated_msgs = torch.mm(A, potential_msgs)
        root_update = torch.mm(X, self.W1)
        output = propagated_msgs + root_update + self.bias
        return output


class NodeNetwork(torch.nn.Module):

    def __init__(self, input_features):
        super().__init__()
        self.conv_1 = BasicGraphConvolutionLayer(input_features, 32)
        self.conv_2 = BasicGraphConvolutionLayer(32, 32)
        self.fc_1 = torch.nn.Linear(32, 16)
        self.out_layer = torch.nn.Linear(16, 2)

    def forward(self, X, A, batch_mat):
        x = self.conv_1(X, A).clamp(0)
        x = self.conv_2(x, A).clamp(0)
        output = global_sum_pool(x, batch_mat)
        output = self.fc_1(output)
        output = self.out_layer(output)
        return F.softmax(output, dim=1)


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


def get_init_inputs():
    return [[], {'input_features': 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 math as tl_math
from torch.nn.parameter import Parameter
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_add_clamp_ge_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp2 >= tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_clamp_ge_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tmp4 >= tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 - tmp3
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp5 / tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 32), (32, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 32), (32, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 32), (32, 1))
    assert_size_stride(primals_7, (32, 32), (32, 1))
    assert_size_stride(primals_8, (32,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (16, 32), (32, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (2, 16), (16, 1))
    assert_size_stride(primals_13, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels._mm_plus_mm(primals_3, buf0, primals_2, primals_4,
            out=buf1)
        del primals_4
        buf2 = buf0
        del buf0
        buf12 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_clamp_ge_0[grid(128)](buf1, primals_5, buf2,
            buf12, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf3 = buf1
        del buf1
        extern_kernels.mm(buf2, primals_6, out=buf3)
        buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = buf3
        del buf3
        extern_kernels.mm(buf2, primals_7, out=buf5)
        buf6 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 32), (32, 1), torch.bool)
        triton_poi_fused_add_clamp_ge_1[grid(128)](buf4, buf5, primals_8,
            buf6, buf11, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_8
        buf7 = buf5
        del buf5
        extern_kernels.mm(primals_9, buf6, out=buf7)
        del buf6
        buf8 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(
            primals_10, (32, 16), (1, 32), 0), alpha=1, beta=1, out=buf8)
        del primals_11
        buf9 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(
            primals_12, (16, 2), (1, 16), 0), alpha=1, beta=1, out=buf9)
        del primals_13
        buf10 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(8)](buf9, buf10, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        del buf9
    return (buf10, buf7, buf8, buf10, primals_12, primals_10,
        reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), buf11,
        reinterpret_tensor(buf2, (32, 4), (1, 32), 0), reinterpret_tensor(
        primals_7, (32, 32), (1, 32), 0), reinterpret_tensor(primals_3, (4,
        4), (1, 4), 0), reinterpret_tensor(primals_6, (32, 32), (1, 32), 0),
        buf12, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0))


def global_sum_pool(X, batch_mat):
    if batch_mat is None or batch_mat.dim() == 1:
        return torch.sum(X, dim=0).unsqueeze(0)
    else:
        return torch.mm(batch_mat, X)


class BasicGraphConvolutionLayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.W2 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.W1 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.bias = Parameter(torch.zeros(out_channels, dtype=torch.float32))

    def forward(self, X, A):
        potential_msgs = torch.mm(X, self.W2)
        propagated_msgs = torch.mm(A, potential_msgs)
        root_update = torch.mm(X, self.W1)
        output = propagated_msgs + root_update + self.bias
        return output


class NodeNetworkNew(torch.nn.Module):

    def __init__(self, input_features):
        super().__init__()
        self.conv_1 = BasicGraphConvolutionLayer(input_features, 32)
        self.conv_2 = BasicGraphConvolutionLayer(32, 32)
        self.fc_1 = torch.nn.Linear(32, 16)
        self.out_layer = torch.nn.Linear(16, 2)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.conv_1.W2
        primals_4 = self.conv_1.W1
        primals_5 = self.conv_1.bias
        primals_6 = self.conv_2.W2
        primals_7 = self.conv_2.W1
        primals_8 = self.conv_2.bias
        primals_10 = self.fc_1.weight
        primals_11 = self.fc_1.bias
        primals_12 = self.out_layer.weight
        primals_13 = self.out_layer.bias
        primals_2 = input_0
        primals_3 = input_1
        primals_9 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
