# 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/qz/cqz2secr535r76gqduuhmyhjt4aplvu6fvum4xsh5e3ep5fhn6kv.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%expand, %expand_1], -1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    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_cat_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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x4 = (xindex // 32)
    x1 = (xindex // 8) % 4
    x3 = (xindex // 128)
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x4) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + ((4*x1) + (16*x3) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x5), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/mf/cmffxi4mvuzp34r5n7jtektcfuii5ruvm4ohtlw23b3k2hkyj3uv.py
# Topologically Sorted Source Nodes: [mul, mul_1, A_esm, A_mask, eq, masked_fill], Original ATen: [aten.mul, aten.add, aten.eq, aten.masked_fill]
# Source node to ATen node mapping:
#   A_esm => add
#   A_mask => mul_2
#   eq => eq
#   masked_fill => full_default, where
#   mul => mul
#   mul_1 => mul_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, 0.6), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, 0.4), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_2, %expand_3), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%mul_2, 0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 9.999999717180685e-10), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %add), kwargs = {})
triton_poi_fused_add_eq_masked_fill_mul_1 = async_compile.triton('triton_poi_fused_add_eq_masked_fill_mul_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_eq_masked_fill_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_eq_masked_fill_mul_1(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x2 = (xindex // 16) % 4
    x4 = (xindex // 4) % 16
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (x6), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 0.0
    tmp4 = tmp2 == tmp3
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = 0.6
    tmp8 = tmp6 * tmp7
    tmp10 = 0.4
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = 9.999999717180685e-10
    tmp14 = tl.where(tmp4, tmp13, tmp12)
    tl.store(out_ptr0 + (x6), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 8), (8, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, buf0, 512, grid=grid(512), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_2, (8, 1), (1, 8), 0), alpha=1, beta=1, out=buf2)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, A_esm, A_mask, eq, masked_fill], Original ATen: [aten.mul, aten.add, aten.eq, aten.masked_fill]
        triton_poi_fused_add_eq_masked_fill_mul_1.run(primals_5, buf2, primals_4, buf3, 256, grid=grid(256), stream=stream0)
        del primals_4
    return (buf3, primals_5, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), buf2, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (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)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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


class SentenceMatrixLayer(nn.Module):

    def __init__(self, in_size, out_size=1, p_Asem=0.6):
        super(SentenceMatrixLayer, self).__init__()
        self.in_size = in_size
        self.out_size = out_size
        self.p_Asem = p_Asem
        self.linear = nn.Linear(in_size * 2, out_size)

    def forward(self, x, adj, mask):
        seq_len = x.shape[1]
        xi = x.unsqueeze(2).expand(-1, -1, seq_len, -1)
        xj = x.unsqueeze(1).expand(-1, seq_len, -1, -1)
        xij = torch.sigmoid(self.linear(torch.cat((xi, xj), dim=-1))).squeeze(
            -1)
        A_esm = self.p_Asem * xij + (1 - self.p_Asem) * adj
        assert mask.shape[1] == seq_len, 'seq_len inconsistent'
        mask_i = mask.unsqueeze(1).expand(-1, seq_len, -1)
        mask_j = mask.unsqueeze(2).expand(-1, -1, seq_len)
        A_mask = mask_i * mask_j
        return A_esm.masked_fill(A_mask == 0, 1e-09)


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


def get_init_inputs():
    return [[], {'in_size': 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
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x4 = xindex // 32
    x1 = xindex // 8 % 4
    x3 = xindex // 128
    x5 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x4 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (4 * x1 + 16 * x3 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x5, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_eq_masked_fill_mul_1(in_ptr0, in_ptr1, in_ptr2,
    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 % 4
    x2 = xindex // 16 % 4
    x4 = xindex // 4 % 16
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x6, xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 0.0
    tmp4 = tmp2 == tmp3
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = 0.6
    tmp8 = tmp6 * tmp7
    tmp10 = 0.4
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 + tmp11
    tmp13 = 9.999999717180685e-10
    tmp14 = tl.where(tmp4, tmp13, tmp12)
    tl.store(out_ptr0 + x6, tmp14, 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), (16, 4, 1))
    assert_size_stride(primals_2, (1, 8), (8, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, buf0, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_2, (8, 1), (1, 8), 0),
            alpha=1, beta=1, out=buf2)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_eq_masked_fill_mul_1[grid(256)](primals_5,
            buf2, primals_4, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
    return buf3, primals_5, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), buf2


class SentenceMatrixLayerNew(nn.Module):

    def __init__(self, in_size, out_size=1, p_Asem=0.6):
        super(SentenceMatrixLayerNew, self).__init__()
        self.in_size = in_size
        self.out_size = out_size
        self.p_Asem = p_Asem
        self.linear = nn.Linear(in_size * 2, out_size)

    def forward(self, input_0, input_1, input_2):
        primals_2 = self.linear.weight
        primals_3 = self.linear.bias
        primals_1 = input_0
        primals_4 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
