# 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/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   h => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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: '*fp32', 2: '*i1', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_threshold_backward_0(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/z4/cz4cbw4gpj7elswxd76xfi2rczcz7clesffiyvgdrdahgny5dakh.py
# Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, sum_1], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.sum]
# Source node to ATen node mapping:
#   e_x => exp
#   e_x_1 => mul_1
#   max_1 => max_1
#   sub => sub
#   sum_1 => sum_1
#   x => mul
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_2, %primals_6), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 1, True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %primals_6), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1], True), kwargs = {})
triton_poi_fused_exp_max_mul_sub_sum_1 = async_compile.triton('triton_poi_fused_exp_max_mul_sub_sum_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: '*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_exp_max_mul_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_exp_max_mul_sub_sum_1(in_ptr0, in_ptr1, 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
    x3 = (xindex // 4)
    x2 = (xindex // 16)
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4 + (64*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x3), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x4 + (64*x2)), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x4 + (64*x2)), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x3), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x4 + (64*x2)), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 * tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 * tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 * tmp1
    tmp18 = tmp5 - tmp14
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp19 * tmp4
    tmp21 = tmp17 + tmp20
    tmp22 = tmp9 - tmp14
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp23 * tmp8
    tmp25 = tmp21 + tmp24
    tmp26 = tmp13 - tmp14
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp27 * tmp12
    tmp29 = tmp25 + tmp28
    tl.store(out_ptr0 + (x5), tmp14, xmask)
    tl.store(out_ptr1 + (x5), tmp29, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6m/c6maj6noglgg3okzmxiv4u3srhu54pov7obv57fn6vlkqal326zf.py
# Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, add, softmax], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   e_x => exp
#   e_x_1 => mul_1
#   max_1 => max_1
#   softmax => div
#   sub => sub
#   x => mul
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_2, %primals_6), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 1, True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %primals_6), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add), kwargs = {})
triton_poi_fused_add_div_exp_max_mul_sub_2 = async_compile.triton('triton_poi_fused_add_div_exp_max_mul_sub_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: '*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_add_div_exp_max_mul_sub_2', '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_div_exp_max_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    x5 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x1 + (4*x3) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask)
    tmp3 = tl.load(in_ptr2 + (x5 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x5 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 * tmp1
    tmp8 = 1e-06
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 / tmp9
    tl.store(out_ptr0 + (x5 + (16*x3) + (64*x2)), tmp10, 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 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (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: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf7, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [o], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, sum_1], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.sum]
        triton_poi_fused_exp_max_mul_sub_sum_1.run(buf3, primals_6, buf4, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, add, softmax], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.add, aten.div]
        triton_poi_fused_add_div_exp_max_mul_sub_2.run(buf3, primals_6, buf4, buf5, buf6, 256, grid=grid(256), stream=stream0)
        del buf4
        del buf5
    return (buf6, primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf7, )


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, ), (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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = 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, primals_5, primals_6])
    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.utils.data
import torch.nn.functional as F


def masked_softmax(x, m=None, dim=-1):
    """
    Softmax with mask
    :param x:
    :param m:
    :param dim:
    :return:
    """
    if m is not None:
        m = m.float()
        x = x * m
    e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
    if m is not None:
        e_x = e_x * m
    softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
    return softmax


class ForwardNet(torch.nn.Module):
    """
    one hidden layer and one softmax layer.
    Args:
        - input_size:
        - hidden_size:
        - output_size:
        - dropout_p:
    Inputs:
        - x: (seq_len, batch, input_size)
        - x_mask: (batch, seq_len)
    Outputs:
        - beta: (batch, seq_len)
    """

    def __init__(self, input_size, hidden_size, dropout_p):
        super(ForwardNet, self).__init__()
        self.linear_h = torch.nn.Linear(input_size, hidden_size)
        self.linear_o = torch.nn.Linear(hidden_size, 1)
        self.dropout = torch.nn.Dropout(p=dropout_p)

    def forward(self, x, x_mask):
        h = F.relu(self.linear_h(x))
        h = self.dropout(h)
        o = self.linear_o(h)
        o = o.squeeze(2).transpose(0, 1)
        beta = masked_softmax(o, x_mask, dim=1)
        return beta


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 4, 'dropout_p': 0.5}]

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
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_relu_threshold_backward_0(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_exp_max_mul_sub_sum_1(in_ptr0, in_ptr1, 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
    x3 = xindex // 4
    x2 = xindex // 16
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4 + 64 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x3), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x4 + 64 * x2), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x4 + 64 * x2), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x3), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x4 + 64 * x2), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 * tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 * tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 * tmp1
    tmp18 = tmp5 - tmp14
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp19 * tmp4
    tmp21 = tmp17 + tmp20
    tmp22 = tmp9 - tmp14
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp23 * tmp8
    tmp25 = tmp21 + tmp24
    tmp26 = tmp13 - tmp14
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp27 * tmp12
    tmp29 = tmp25 + tmp28
    tl.store(out_ptr0 + x5, tmp14, xmask)
    tl.store(out_ptr1 + x5, tmp29, xmask)


@triton.jit
def triton_poi_fused_add_div_exp_max_mul_sub_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    x5 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * x3 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask)
    tmp3 = tl.load(in_ptr2 + (x5 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr3 + (x5 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 * tmp1
    tmp8 = 1e-06
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 / tmp9
    tl.store(out_ptr0 + (x5 + 16 * x3 + 64 * x2), tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (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_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_poi_fused_exp_max_mul_sub_sum_1[grid(64)](buf3, primals_6,
            buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_poi_fused_add_div_exp_max_mul_sub_2[grid(256)](buf3,
            primals_6, buf4, buf5, buf6, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf4
        del buf5
    return buf6, primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf7


def masked_softmax(x, m=None, dim=-1):
    """
    Softmax with mask
    :param x:
    :param m:
    :param dim:
    :return:
    """
    if m is not None:
        m = m.float()
        x = x * m
    e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
    if m is not None:
        e_x = e_x * m
    softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
    return softmax


class ForwardNetNew(torch.nn.Module):
    """
    one hidden layer and one softmax layer.
    Args:
        - input_size:
        - hidden_size:
        - output_size:
        - dropout_p:
    Inputs:
        - x: (seq_len, batch, input_size)
        - x_mask: (batch, seq_len)
    Outputs:
        - beta: (batch, seq_len)
    """

    def __init__(self, input_size, hidden_size, dropout_p):
        super(ForwardNetNew, self).__init__()
        self.linear_h = torch.nn.Linear(input_size, hidden_size)
        self.linear_o = torch.nn.Linear(hidden_size, 1)
        self.dropout = torch.nn.Dropout(p=dropout_p)

    def forward(self, input_0, input_1):
        primals_1 = self.linear_h.weight
        primals_2 = self.linear_h.bias
        primals_4 = self.linear_o.weight
        primals_5 = self.linear_o.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
