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

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


# kernel path: runs/run_shard_6/inductor_cache/ww/cwwpir5gsc7d5dokiuwldad3gry5gyxakinvothybmaie6l5e22x.py
# Topologically Sorted Source Nodes: [sub, bool_1, masked_vector, result], Original ATen: [aten.rsub, aten._to_copy, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   bool_1 => convert_element_type
#   masked_vector => full_default, where
#   result => amax, exp, sub_1, sum_1
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %unsqueeze_1), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sub, torch.bool), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1.0000000331813535e+32), 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 = (%convert_element_type, %full_default, %view_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [1], True), kwargs = {})
#   %sub_1 : [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_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax__to_copy_masked_fill_rsub_0 = async_compile.triton('triton_poi_fused__softmax__to_copy_masked_fill_rsub_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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__to_copy_masked_fill_rsub_0', 'mutated_arg_names': [], '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__to_copy_masked_fill_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 4
    x2 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp4 = tl.load(in_ptr1 + (x1 + (16*x2)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x1 + (16*x2)), xmask)
    tmp10 = tl.load(in_ptr1 + (8 + x1 + (16*x2)), xmask)
    tmp13 = tl.load(in_ptr1 + (12 + x1 + (16*x2)), xmask)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp3 = (tmp2 != 0)
    tmp5 = -1.0000000331813535e+32
    tmp6 = tl.where(tmp3, tmp5, tmp4)
    tmp8 = tl.where(tmp3, tmp5, tmp7)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tl.where(tmp3, tmp5, tmp10)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tl.where(tmp3, tmp5, tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp16 = tmp6 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp8 - tmp15
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp15
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp14 - tmp15
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tl.store(out_ptr0 + (x0), tmp3, xmask)
    tl.store(out_ptr1 + (x0), tmp15, xmask)
    tl.store(out_ptr2 + (x0), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yl/cylfvunc2uua2jyiaihve72db7ae6ut5megnvfzqnlektcibh2jy.py
# Topologically Sorted Source Nodes: [masked_vector, result, attw, attn_feats], Original ATen: [aten.masked_fill, aten._softmax, aten.add, aten.mul]
# Source node to ATen node mapping:
#   attn_feats => mul
#   attw => add
#   masked_vector => full_default, where
#   result => amax, div, exp, sub_1, sum_1
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1.0000000331813535e+32), 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 = (%convert_element_type, %full_default, %view_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [1], True), kwargs = {})
#   %sub_1 : [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_1,), 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 = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 1e-13), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %add), kwargs = {})
triton_poi_fused__softmax_add_masked_fill_mul_1 = async_compile.triton('triton_poi_fused__softmax_add_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: '*i1', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_masked_fill_mul_1', 'mutated_arg_names': [], '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_add_masked_fill_mul_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = (xindex // 4) % 4
    x3 = (xindex // 64)
    x5 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr1 + (x1 + (4*x3)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp2 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr4 + (x1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp3 = -1.0000000331813535e+32
    tmp4 = tl.where(tmp1, tmp3, tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tmp10 = 1e-13
    tmp11 = tmp9 + tmp10
    tmp12 = tmp0 * tmp11
    tl.store(out_ptr0 + (x4), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/67/c67xtgksmpjvrx5otnwmnt5tn4vpspb4ftjik6qpv57g4ff7cilq.py
# Topologically Sorted Source Nodes: [masked_vector, result, attw], Original ATen: [aten.masked_fill, aten._softmax, aten.add]
# Source node to ATen node mapping:
#   attw => add
#   masked_vector => full_default, where
#   result => amax, div, exp, sub_1, sum_1
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1.0000000331813535e+32), 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 = (%convert_element_type, %full_default, %view_1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [1], True), kwargs = {})
#   %sub_1 : [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_1,), 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 = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 1e-13), kwargs = {})
triton_poi_fused__softmax_add_masked_fill_2 = async_compile.triton('triton_poi_fused__softmax_add_masked_fill_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=[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_add_masked_fill_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__softmax_add_masked_fill_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (x3), xmask)
    tmp4 = tl.load(in_ptr2 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = -1.0000000331813535e+32
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = 1e-13
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 4, 1), (4, 4, 1, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        buf3 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [sub, bool_1, masked_vector, result], Original ATen: [aten.rsub, aten._to_copy, aten.masked_fill, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax__to_copy_masked_fill_rsub_0.run(primals_3, buf0, buf1, buf2, buf3, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [masked_vector, result, attw, attn_feats], Original ATen: [aten.masked_fill, aten._softmax, aten.add, aten.mul]
        triton_poi_fused__softmax_add_masked_fill_mul_1.run(primals_2, buf1, buf0, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [res], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [masked_vector, result, attw], Original ATen: [aten.masked_fill, aten._softmax, aten.add]
        triton_poi_fused__softmax_add_masked_fill_2.run(buf1, buf0, buf2, buf3, buf6, 64, grid=grid(64), stream=stream0)
        del buf2
        del buf3
    return (reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0), primals_2, buf0, buf1, reinterpret_tensor(buf4, (64, 4), (4, 1), 0), primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((1, 4), (4, 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, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (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.distributed
import torch.optim.lr_scheduler
import torch.utils.data


class AttwNetHead(nn.Module):

    def __init__(self, idim, hdim, odim):
        super().__init__()
        self.mlp_attn = nn.Linear(idim, 1, bias=False)
        self.mlp_out = nn.Linear(idim, odim, bias=False)

    def masked_softmax(self, vector: 'torch.Tensor', mask: 'torch.Tensor',
        dim: 'int'=-1, memory_efficient: 'bool'=False, mask_fill_value:
        'float'=-1e+32):
        if mask is None:
            result = torch.nn.functional.softmax(vector, dim=dim)
        else:
            mask = mask.float()
            while mask.dim() < vector.dim():
                mask = mask.unsqueeze(1)
            if not memory_efficient:
                result = torch.nn.functional.softmax(vector * mask, dim=dim)
                result = result * mask
                result = result / (result.sum(dim=dim, keepdim=True) + 1e-13)
            else:
                masked_vector = vector.masked_fill((1 - mask).bool(),
                    mask_fill_value)
                result = torch.nn.functional.softmax(masked_vector, dim=dim)
        return result + 1e-13

    def mask_softmax(self, feat, mask, dim=-1):
        return self.masked_softmax(feat, mask, memory_efficient=True, dim=dim)

    def get_mask_from_sequence_lengths(self, sequence_lengths:
        'torch.Tensor', max_length: 'int'):
        ones = sequence_lengths.new_ones(sequence_lengths.size(0), max_length)
        range_tensor = ones.cumsum(dim=1)
        return (sequence_lengths.unsqueeze(1) >= range_tensor).long()

    def forward(self, mfeats, mask):
        logits = self.mlp_attn(mfeats)
        attw = self.mask_softmax(logits, mask.unsqueeze(-1).repeat(1, 1,
            logits.shape[-1]), dim=1)
        attn_feats = mfeats * attw
        res = self.mlp_out(attn_feats)
        return res, attw.squeeze()


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


def get_init_inputs():
    return [[], {'idim': 4, 'hdim': 4, 'odim': 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
import torch.nn as nn
import torch.distributed
import torch.optim.lr_scheduler
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__softmax__to_copy_masked_fill_rsub_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 4
    x2 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp4 = tl.load(in_ptr1 + (x1 + 16 * x2), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x1 + 16 * x2), xmask)
    tmp10 = tl.load(in_ptr1 + (8 + x1 + 16 * x2), xmask)
    tmp13 = tl.load(in_ptr1 + (12 + x1 + 16 * x2), xmask)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp3 = tmp2 != 0
    tmp5 = -1.0000000331813535e+32
    tmp6 = tl.where(tmp3, tmp5, tmp4)
    tmp8 = tl.where(tmp3, tmp5, tmp7)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tl.where(tmp3, tmp5, tmp10)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tl.where(tmp3, tmp5, tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp16 = tmp6 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp8 - tmp15
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp15
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp14 - tmp15
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tl.store(out_ptr0 + x0, tmp3, xmask)
    tl.store(out_ptr1 + x0, tmp15, xmask)
    tl.store(out_ptr2 + x0, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_add_masked_fill_mul_1(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + (x1 + 4 * x3), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp2 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr4 + (x1 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp3 = -1.0000000331813535e+32
    tmp4 = tl.where(tmp1, tmp3, tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tmp10 = 1e-13
    tmp11 = tmp9 + tmp10
    tmp12 = tmp0 * tmp11
    tl.store(out_ptr0 + x4, tmp12, xmask)


@triton.jit
def triton_poi_fused__softmax_add_masked_fill_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr3 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = -1.0000000331813535e+32
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tmp9 = 1e-13
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x3, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 4, 1), (4, 4, 1, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        buf3 = empty_strided_cuda((4, 1, 4, 1), (4, 16, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax__to_copy_masked_fill_rsub_0[grid(16)](
            primals_3, buf0, buf1, buf2, buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_add_masked_fill_mul_1[grid(256)](primals_2,
            buf1, buf0, buf2, buf3, buf4, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused__softmax_add_masked_fill_2[grid(64)](buf1, buf0,
            buf2, buf3, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
        del buf3
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0
        ), primals_2, buf0, buf1, reinterpret_tensor(buf4, (64, 4), (4, 1), 0
        ), primals_4


class AttwNetHeadNew(nn.Module):

    def __init__(self, idim, hdim, odim):
        super().__init__()
        self.mlp_attn = nn.Linear(idim, 1, bias=False)
        self.mlp_out = nn.Linear(idim, odim, bias=False)

    def masked_softmax(self, vector: 'torch.Tensor', mask: 'torch.Tensor',
        dim: 'int'=-1, memory_efficient: 'bool'=False, mask_fill_value:
        'float'=-1e+32):
        if mask is None:
            result = torch.nn.functional.softmax(vector, dim=dim)
        else:
            mask = mask.float()
            while mask.dim() < vector.dim():
                mask = mask.unsqueeze(1)
            if not memory_efficient:
                result = torch.nn.functional.softmax(vector * mask, dim=dim)
                result = result * mask
                result = result / (result.sum(dim=dim, keepdim=True) + 1e-13)
            else:
                masked_vector = vector.masked_fill((1 - mask).bool(),
                    mask_fill_value)
                result = torch.nn.functional.softmax(masked_vector, dim=dim)
        return result + 1e-13

    def mask_softmax(self, feat, mask, dim=-1):
        return self.masked_softmax(feat, mask, memory_efficient=True, dim=dim)

    def get_mask_from_sequence_lengths(self, sequence_lengths:
        'torch.Tensor', max_length: 'int'):
        ones = sequence_lengths.new_ones(sequence_lengths.size(0), max_length)
        range_tensor = ones.cumsum(dim=1)
        return (sequence_lengths.unsqueeze(1) >= range_tensor).long()

    def forward(self, input_0, input_1):
        primals_1 = self.mlp_attn.weight
        primals_3 = self.mlp_out.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
