# 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/oy/coyyh4komknigd6s5bag6tix6g4xmd2zqzeqo5stuxj2oszsrdim.py
# Topologically Sorted Source Nodes: [eq], Original ATen: [aten.eq]
# Source node to ATen node mapping:
#   eq => eq
# Graph fragment:
#   %eq : [num_users=2] = call_function[target=torch.ops.aten.eq.Scalar](args = (%unsqueeze, 0), kwargs = {})
triton_poi_fused_eq_0 = async_compile.triton('triton_poi_fused_eq_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: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vk/cvkpoiqic3bn6zjxecjwsebrahs3rln5jyritpcz6whedq7ub3ve.py
# Topologically Sorted Source Nodes: [m2m, m2m_1, m2m_w], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   m2m => div
#   m2m_1 => full_default, where
#   m2m_w => amax, exp, sub, sum_1
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 2.0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1000000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %div), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
triton_poi_fused__softmax_div_masked_fill_1 = async_compile.triton('triton_poi_fused__softmax_div_masked_fill_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i1', 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__softmax_div_masked_fill_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__softmax_div_masked_fill_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tmp12 * tmp2
    tmp14 = tl.where(tmp11, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tmp17 * tmp2
    tmp19 = tl.where(tmp16, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + (x2), tmp20, xmask)
    tl.store(out_ptr1 + (x2), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/o2/co2fd63la4ruc3ar2mmbrotudbyv4lpgh5osbtpthwipraxolkpt.py
# Topologically Sorted Source Nodes: [m2m, m2m_1, m2m_w], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   m2m => div
#   m2m_1 => full_default, where
#   m2m_w => amax, div_1, exp, sub
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 2.0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1000000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %div), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div_1 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_div_masked_fill_2 = async_compile.triton('triton_poi_fused__softmax_div_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: '*fp32', 1: '*i1', 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__softmax_div_masked_fill_2', 'mutated_arg_names': ['in_out_ptr0'], '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_div_masked_fill_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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
    x4 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp6 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/q2/cq26fybzn7g3la3efd7ehhsrxi2lr7ymr4nvyqdxrrt6bjjoxks4.py
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %bmm_1), kwargs = {})
triton_poi_fused_add_3 = async_compile.triton('triton_poi_fused_add_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    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_add_3', '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_add_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (8, 4), (4, 1))
    assert_size_stride(primals_6, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [m_k], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_3
        del primals_4
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [m_trans], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 8), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [eq], Original ATen: [aten.eq]
        stream0 = get_raw_stream(0)
        triton_poi_fused_eq_0.run(primals_1, buf3, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [m2m, m2m_1, m2m_w], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
        triton_poi_fused__softmax_div_masked_fill_1.run(buf3, buf2, buf4, buf5, 16, grid=grid(16), stream=stream0)
        buf6 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [m2m, m2m_1, m2m_w], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
        triton_poi_fused__softmax_div_masked_fill_2.run(buf6, buf3, buf4, buf5, 64, grid=grid(64), stream=stream0)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [r], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf6, reinterpret_tensor(buf1, (4, 4, 4), (32, 8, 1), 4), out=buf7)
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
        triton_poi_fused_add_3.run(buf8, primals_2, 64, grid=grid(64), stream=stream0)
    return (buf8, reinterpret_tensor(buf6, (4, 1, 4, 4), (16, 16, 4, 1), 0), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf3, buf6, reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 4), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 4), (32, 8, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, ), (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.nn as nn
import torch.nn.functional as F
import torch.distributed
import torch.optim.lr_scheduler
import torch.utils.data


class MultiHeadAttention(nn.Module):

    def __init__(self, idim, odim, nhead=1, use_bias=True):
        super(MultiHeadAttention, self).__init__()
        self.idim = idim
        self.odim = odim
        self.nheads = nhead
        self.use_bias = use_bias
        self.c_lin = nn.Linear(self.idim, self.odim * 2, bias=self.use_bias)
        self.v_lin = nn.Linear(self.idim, self.odim, bias=self.use_bias)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.drop = nn.Dropout(0)

    def forward(self, m_feats, mask):
        """
        apply muti-head attention
        Inputs:
            m_feats: multimodal features
            mask: mask for features
        Outputs:
            updated_m: updated multimodal features
        """
        mask = mask.float()
        _B, _nseg = mask.size()
        m_k = self.v_lin(self.drop(m_feats))
        m_trans = self.c_lin(self.drop(m_feats))
        m_q, m_v = torch.split(m_trans, m_trans.size(2) // 2, dim=2)
        new_mq = m_q
        new_mk = m_k
        w_list = []
        mk_set = torch.split(new_mk, new_mk.size(2) // self.nheads, dim=2)
        mq_set = torch.split(new_mq, new_mq.size(2) // self.nheads, dim=2)
        mv_set = torch.split(m_v, m_v.size(2) // self.nheads, dim=2)
        for i in range(self.nheads):
            mk_slice, mq_slice, mv_slice = mk_set[i], mq_set[i], mv_set[i]
            m2m = mk_slice @ mq_slice.transpose(1, 2) / (self.odim // self.
                nheads) ** 0.5
            m2m = m2m.masked_fill(mask.unsqueeze(1).eq(0), -1000000000.0 if
                m2m.dtype == torch.float32 else -10000.0)
            m2m_w = F.softmax(m2m, dim=2)
            w_list.append(m2m_w)
            r = m2m_w @ mv_slice if i == 0 else torch.cat((r, m2m_w @
                mv_slice), dim=2)
        updated_m = self.drop(m_feats + r)
        return updated_m, torch.stack(w_list, dim=1)


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


def get_init_inputs():
    return [[], {'idim': 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_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tmp12 * tmp2
    tmp14 = tl.where(tmp11, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tmp17 * tmp2
    tmp19 = tl.where(tmp16, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + x2, tmp20, xmask)
    tl.store(out_ptr1 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_2(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp6 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = -1000000000.0
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_out_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (8, 4), (4, 1))
    assert_size_stride(primals_6, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_3
        del primals_4
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 8), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_5
        del primals_6
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(16)](primals_1, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_1
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_1[grid(16)](buf3, buf2,
            buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = buf2
        del buf2
        triton_poi_fused__softmax_div_masked_fill_2[grid(64)](buf6, buf3,
            buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf1, (4, 4, 4), (32, 8,
            1), 4), out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_add_3[grid(64)](buf8, primals_2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf8, reinterpret_tensor(buf6, (4, 1, 4, 4), (16, 16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), buf3, buf6, reinterpret_tensor(buf1, (4, 4, 4), (32, 1, 8), 4
        ), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 4), (32, 8, 1), 0)


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, idim, odim, nhead=1, use_bias=True):
        super(MultiHeadAttentionNew, self).__init__()
        self.idim = idim
        self.odim = odim
        self.nheads = nhead
        self.use_bias = use_bias
        self.c_lin = nn.Linear(self.idim, self.odim * 2, bias=self.use_bias)
        self.v_lin = nn.Linear(self.idim, self.odim, bias=self.use_bias)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.drop = nn.Dropout(0)

    def forward(self, input_0, input_1):
        primals_5 = self.c_lin.weight
        primals_6 = self.c_lin.bias
        primals_1 = self.v_lin.weight
        primals_4 = self.v_lin.bias
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
