# 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/sb/csbkytvktsi5xvyifabpordrodcrbhhsdje4rbbt3znhexh2yg7g.py
# Topologically Sorted Source Nodes: [x_proj_1, y_proj_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_proj_1 => relu
#   y_proj_1 => relu_1
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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')
    tmp5 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = 0.0
    tmp9 = tmp7 <= tmp8
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(in_out_ptr1 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zg/czglmzisr33fo6qj23az2oiaroufqbaddga7xvj7zutdk4pqyeve.py
# Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   alpha_flat => amax, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_4, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_4, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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: '*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__softmax_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_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*(x0 // 4)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*(x0 // 4))), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + (4*(x0 // 4))), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + (4*(x0 // 4))), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = -1.0000000150474662e+30
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp6 == tmp1
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp12 = tmp11 == tmp1
    tmp14 = tl.where(tmp12, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp17 = tmp16 == tmp1
    tmp19 = tl.where(tmp17, 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 + (x0), tmp20, xmask)
    tl.store(out_ptr1 + (x0), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rs/crsxbmq5wxmlwnh3y33hktmvwmwqhh42xzakxys2s2f2e3b2ck4o.py
# Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   alpha_flat => amax, div, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_4, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_4, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    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__softmax_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_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
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*(x1 // 4))), xmask)
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp6 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = -1.0000000150474662e+30
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + (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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4), (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((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf2)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0  # reuse
        buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0); del buf2  # reuse
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_proj_1, y_proj_1], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, buf3, primals_3, buf9, 64, grid=grid(64), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        buf6 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(primals_5, buf4, buf5, buf6, 16, grid=grid(16), stream=stream0)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha_flat], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(primals_5, buf4, buf5, buf6, buf7, 64, grid=grid(64), stream=stream0)
        del buf5
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matched_seq], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0), primals_4, out=buf8)
        del buf7
    return (buf8, primals_4, primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf1, buf4, buf3, buf9, )


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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (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
from torch.nn import functional as F


class SeqAttnMatch(nn.Module):
    """
    Given sequences X and Y, match sequence Y to each element in X.
    * o_i = sum(alpha_j * y_j) for i in X
    * alpha_j = softmax(y_j * x_i)
    """

    def __init__(self, embed_dim, identity=False):
        super(SeqAttnMatch, self).__init__()
        if not identity:
            self.linear = nn.Linear(embed_dim, embed_dim)
        else:
            self.linear = None

    def forward(self, x, y, y_mask):
        if self.linear:
            x_proj = self.linear(x.view(-1, x.size(2))).view(x.size())
            x_proj = F.relu(x_proj)
            y_proj = self.linear(y.view(-1, y.size(2))).view(y.size())
            y_proj = F.relu(y_proj)
        else:
            x_proj = x
            y_proj = y
        scores = x_proj.bmm(y_proj.transpose(2, 1))
        y_mask = y_mask.unsqueeze(1).expand(scores.size())
        scores = scores.masked_fill(y_mask == 0, -1e+30)
        alpha_flat = F.softmax(scores.view(-1, y.size(1)), -1)
        alpha = alpha_flat.view(-1, x.size(1), y.size(1))
        matched_seq = alpha.bmm(y)
        return matched_seq


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


def get_init_inputs():
    return [[], {'embed_dim': 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
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_out_ptr1,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = 0.0
    tmp9 = tmp7 <= tmp8
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * (x0 // 4), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = -1.0000000150474662e+30
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp6 == tmp1
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp12 = tmp11 == tmp1
    tmp14 = tl.where(tmp12, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp17 = tmp16 == tmp1
    tmp19 = tl.where(tmp17, 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 + x0, tmp20, xmask)
    tl.store(out_ptr1 + x0, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4)), xmask)
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = -1.0000000150474662e+30
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(out_ptr0 + x2, tmp10, 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, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (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((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf2)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
        del buf2
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1, buf3,
            primals_3, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1,
            4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        buf6 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](primals_5, buf4, buf5, buf6, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](primals_5, buf4, buf5, buf6,
            buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf5
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 
            0), primals_4, out=buf8)
        del buf7
    return buf8, primals_4, primals_5, reinterpret_tensor(primals_1, (16, 4
        ), (4, 1), 0), buf1, buf4, buf3, buf9


class SeqAttnMatchNew(nn.Module):
    """
    Given sequences X and Y, match sequence Y to each element in X.
    * o_i = sum(alpha_j * y_j) for i in X
    * alpha_j = softmax(y_j * x_i)
    """

    def __init__(self, embed_dim, identity=False):
        super(SeqAttnMatchNew, self).__init__()
        if not identity:
            self.linear = nn.Linear(embed_dim, embed_dim)
        else:
            self.linear = None

    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]
