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

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


# kernel path: runs/run_shard_4/inductor_cache/vu/cvuv73ijvl45rys2kvk2dud7shg4nznn622fzyldpf7pmxppx3o5.py
# Topologically Sorted Source Nodes: [key_1], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   key_1 => tanh
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {})
triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_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: '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_tanh_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_tanh_0(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
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3x/c3xofha4qqmlee5fyb5f3isucqozm7lantx7oxuqmlenwkdzt4m7.py
# Topologically Sorted Source Nodes: [exp_vector, masked_vector, sum_1], Original ATen: [aten.exp, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   exp_vector => exp
#   masked_vector => mul
#   sum_1 => sum_1
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%squeeze,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %primals_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1], True), kwargs = {})
triton_poi_fused_exp_mul_sum_1 = async_compile.triton('triton_poi_fused_exp_mul_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_exp_mul_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_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + (x0), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/e7/ce77ixqcx5f2pzrwryx4st3pdytainzy374bkfl2dcz3esslw3n4.py
# Topologically Sorted Source Nodes: [attentioned_value, vector], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   attentioned_value => mul_1
#   vector => sum_2
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %unsqueeze), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
triton_poi_fused_mul_sum_2 = async_compile.triton('triton_poi_fused_mul_sum_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=[16], 
    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_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 13, '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_mul_sum_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp9 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp17 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp25 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 / tmp5
    tmp7 = tmp0 * tmp6
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 / tmp5
    tmp14 = tmp8 * tmp13
    tmp15 = tmp7 + tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp20 / tmp5
    tmp22 = tmp16 * tmp21
    tmp23 = tmp15 + tmp22
    tmp26 = tl_math.exp(tmp25)
    tmp28 = tmp26 * tmp27
    tmp29 = tmp28 / tmp5
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tl.store(out_ptr0 + (x2), tmp31, 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, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (1, 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_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [key_1], Original ATen: [aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_tanh_0.run(buf1, primals_4, 64, grid=grid(64), stream=stream0)
        del primals_4
        buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [exp_vector, masked_vector, sum_1], Original ATen: [aten.exp, aten.mul, aten.sum]
        triton_poi_fused_exp_mul_sum_1.run(buf2, primals_2, buf3, 4, grid=grid(4), stream=stream0)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attentioned_value, vector], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_2.run(primals_1, buf2, primals_2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del buf3
    return (buf4, primals_1, primals_2, buf1, buf2, primals_5, )


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, 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((1, 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)

from torch.nn import Module
import torch
from torch.nn import Linear
from typing import Optional
from torch.nn import Tanh


def masked_softmax(vector: 'torch.FloatTensor', mask: 'torch.ByteTensor'):
    """
    计算带有 masked 的 softmax
    :param vector: shape: (B, seq_len)
    :param mask: shape: (B, seq_len),
    :return: (B, seq_len)
    """
    exp_vector = vector.exp()
    masked_vector = exp_vector * mask.float()
    return masked_vector / torch.sum(masked_vector, dim=-1, keepdim=True)


class AttentionSeq2Vec(Module):
    """
    基于 attention 将 seq2vec. 具体操作如下:

    1. sequence: (B, seq_len, input_size)
    2. K = WkSeqeunce 将 sequence 进行变换, K shape: (B, seq_len, query_hidden_size)
    3. Q = Shape: (query_hidden_size)
    4. attention = softmax(KQ), shape: (B, seq_len)
    5. V = WvSequence, shape: (B, seq_len, value_hidden_size); 如果 value_hidden_size is None,
    shape: (B, seq_len, input_size)
    6. sum(V*attention, dim=-1), shape: (B, input_size)
    """

    def __init__(self, input_size: 'int', query_hidden_size: 'int',
        value_hidden_size: 'Optional[int]'=None):
        """
        初始化。遵循 Q K V，计算 attention 方式。
        :param input_size: 输入的 sequence token 的 embedding dim
        :param query_hidden_size: 将 seqence 变成 Q 的时候，变换后的 token embedding dim.
        :param value_hidden_size: 将 seqence 变成 V 的时候, 变换后的 token embedding dim.
        如果 value_hidden_size is None, 那么，该模型就与 2016-Hierarchical Attention Networks for Document Classification
        是一致的, 最后的输出结果 shape (B, seq_len, input_size);
        如果 value_hidden_size 被设置了, 那么，就与 Attention is All your Need 中 变换是一致的, 最后的输出结果
        shape (B, seq_len, value_hidden_size)
        """
        super().__init__()
        self.wk = Linear(in_features=input_size, out_features=
            query_hidden_size, bias=True)
        self.key_activation = Tanh()
        self.attention = Linear(in_features=query_hidden_size, out_features
            =1, bias=False)
        self.wv = None
        if value_hidden_size is not None:
            self.wv = Linear(in_features=input_size, out_features=
                value_hidden_size, bias=True)
        self.reset_parameters()

    def reset_parameters(self):
        pass

    def forward(self, sequence: 'torch.LongTensor', mask:
        'Optional[torch.ByteTensor]') ->torch.FloatTensor:
        """
        执行 attetion seq2vec
        :param sequence: 输入的token 序列, shape: (batch_size, seq_len, input_size)
        :param mask: mask shape: (batch_size, seq_len)
        :return: attention 编码向量, shape: (batch_size, value_hidden_size or input_size)
        """
        assert sequence.dim(
            ) == 3, 'sequence shape: (batch_size, seq_len, input_size)'
        if mask is not None:
            assert mask.dim() == 2, 'mask shape: (batch_size, seq_len)'
        key = self.wk(sequence)
        key = self.key_activation(key)
        attention = self.attention(key)
        attention = torch.squeeze(attention, dim=-1)
        if mask is not None:
            attention = masked_softmax(vector=attention, mask=mask)
        else:
            attention = torch.softmax(attention, dim=-1)
        if self.wv is not None:
            value = self.wv(sequence)
        else:
            value = sequence
        attentioned_value = value * attention.unsqueeze(dim=-1)
        vector = torch.sum(attentioned_value, dim=1)
        return vector


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


def get_init_inputs():
    return [[], {'input_size': 4, 'query_hidden_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.nn import Module
from torch.nn import Linear
from typing import Optional
from torch.nn import Tanh
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_tanh_0(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
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_exp_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + x0, tmp18, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp9 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp17 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr2 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp25 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr2 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 / tmp5
    tmp7 = tmp0 * tmp6
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 / tmp5
    tmp14 = tmp8 * tmp13
    tmp15 = tmp7 + tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp20 / tmp5
    tmp22 = tmp16 * tmp21
    tmp23 = tmp15 + tmp22
    tmp26 = tl_math.exp(tmp25)
    tmp28 = tmp26 * tmp27
    tmp29 = tmp28 / tmp5
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tl.store(out_ptr0 + x2, tmp31, 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, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 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_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(64)](buf1, primals_4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_exp_mul_sum_1[grid(4)](buf2, primals_2, buf3, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sum_2[grid(16)](primals_1, buf2, primals_2,
            buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf3
    return buf4, primals_1, primals_2, buf1, buf2, primals_5


def masked_softmax(vector: 'torch.FloatTensor', mask: 'torch.ByteTensor'):
    """
    计算带有 masked 的 softmax
    :param vector: shape: (B, seq_len)
    :param mask: shape: (B, seq_len),
    :return: (B, seq_len)
    """
    exp_vector = vector.exp()
    masked_vector = exp_vector * mask.float()
    return masked_vector / torch.sum(masked_vector, dim=-1, keepdim=True)


class AttentionSeq2VecNew(Module):
    """
    基于 attention 将 seq2vec. 具体操作如下:

    1. sequence: (B, seq_len, input_size)
    2. K = WkSeqeunce 将 sequence 进行变换, K shape: (B, seq_len, query_hidden_size)
    3. Q = Shape: (query_hidden_size)
    4. attention = softmax(KQ), shape: (B, seq_len)
    5. V = WvSequence, shape: (B, seq_len, value_hidden_size); 如果 value_hidden_size is None,
    shape: (B, seq_len, input_size)
    6. sum(V*attention, dim=-1), shape: (B, input_size)
    """

    def __init__(self, input_size: 'int', query_hidden_size: 'int',
        value_hidden_size: 'Optional[int]'=None):
        """
        初始化。遵循 Q K V，计算 attention 方式。
        :param input_size: 输入的 sequence token 的 embedding dim
        :param query_hidden_size: 将 seqence 变成 Q 的时候，变换后的 token embedding dim.
        :param value_hidden_size: 将 seqence 变成 V 的时候, 变换后的 token embedding dim.
        如果 value_hidden_size is None, 那么，该模型就与 2016-Hierarchical Attention Networks for Document Classification
        是一致的, 最后的输出结果 shape (B, seq_len, input_size);
        如果 value_hidden_size 被设置了, 那么，就与 Attention is All your Need 中 变换是一致的, 最后的输出结果
        shape (B, seq_len, value_hidden_size)
        """
        super().__init__()
        self.wk = Linear(in_features=input_size, out_features=
            query_hidden_size, bias=True)
        self.key_activation = Tanh()
        self.attention = Linear(in_features=query_hidden_size, out_features
            =1, bias=False)
        self.wv = None
        if value_hidden_size is not None:
            self.wv = Linear(in_features=input_size, out_features=
                value_hidden_size, bias=True)
        self.reset_parameters()

    def reset_parameters(self):
        pass

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