# 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/aq/caqwqjdiinvvk6gcwm6mkr5l2xr66324dq6z6qsyxf3s2e6ey3jo.py
# Topologically Sorted Source Nodes: [scores, eq, scores_1, attention_matrix], Original ATen: [aten.div, aten.eq, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   attention_matrix => amax, exp, sub, sum_1
#   eq => eq
#   scores => div
#   scores_1 => full_default, where
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 2.0), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%expand_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, [-1], 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, [-1], True), kwargs = {})
triton_poi_fused__softmax_div_eq_masked_fill_0 = async_compile.triton('triton_poi_fused__softmax_div_eq_masked_fill_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: '*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_eq_masked_fill_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_div_eq_masked_fill_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tmp6 = -1000000000.0
    tmp7 = tl.where(tmp2, tmp6, tmp5)
    tmp9 = tmp8 * tmp4
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp11 = triton_helpers.maximum(tmp7, tmp10)
    tmp13 = tmp12 * tmp4
    tmp14 = tl.where(tmp2, tmp6, tmp13)
    tmp15 = triton_helpers.maximum(tmp11, tmp14)
    tmp17 = tmp16 * tmp4
    tmp18 = tl.where(tmp2, tmp6, tmp17)
    tmp19 = triton_helpers.maximum(tmp15, tmp18)
    tmp20 = tmp7 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp10 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp14 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x2), tmp19, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/o7/co745ttqsjpcy7x7gy5tcygrr6hg3ejh35vgcjzvttufm4rvgjub.py
# Topologically Sorted Source Nodes: [scores, eq, scores_1, attention_matrix], Original ATen: [aten.div, aten.eq, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   attention_matrix => amax, div_1, exp, sub, sum_1
#   eq => eq
#   scores => div
#   scores_1 => full_default, where
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 2.0), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%expand_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, [-1], 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, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_div_eq_masked_fill_1 = async_compile.triton('triton_poi_fused__softmax_div_eq_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=[256], 
    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_div_eq_masked_fill_1', '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_eq_masked_fill_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 16
    x3 = xindex
    x4 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp8 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tmp6 = -1000000000.0
    tmp7 = tl.where(tmp2, tmp6, tmp5)
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 / tmp11
    tl.store(in_out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/h2/ch2e77yh44s6lkqiqyg7nvfi4moh7imiwqn56mevhmasajj4t7td.py
# Topologically Sorted Source Nodes: [att_output_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   att_output_2 => amax_1, div_2, exp_1, sub_1, sum_2
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_13, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_13, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), 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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': ['in_out_ptr0'], '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__softmax_2(in_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
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, 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, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [key], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [query], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [value], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [scores, eq, scores_1, attention_matrix], Original ATen: [aten.div, aten.eq, aten.masked_fill, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_div_eq_masked_fill_0.run(primals_8, buf3, buf4, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [scores, eq, scores_1, attention_matrix], Original ATen: [aten.div, aten.eq, aten.masked_fill, aten._softmax]
        triton_poi_fused__softmax_div_eq_masked_fill_1.run(buf6, primals_8, buf4, buf5, 256, grid=grid(256), stream=stream0)
        del buf4
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), out=buf7)
        buf8 = reinterpret_tensor(buf5, (64, 1), (1, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [att_output_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 1), (1, 4), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [att_output_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf9, 64, grid=grid(64), stream=stream0)
    return (buf9, primals_8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf6, reinterpret_tensor(buf7, (64, 4), (4, 1), 0), buf9, primals_9, reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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, primals_6, primals_7, primals_8, primals_9])
    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 math
import torch
import torch.nn as nn


def calculate_attention(query: 'torch.Tensor', key: 'torch.Tensor', value:
    'torch.Tensor', mask: 'torch.Tensor'):
    """Calclulate Attention 
    @param:
        query: torch.Tensor (Batch_size, max_seq_len, hidden_size)
        key: torch.Tensor (Batch_size, max_seq_len, hidden_size)
        value : torch.Tensor(Batch_size, max_seq_len, word_vector_size)
        mask : torch.Tensor.Boolean(Batch_size, max_seq_len);
    """
    hidden_size = query.size(-1)
    max_seq_size = mask.size(-1)
    scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(hidden_size
        )
    mask_ = mask.unsqueeze(-1).expand(-1, -1, max_seq_size)
    scores = scores.masked_fill(mask_ == 0, -1000000000.0)
    attention_matrix = torch.softmax(scores, dim=-1)
    output = torch.matmul(attention_matrix, value)
    return output


class Attention_layer(nn.Module):
    """Attention Unit"""

    def __init__(self, kernel_input: 'int', kernel_output: 'int', dropout:
        'int'=0.2):
        super(Attention_layer, self).__init__()
        self.key_kernel = nn.Linear(kernel_input, kernel_output)
        self.query_kernel = nn.Linear(kernel_input, kernel_output)
        self.value_kernel = nn.Linear(kernel_input, kernel_output)
        self.normalize_kernel = nn.Linear(kernel_output, 1, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x: 'torch.Tensor', mask: 'torch.Tensor'):
        """
        @params:
            x: (batch_size, max_seq_len, word_emb_size)
            mask: (batch_size, max_seq_len)
        """
        key = self.key_kernel(x)
        query = self.query_kernel(x)
        value = self.value_kernel(x)
        att_output = calculate_attention(query, key, value, mask)
        att_output = self.dropout(att_output)
        att_output = self.normalize_kernel(att_output)
        att_output = torch.softmax(att_output, -1)
        return att_output


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


def get_init_inputs():
    return [[], {'kernel_input': 4, 'kernel_output': 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 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__softmax_div_eq_masked_fill_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tmp6 = -1000000000.0
    tmp7 = tl.where(tmp2, tmp6, tmp5)
    tmp9 = tmp8 * tmp4
    tmp10 = tl.where(tmp2, tmp6, tmp9)
    tmp11 = triton_helpers.maximum(tmp7, tmp10)
    tmp13 = tmp12 * tmp4
    tmp14 = tl.where(tmp2, tmp6, tmp13)
    tmp15 = triton_helpers.maximum(tmp11, tmp14)
    tmp17 = tmp16 * tmp4
    tmp18 = tl.where(tmp2, tmp6, tmp17)
    tmp19 = triton_helpers.maximum(tmp15, tmp18)
    tmp20 = tmp7 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp10 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp14 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x2, tmp19, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused__softmax_div_eq_masked_fill_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x3 = xindex
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x3, xmask)
    tmp8 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = 0.5
    tmp5 = tmp3 * tmp4
    tmp6 = -1000000000.0
    tmp7 = tl.where(tmp2, tmp6, tmp5)
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 / tmp11
    tl.store(in_out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_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
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_div_eq_masked_fill_0[grid(64)](primals_8,
            buf3, buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_div_eq_masked_fill_1[grid(256)](buf6,
            primals_8, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), out=buf7)
        buf8 = reinterpret_tensor(buf5, (64, 1), (1, 1), 0)
        del buf5
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 1), (1, 4), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf8
        triton_poi_fused__softmax_2[grid(64)](buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf9, primals_8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf7, (64, 4), (4, 1), 0
        ), buf9, primals_9, reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0)


def calculate_attention(query: 'torch.Tensor', key: 'torch.Tensor', value:
    'torch.Tensor', mask: 'torch.Tensor'):
    """Calclulate Attention 
    @param:
        query: torch.Tensor (Batch_size, max_seq_len, hidden_size)
        key: torch.Tensor (Batch_size, max_seq_len, hidden_size)
        value : torch.Tensor(Batch_size, max_seq_len, word_vector_size)
        mask : torch.Tensor.Boolean(Batch_size, max_seq_len);
    """
    hidden_size = query.size(-1)
    max_seq_size = mask.size(-1)
    scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(hidden_size
        )
    mask_ = mask.unsqueeze(-1).expand(-1, -1, max_seq_size)
    scores = scores.masked_fill(mask_ == 0, -1000000000.0)
    attention_matrix = torch.softmax(scores, dim=-1)
    output = torch.matmul(attention_matrix, value)
    return output


class Attention_layerNew(nn.Module):
    """Attention Unit"""

    def __init__(self, kernel_input: 'int', kernel_output: 'int', dropout:
        'int'=0.2):
        super(Attention_layerNew, self).__init__()
        self.key_kernel = nn.Linear(kernel_input, kernel_output)
        self.query_kernel = nn.Linear(kernel_input, kernel_output)
        self.value_kernel = nn.Linear(kernel_input, kernel_output)
        self.normalize_kernel = nn.Linear(kernel_output, 1, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, input_0, input_1):
        primals_1 = self.key_kernel.weight
        primals_2 = self.key_kernel.bias
        primals_4 = self.query_kernel.weight
        primals_5 = self.query_kernel.bias
        primals_6 = self.value_kernel.weight
        primals_7 = self.value_kernel.bias
        primals_9 = self.normalize_kernel.weight
        primals_3 = input_0
        primals_8 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
