# 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/2x/c2x66b6sza3svon43c774fqn45xzpdlajk7fj3gf6dzmp6nxl7jx.py
# Topologically Sorted Source Nodes: [add, tanh], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   tanh => tanh
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view_3), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_tanh_0 = async_compile.triton('triton_poi_fused_add_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=[1024], 
    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_add_tanh_0', '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_add_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 256
    x0 = xindex % 4
    x3 = (xindex // 256)
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(out_ptr0 + (x6), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/oe/coezvwyo2fyfsqghp3geqsqeeuhyvxcoe7thtujlv6einfmi73d5.py
# Topologically Sorted Source Nodes: [mask, invert, setitem], Original ATen: [aten._to_copy, aten.bitwise_not, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   invert => bitwise_not
#   mask => convert_element_type
#   setitem => full_default, index_put
# Graph fragment:
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%primals_9, torch.bool), kwargs = {})
#   %bitwise_not : [num_users=2] = call_function[target=torch.ops.aten.bitwise_not.default](args = (%convert_element_type,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -inf), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%squeeze, [%bitwise_not], %full_default), kwargs = {})
triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_1 = async_compile.triton('triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_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: '*i1', 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__to_copy_bitwise_not_index_put_lift_fresh_1', 'mutated_arg_names': ['in_ptr1', 'out_ptr2'], '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__to_copy_bitwise_not_index_put_lift_fresh_1(in_ptr0, in_ptr1, out_ptr0, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = (tmp0 != 0)
    tmp2 = tmp1 == 0
    tmp4 = float("-inf")
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr2 + (x0), tmp5, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1, ), (1, ))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 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: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, tanh], Original ATen: [aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_tanh_0.run(buf0, primals_2, buf1, primals_6, buf2, 1024, grid=grid(1024), stream=stream0)
        del buf0
        del primals_2
        del primals_6
        buf4 = reinterpret_tensor(buf1, (256, 1), (1, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [mask, invert, setitem], Original ATen: [aten._to_copy, aten.bitwise_not, aten.lift_fresh, aten.index_put]
        triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_1.run(primals_9, buf4, buf5, buf4, 256, grid=grid(256), stream=stream0)
        del primals_9
    return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), buf2, buf5, primals_7, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 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 torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn


class AdditiveAttention(nn.Module):
    """
    Originally from:
        https://arxiv.org/pdf/1409.0473v5.pdf

    Also referenced to as Content Based Attention:
        https://arxiv.org/pdf/1506.03134v1.pdf

    Attention is learned for a query vector over a set of vectors. 
    If we have a query vector and then a key matrix with the size (n,k), 
    we will create attention vector over n

    
    NOTE!
    We mask out the attention scores for the positions which its impossible for 
    the segments to attend to. I.e. the padding.


    """

    def __init__(self, input_dim: 'int'):
        super().__init__()
        self.W1 = nn.Linear(input_dim, input_dim)
        self.W2 = nn.Linear(input_dim, input_dim)
        self.v = nn.Linear(input_dim, 1)

    def forward(self, query: 'Tensor', key: 'Tensor', mask: 'Tensor',
        softmax=False):
        key_out = self.W1(key)
        query_out = self.W2(query.unsqueeze(1))
        ui = self.v(torch.tanh(key_out + query_out)).squeeze(-1)
        mask = mask.type(torch.bool)
        ui[~mask] = float('-inf')
        if softmax:
            ui = torch.softmax(ui)
        return ui


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


def get_init_inputs():
    return [[], {'input_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.triton_helpers import libdevice
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_add_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 256
    x0 = xindex % 4
    x3 = xindex // 256
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(out_ptr0 + x6, tmp7, xmask)


@triton.jit
def triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_1(in_ptr0,
    in_ptr1, out_ptr0, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = tmp0 != 0
    tmp2 = tmp1 == 0
    tmp4 = float('-inf')
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr2 + x0, tmp5, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (1, 4), (4, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(1024)](buf0, primals_2, buf1,
            primals_6, buf2, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        del primals_6
        buf4 = reinterpret_tensor(buf1, (256, 1), (1, 1), 0)
        del buf1
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (256, 4),
            (4, 1), 0), reinterpret_tensor(primals_7, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused__to_copy_bitwise_not_index_put_lift_fresh_1[grid(256)
            ](primals_9, buf4, buf5, buf4, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_9
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0
        ), buf2, buf5, primals_7


class AdditiveAttentionNew(nn.Module):
    """
    Originally from:
        https://arxiv.org/pdf/1409.0473v5.pdf

    Also referenced to as Content Based Attention:
        https://arxiv.org/pdf/1506.03134v1.pdf

    Attention is learned for a query vector over a set of vectors. 
    If we have a query vector and then a key matrix with the size (n,k), 
    we will create attention vector over n

    
    NOTE!
    We mask out the attention scores for the positions which its impossible for 
    the segments to attend to. I.e. the padding.


    """

    def __init__(self, input_dim: 'int'):
        super().__init__()
        self.W1 = nn.Linear(input_dim, input_dim)
        self.W2 = nn.Linear(input_dim, input_dim)
        self.v = nn.Linear(input_dim, 1)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.W1.weight
        primals_2 = self.W1.bias
        primals_5 = self.W2.weight
        primals_6 = self.W2.bias
        primals_7 = self.v.weight
        primals_8 = self.v.bias
        primals_3 = input_0
        primals_4 = input_1
        primals_9 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
