# 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/ch/cchblwxibhbejgpezaajsszmynwayrtalecnd5ksg22infzh2v3o.py
# Topologically Sorted Source Nodes: [dot, dot_1], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   dot => add
#   dot_1 => tanh
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view, %expand), 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=[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_add_tanh_0', 'mutated_arg_names': [], '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_add_tanh_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pw/cpw7p22sygsv72j67klg2k3xeokdqrvalgmkjpkzpqpj5pv4xmkc.py
# Topologically Sorted Source Nodes: [weight], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   weight => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%view_2, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_1 = async_compile.triton('triton_per_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp11, xmask)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
    tl.store(out_ptr1 + (x0), 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, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (1, 4), (4, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot, dot_1], Original ATen: [aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_tanh_0.run(primals_2, buf0, primals_4, buf1, 256, grid=grid(256), stream=stream0)
        del primals_2
        del primals_4
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dot_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weight], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf3, buf4, buf5, buf6, 4, 16, grid=grid(4), stream=stream0)
        buf7 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 1, 16), (16, 0, 1), 0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 4, 1), 0), out=buf7)
        del buf6
    return (reinterpret_tensor(buf7, (4, 4), (4, 1), 0), primals_5, buf1, buf3, buf4, buf5, reinterpret_tensor(primals_1, (4, 4, 16), (64, 1, 4), 0), primals_6, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *


class Attention(nn.Module):

    def __init__(self, opt):
        super(Attention, self).__init__()
        self.rnn_size = opt.rnn_size
        self.att_hid_size = opt.att_hid_size
        self.h2att = nn.Linear(self.rnn_size, self.att_hid_size)
        self.alpha_net = nn.Linear(self.att_hid_size, 1)

    def forward(self, h, att_feats, p_att_feats, att_masks=None):
        att_size = att_feats.numel() // att_feats.size(0) // att_feats.size(-1)
        att = p_att_feats.view(-1, att_size, self.att_hid_size)
        att_h = self.h2att(h)
        att_h = att_h.unsqueeze(1).expand_as(att)
        dot = att + att_h
        dot = torch.tanh(dot)
        dot = dot.view(-1, self.att_hid_size)
        dot = self.alpha_net(dot)
        dot = dot.view(-1, att_size)
        weight = F.softmax(dot, dim=1)
        if att_masks is not None:
            weight = weight * att_masks.view(-1, att_size).float()
            weight = weight / weight.sum(1, keepdim=True)
        att_feats_ = att_feats.view(-1, att_size, att_feats.size(-1))
        att_res = torch.bmm(weight.unsqueeze(1), att_feats_).squeeze(1)
        return att_res


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


def get_init_inputs():
    return [[], {'opt': _mock_config(rnn_size=4, att_hid_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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
from torch.autograd import *
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, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(out_ptr0 + x3, tmp5, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (1, 4), (4, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](primals_2, buf0, primals_4,
            buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        del primals_4
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(4)](buf3, buf4, buf5, buf6, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 1, 16), (16, 0, 1),
            0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 4, 1), 0),
            out=buf7)
        del buf6
    return reinterpret_tensor(buf7, (4, 4), (4, 1), 0
        ), primals_5, buf1, buf3, buf4, buf5, reinterpret_tensor(primals_1,
        (4, 4, 16), (64, 1, 4), 0), primals_6


class AttentionNew(nn.Module):

    def __init__(self, opt):
        super(AttentionNew, self).__init__()
        self.rnn_size = opt.rnn_size
        self.att_hid_size = opt.att_hid_size
        self.h2att = nn.Linear(self.rnn_size, self.att_hid_size)
        self.alpha_net = nn.Linear(self.att_hid_size, 1)

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.h2att.weight
        primals_4 = self.h2att.bias
        primals_6 = self.alpha_net.weight
        primals_7 = self.alpha_net.bias
        primals_5 = input_0
        primals_1 = input_1
        primals_2 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
