# 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_8/inductor_cache/iq/ciqftckmgqckpktocc4vnzvzxloichfo465idr3x42oonjq74tp7.py
# Topologically Sorted Source Nodes: [inner_sum, tanh], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   inner_sum => 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=[256], 
    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_add_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_add_tanh_0(in_out_ptr0, in_ptr0, 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_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/dq/cdqbbqy7obiw5h7sjtb2zv2fvtkcjtt2cb3ee5uvgfirualk4m2f.py
# Topologically Sorted Source Nodes: [alpha], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   alpha => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%squeeze, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%squeeze, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), 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=[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__softmax_1', '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_1(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 % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/f5/cf5347orlkm5ylmh4iouw6qvowwebewwg66dnqrhzt7tg7a4irp5.py
# Topologically Sorted Source Nodes: [alpha], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   alpha => div_1, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0], True), kwargs = {})
#   %div_1 : [num_users=2] = 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: '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__softmax_2', '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_2(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 % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/nt/cntzaspueq6fx2yeimpeg4uzm6tlchzf2wwq7mhodo7u3mdomp62.py
# Topologically Sorted Source Nodes: [mul, sum_1], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   sum_1 => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %primals_2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [0]), kwargs = {})
triton_poi_fused_mul_sum_3 = async_compile.triton('triton_poi_fused_mul_sum_3', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_3', '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_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (64 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (128 + x2), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (192 + x2), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + (x2), tmp14, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (4, 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: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (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: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [inner_sum, tanh], Original ATen: [aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_tanh_0.run(buf2, buf1, 256, grid=grid(256), stream=stream0)
        del buf1
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [alpha], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf4, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [mul, sum_1], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_3.run(buf5, primals_2, buf6, 64, grid=grid(64), stream=stream0)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf7)
    return (buf5, reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0), primals_2, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), buf2, buf5, reinterpret_tensor(buf6, (16, 4), (4, 1), 0), primals_6, 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, 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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = 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, primals_6])
    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.functional as F
from torch import nn
from typing import Optional
import torch.optim


def get_activation_fn(name: 'Optional[str]'):
    """Returns a callable activation function from `torch`."""
    if name in (None, 'linear'):
        return lambda x: x
    elif name in ('sigmoid', 'tanh'):
        return getattr(torch, name)
    else:
        return getattr(F, name)


class DotAttention(nn.Module):
    """Attention layer with dot product."""

    def __init__(self, ctx_dim, hid_dim, att_bottleneck='ctx',
        transform_ctx=True, att_activ='tanh', temp=1.0, ctx2hid=True,
        mlp_bias=None):
        super().__init__()
        self.ctx_dim = ctx_dim
        self.hid_dim = hid_dim
        self._ctx2hid = ctx2hid
        self.temperature = temp
        self.activ = get_activation_fn(att_activ)
        if isinstance(att_bottleneck, int):
            self.mid_dim = att_bottleneck
        else:
            self.mid_dim = getattr(self, '{}_dim'.format(att_bottleneck))
        self.hid2ctx = nn.Linear(self.hid_dim, self.mid_dim, bias=False)
        if transform_ctx or self.mid_dim != self.ctx_dim:
            self.ctx2ctx = nn.Linear(self.ctx_dim, self.mid_dim, bias=False)
        else:
            self.ctx2ctx = lambda x: x
        if self._ctx2hid:
            self.ctx2hid = nn.Linear(self.ctx_dim, self.hid_dim, bias=False)
        else:
            self.ctx2hid = lambda x: x

    def forward(self, hid, ctx, ctx_mask=None):
        """Computes attention probabilities and final context using
        decoder's hidden state and source annotations.

        Arguments:
            hid(Tensor): A set of decoder hidden states of shape `T*B*H`
                where `T` == 1, `B` is batch dim and `H` is hidden state dim.
            ctx(Tensor): A set of annotations of shape `S*B*C` where `S`
                is the source timestep dim, `B` is batch dim and `C`
                is annotation dim.
            ctx_mask(FloatTensor): A binary mask of shape `S*B` with zeroes
                in the padded positions.

        Returns:
            scores(Tensor): A tensor of shape `S*B` containing normalized
                attention scores for each position and sample.
            z_t(Tensor): A tensor of shape `B*H` containing the final
                attended context vector for this target decoding timestep.

        Notes:
            This will only work when `T==1` for now.
        """
        ctx_ = self.ctx2ctx(ctx)
        hid_ = self.hid2ctx(hid)
        scores = torch.bmm(hid_.permute(1, 0, 2), ctx_.permute(1, 2, 0)).div(
            self.temperature).squeeze(1).t()
        if ctx_mask is not None:
            scores.masked_fill_((1 - ctx_mask).bool(), -100000000.0)
        alpha = F.softmax(scores, dim=0)
        return alpha, self.ctx2hid((alpha.unsqueeze(-1) * ctx).sum(0))


class MLPAttention(DotAttention):
    """Attention layer with feed-forward layer."""

    def __init__(self, ctx_dim, hid_dim, att_bottleneck='ctx',
        transform_ctx=True, att_activ='tanh', mlp_bias=False, temp=1.0,
        ctx2hid=True):
        super().__init__(ctx_dim, hid_dim, att_bottleneck, transform_ctx,
            att_activ, temp, ctx2hid)
        if mlp_bias:
            self.bias = nn.Parameter(torch.Tensor(self.mid_dim))
            self.bias.data.zero_()
        else:
            self.register_parameter('bias', None)
        self.mlp = nn.Linear(self.mid_dim, 1, bias=False)

    def forward(self, hid, ctx, ctx_mask=None):
        """Computes attention probabilities and final context using
        decoder's hidden state and source annotations.

        Arguments:
            hid(Tensor): A set of decoder hidden states of shape `T*B*H`
                where `T` == 1, `B` is batch dim and `H` is hidden state dim.
            ctx(Tensor): A set of annotations of shape `S*B*C` where `S`
                is the source timestep dim, `B` is batch dim and `C`
                is annotation dim.
            ctx_mask(FloatTensor): A binary mask of shape `S*B` with zeroes
                in the padded positions.

        Returns:
            scores(Tensor): A tensor of shape `S*B` containing normalized
                attention scores for each position and sample.
            z_t(Tensor): A tensor of shape `B*H` containing the final
                attended context vector for this target decoding timestep.

        Notes:
            This will only work when `T==1` for now.
        """
        inner_sum = self.ctx2ctx(ctx) + self.hid2ctx(hid)
        if self.bias is not None:
            inner_sum.add_(self.bias)
        scores = self.mlp(self.activ(inner_sum)).div(self.temperature).squeeze(
            -1)
        if ctx_mask is not None:
            scores.masked_fill_((1 - ctx_mask).bool(), -100000000.0)
        alpha = F.softmax(scores, dim=0)
        return alpha, self.ctx2hid((alpha.unsqueeze(-1) * ctx).sum(0))


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


def get_init_inputs():
    return [[], {'ctx_dim': 4, 'hid_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 libdevice, math as tl_math
import torch.nn.functional as F
from torch import nn
from typing import Optional
import torch.optim
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_out_ptr0, in_ptr0, 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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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 % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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 % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + (16 + x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (64 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (32 + x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (128 + x2), xmask)
    tmp11 = tl.load(in_ptr0 + (48 + x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (192 + x2), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (4, 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.mm(reinterpret_tensor(primals_2, (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_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](buf2, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_2[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused_mul_sum_3[grid(64)](buf5, primals_2, buf6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf7)
    return buf5, reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0
        ), primals_2, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0
        ), buf2, buf5, reinterpret_tensor(buf6, (16, 4), (4, 1), 0
        ), primals_6, primals_5


def get_activation_fn(name: 'Optional[str]'):
    """Returns a callable activation function from `torch`."""
    if name in (None, 'linear'):
        return lambda x: x
    elif name in ('sigmoid', 'tanh'):
        return getattr(torch, name)
    else:
        return getattr(F, name)


class DotAttention(nn.Module):
    """Attention layer with dot product."""

    def __init__(self, ctx_dim, hid_dim, att_bottleneck='ctx',
        transform_ctx=True, att_activ='tanh', temp=1.0, ctx2hid=True,
        mlp_bias=None):
        super().__init__()
        self.ctx_dim = ctx_dim
        self.hid_dim = hid_dim
        self._ctx2hid = ctx2hid
        self.temperature = temp
        self.activ = get_activation_fn(att_activ)
        if isinstance(att_bottleneck, int):
            self.mid_dim = att_bottleneck
        else:
            self.mid_dim = getattr(self, '{}_dim'.format(att_bottleneck))
        self.hid2ctx = nn.Linear(self.hid_dim, self.mid_dim, bias=False)
        if transform_ctx or self.mid_dim != self.ctx_dim:
            self.ctx2ctx = nn.Linear(self.ctx_dim, self.mid_dim, bias=False)
        else:
            self.ctx2ctx = lambda x: x
        if self._ctx2hid:
            self.ctx2hid = nn.Linear(self.ctx_dim, self.hid_dim, bias=False)
        else:
            self.ctx2hid = lambda x: x

    def forward(self, hid, ctx, ctx_mask=None):
        """Computes attention probabilities and final context using
        decoder's hidden state and source annotations.

        Arguments:
            hid(Tensor): A set of decoder hidden states of shape `T*B*H`
                where `T` == 1, `B` is batch dim and `H` is hidden state dim.
            ctx(Tensor): A set of annotations of shape `S*B*C` where `S`
                is the source timestep dim, `B` is batch dim and `C`
                is annotation dim.
            ctx_mask(FloatTensor): A binary mask of shape `S*B` with zeroes
                in the padded positions.

        Returns:
            scores(Tensor): A tensor of shape `S*B` containing normalized
                attention scores for each position and sample.
            z_t(Tensor): A tensor of shape `B*H` containing the final
                attended context vector for this target decoding timestep.

        Notes:
            This will only work when `T==1` for now.
        """
        ctx_ = self.ctx2ctx(ctx)
        hid_ = self.hid2ctx(hid)
        scores = torch.bmm(hid_.permute(1, 0, 2), ctx_.permute(1, 2, 0)).div(
            self.temperature).squeeze(1).t()
        if ctx_mask is not None:
            scores.masked_fill_((1 - ctx_mask).bool(), -100000000.0)
        alpha = F.softmax(scores, dim=0)
        return alpha, self.ctx2hid((alpha.unsqueeze(-1) * ctx).sum(0))


class MLPAttentionNew(DotAttention):
    """Attention layer with feed-forward layer."""

    def __init__(self, ctx_dim, hid_dim, att_bottleneck='ctx',
        transform_ctx=True, att_activ='tanh', mlp_bias=False, temp=1.0,
        ctx2hid=True):
        super().__init__(ctx_dim, hid_dim, att_bottleneck, transform_ctx,
            att_activ, temp, ctx2hid)
        if mlp_bias:
            self.bias = nn.Parameter(torch.Tensor(self.mid_dim))
            self.bias.data.zero_()
        else:
            self.register_parameter('bias', None)
        self.mlp = nn.Linear(self.mid_dim, 1, bias=False)

    def forward(self, input_0, input_1):
        primals_1 = self.hid2ctx.weight
        primals_3 = self.ctx2ctx.weight
        primals_6 = self.ctx2hid.weight
        primals_5 = self.mlp.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
