# 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/nc/cnc6a3vkphurm472zdavmn3qnff4lmaezxs63jlllw2kks2e62a4.py
# Topologically Sorted Source Nodes: [input_tensor], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_tensor => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %primals_3], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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_cat_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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = (xindex // 12)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + ((4*x1) + ((-8) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/as/castj5utepsqaf7nmxz7ydwohxvblspgu7zqqkf5geoow2xnjdtg.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[32], 
    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_cat_1', 'mutated_arg_names': [], '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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/42/c42q6xmrtx4mk6cvsm764oigvfmjedisa43isepa27ioqcfzfgtm.py
# Topologically Sorted Source Nodes: [z, sub, mul, mul_1, add, tanh], Original ATen: [aten.sigmoid, aten.rsub, aten.mul, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
#   tanh => tanh
#   z => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %sigmoid), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %addmm_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %addmm_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_tanh_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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_mul_rsub_sigmoid_tanh_2', '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_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp4 = tl.load(in_ptr1 + (x0), xmask)
    tmp6 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp5 = tmp3 * tmp4
    tmp7 = tmp1 * tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = libdevice.tanh(tmp8)
    tl.store(out_ptr0 + (x0), tmp9, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 12), (12, 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, 8), (8, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_tensor], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, primals_3, buf0, 48, grid=grid(48), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4, (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [proj_source], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, primals_3, 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((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, primals_2, buf3, 32, grid=grid(32), stream=stream0)
        del primals_1
        del primals_2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [proj_target], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf3, reinterpret_tensor(primals_8, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        del primals_9
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [z, sub, mul, mul_1, add, tanh], Original ATen: [aten.sigmoid, aten.rsub, aten.mul, aten.add, aten.tanh]
        triton_poi_fused_add_mul_rsub_sigmoid_tanh_2.run(buf1, buf4, buf2, buf5, 16, grid=grid(16), stream=stream0)
    return (buf5, primals_3, buf0, buf1, buf2, buf3, buf4, buf5, )


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, 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, 12), (12, 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, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((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
import torch.nn as nn
import torch.cuda
import torch.distributed


class ContextGate(nn.Module):
    """
    Context gate is a decoder module that takes as input the previous word
    embedding, the current decoder state and the attention state, and
    produces a gate.
    The gate can be used to select the input from the target side context
    (decoder state), from the source context (attention state) or both.
    """

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(ContextGate, self).__init__()
        input_size = embeddings_size + decoder_size + attention_size
        self.gate = nn.Linear(input_size, output_size, bias=True)
        self.sig = nn.Sigmoid()
        self.source_proj = nn.Linear(attention_size, output_size)
        self.target_proj = nn.Linear(embeddings_size + decoder_size,
            output_size)

    def forward(self, prev_emb, dec_state, attn_state):
        input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
        z = self.sig(self.gate(input_tensor))
        proj_source = self.source_proj(attn_state)
        proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
        return z, proj_source, proj_target


class BothContextGate(nn.Module):
    """Apply the context gate to both contexts"""

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(BothContextGate, self).__init__()
        self.context_gate = ContextGate(embeddings_size, decoder_size,
            attention_size, output_size)
        self.tanh = nn.Tanh()

    def forward(self, prev_emb, dec_state, attn_state):
        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)
        return self.tanh((1.0 - z) * target + z * source)


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


def get_init_inputs():
    return [[], {'embeddings_size': 4, 'decoder_size': 4, 'attention_size':
        4, 'output_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.cuda
import torch.distributed
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp5 = tmp3 * tmp4
    tmp7 = tmp1 * tmp6
    tmp8 = tmp5 + tmp7
    tmp9 = libdevice.tanh(tmp8)
    tl.store(out_ptr0 + x0, tmp9, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 12), (12, 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, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(48)](primals_1, primals_2, primals_3,
            buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4,
            (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, primals_3, 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((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](primals_1, primals_2, buf3, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf3, reinterpret_tensor(primals_8,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        del primals_9
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_tanh_2[grid(16)](buf1, buf4,
            buf2, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf5, primals_3, buf0, buf1, buf2, buf3, buf4, buf5


class ContextGate(nn.Module):
    """
    Context gate is a decoder module that takes as input the previous word
    embedding, the current decoder state and the attention state, and
    produces a gate.
    The gate can be used to select the input from the target side context
    (decoder state), from the source context (attention state) or both.
    """

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(ContextGate, self).__init__()
        input_size = embeddings_size + decoder_size + attention_size
        self.gate = nn.Linear(input_size, output_size, bias=True)
        self.sig = nn.Sigmoid()
        self.source_proj = nn.Linear(attention_size, output_size)
        self.target_proj = nn.Linear(embeddings_size + decoder_size,
            output_size)

    def forward(self, prev_emb, dec_state, attn_state):
        input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
        z = self.sig(self.gate(input_tensor))
        proj_source = self.source_proj(attn_state)
        proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
        return z, proj_source, proj_target


class BothContextGateNew(nn.Module):
    """Apply the context gate to both contexts"""

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(BothContextGateNew, self).__init__()
        self.context_gate = ContextGate(embeddings_size, decoder_size,
            attention_size, output_size)
        self.tanh = nn.Tanh()

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.context_gate.gate.weight
        primals_5 = self.context_gate.gate.bias
        primals_1 = self.context_gate.source_proj.weight
        primals_7 = self.context_gate.source_proj.bias
        primals_8 = self.context_gate.target_proj.weight
        primals_9 = self.context_gate.target_proj.bias
        primals_2 = input_0
        primals_3 = input_1
        primals_6 = 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]
