# 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/lg/clgtnw2bgvc3mnanbov77yxt2a6ztfqy7qpuhudozzijvzjgbgly.py
# Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_next, sigmoid_2, tanh_1, h_next], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   c_next => add_1
#   h_next => mul_2
#   mul => mul
#   mul_1 => mul_1
#   sigmoid => sigmoid
#   sigmoid_1 => sigmoid_1
#   sigmoid_2 => sigmoid_2
#   tanh => tanh
#   tanh_1 => tanh_1
# Graph fragment:
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_6), kwargs = {})
#   %sigmoid_1 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_2,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %tanh), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_3,), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_4), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_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: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '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, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 13, '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_sigmoid_sigmoid_backward_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (16*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (8 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp18 = tl.load(in_ptr3 + (x2), xmask)
    tmp25 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp26 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tl.sigmoid(tmp16)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.sigmoid(tmp29)
    tmp31 = libdevice.tanh(tmp21)
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp21, xmask)
    tl.store(out_ptr3 + (x2), tmp24, xmask)
    tl.store(out_ptr4 + (x2), tmp30, xmask)
    tl.store(out_ptr5 + (x2), tmp32, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 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, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_next, sigmoid_2, tanh_1, h_next], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0.run(buf0, primals_2, buf1, primals_6, buf2, buf3, buf4, buf7, buf5, buf6, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf1
        del primals_2
    return (buf6, buf4, primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), buf2, buf3, buf4, buf5, buf7, )


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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = 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])
    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 torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd


class LSTMCell(Module):
    """
    ## Long Short-Term Memory Cell

    LSTM Cell computes $c$, and $h$. $c$ is like the long-term memory,
    and $h$ is like the short term memory.
    We use the input $x$ and $h$ to update the long term memory.
    In the update, some features of $c$ are cleared with a forget gate $f$,
    and some features $i$ are added through a gate $g$.

    The new short term memory is the $	anh$ of the long-term memory
    multiplied by the output gate $o$.

    Note that the cell doesn't look at long term memory $c$ when doing the update. It only modifies it.
    Also $c$ never goes through a linear transformation.
    This is what solves vanishing and exploding gradients.

    Here's the update rule.

    egin{align}
    c_t &= \\sigma(f_t) \\odot c_{t-1} + \\sigma(i_t) \\odot 	anh(g_t) \\
    h_t &= \\sigma(o_t) \\odot 	anh(c_t)
    \\end{align}

    $\\odot$ stands for element-wise multiplication.

    Intermediate values and gates are computed as linear transformations of the hidden
    state and input.

    egin{align}
    i_t &= lin_x^i(x_t) + lin_h^i(h_{t-1}) \\
    f_t &= lin_x^f(x_t) + lin_h^f(h_{t-1}) \\
    g_t &= lin_x^g(x_t) + lin_h^g(h_{t-1}) \\
    o_t &= lin_x^o(x_t) + lin_h^o(h_{t-1})
    \\end{align}
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', layer_norm:
        'bool'=False):
        super().__init__()
        self.hidden_lin = nn.Linear(hidden_size, 4 * hidden_size)
        self.input_lin = nn.Linear(input_size, 4 * hidden_size, bias=False)
        if layer_norm:
            self.layer_norm = nn.ModuleList([nn.LayerNorm(hidden_size) for
                _ in range(4)])
            self.layer_norm_c = nn.LayerNorm(hidden_size)
        else:
            self.layer_norm = nn.ModuleList([nn.Identity() for _ in range(4)])
            self.layer_norm_c = nn.Identity()

    def forward(self, x: 'torch.Tensor', h: 'torch.Tensor', c: 'torch.Tensor'):
        ifgo = self.hidden_lin(h) + self.input_lin(x)
        ifgo = ifgo.chunk(4, dim=-1)
        ifgo = [self.layer_norm[i](ifgo[i]) for i in range(4)]
        i, f, g, o = ifgo
        c_next = torch.sigmoid(f) * c + torch.sigmoid(i) * torch.tanh(g)
        h_next = torch.sigmoid(o) * torch.tanh(self.layer_norm_c(c_next))
        return h_next, c_next


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_size': 4, 'hidden_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
from torch.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
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_mul_sigmoid_sigmoid_backward_tanh_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3,
    out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp18 = tl.load(in_ptr3 + x2, xmask)
    tmp25 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp26 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tl.sigmoid(tmp16)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.sigmoid(tmp29)
    tmp31 = libdevice.tanh(tmp21)
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp21, xmask)
    tl.store(out_ptr3 + x2, tmp24, xmask)
    tl.store(out_ptr4 + x2, tmp30, xmask)
    tl.store(out_ptr5 + x2, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0[grid(256)](
            buf0, primals_2, buf1, primals_6, buf2, buf3, buf4, buf7, buf5,
            buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del primals_2
    return buf6, buf4, primals_6, reinterpret_tensor(primals_3, (64, 4), (4,
        1), 0), reinterpret_tensor(primals_5, (64, 4), (4, 1), 0
        ), buf2, buf3, buf4, buf5, buf7


class LSTMCellNew(Module):
    """
    ## Long Short-Term Memory Cell

    LSTM Cell computes $c$, and $h$. $c$ is like the long-term memory,
    and $h$ is like the short term memory.
    We use the input $x$ and $h$ to update the long term memory.
    In the update, some features of $c$ are cleared with a forget gate $f$,
    and some features $i$ are added through a gate $g$.

    The new short term memory is the $	anh$ of the long-term memory
    multiplied by the output gate $o$.

    Note that the cell doesn't look at long term memory $c$ when doing the update. It only modifies it.
    Also $c$ never goes through a linear transformation.
    This is what solves vanishing and exploding gradients.

    Here's the update rule.

    egin{align}
    c_t &= \\sigma(f_t) \\odot c_{t-1} + \\sigma(i_t) \\odot 	anh(g_t) \\
    h_t &= \\sigma(o_t) \\odot 	anh(c_t)
    \\end{align}

    $\\odot$ stands for element-wise multiplication.

    Intermediate values and gates are computed as linear transformations of the hidden
    state and input.

    egin{align}
    i_t &= lin_x^i(x_t) + lin_h^i(h_{t-1}) \\
    f_t &= lin_x^f(x_t) + lin_h^f(h_{t-1}) \\
    g_t &= lin_x^g(x_t) + lin_h^g(h_{t-1}) \\
    o_t &= lin_x^o(x_t) + lin_h^o(h_{t-1})
    \\end{align}
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', layer_norm:
        'bool'=False):
        super().__init__()
        self.hidden_lin = nn.Linear(hidden_size, 4 * hidden_size)
        self.input_lin = nn.Linear(input_size, 4 * hidden_size, bias=False)
        if layer_norm:
            self.layer_norm = nn.ModuleList([nn.LayerNorm(hidden_size) for
                _ in range(4)])
            self.layer_norm_c = nn.LayerNorm(hidden_size)
        else:
            self.layer_norm = nn.ModuleList([nn.Identity() for _ in range(4)])
            self.layer_norm_c = nn.Identity()

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.hidden_lin.weight
        primals_2 = self.hidden_lin.bias
        primals_4 = self.input_lin.weight
        primals_3 = input_0
        primals_5 = input_1
        primals_6 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
