# 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/cm/ccmo7fnacrzz2bpb3kjhjwznydxc2ydumd6dycmnumtbvfyp23ld.py
# Topologically Sorted Source Nodes: [i_t, f_t, g_t, o_t, mul, mul_1, c_t, tanh_1, h_t], Original ATen: [aten.sigmoid, aten.tanh, aten.mul, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   c_t => add_1
#   f_t => sigmoid_1
#   g_t => tanh
#   h_t => mul_2
#   i_t => sigmoid
#   mul => mul
#   mul_1 => mul_1
#   o_t => sigmoid_2
#   tanh_1 => tanh_1
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_2,), kwargs = {})
#   %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_7), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), 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_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_3), 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=[16], 
    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: '*fp32', 11: '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, 11), 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': 17, '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, in_ptr4, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    tmp4 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp9 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (12 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr3 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp17 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (8 + x0 + (16*x1)), xmask)
    tmp20 = tl.load(in_ptr3 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp25 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp28 = tl.load(in_ptr3 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr4 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp10 = tmp8 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp18 = tmp16 + tmp17
    tmp21 = tmp19 + tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = libdevice.tanh(tmp22)
    tmp26 = tmp24 + tmp25
    tmp29 = tmp27 + tmp28
    tmp30 = tmp26 + tmp29
    tmp31 = tl.sigmoid(tmp30)
    tmp33 = tmp31 * tmp32
    tmp34 = tmp7 * tmp23
    tmp35 = tmp33 + tmp34
    tmp36 = 1.0
    tmp37 = tmp36 - tmp31
    tmp38 = tmp31 * tmp37
    tmp39 = libdevice.tanh(tmp35)
    tmp40 = tmp15 * tmp39
    tl.store(out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr1 + (x2), tmp15, xmask)
    tl.store(out_ptr2 + (x2), tmp23, xmask)
    tl.store(out_ptr3 + (x2), tmp35, xmask)
    tl.store(out_ptr4 + (x2), tmp38, xmask)
    tl.store(out_ptr5 + (x2), tmp40, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_6, reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [i_t, f_t, g_t, o_t, mul, mul_1, c_t, tanh_1, h_t], Original ATen: [aten.sigmoid, aten.tanh, aten.mul, 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_5, primals_7, buf2, buf4, buf3, buf5, buf7, buf6, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del primals_2
        del primals_5
    return (buf6, buf5, primals_3, primals_6, primals_7, 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, 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((16, ), (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, 4), (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])
    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 math
import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Optional
from typing import Tuple


class LSTMLinear(nn.Linear):
    """
    This function is the same as a nn.Linear layer, except that in the backward pass
    the grad_samples get accumulated (instead of being concatenated as in the standard
    nn.Linear)
    """

    def __init__(self, in_features: 'int', out_features: 'int', bias:
        'bool'=True):
        super().__init__(in_features, out_features, bias)


class DPLSTMCell(nn.Module):
    """
    Internal-only class. Implements *one* step of LSTM so that a LSTM layer can be seen as repeated
    applications of this class.
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', bias: 'bool'):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.bias = bias
        self.ih = LSTMLinear(input_size, 4 * hidden_size, bias=self.bias)
        self.hh = LSTMLinear(hidden_size, 4 * hidden_size, bias=self.bias)
        self.reset_parameters()

    def reset_parameters(self):
        """
        Resets parameters by initializing them from an uniform distribution.
        """
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            nn.init.uniform_(weight, -stdv, stdv)

    def set_max_batch_length(self, max_batch_length: 'int') ->None:
        """
        Sets max batch length
        """
        self.ih.max_batch_len = max_batch_length
        self.hh.max_batch_len = max_batch_length

    def forward(self, x: 'torch.Tensor', h_prev: 'torch.Tensor', c_prev:
        'torch.Tensor', batch_size_t: 'Optional[int]'=None) ->Tuple[torch.
        Tensor, torch.Tensor]:
        if batch_size_t is None:
            gates = self.ih(x) + self.hh(h_prev)
        else:
            gates = self.ih(x) + self.hh(h_prev[:batch_size_t, :])
        i_t_input, f_t_input, g_t_input, o_t_input = torch.split(gates,
            self.hidden_size, 1)
        i_t = torch.sigmoid(i_t_input)
        f_t = torch.sigmoid(f_t_input)
        g_t = torch.tanh(g_t_input)
        o_t = torch.sigmoid(o_t_input)
        if batch_size_t is None:
            c_t = f_t * c_prev + i_t * g_t
        else:
            c_t = f_t * c_prev[:batch_size_t, :] + i_t * g_t
        h_t = o_t * torch.tanh(c_t)
        return h_t, c_t


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 4, 'bias': 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 math
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
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, in_ptr4, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, out_ptr4, out_ptr5, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp4 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp9 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr3 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp17 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp20 = tl.load(in_ptr3 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp25 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp28 = tl.load(in_ptr3 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr4 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp10 = tmp8 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp18 = tmp16 + tmp17
    tmp21 = tmp19 + tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = libdevice.tanh(tmp22)
    tmp26 = tmp24 + tmp25
    tmp29 = tmp27 + tmp28
    tmp30 = tmp26 + tmp29
    tmp31 = tl.sigmoid(tmp30)
    tmp33 = tmp31 * tmp32
    tmp34 = tmp7 * tmp23
    tmp35 = tmp33 + tmp34
    tmp36 = 1.0
    tmp37 = tmp36 - tmp31
    tmp38 = tmp31 * tmp37
    tmp39 = libdevice.tanh(tmp35)
    tmp40 = tmp15 * tmp39
    tl.store(out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)
    tl.store(out_ptr2 + x2, tmp23, xmask)
    tl.store(out_ptr3 + x2, tmp35, xmask)
    tl.store(out_ptr4 + x2, tmp38, xmask)
    tl.store(out_ptr5 + x2, tmp40, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 16),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_6, reinterpret_tensor(primals_4, (4, 16),
            (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0[grid(16)](buf0
            , primals_2, buf1, primals_5, primals_7, buf2, buf4, buf3, buf5,
            buf7, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del primals_2
        del primals_5
    return (buf6, buf5, primals_3, primals_6, primals_7, buf2, buf3, buf4,
        buf5, buf7)


class LSTMLinear(nn.Linear):
    """
    This function is the same as a nn.Linear layer, except that in the backward pass
    the grad_samples get accumulated (instead of being concatenated as in the standard
    nn.Linear)
    """

    def __init__(self, in_features: 'int', out_features: 'int', bias:
        'bool'=True):
        super().__init__(in_features, out_features, bias)


class DPLSTMCellNew(nn.Module):
    """
    Internal-only class. Implements *one* step of LSTM so that a LSTM layer can be seen as repeated
    applications of this class.
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', bias: 'bool'):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.bias = bias
        self.ih = LSTMLinear(input_size, 4 * hidden_size, bias=self.bias)
        self.hh = LSTMLinear(hidden_size, 4 * hidden_size, bias=self.bias)
        self.reset_parameters()

    def reset_parameters(self):
        """
        Resets parameters by initializing them from an uniform distribution.
        """
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            nn.init.uniform_(weight, -stdv, stdv)

    def set_max_batch_length(self, max_batch_length: 'int') ->None:
        """
        Sets max batch length
        """
        self.ih.max_batch_len = max_batch_length
        self.hh.max_batch_len = max_batch_length

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.ih.weight
        primals_2 = self.ih.bias
        primals_4 = self.hh.weight
        primals_5 = self.hh.bias
        primals_3 = input_0
        primals_6 = input_1
        primals_7 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
