# 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_7/inductor_cache/l4/cl4boort6vfsvh6h6bfd4lck36kbmtipkqcrnhckuuxer6sfib77.py
# Topologically Sorted Source Nodes: [hx], Original ATen: [aten.zeros]
# Source node to ATen node mapping:
#   hx => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_zeros_0 = async_compile.triton('triton_poi_fused_zeros_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_zeros_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_zeros_0(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 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qp/cqplgpkxuj6ap4usdcijdffbeqxnixaqpkxfz2lr5j4d2olvaf6r.py
# Topologically Sorted Source Nodes: [gates, h_t], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   gates => add
#   h_t => tanh
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %add_tensor), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_tanh_1 = async_compile.triton('triton_poi_fused_add_tanh_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x4 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hx], Original ATen: [aten.zeros]
        stream0 = get_raw_stream(0)
        triton_poi_fused_zeros_0.run(buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [gates, h_t], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_1.run(buf3, primals_3, buf2, primals_5, 256, grid=grid(256), stream=stream0)
        del buf2
        del primals_3
        del primals_5
    return (buf3, buf0, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
from torch import Tensor
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
from typing import Optional


class RNNLinear(nn.Linear):
    """Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

    This module is the same as a ``torch.nn.Linear``` layer, except that in the backward pass
    the grad_samples get accumulated (instead of being concatenated as in the standard
    nn.Linear).

    When used with `PackedSequence`s, additional attribute `max_batch_len` is defined to determine
    the size of per-sample grad tensor.
    """
    max_batch_len: 'int'

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


class DPRNNCellBase(nn.Module):
    has_cell_state: 'bool' = False

    def __init__(self, input_size: 'int', hidden_size: 'int', bias: 'bool',
        num_chunks: 'int') ->None:
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.bias = bias
        self.ih = RNNLinear(input_size, num_chunks * hidden_size, bias)
        self.hh = RNNLinear(hidden_size, num_chunks * hidden_size, bias)
        self.reset_parameters()

    def reset_parameters(self) ->None:
        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:
        self.ih.max_batch_len = max_batch_length
        self.hh.max_batch_len = max_batch_length


class DPRNNCell(DPRNNCellBase):
    """An Elman RNN cell with tanh or ReLU non-linearity.

    DP-friendly drop-in replacement of the ``torch.nn.RNNCell`` module to use in ``DPRNN``.
    Refer to ``torch.nn.RNNCell`` documentation for the model description, parameters and inputs/outputs.
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', bias: 'bool',
        nonlinearity: 'str'='tanh') ->None:
        super().__init__(input_size, hidden_size, bias, num_chunks=1)
        if nonlinearity not in ('tanh', 'relu'):
            raise ValueError(f'Unsupported nonlinearity: {nonlinearity}')
        self.nonlinearity = nonlinearity

    def forward(self, input: 'Tensor', hx: 'Optional[Tensor]'=None,
        batch_size_t: 'Optional[int]'=None) ->Tensor:
        if hx is None:
            hx = torch.zeros(input.shape[0], self.hidden_size, dtype=input.
                dtype, device=input.device)
        h_prev = hx
        gates = self.ih(input) + self.hh(h_prev if batch_size_t is None else
            h_prev[:batch_size_t, :])
        if self.nonlinearity == 'tanh':
            h_t = torch.tanh(gates)
        elif self.nonlinearity == 'relu':
            h_t = torch.relu(gates)
        else:
            raise RuntimeError(f'Unknown nonlinearity: {self.nonlinearity}')
        return h_t


def get_inputs():
    return [torch.rand([4, 4, 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
from torch import nn
import torch.utils.data
import torch.utils.data.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_zeros_0(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 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x4 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_zeros_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf2)
        del primals_4
        buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_add_tanh_1[grid(256)](buf3, primals_3, buf2,
            primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
        del primals_3
        del primals_5
    return buf3, buf0, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf3


class RNNLinear(nn.Linear):
    """Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

    This module is the same as a ``torch.nn.Linear``` layer, except that in the backward pass
    the grad_samples get accumulated (instead of being concatenated as in the standard
    nn.Linear).

    When used with `PackedSequence`s, additional attribute `max_batch_len` is defined to determine
    the size of per-sample grad tensor.
    """
    max_batch_len: 'int'

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


class DPRNNCellBase(nn.Module):
    has_cell_state: 'bool' = False

    def __init__(self, input_size: 'int', hidden_size: 'int', bias: 'bool',
        num_chunks: 'int') ->None:
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.bias = bias
        self.ih = RNNLinear(input_size, num_chunks * hidden_size, bias)
        self.hh = RNNLinear(hidden_size, num_chunks * hidden_size, bias)
        self.reset_parameters()

    def reset_parameters(self) ->None:
        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:
        self.ih.max_batch_len = max_batch_length
        self.hh.max_batch_len = max_batch_length


class DPRNNCellNew(DPRNNCellBase):
    """An Elman RNN cell with tanh or ReLU non-linearity.

    DP-friendly drop-in replacement of the ``torch.nn.RNNCell`` module to use in ``DPRNN``.
    Refer to ``torch.nn.RNNCell`` documentation for the model description, parameters and inputs/outputs.
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', bias: 'bool',
        nonlinearity: 'str'='tanh') ->None:
        super().__init__(input_size, hidden_size, bias, num_chunks=1)
        if nonlinearity not in ('tanh', 'relu'):
            raise ValueError(f'Unsupported nonlinearity: {nonlinearity}')
        self.nonlinearity = nonlinearity

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