# 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/x2/cx2opldmv23p224bzsdeqf4rnb3hnj2sia22rfsabdb42yyrrphc.py
# Topologically Sorted Source Nodes: [forget_rates, sub, weighted_inputs, mul_1, new_step_state, relu], Original ATen: [aten.sigmoid, aten.rsub, aten.mul, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   forget_rates => sigmoid
#   mul_1 => mul_1
#   new_step_state => add
#   relu => relu
#   sub => sub
#   weighted_inputs => mul
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %view_3), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_5), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_0 = async_compile.triton('triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_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: '*i1', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_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_add_mul_relu_rsub_sigmoid_threshold_backward_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, 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_ptr0 + (x0), xmask)
    tmp2 = tl.load(in_ptr1 + (x0), xmask)
    tmp6 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = 0.0
    tmp12 = tmp10 <= tmp11
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp10, xmask)
    tl.store(out_ptr2 + (x0), tmp12, 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, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 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.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        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_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (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.bool)
        # Topologically Sorted Source Nodes: [forget_rates, sub, weighted_inputs, mul_1, new_step_state, relu], Original ATen: [aten.sigmoid, aten.rsub, aten.mul, aten.add, aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_0.run(buf0, primals_5, buf1, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0)
    return (buf3, buf2, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf1, buf4, )


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, ), (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((4, 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)
    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 torch
from typing import Tuple
from abc import abstractmethod
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors


class AutoregressiveLayer(pt.nn.Module):

    @property
    @abstractmethod
    def num_state_tensors(self) ->int:
        """ Number of state tensors returned by the layer """
        raise NotImplementedError

    @property
    @abstractmethod
    def needs_mask(self) ->bool:
        """ Whether the layer makes use of a mask tensor or not """
        raise NotImplementedError

    @abstractmethod
    def get_state_shape(self, batch_size) ->Tuple:
        """
        :param batch_size: current batch size
        :return: dimensions of each output state (assuming all of them have the same shape)
        """
        raise NotImplementedError

    @abstractmethod
    def forward(self, inputs: 'pt.Tensor', previous_states: 'pt.Tensor', *args
        ) ->Tuple:
        """
        :param inputs: layer input
        :param previous_states: Previous states array or list of arrays
        :param args: layer-specific arguments and/or arguments to be ignored
        :return: layer output and new states
        """
        raise NotImplementedError


class PyTorchSSRU(AutoregressiveLayer):
    """
    Simpler Simple Recurrent Unit

    Kim et al, "From Research to Production and Back: Ludicrously Fast Neural Machine Translation" WNGT 2019

    Variant of an LSTM cell aimed at reducing computational dependency across time steps.
    Formally described as:

    (1) f[t] = sigmoid(W1[t] * x[t] + b[t])
    (2) c[t] = f[t] . c[t-1] + (1 - f[t]) . W2[t] * x[t]
    (3) h = ReLU(c[t])

    where:
        . represents elementwise multiplication;
        x[t] is the input at time step t;
        f[t] is the output of the forget gate at time step t;
        c[t] is the cell state at time step t;
        h is the output of the unit.

    :param model_size: number of hidden units
    :param inference_only: flag used to indicate execution at inference time
    """

    def __init__(self, model_size: 'int', inference_only: 'bool') ->None:
        super().__init__()
        self.model_size = model_size
        self.inference_only = inference_only
        self.cell_state_transform = (self._inference_cell_state_transform if
            inference_only else self._training_cell_state_transform)
        self.forget_gate = pt.nn.Linear(in_features=model_size,
            out_features=model_size, bias=True)
        self.forget_gate_act = pt.nn.Sigmoid()
        self.linear = pt.nn.Linear(in_features=model_size, out_features=
            model_size, bias=False)
        self.relu = pt.nn.ReLU(inplace=False)

    @property
    def num_state_tensors(self) ->int:
        """ Number of state tensors returned by the layer """
        return 1

    @property
    def needs_mask(self) ->bool:
        """ Whether the layer makes use of a mask tensor or not """
        return False

    def get_state_shape(self, batch_size: 'int') ->Tuple:
        """
        :param batch_size: current batch size
        :return: dimensions of each output state (assuming all of them have the same shape)
        """
        return 1, batch_size, self.model_size

    @staticmethod
    @pt.jit.script_if_tracing
    def _training_cell_state_transform(previous_cell_state, weighted_inputs,
        forget_rates) ->Tuple[pt.Tensor, pt.Tensor]:
        """Update SSRU cell at training time"""
        steps = weighted_inputs.size()[0]
        cell_state = previous_cell_state.squeeze(0)
        states = []
        for t in range(steps):
            cell_state = forget_rates[t, :, :] * cell_state + weighted_inputs[
                t, :, :]
            states.append(cell_state)
        states = pt.stack(states, dim=0)
        return states, cell_state.unsqueeze(0)

    @staticmethod
    def _inference_cell_state_transform(previous_cell_state,
        weighted_inputs, forget_rates) ->Tuple[pt.Tensor, pt.Tensor]:
        """Update SSRU cell at inference time"""
        new_step_state = forget_rates * previous_cell_state + weighted_inputs
        return new_step_state, new_step_state

    def forward(self, inputs: 'pt.Tensor', previous_states: 'pt.Tensor', **args
        ) ->Tuple[pt.Tensor, pt.Tensor]:
        """
        :param inputs: input data. Shape: (max_length, batch, input_depth).
        :param previous_states: previous cell states. Shape: (max_length, batch, input_depth)
        :return: cell output and new cell states.  Both with shape (max_length, batch, input_depth).
        """
        forget_rates = self.forget_gate_act(self.forget_gate(inputs))
        weighted_inputs = (1 - forget_rates) * self.linear(inputs)
        cell_state, last_step_state = self.cell_state_transform(previous_states
            , weighted_inputs, forget_rates)
        return self.relu(cell_state), last_step_state

    def weights_from_mxnet_block(self, block_mx: "'SSRU'"):
        self.forget_gate.weight.data[:] = pt.as_tensor(block_mx.forget_gate
            .weight.data().asnumpy())
        self.forget_gate.bias.data[:] = pt.as_tensor(block_mx.forget_gate.
            bias.data().asnumpy())
        self.linear.weight.data[:] = pt.as_tensor(block_mx.linear.weight.
            data().asnumpy())


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


def get_init_inputs():
    return [[], {'model_size': 4, 'inference_only': 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 typing import Tuple
from abc import abstractmethod
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
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_relu_rsub_sigmoid_threshold_backward_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, 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_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = 0.0
    tmp12 = tmp10 <= tmp11
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)
    tl.store(out_ptr2 + x0, tmp12, 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, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (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.bool)
        get_raw_stream(0)
        triton_poi_fused_add_mul_relu_rsub_sigmoid_threshold_backward_0[grid
            (256)](buf0, primals_5, buf1, buf2, buf3, buf4, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf3, buf2, primals_5, reinterpret_tensor(primals_3, (64, 4), (4,
        1), 0), buf0, buf1, buf4


class AutoregressiveLayer(pt.nn.Module):

    @property
    @abstractmethod
    def num_state_tensors(self) ->int:
        """ Number of state tensors returned by the layer """
        raise NotImplementedError

    @property
    @abstractmethod
    def needs_mask(self) ->bool:
        """ Whether the layer makes use of a mask tensor or not """
        raise NotImplementedError

    @abstractmethod
    def get_state_shape(self, batch_size) ->Tuple:
        """
        :param batch_size: current batch size
        :return: dimensions of each output state (assuming all of them have the same shape)
        """
        raise NotImplementedError

    @abstractmethod
    def forward(self, inputs: 'pt.Tensor', previous_states: 'pt.Tensor', *args
        ) ->Tuple:
        """
        :param inputs: layer input
        :param previous_states: Previous states array or list of arrays
        :param args: layer-specific arguments and/or arguments to be ignored
        :return: layer output and new states
        """
        raise NotImplementedError


class PyTorchSSRUNew(AutoregressiveLayer):
    """
    Simpler Simple Recurrent Unit

    Kim et al, "From Research to Production and Back: Ludicrously Fast Neural Machine Translation" WNGT 2019

    Variant of an LSTM cell aimed at reducing computational dependency across time steps.
    Formally described as:

    (1) f[t] = sigmoid(W1[t] * x[t] + b[t])
    (2) c[t] = f[t] . c[t-1] + (1 - f[t]) . W2[t] * x[t]
    (3) h = ReLU(c[t])

    where:
        . represents elementwise multiplication;
        x[t] is the input at time step t;
        f[t] is the output of the forget gate at time step t;
        c[t] is the cell state at time step t;
        h is the output of the unit.

    :param model_size: number of hidden units
    :param inference_only: flag used to indicate execution at inference time
    """

    def __init__(self, model_size: 'int', inference_only: 'bool') ->None:
        super().__init__()
        self.model_size = model_size
        self.inference_only = inference_only
        self.cell_state_transform = (self._inference_cell_state_transform if
            inference_only else self._training_cell_state_transform)
        self.forget_gate = pt.nn.Linear(in_features=model_size,
            out_features=model_size, bias=True)
        self.forget_gate_act = pt.nn.Sigmoid()
        self.linear = pt.nn.Linear(in_features=model_size, out_features=
            model_size, bias=False)
        self.relu = pt.nn.ReLU(inplace=False)

    @property
    def num_state_tensors(self) ->int:
        """ Number of state tensors returned by the layer """
        return 1

    @property
    def needs_mask(self) ->bool:
        """ Whether the layer makes use of a mask tensor or not """
        return False

    def get_state_shape(self, batch_size: 'int') ->Tuple:
        """
        :param batch_size: current batch size
        :return: dimensions of each output state (assuming all of them have the same shape)
        """
        return 1, batch_size, self.model_size

    @staticmethod
    @pt.jit.script_if_tracing
    def _training_cell_state_transform(previous_cell_state, weighted_inputs,
        forget_rates) ->Tuple[pt.Tensor, pt.Tensor]:
        """Update SSRU cell at training time"""
        steps = weighted_inputs.size()[0]
        cell_state = previous_cell_state.squeeze(0)
        states = []
        for t in range(steps):
            cell_state = forget_rates[t, :, :] * cell_state + weighted_inputs[
                t, :, :]
            states.append(cell_state)
        states = pt.stack(states, dim=0)
        return states, cell_state.unsqueeze(0)

    @staticmethod
    def _inference_cell_state_transform(previous_cell_state,
        weighted_inputs, forget_rates) ->Tuple[pt.Tensor, pt.Tensor]:
        """Update SSRU cell at inference time"""
        new_step_state = forget_rates * previous_cell_state + weighted_inputs
        return new_step_state, new_step_state

    def weights_from_mxnet_block(self, block_mx: "'SSRU'"):
        self.forget_gate.weight.data[:] = pt.as_tensor(block_mx.forget_gate
            .weight.data().asnumpy())
        self.forget_gate.bias.data[:] = pt.as_tensor(block_mx.forget_gate.
            bias.data().asnumpy())
        self.linear.weight.data[:] = pt.as_tensor(block_mx.linear.weight.
            data().asnumpy())

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