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]


Measured latency_ms: 0.015144