Repository: pytorch
Issue: Custom recurrent network takes very long to compile for long sequences
Labels: triaged,ezyang's list,oncall: pt2,module: dynamo,module: python dispatcher,module: startup-tracing-compile

### 🐛 Describe the bug

I am currently experimenting around `torch.compile` for use with a custom, pure-Python LiGRU implementation, which is a variant of the typical GRU architecture. This is not a crash/miscompile issue but a performance bug, resulting in really long compile times (>15 minutes for 1024 sequence length as an extreme example), ~~and worsened runtime performance (worse than eager even at a 64 sequence length on an A100 40GB)~~ (that part needs double checking).  
For smaller sequences, the compiled variant is typically much faster than either eager mode or `torch.jit.script` compilation.

It seems likely to me that the recurrent architecture is being fully unrolled. Is this the expected behavior (e.g. at graph construction time)? If so, what can be done to work around the issue?  
Note that I have been unable to try `dynamic=True` for now, as I am encountering an error with it enabled. If you believe dynamic shape support can help with this issue, I can also look deeper into that and report back.

The recurrent architecture in the below code is implemented as a `for`-loop over the sequence length as determined from the shape of the input.  
I am not very familiar with `torch.compile`'s architecture, or with GPU compilers in general. However, I am wondering if something like partial unrolling in the CPU world could be applied here.

I have attached the full real-world code including the toy benchmark I've been using, which hopefully is correct.  
I assumed it would not be helpful to create a smaller repro as this is not a crash/miscompile. If I'm wrong, I could try creating a smaller repro that shows similar behavior.

**`def _ligru_cell(self, w, ht):` is the relevant function.**

### Error logs

_No response_

### Repro

```python
import logging
logging.basicConfig(
    format='%(asctime)s,%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d:%H:%M:%S',
    level=logging.INFO
)
logging.info("importing pytorch")

import torch
import torch._dynamo as dynamo
import torch.nn as nn
import time

from torch import Tensor
from typing import Optional

class LiGRU(torch.nn.Module):
    """ This function implements a Light GRU (liGRU).
    LiGRU is single-gate GRU model based on batch-norm + relu
    activations + recurrent dropout. For more info see:
    "M. Ravanelli, P. Brakel, M. Omologo, Y. Bengio,
    Light Gated Recurrent Units for Speech Recognition,
    in IEEE Transactions on Emerging Topics in Computational Intelligence,
    2018" (https://arxiv.org/abs/1803.10225)
    This is a custm RNN and to speed it up it must be compiled with
    the torch just-in-time compiler (jit) right before using it.
    You can compile it with:
    compiled_model = torch.jit.script(model)
    It accepts in input tensors formatted as (batch, time, fea).
    In the case of 4d inputs like (batch, time, fea, channel) the tensor is
    flattened as (batch, time, fea*channel).
    Arguments
    ---------
    hidden_size : int
        Number of output neurons (i.e, the dimensionality of the output).
        values (i.e, time and frequency kernel sizes respectively).
    input_shape : tuple
        The shape of an example input.
    nonlinearity : str
        Type of nonlinearity (tanh, relu).
    normalization : str
        Type of normalization for the ligru model (batchnorm, layernorm).
        Every string different from batchnorm and layernorm will result
        in no normalization.
    num_layers : int
        Number of layers to employ in the RNN architecture.
    bias : bool
        If True, the additive bias b is adopted.
    dropout : float
        It is the dropout factor (must be between 0 and 1).
    re_init : bool
        If True, orthogonal initialization is used for the recurrent weights.
        Xavier initialization is used for the input connection weights.
    bidirectional : bool
        If True, a bidirectional model that scans the sequence both
        right-to-left and left-to-right is used.
    Example
    -------
    >>> inp_tensor = torch.rand([4, 10, 20])
    >>> net = LiGRU(input_shape=inp_tensor.shape, hidden_size=5)
    >>> out_tensor, _ = net(inp_tensor)
    >>>
    torch.Size([4, 10, 5])
    """

    def __init__(
        self,
        hidden_size,
        input_shape,
        nonlinearity="relu",
        normalization="batchnorm",
        num_layers=1,
        bias=True,
        dropout=0.0,
        re_init=True,
        bidirectional=False,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        self.nonlinearity = nonlinearity
        self.num_layers = num_layers
        self.normalization = normalization
        self.bias = bias
        self.dropout = dropout
        self.re_init = re_init
        self.bidirectional = bidirectional
        self.reshape = False

        # Computing the feature dimensionality
        if len(input_shape) > 3:
            self.reshape = True
        self.fea_dim = float(torch.prod(torch.tensor(input_shape[2:])))
        self.batch_size = input_shape[0]
        self.rnn = self._init_layers()

        if self.re_init:
            rnn_init(self.rnn)

    def _init_layers(self):
        """Initializes the layers of the liGRU."""
        rnn = torch.nn.ModuleList([])
        current_dim = self.fea_dim

        for i in range(self.num_layers):
            rnn_lay = LiGRU_Layer(
                current_dim,
                self.hidden_size,
                self.num_layers,
                self.batch_size,
                dropout=self.dropout,
                nonlinearity=self.nonlinearity,
                normalization=self.normalization,
                bidirectional=self.bidirectional,
            )
            rnn.append(rnn_lay)

            if self.bidirectional:
                current_dim = self.hidden_size * 2
            else:
                current_dim = self.hidden_size
        return rnn

    def forward(self, x, hx: Optional[Tensor] = None):
        """Returns the output of the liGRU.
        Arguments
        ---------
        x : torch.Tensor
            The input tensor.
        hx : torch.Tensor
            Starting hidden state.
        """
        # Reshaping input tensors for 4d inputs
        if self.reshape:
            if x.ndim == 4:
                x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3])

        # run ligru
        output, hh = self._forward_ligru(x, hx=hx)

        return output, hh

    def _forward_ligru(self, x, hx: Optional[Tensor]):
        """Returns the output of the vanilla liGRU.
        Arguments
        ---------
        x : torch.Tensor
            Input tensor.
        hx : torch.Tensor
        """
        h = []
        if hx is not None:
            if self.bidirectional:
                hx = hx.reshape(
                    self.num_layers, self.batch_size * 2, self.hidden_size
                )
        # Processing the different layers
        for i, ligru_lay in enumerate(self.rnn):
            if hx is not None:
                x = ligru_lay(x, hx=hx[i])
            else:
                x = ligru_lay(x, hx=None)
            h.append(x[:, -1, :])
        h = torch.stack(h, dim=1)

        if self.bidirectional:
            h = h.reshape(h.shape[1] * 2, h.shape[0], self.hidden_size)
        else:
            h = h.transpose(0, 1)

        return x, h


def chunker(seq, size):
    return (seq[pos:pos + size] for pos in range(0, len(seq), size))


class LiGRU_Layer(torch.nn.Module):
    """ This function implements Light-Gated Recurrent Units (ligru) layer.
    Arguments
    ---------
    input_size : int
        Feature dimensionality of the input tensors.
    batch_size : int
        Batch size of the input tensors.
    hidden_size : int
        Number of output neurons.
    num_layers : int
        Number of layers to employ in the RNN architecture.
    nonlinearity : str
        Type of nonlinearity (tanh, relu).
    normalization : str
        Type of normalization (batchnorm, layernorm).
        Every string different from batchnorm and layernorm will result
        in no normalization.
    dropout : float
        It is the dropout factor (must be between 0 and 1).
    bidirectional : bool
        if True, a bidirectional model that scans the sequence both
        right-to-left and left-to-right is used.
    """

    def __init__(
        self,
        input_size,
        hidden_size,
        num_layers,
        batch_size,
        dropout=0.0,
        nonlinearity="relu",
        normalization="batchnorm",
        bidirectional=False,
    ):

        super(LiGRU_Layer, self).__init__()
        self.hidden_size = int(hidden_size)
        self.input_size = int(input_size)
        self.batch_size = batch_size
        self.bidirectional = bidirectional
        self.dropout = dropout

        self.w = nn.Linear(self.input_size, 2 * self.hidden_size, bias=False)

        self.u = nn.Linear(self.hidden_size, 2 * self.hidden_size, bias=False)

        if self.bidirectional:
            self.batch_size = self.batch_size * 2

        # Initializing batch norm
        self.normalize = False

        if normalization == "batchnorm":
            self.norm = nn.BatchNorm1d(2 * self.hidden_size, momentum=0.05)
            self.normalize = True

        elif normalization == "layernorm":
            self.norm = torch.nn.LayerNorm(2 * self.hidden_size)
            self.normalize = True
        else:
            # Normalization is disabled here. self.norm is only  formally
            # initialized to avoid jit issues.
            self.norm = torch.nn.LayerNorm(2 * self.hidden_size)
            self.normalize = True

        # Initial state
        self.register_buffer("h_init", torch.zeros(1, self.hidden_size))

        # Preloading dropout masks (gives some speed improvement)
        self._init_drop(self.batch_size)

        # Setting the activation function
        if nonlinearity == "tanh":
            self.act = torch.nn.Tanh()
        elif nonlinearity == "sin":
            self.act = torch.sin
        elif nonlinearity == "leaky_relu":
            self.act = torch.nn.LeakyReLU()
        else:
            self.act = torch.nn.ReLU()

    def forward(self, x, hx: Optional[Tensor] = None):
        # type: (Tensor, Optional[Tensor]) -> Tensor # noqa F821
        """Returns the output of the liGRU layer.
        Arguments
        ---------
        x : torch.Tensor
            Input tensor.
        """
        if self.bidirectional:
            x_flip = x.flip(1)
            x = torch.cat([x, x_flip], dim=0)

        # Change batch size if needed
        self._change_batch_size(x)

        # Feed-forward affine transformations (all steps in parallel)
        w = self.w(x)

        # Apply batch normalization
        if self.normalize:
            w_bn = self.norm(w.reshape(w.shape[0] * w.shape[1], w.shape[2]))
            w = w_bn.reshape(w.shape[0], w.shape[1], w.shape[2])

        # Processing time steps
        if hx is not None:
            h = self._ligru_cell(w, hx)
        else:
            h = self._ligru_cell(w, self.h_init)

        if self.bidirectional:
            h_f, h_b = h.chunk(2, dim=0)
            h_b = h_b.flip(1)
            h = torch.cat([h_f, h_b], dim=2)

        return h

    def _ligru_cell(self, w, ht):
        """Returns the hidden states for each time step.
        Arguments
        ---------
        wx : torch.Tensor
            Linearly transformed input.
        """
        hiddens = []

        # Sampling dropout mask
        drop_mask = self._sample_drop_mask(w)

        # Loop over time axis
        for k in range(w.shape[1]):
            # HACK: compile: broadcast_to is necessary for the compiler to understand
            # the initial shape of ht correctly
            gates = w[:, k] + self.u(ht).broadcast_to(w[:, k].shape)
            at, zt = gates.chunk(2, 1)
            zt = torch.sigmoid(zt)
            hcand = self.act(at) * drop_mask
            ht = zt * ht + (1 - zt) * hcand
            hiddens.append(ht)

        # Stacking hidden states
        h = torch.stack(hiddens, dim=1)
        return h

    def _init_drop(self, batch_size):
        """Initializes the recurrent dropout operation. To speed it up,
        the dropout masks are sampled in advance.
        """
        self.drop = torch.nn.Dropout(p=self.dropout, inplace=False)
        self.N_drop_masks = 16000
        self.drop_mask_cnt = 0

        self.register_buffer(
            "drop_masks",
            self.drop(torch.ones(self.N_drop_masks, self.hidden_size)).data,
        )
        self.register_buffer("drop_mask_te", torch.tensor([1.0]).float())

    def _sample_drop_mask(self, w):
        """Selects one of the pre-defined dropout masks"""
        if self.training:

            # Sample new masks when needed
            if self.drop_mask_cnt + self.batch_size > self.N_drop_masks:
                self.drop_mask_cnt = 0
                self.drop_masks = self.drop(
                    torch.ones(
                        self.N_drop_masks, self.hidden_size, device=w.device
                    )
                ).data

            # Sampling the mask
            drop_mask = self.drop_masks[
                self.drop_mask_cnt : self.drop_mask_cnt + self.batch_size
            ]
            self.drop_mask_cnt = self.drop_mask_cnt + self.batch_size

        else:
            # FIXME: compile: breaks fullgraph capture
            # self.drop_mask_te = self.drop_mask_te.to(w.device)
            drop_mask = self.drop_mask_te.to(w.device)

        return drop_mask

    def _change_batch_size(self, x):
        """This function changes the batch size when it is different from
        the one detected in the initialization method. This might happen in
        the case of multi-gpu or when we have different batch sizes in train
        and test. We also update the h_int and drop masks.
        """
        if self.batch_size != x.shape[0]:
            self.batch_size = x.shape[0]

            if self.training:
                self.drop_masks = self.drop(
                    torch.ones(
                        self.N_drop_masks, self.hidden_size, device=x.device,
                    )
                ).data
def rnn_init(module):
    """This function is used to initialize the RNN weight.
    Recurrent connection: orthogonal initialization.
    Arguments
    ---------
    module: torch.nn.Module
        Recurrent neural network module.
    Example
    -------
    >>> inp_tensor = torch.rand([4, 10, 20])
    >>> net = RNN(hidden_size=5, input_shape=inp_tensor.shape)
    >>> out_tensor = net(inp_tensor)
    >>> rnn_init(net)
    """
    for name, param in module.named_parameters():
        if "weight_hh" in name or ".u.weight" in name:
            nn.init.orthogonal_(param)


def time_it(func):
    start = time.time()
    ret = func()
    end = time.time()
    logging.info(f"... took {end - start:.2f}s")
    return ret


def benchmark(func, count=100):
    logging.info("running 10 dry runs for function")
    for _ in range(10):
        func()

    torch.cuda.synchronize()

    logging.info("true runs:")
    start = time.time()
    for _ in range(count):
        torch.cuda.synchronize()
        func()
        torch.cuda.synchronize()
    end = time.time()
    spent = end - start
    logging.info(f"{spent / count:.4f}s/iter for {count} total")


if __name__ == "__main__":
    batch, time_steps, feats = 128, 64, 512
    hidden_size, num_layer, dropout = 1024, 4, 0.0
    nonlinearity = "relu" # works also with sine, leakyrelu and tanh

    device = "cuda"

    # torch._dynamo.config.verbose=False
    
    # smaller_toy = torch.randn((batch, 8, feats), requires_grad=False).to(device).half()
    # frozen_toy_example = dynamo.run(toy_example)

    logging.info("loading model & input onto device")
    torch.manual_seed(0)
    inp_tensor = torch.randn((batch, time_steps, feats), requires_grad=False).to(device).half()
    net = LiGRU(
        input_shape=inp_tensor.shape,
        hidden_size=hidden_size,
        num_layers=num_layer,
        dropout=dropout,
        nonlinearity=nonlinearity,
    ).to(device).half()

    net.eval()

    logging.info("=== evaluating model (not compiled)")
    benchmark(lambda: net(inp_tensor)[0].sum().backward())

    logging.info("=== torch.jit.script (JIT)")
    net = time_it(lambda: torch.jit.script(net))

    logging.info("=== testing JIT model")
    benchmark(lambda: net(inp_tensor)[0].sum().backward())

    logging.info("=== torch.compile (torchinductor)")
    net = LiGRU(
        input_shape=inp_tensor.shape,
        hidden_size=hidden_size,
        num_layers=num_layer,
        dropout=dropout,
        nonlinearity=nonlinearity,
    ).to(device).half()
    net.eval()

    net = time_it(lambda: torch.compile(net, mode="reduce-overhead", fullgraph=True))

    logging.info("=== testing compiled model")
    benchmark(lambda: net(inp_tensor)[0].sum().backward())
```

### Versions

```
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final)
CMake version: version 3.10.2
Libc version: glibc-2.27

Python version: 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.27
Is CUDA available: False
CUDA runtime version: 11.3.58
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Probably one of the following:
/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.2.1
/usr/local/cuda-10.2/targets/x86_64-linux/lib/libcudnn.so.7.2.1
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.2.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.2.0
/usr/local/cuda-9.2/lib64/libcudnn.so.7.2.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              16
On-line CPU(s) list: 0-15
Thread(s) per core:  2
Core(s) per socket:  4
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               44
Model name:          Intel(R) Xeon(R) CPU           E5640  @ 2.67GHz
Stepping:            2
CPU MHz:             1600.237
BogoMIPS:            5334.11
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            256K
L3 cache:            12288K
NUMA node0 CPU(s):   0,2,4,6,8,10,12,14
NUMA node1 CPU(s):   1,3,5,7,9,11,13,15
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr pdcm pcid dca sse4_1 sse4_2 popcnt aes lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida arat flush_l1d

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.0.0
[pip3] torchaudio==2.0.0
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0           py310h7f8727e_0  
[conda] mkl_fft                   1.3.1           py310hd6ae3a3_0  
[conda] mkl_random                1.2.2           py310h00e6091_0  
[conda] numpy                     1.23.5          py310hd5efca6_0  
[conda] numpy-base                1.23.5          py310h8e6c178_0  
[conda] pytorch                   2.0.0           py3.10_cuda11.8_cudnn8.7.0_0    pytorch
[conda] pytorch-cuda              11.8                 h7e8668a_3    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.0.0               py310_cu118    pytorch
[conda] torchtriton               2.0.0                     py310    pytorch
[conda] torchvision               0.15.0              py310_cu118    pytorch
```

cc @ezyang @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @oulgen @jamesjwu @aorenste @laithsakka @msaroufim @bdhirsh @zou3519 @soumith @wconstab @ngimel