rnnt.py

from tinygrad.tensor import Tensor
from tinygrad.jit import TinyJit
from tinygrad.nn import Linear, Embedding
import numpy as np
from extra.utils import download_file
from pathlib import Path


class RNNT:
  def __init__(self, input_features=240, vocab_size=29, enc_hidden_size=1024, pred_hidden_size=320, joint_hidden_size=512, pre_enc_layers=2, post_enc_layers=3, pred_layers=2, stack_time_factor=2, dropout=0.32):
    self.encoder = Encoder(input_features, enc_hidden_size, pre_enc_layers, post_enc_layers, stack_time_factor, dropout)
    self.prediction = Prediction(vocab_size, pred_hidden_size, pred_layers, dropout)
    self.joint = Joint(vocab_size, pred_hidden_size, enc_hidden_size, joint_hidden_size, dropout)

  @TinyJit
  def __call__(self, x, y, hc=None):
    f, _ = self.encoder(x, None)
    g, _ = self.prediction(y, hc, Tensor.ones(1, requires_grad=False))
    out = self.joint(f, g)
    return out.realize()

  def decode(self, x, x_lens):
    logits, logit_lens = self.encoder(x, x_lens)
    outputs = []
    for b in range(logits.shape[0]):
      inseq = logits[b, :, :].unsqueeze(1)
      logit_len = logit_lens[b]
      seq = self._greedy_decode(inseq, int(np.ceil(logit_len.numpy()).item()))
      outputs.append(seq)
    return outputs

  def _greedy_decode(self, logits, logit_len):
    hc = Tensor.zeros(self.prediction.rnn.layers, 2, self.prediction.hidden_size, requires_grad=False)
    labels = []
    label = Tensor.zeros(1, 1, requires_grad=False)
    mask = Tensor.zeros(1, requires_grad=False)
    for time_idx in range(logit_len):
      logit = logits[time_idx, :, :].unsqueeze(0)
      not_blank = True
      added = 0
      while not_blank and added < 30:
        if len(labels) > 0:
          mask = (mask + 1).clip(0, 1)
          label = Tensor([[labels[-1] if labels[-1] <= 28 else labels[-1] - 1]], requires_grad=False) + 1 - 1
        jhc = self._pred_joint(Tensor(logit.numpy()), label, hc, mask)
        k = np.argmax(jhc[0, 0, :29].numpy(), axis=0)
        not_blank = k != 28
        if not_blank:
          labels.append(k)
          hc = jhc[:, :, 29:] + 1 - 1
        added += 1
    return labels

  @TinyJit
  def _pred_joint(self, logit, label, hc, mask):
    g, hc = self.prediction(label, hc, mask)
    j = self.joint(logit, g)[0]
    j = j.pad(((0, 1), (0, 1), (0, 0)))
    out = j.cat(hc, dim=2)
    return out.realize()

  def load_from_pretrained(self):
    fn = Path(__file__).parent.parent / "weights/rnnt.pt"
    download_file("https://zenodo.org/record/3662521/files/DistributedDataParallel_1576581068.9962234-epoch-100.pt?download=1", fn)

    import torch
    with open(fn, "rb") as f:
      state_dict = torch.load(f, map_location="cpu")["state_dict"]

    # encoder
    for i in range(2):
      self.encoder.pre_rnn.cells[i].weights_ih.assign(state_dict[f"encoder.pre_rnn.lstm.weight_ih_l{i}"].numpy())
      self.encoder.pre_rnn.cells[i].weights_hh.assign(state_dict[f"encoder.pre_rnn.lstm.weight_hh_l{i}"].numpy())
      self.encoder.pre_rnn.cells[i].bias_ih.assign(state_dict[f"encoder.pre_rnn.lstm.bias_ih_l{i}"].numpy())
      self.encoder.pre_rnn.cells[i].bias_hh.assign(state_dict[f"encoder.pre_rnn.lstm.bias_hh_l{i}"].numpy())
    for i in range(3):
      self.encoder.post_rnn.cells[i].weights_ih.assign(state_dict[f"encoder.post_rnn.lstm.weight_ih_l{i}"].numpy())
      self.encoder.post_rnn.cells[i].weights_hh.assign(state_dict[f"encoder.post_rnn.lstm.weight_hh_l{i}"].numpy())
      self.encoder.post_rnn.cells[i].bias_ih.assign(state_dict[f"encoder.post_rnn.lstm.bias_ih_l{i}"].numpy())
      self.encoder.post_rnn.cells[i].bias_hh.assign(state_dict[f"encoder.post_rnn.lstm.bias_hh_l{i}"].numpy())

    # prediction
    self.prediction.emb.weight.assign(state_dict["prediction.embed.weight"].numpy())
    for i in range(2):
      self.prediction.rnn.cells[i].weights_ih.assign(state_dict[f"prediction.dec_rnn.lstm.weight_ih_l{i}"].numpy())
      self.prediction.rnn.cells[i].weights_hh.assign(state_dict[f"prediction.dec_rnn.lstm.weight_hh_l{i}"].numpy())
      self.prediction.rnn.cells[i].bias_ih.assign(state_dict[f"prediction.dec_rnn.lstm.bias_ih_l{i}"].numpy())
      self.prediction.rnn.cells[i].bias_hh.assign(state_dict[f"prediction.dec_rnn.lstm.bias_hh_l{i}"].numpy())

    # joint
    self.joint.l1.weight.assign(state_dict["joint_net.0.weight"].numpy())
    self.joint.l1.bias.assign(state_dict["joint_net.0.bias"].numpy())
    self.joint.l2.weight.assign(state_dict["joint_net.3.weight"].numpy())
    self.joint.l2.bias.assign(state_dict["joint_net.3.bias"].numpy())


class LSTMCell:
  def __init__(self, input_size, hidden_size, dropout):
    self.dropout = dropout

    self.weights_ih = Tensor.uniform(hidden_size * 4, input_size)
    self.bias_ih = Tensor.uniform(hidden_size * 4)
    self.weights_hh = Tensor.uniform(hidden_size * 4, hidden_size)
    self.bias_hh = Tensor.uniform(hidden_size * 4)

  def __call__(self, x, hc):
    gates = x.linear(self.weights_ih.T, self.bias_ih) + hc[:x.shape[0]].linear(self.weights_hh.T, self.bias_hh)

    i, f, g, o = gates.chunk(4, 1)
    i, f, g, o = i.sigmoid(), f.sigmoid(), g.tanh(), o.sigmoid()

    c = (f * hc[x.shape[0]:]) + (i * g)
    h = (o * c.tanh()).dropout(self.dropout)

    return Tensor.cat(h, c).realize()


class LSTM:
  def __init__(self, input_size, hidden_size, layers, dropout):
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.layers = layers

    self.cells = [LSTMCell(input_size, hidden_size, dropout) if i == 0 else LSTMCell(hidden_size, hidden_size, dropout if i != layers - 1 else 0) for i in range(layers)]

  def __call__(self, x, hc):
    @TinyJit
    def _do_step(x_, hc_):
      return self.do_step(x_, hc_)

    if hc is None:
      hc = Tensor.zeros(self.layers, 2 * x.shape[1], self.hidden_size, requires_grad=False)

    output = None
    for t in range(x.shape[0]):
      hc = _do_step(x[t] + 1 - 1, hc) # TODO: why do we need to do this?
      if output is None:
        output = hc[-1:, :x.shape[1]]
      else:
        output = output.cat(hc[-1:, :x.shape[1]], dim=0).realize()

    return output, hc

  def do_step(self, x, hc):
    new_hc = [x]
    for i, cell in enumerate(self.cells):
      new_hc.append(cell(new_hc[i][:x.shape[0]], hc[i]))
    return Tensor.stack(new_hc[1:]).realize()


class StackTime:
  def __init__(self, factor):
    self.factor = factor

  def __call__(self, x, x_lens):
    x = x.pad(((0, (-x.shape[0]) % self.factor), (0, 0), (0, 0)))
    x = x.reshape(x.shape[0] // self.factor, x.shape[1], x.shape[2] * self.factor)
    return x, x_lens / self.factor if x_lens is not None else None


class Encoder:
  def __init__(self, input_size, hidden_size, pre_layers, post_layers, stack_time_factor, dropout):
    self.pre_rnn = LSTM(input_size, hidden_size, pre_layers, dropout)
    self.stack_time = StackTime(stack_time_factor)
    self.post_rnn = LSTM(stack_time_factor * hidden_size, hidden_size, post_layers, dropout)

  def __call__(self, x, x_lens):
    x, _ = self.pre_rnn(x, None)
    x, x_lens = self.stack_time(x, x_lens)
    x, _ = self.post_rnn(x, None)
    return x.transpose(0, 1), x_lens


class Prediction:
  def __init__(self, vocab_size, hidden_size, layers, dropout):
    self.hidden_size = hidden_size

    self.emb = Embedding(vocab_size - 1, hidden_size)
    self.rnn = LSTM(hidden_size, hidden_size, layers, dropout)

  def __call__(self, x, hc, m):
    emb = self.emb(x) * m
    x_, hc = self.rnn(emb.transpose(0, 1), hc)
    return x_.transpose(0, 1), hc


class Joint:
  def __init__(self, vocab_size, pred_hidden_size, enc_hidden_size, joint_hidden_size, dropout):
    self.dropout = dropout

    self.l1 = Linear(pred_hidden_size + enc_hidden_size, joint_hidden_size)
    self.l2 = Linear(joint_hidden_size, vocab_size)

  def __call__(self, f, g):
    (_, T, H), (B, U, H2) = f.shape, g.shape
    f = f.unsqueeze(2).expand(B, T, U, H)
    g = g.unsqueeze(1).expand(B, T, U, H2)

    inp = f.cat(g, dim=3)
    t = self.l1(inp).relu()
    t = t.dropout(self.dropout)
    return self.l2(t)