How to use the trax.backend.random.split function in trax

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github google / trax / trax / layers / research / View on Github external
while factor > 0 and not (
            self.n_buckets % factor == 0 and
            factor % 2 == 0 and
            (self.n_buckets // factor) % 2 == 0):
          factor -= 1
        if factor > 2:  # Factor of 2 does not warrant the effort.
          rot_size = factor + (self.n_buckets // factor)
          factor_list = [factor, self.n_buckets // factor]

    rotations_shape = (
        self.n_hashes if self._rehash_each_round else 1,
        rot_size // 2)

    rng = jax.lax.tie_in(vecs, rng)
    rng, subrng = backend.random.split(rng)
    random_rotations = self._sample_rotation(rotations_shape, vecs, rng)

    # TODO(lukaszkaiser): the dropout mask will be used for all rounds of
    # hashing, so it's shared between them. Check if that's what we want.
    dropped_vecs = self.drop_for_hash(vecs, subrng)
    rotated_vecs = np.einsum('tf,fhb->htb', dropped_vecs, random_rotations)

    if self._rehash_each_round:
      if self._factorize_hash and len(factor_list) > 1:
        # We factorized self.n_buckets as the product of factor_list.
        # Get the buckets for them and combine.
        buckets, cur_sum, cur_product = None, 0, 1
        for factor in factor_list:
          rv = rotated_vecs[..., cur_sum:cur_sum + (factor // 2)]
          cur_sum += factor // 2
          rv = np.concatenate([rv, -rv], axis=-1)
github google / trax / trax / models / research / View on Github external
def reverse(self, output, weights=(), state=(), new_state=(), **kwargs):
    reconstructed_x = output
    rng = kwargs.pop('rng', None)
    rngs = (None,) * self._n_layers
    if rng is not None:
      rngs = backend.random.split(rng, self._n_layers)
    # Note that self.sublayers aligns exactly with self.reverse_layers in
    # terms of parameter and rng usage, so no re-ordering is required.
    for layer, p, s, ns, rng in zip(
        self.reverse_layers, weights, state, new_state, rngs):
      reconstructed_x = layer(reconstructed_x, weights=p,
                              state=s, new_state=ns, rng=rng, **kwargs)
    return reconstructed_x
github google / trax / trax / supervised / View on Github external
def mapped_compute_loss(opt_state, batch, state, rng):
    """This is a multi-device version of the update function above."""
    # We assume all tensors have the first dimension = n_devices.
    rng, subrng = jax_random.split(rng)
    loss_val, state = loss_fn(opt_state[0], batch, predict_fn, state, rng)
    return loss_val, state, subrng
github google / trax / trax / supervised / View on Github external
def mapped_update(i, opt_state, batch, state, rng):
    """This is a multi-device version of the update function above."""
    # We assume all tensors have the first dimension = n_devices.
    weights, slots, opt_params = opt_state
    rng, subrng = jax_random.split(rng)
    grad_fn = backend.grad(model_and_loss_call, has_aux=True)
    grads, state = grad_fn(weights, batch, state, rng)
    # We do a psum(1.0) here instead of `n_devices` since `n_devices` is just
    # the number of devices on this host machine, however psum goes over all
    # devices of all hosts (ex: a TPU pod) and we need to be averaging over all
    # of them.
    grads = jax.tree_util.tree_map(
        lambda g: backend.psum(g, 'batch') / backend.psum(1.0, 'batch'), grads)
    return optimizer.tree_update(
        i, grads, weights, slots, opt_params), state, subrng
github google / trax / trax / layers / research / View on Github external
def body_fun(vals):
      """Performs attention for a single batch element and head."""
      batch_loop_idx = vals[0]
      if self._prng is None:
        hash_slice_rng = jax.random.fold_in(rng, batch_loop_idx)
        hash_rng, slice_rng = backend.random.split(hash_slice_rng)
        # TODO(kitaev): Maybe use the same RNG across examples (but not heads)?
        hash_rng = jax.random.fold_in(self._prng, batch_loop_idx)
        slice_rng = jax.random.fold_in(rng, batch_loop_idx)
      qk_slice = jax.lax.dynamic_index_in_dim(
          qk, batch_loop_idx, axis=0, keepdims=False)
      v_slice = jax.lax.dynamic_index_in_dim(
          v, batch_loop_idx, axis=0, keepdims=False)

      if buckets is None:
        buckets_slice = self.hash_vectors(qk_slice, rng=hash_rng)
        buckets_slice = jax.lax.dynamic_index_in_dim(
            buckets, batch_loop_idx, axis=0, keepdims=False)

      if ct is None:
github google / trax / trax / supervised / View on Github external
def evaluate(self, n_eval_steps):
    """Evaluate the model and log metrics."""
    _, rng = jax_random.split(self._rngs[0])
    # TODO(lukaszkaiser): both model state and parameters by default include
    # the loss layer. Currently, we access the pure-model parameters by just
    # indexing, [0] here. But we should make it more explicit in a better API.
    weights = (self._opt_state[0][0], self._metrics_weights)
    state = (self._model_state[0], self._metrics_state)
    train_eval_slice = itertools.islice(self._train_eval_stream, n_eval_steps)
    train_metrics, _ = self.evaluation_round(train_eval_slice, weights, state,
    self.log_metrics(train_metrics, self._train_sw, 'train')
    eval_slice = itertools.islice(self._eval_stream, n_eval_steps)
    eval_metrics, _ = self.evaluation_round(eval_slice, weights, state, rng)
    self.log_metrics(eval_metrics, self._eval_sw, 'eval')
    self.log_step('Finished evaluation')

    # Save the optimizer weights in the history
github google / trax / trax / supervised / View on Github external
self._mask_id = mask_id
    self._metrics_dict = metrics if metrics is not None else _DEFAULT_METRICS
    loss_fn = loss_fn(has_weights=has_weights, mask_id=mask_id)
    inputs = inputs(self._n_devices)
    self._inputs = inputs

    # Initialize the learning rate to a dummy value. It will be set in reset().
    opt = optimizer(learning_rate=0.0)

    # Setup the model.
    model_train = model(mode='train')
    model_predict_eval = model(mode='eval')

    # Setup state.
    rng, init_rng = jax_random.split(rng)
    self._rngs = np.stack(jax_random.split(rng, self._n_devices))
    first_shape = inputs.input_shape[0]
    # If the inputs are a tuple/list, add [None] (batch) to each element.
    if isinstance(first_shape, (list, tuple)):
      model_input_shape = tuple(
          tuple([None] + list(shape)) for shape in inputs.input_shape)
      model_target_shape = tuple(
          tuple([None] + list(shape)) for shape in inputs.target_shape)
    else:  # Otherwise just add [None] to the input shape.
      model_input_shape = tuple([None] + list(inputs.input_shape))
      model_target_shape = tuple([None] + list(inputs.target_shape))
    # Change all None to 1 in input and target shape.
    model_input_shape = backend.nested_map(lambda x: x or 1, model_input_shape)
    model_target_shape = backend.nested_map(lambda x: x or 1,

    def new_opt_state_and_model_state(input_shape, input_dtype, target_shape,