# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Trains a transformer for language modeling on a small text dataset.

This example serves to demonstrate:
  - A clean Haiku transformer implementation.
  - An example minimal training loop around it.

This example runs on ASCII text files.
We have not tuned the hyperparameters at all.

Example, using Karpathy's tiny_shakespeare dataset:
$ wget -O /tmp/shakespeare.txt \
    https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
$ python3 examples/transformer/train.py \
    --dataset_path=/tmp/shakespeare.txt --alsologtostderr
"""

import time
from typing import Any, MutableMapping, NamedTuple, Tuple, Union

from absl import app
from absl import flags
from absl import logging
import haiku as hk
from examples.transformer import dataset
from examples.transformer import model
import jax
import jax.numpy as jnp
import numpy as np
import optax

DATASET_PATH = flags.DEFINE_string(
    'dataset_path', None, help='Path to raw dataset file', required=True)

# Training hyperparameters.
BATCH_SIZE = 2
SEQUENCE_LENGTH = 64
LEARNING_RATE = 3e-4
GRAD_CLIP_VALUE = 1
LOG_EVERY = 50
MAX_STEPS = 10**6
SEED = 0

# Model hyperparameters.
NUM_LAYERS = 6
NUM_HEADS = 8  # Number of attention heads.
MODEL_SIZE = 128
KEY_SIZE = 32
DROPOUT_RATE = 0.1

# Helpful type aliases.
_Batch = dataset.Batch
_Metrics = MutableMapping[str, Any]


class TrainingState(NamedTuple):
  """Container for the training state."""
  params: hk.Params  # Current network parameters.
  opt_state: optax.OptState  # Optimiser state (e.g. gradient moments).
  rng_key: jax.Array  # RNG used for e.g. dropout. Split on each update step.
  step: jax.Array  # Tracks the number of training steps.


def forward_pass(tokens: Union[np.ndarray, jax.Array]) -> jax.Array:
  """Defines the forward pass of the language model."""
  lm = model.LanguageModel(
      model_size=MODEL_SIZE,
      vocab_size=dataset.VOCAB_SIZE,
      pad_token=dataset.PAD_TOKEN,
      transformer=model.Transformer(
          num_heads=NUM_HEADS,
          num_layers=NUM_LAYERS,
          attn_size=KEY_SIZE,
          dropout_rate=DROPOUT_RATE,
      ),
  )
  return lm(tokens)  # Logits, shape [B, T, V].


def optimiser() -> optax.GradientTransformation:
  return optax.chain(
      optax.clip_by_global_norm(GRAD_CLIP_VALUE),
      optax.adam(LEARNING_RATE, b1=0.9, b2=0.99),
  )


@hk.transform
def loss_fn(data: _Batch) -> jax.Array:
  """Computes the (scalar) language modelling loss on `data` w.r.t. params."""
  logits = forward_pass(data.inputs)
  log_probs = jax.nn.log_softmax(logits)  # [B, T, V]
  onehot_targets = jax.nn.one_hot(data.targets, dataset.VOCAB_SIZE)
  log_likelihood = jnp.sum(onehot_targets * log_probs, axis=-1)  # [B, T]

  # Loss is the average negative log-likelihood per (non-masked) token.
  mask = jnp.not_equal(data.inputs, dataset.PAD_TOKEN)  # [B, T]
  return -jnp.sum(log_likelihood * mask) / jnp.sum(mask)  # []


@jax.jit
def init(rng: jax.Array, data: _Batch) -> TrainingState:
  """Makes an initial training state (random parameters)."""
  rng, init_rng = jax.random.split(rng)
  initial_params = loss_fn.init(init_rng, data)
  initial_opt_state = optimiser().init(initial_params)
  return TrainingState(
      params=initial_params,
      opt_state=initial_opt_state,
      rng_key=rng,
      step=jnp.array(0),
  )


@jax.jit
def update(
    state: TrainingState, data: _Batch) -> Tuple[TrainingState, _Metrics]:
  """Does an SGD step, returning a new training state and metrics."""
  rng, net_rng = jax.random.split(state.rng_key)
  loss_and_grad_fn = jax.value_and_grad(loss_fn.apply)
  loss, gradients = loss_and_grad_fn(state.params, net_rng, data)

  updates, new_opt_state = optimiser().update(gradients, state.opt_state)
  new_params = optax.apply_updates(state.params, updates)

  new_state = TrainingState(
      params=new_params,
      opt_state=new_opt_state,
      rng_key=rng,
      step=state.step + 1,
  )

  metrics = {
      'step': state.step,
      'loss': loss,
  }
  return new_state, metrics


def main(_):

  # Create the dataset.
  with open(DATASET_PATH.value, mode='r') as file:
    train_dataset = dataset.load_ascii_dataset(
        corpus=file.read(),
        batch_size=BATCH_SIZE,
        sequence_length=SEQUENCE_LENGTH,
    )

  # Initialise the model parameters.
  rng = jax.random.PRNGKey(SEED)
  data = next(train_dataset)
  state = init(rng, data)

  # Training loop (note we don't include any explicit eval in this example).
  prev_time = time.time()
  for step in range(MAX_STEPS):
    state, metrics = update(state, data)
    data = next(train_dataset)
    # We use JAX runahead to mask data preprocessing and JAX dispatch overheads.
    # Using values from state/metrics too often will block the runahead and can
    # cause these overheads to become more prominent.
    if step % LOG_EVERY == 0:
      steps_per_sec = LOG_EVERY / (time.time() - prev_time)
      prev_time = time.time()
      metrics |= {'steps_per_sec': steps_per_sec}
      logging.info({k: float(v) for k, v in metrics.items()})


if __name__ == '__main__':
  app.run(main)
