# Copyright 2018 MLBenchmark Group. 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.
# ==============================================================================
"""Defines the Transformer model, and its encoder and decoder stacks.

Model paper: https://arxiv.org/pdf/1706.03762.pdf
Transformer model code source: https://github.com/tensorflow/tensor2tensor
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from mlperf_compliance import mlperf_log

from model import attention_layer
from model import beam_search
from model import embedding_layer
from model import ffn_layer
from model import model_utils
from utils.tokenizer import EOS_ID

_NEG_INF = -1e9

# Define defaults for parameters


class Transformer(object):
  """Transformer model for sequence to sequence data.

  Implemented as described in: https://arxiv.org/pdf/1706.03762.pdf

  The Transformer model consists of an encoder and decoder. The input is an int
  sequence (or a batch of sequences). The encoder produces a continous
  representation, and the decoder uses the encoder output to generate
  probabilities for the output sequence.
  """

  def __init__(self, params, train):
    """Initialize layers to build Transformer model.

    Args:
      params: hyperparameter object defining layer sizes, dropout values, etc.
      train: boolean indicating whether the model is in training mode. Used to
        determine if dropout layers should be added.
    """
    self.train = train
    self.params = params

    self.embedding_softmax_layer = embedding_layer.EmbeddingSharedWeights(
        params.vocab_size, params.hidden_size)
    self.encoder_stack = EncoderStack(params, train)
    self.decoder_stack = DecoderStack(params, train)

  def __call__(self, inputs, targets=None):
    """Calculate target logits or inferred target sequences.

    Args:
      inputs: int tensor with shape [batch_size, input_length].
      targets: None or int tensor with shape [batch_size, target_length].

    Returns:
      If targets is defined, then return logits for each word in the target
      sequence. float tensor with shape [batch_size, target_length, vocab_size]
      If target is none, then generate output sequence one token at a time.
        returns a dictionary {
          output: [batch_size, decoded length]
          score: [batch_size, float]}
    """
    # Variance scaling is used here because it seems to work in many problems.
    # Other reasonable initializers may also work just as well.
    mlperf_log.transformer_print(key=mlperf_log.MODEL_HP_INITIALIZER_GAIN,
                                 value=self.params.initializer_gain)
    initializer = tf.variance_scaling_initializer(
        self.params.initializer_gain, mode="fan_avg", distribution="uniform")
    with tf.variable_scope("Transformer", initializer=initializer):
      # Calculate attention bias for encoder self-attention and decoder
      # multi-headed attention layers.
      attention_bias = model_utils.get_padding_bias(inputs)

      # Run the inputs through the encoder layer to map the symbol
      # representations to continuous representations.
      encoder_outputs = self.encode(inputs, attention_bias)

      # Generate output sequence if targets is None, or return logits if target
      # sequence is known.
      if targets is None:
        return self.predict(encoder_outputs, attention_bias)
      else:
        logits = self.decode(targets, encoder_outputs, attention_bias)
        return logits

  def encode(self, inputs, attention_bias):
    """Generate continuous representation for inputs.

    Args:
      inputs: int tensor with shape [batch_size, input_length].
      attention_bias: float tensor with shape [batch_size, 1, 1, input_length]

    Returns:
      float tensor with shape [batch_size, input_length, hidden_size]
    """
    with tf.name_scope("encode"):
      # Prepare inputs to the layer stack by adding positional encodings and
      # applying dropout.
      embedded_inputs = self.embedding_softmax_layer(inputs)
      inputs_padding = model_utils.get_padding(inputs)

      with tf.name_scope("add_pos_encoding"):
        length = tf.shape(embedded_inputs)[1]
        pos_encoding = model_utils.get_position_encoding(
            length, self.params.hidden_size)
        encoder_inputs = embedded_inputs + pos_encoding

      if self.train:
        mlperf_log.transformer_print(
            key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
            value=self.params.layer_postprocess_dropout)
        encoder_inputs = tf.nn.dropout(
            encoder_inputs, 1 - self.params.layer_postprocess_dropout)

      return self.encoder_stack(encoder_inputs, attention_bias, inputs_padding)

  def decode(self, targets, encoder_outputs, attention_bias):
    """Generate logits for each value in the target sequence.

    Args:
      targets: target values for the output sequence.
        int tensor with shape [batch_size, target_length]
      encoder_outputs: continuous representation of input sequence.
        float tensor with shape [batch_size, input_length, hidden_size]
      attention_bias: float tensor with shape [batch_size, 1, 1, input_length]

    Returns:
      float32 tensor with shape [batch_size, target_length, vocab_size]
    """
    with tf.name_scope("decode"):
      # Prepare inputs to decoder layers by shifting targets, adding positional
      # encoding and applying dropout.
      decoder_inputs = self.embedding_softmax_layer(targets)
      with tf.name_scope("shift_targets"):
        # Shift targets to the right, and remove the last element
        decoder_inputs = tf.pad(
            decoder_inputs, [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
      with tf.name_scope("add_pos_encoding"):
        length = tf.shape(decoder_inputs)[1]
        decoder_inputs += model_utils.get_position_encoding(
            length, self.params.hidden_size)
      if self.train:
        mlperf_log.transformer_print(
            key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
            value=self.params.layer_postprocess_dropout)
        decoder_inputs = tf.nn.dropout(
            decoder_inputs, 1 - self.params.layer_postprocess_dropout)

      # Run values
      decoder_self_attention_bias = model_utils.get_decoder_self_attention_bias(
          length)
      outputs = self.decoder_stack(
          decoder_inputs, encoder_outputs, decoder_self_attention_bias,
          attention_bias)
      logits = self.embedding_softmax_layer.linear(outputs)
      return logits

  def _get_symbols_to_logits_fn(self, max_decode_length):
    """Returns a decoding function that calculates logits of the next tokens."""

    timing_signal = model_utils.get_position_encoding(
        max_decode_length + 1, self.params.hidden_size)
    decoder_self_attention_bias = model_utils.get_decoder_self_attention_bias(
        max_decode_length)

    def symbols_to_logits_fn(ids, i, cache):
      """Generate logits for next potential IDs.

      Args:
        ids: Current decoded sequences.
          int tensor with shape [batch_size * beam_size, i + 1]
        i: Loop index
        cache: dictionary of values storing the encoder output, encoder-decoder
          attention bias, and previous decoder attention values.

      Returns:
        Tuple of
          (logits with shape [batch_size * beam_size, vocab_size],
           updated cache values)
      """
      # Set decoder input to the last generated IDs
      decoder_input = ids[:, -1:]

      # Preprocess decoder input by getting embeddings and adding timing signal.
      decoder_input = self.embedding_softmax_layer(decoder_input)
      decoder_input += timing_signal[i:i + 1]

      self_attention_bias = decoder_self_attention_bias[:, :, i:i + 1, :i + 1]
      decoder_outputs = self.decoder_stack(
          decoder_input, cache.get("encoder_outputs"), self_attention_bias,
          cache.get("encoder_decoder_attention_bias"), cache)
      logits = self.embedding_softmax_layer.linear(decoder_outputs)
      logits = tf.squeeze(logits, axis=[1])
      return logits, cache
    return symbols_to_logits_fn

  def predict(self, encoder_outputs, encoder_decoder_attention_bias):
    """Return predicted sequence."""
    batch_size = tf.shape(encoder_outputs)[0]
    input_length = tf.shape(encoder_outputs)[1]
    max_decode_length = input_length + self.params.extra_decode_length

    symbols_to_logits_fn = self._get_symbols_to_logits_fn(max_decode_length)

    # Create initial set of IDs that will be passed into symbols_to_logits_fn.
    initial_ids = tf.zeros([batch_size], dtype=tf.int32)

    # Create cache storing decoder attention values for each layer.
    cache = {
        "layer_%d" % layer: {
            "k": tf.zeros([batch_size, 0, self.params.hidden_size]),
            "v": tf.zeros([batch_size, 0, self.params.hidden_size]),
        } for layer in range(self.params.num_hidden_layers)}

    # Add encoder output and attention bias to the cache.
    cache["encoder_outputs"] = encoder_outputs
    cache["encoder_decoder_attention_bias"] = encoder_decoder_attention_bias

    # Use beam search to find the top beam_size sequences and scores.
    mlperf_log.transformer_print(
      key=mlperf_log.MODEL_HP_SEQ_BEAM_SEARCH,
      value={
        "vocab_size": self.params.vocab_size,
        "beam_size": self.params.beam_size,
        "alpha": self.params.alpha,
        "extra_decode_length": self.params.extra_decode_length})
    decoded_ids, scores = beam_search.sequence_beam_search(
        symbols_to_logits_fn=symbols_to_logits_fn,
        initial_ids=initial_ids,
        initial_cache=cache,
        vocab_size=self.params.vocab_size,
        beam_size=self.params.beam_size,
        alpha=self.params.alpha,
        max_decode_length=max_decode_length,
        eos_id=EOS_ID)

    # Get the top sequence for each batch element
    top_decoded_ids = decoded_ids[:, 0, 1:]
    top_scores = scores[:, 0]

    return {"outputs": top_decoded_ids, "scores": top_scores}


class LayerNormalization(tf.layers.Layer):
  """Applies layer normalization."""

  def __init__(self, hidden_size):
    super(LayerNormalization, self).__init__()
    self.hidden_size = hidden_size

  def build(self, _):
    mlperf_log.transformer_print(
        key=mlperf_log.MODEL_HP_NORM,
        value={"hidden_size": self.hidden_size})
    self.scale = tf.get_variable("layer_norm_scale", [self.hidden_size],
                                 initializer=tf.ones_initializer())
    self.bias = tf.get_variable("layer_norm_bias", [self.hidden_size],
                                initializer=tf.zeros_initializer())
    self.built = True

  def call(self, x, epsilon=1e-6):
    mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
    variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keepdims=True)
    norm_x = (x - mean) * tf.rsqrt(variance + epsilon)
    return norm_x * self.scale + self.bias


class PrePostProcessingWrapper(object):
  """Wrapper class that applies layer pre-processing and post-processing."""

  def __init__(self, layer, params, train):
    self.layer = layer
    self.postprocess_dropout = params.layer_postprocess_dropout
    self.train = train

    # Create normalization layer
    self.layer_norm = LayerNormalization(params.hidden_size)

  def __call__(self, x, *args, **kwargs):
    # Preprocessing: apply layer normalization
    y = self.layer_norm(x)

    # Get layer output
    y = self.layer(y, *args, **kwargs)

    # Postprocessing: apply dropout and residual connection
    if self.train:
      mlperf_log.transformer_print(
            key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
            value=self.postprocess_dropout)
      y = tf.nn.dropout(y, 1 - self.postprocess_dropout)
    return x + y


class EncoderStack(tf.layers.Layer):
  """Transformer encoder stack.

  The encoder stack is made up of N identical layers. Each layer is composed
  of the sublayers:
    1. Self-attention layer
    2. Feedforward network (which is 2 fully-connected layers)
  """

  def __init__(self, params, train):
    super(EncoderStack, self).__init__()
    self.layers = []
    mlperf_log.transformer_print(
        key=mlperf_log.MODEL_HP_NUM_HIDDEN_LAYERS,
        value=params.num_hidden_layers)
    for _ in range(params.num_hidden_layers):
      # Create sublayers for each layer.
      self_attention_layer = attention_layer.SelfAttention(
          params.hidden_size, params.num_heads, params.attention_dropout, train)
      feed_forward_network = ffn_layer.FeedFowardNetwork(
          params.hidden_size, params.filter_size, params.relu_dropout, train)

      self.layers.append([
          PrePostProcessingWrapper(self_attention_layer, params, train),
          PrePostProcessingWrapper(feed_forward_network, params, train)])

    # Create final layer normalization layer.
    self.output_normalization = LayerNormalization(params.hidden_size)

  def call(self, encoder_inputs, attention_bias, inputs_padding):
    for n, layer in enumerate(self.layers):
      # Run inputs through the sublayers.
      self_attention_layer = layer[0]
      feed_forward_network = layer[1]

      with tf.variable_scope("layer_%d" % n):
        with tf.variable_scope("self_attention"):
          encoder_inputs = self_attention_layer(encoder_inputs, attention_bias)
        with tf.variable_scope("ffn"):
          encoder_inputs = feed_forward_network(encoder_inputs, inputs_padding)

    return self.output_normalization(encoder_inputs)


class DecoderStack(tf.layers.Layer):
  """Transformer decoder stack.

  Like the encoder stack, the decoder stack is made up of N identical layers.
  Each layer is composed of the sublayers:
    1. Self-attention layer
    2. Multi-headed attention layer combining encoder outputs with results from
       the previous self-attention layer.
    3. Feedforward network (2 fully-connected layers)
  """

  def __init__(self, params, train):
    super(DecoderStack, self).__init__()
    self.layers = []
    mlperf_log.transformer_print(
        key=mlperf_log.MODEL_HP_NUM_HIDDEN_LAYERS,
        value=params.num_hidden_layers)
    for _ in range(params.num_hidden_layers):
      self_attention_layer = attention_layer.SelfAttention(
          params.hidden_size, params.num_heads, params.attention_dropout, train)
      enc_dec_attention_layer = attention_layer.Attention(
          params.hidden_size, params.num_heads, params.attention_dropout, train)
      feed_forward_network = ffn_layer.FeedFowardNetwork(
          params.hidden_size, params.filter_size, params.relu_dropout, train)

      self.layers.append([
          PrePostProcessingWrapper(self_attention_layer, params, train),
          PrePostProcessingWrapper(enc_dec_attention_layer, params, train),
          PrePostProcessingWrapper(feed_forward_network, params, train)])

    self.output_normalization = LayerNormalization(params.hidden_size)

  def call(self, decoder_inputs, encoder_outputs, decoder_self_attention_bias,
           attention_bias, cache=None):
    for n, layer in enumerate(self.layers):
      self_attention_layer = layer[0]
      enc_dec_attention_layer = layer[1]
      feed_forward_network = layer[2]

      # Run inputs through the sublayers.
      layer_name = "layer_%d" % n
      layer_cache = cache[layer_name] if cache is not None else None
      with tf.variable_scope(layer_name):
        with tf.variable_scope("self_attention"):
          decoder_inputs = self_attention_layer(
              decoder_inputs, decoder_self_attention_bias, cache=layer_cache)
        with tf.variable_scope("encdec_attention"):
          decoder_inputs = enc_dec_attention_layer(
              decoder_inputs, encoder_outputs, attention_bias)
        with tf.variable_scope("ffn"):
          decoder_inputs = feed_forward_network(decoder_inputs)

    return self.output_normalization(decoder_inputs)
