# 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.
# ==============================================================================
"""Implementation of embedding layer with shared weights."""

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 model_utils


class EmbeddingSharedWeights(tf.compat.v1.layers.Layer):
  """Calculates input embeddings and pre-softmax linear with shared weights."""

  def __init__(self, vocab_size, hidden_size):
    super(EmbeddingSharedWeights, self).__init__()
    self.vocab_size = vocab_size
    self.hidden_size = hidden_size

  def build(self, _):
    with tf.compat.v1.variable_scope("embedding_and_softmax", reuse=tf.compat.v1.AUTO_REUSE):
      # Create and initialize weights. The random normal initializer was chosen
      # randomly, and works well.
      mlperf_log.transformer_print(
          key=mlperf_log.MODEL_HP_EMBEDDING_SHARED_WEIGHTS,
          value={
            "vocab_size": self.vocab_size,
            "hidden_size": self.hidden_size
          })
      self.shared_weights = tf.compat.v1.get_variable(
          "weights", [self.vocab_size, self.hidden_size],
          initializer=tf.compat.v1.random_normal_initializer(
              0., self.hidden_size ** -0.5))

    self.built = True

  def call(self, x):
    """Get token embeddings of x.

    Args:
      x: An int64 tensor with shape [batch_size, length]
    Returns:
      embeddings: float32 tensor with shape [batch_size, length, embedding_size]
      padding: float32 tensor with shape [batch_size, length] indicating the
        locations of the padding tokens in x.
    """
    with tf.compat.v1.name_scope("embedding"):
      embeddings = tf.gather(self.shared_weights, x)

      # Scale embedding by the sqrt of the hidden size
      embeddings *= self.hidden_size ** 0.5

      # Create binary array of size [batch_size, length]
      # where 1 = padding, 0 = not padding
      padding = model_utils.get_padding(x)

      # Set all padding embedding values to 0
      with tf.compat.v1.tpu.bfloat16_scope():
         temp = tf.expand_dims(1 - padding, -1)
         embeddings = tf.cast(embeddings, tf.bfloat16)
         embeddings *= temp
      return embeddings

  def linear(self, x):
    """Computes logits by running x through a linear layer.

    Args:
      x: A float32 tensor with shape [batch_size, length, hidden_size]
    Returns:
      float32 tensor with shape [batch_size, length, vocab_size].
    """
    with tf.compat.v1.name_scope("presoftmax_linear"):
      batch_size = tf.shape(input=x)[0]
      length = tf.shape(input=x)[1]

      x = tf.reshape(x, [-1, self.hidden_size])
      x = tf.cast(x, tf.bfloat16)
      self.shared_weights = tf.cast(self.shared_weights, tf.bfloat16)
      logits = tf.matmul(x, self.shared_weights, transpose_b=True)
      logits = tf.cast(logits, tf.float32)
      

      return tf.reshape(logits, [batch_size, length, self.vocab_size])
