Keras QAT Error with tf.IndexedSlices (#2640)

* Add decorator to convert tf.IndexSlices to tf.tensor for grads

Signed-off-by: Matthew Ernst <quic_ernst@quicinc.com>

TrainingExtensions/tensorflow/src/python/aimet_tensorflow/keras/quant_sim/tensor_quantizer.py

@@ -37,7 +37,7 @@
 """ Tensor quantizer for tf 2 keras """
 import abc
 import functools
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, Callable
 import tensorflow as tf
 import tensorflow.keras.backend as K
 
@@ -84,6 +84,21 @@ def _handle(cls, tensor):
     return _handle
 
 
+def _update_grad_to_tf_tensor_if_needed(grad_func: Callable):
+    """
+    Decorator function to convert gradient tensors that are represented as tf.IndexedSlices into
+    tf.Tensor's. This typically occurs with operations such as tf.gather and keras.Embedding layers.
+
+    :param grad_func: The gradient function that will be called after conversion
+    """
+    def wrapper(*args, **kwargs):
+        grad = args[0]
+        if isinstance(grad, tf.IndexedSlices):
+            _logger.debug("Converting %s from tf.IndexedSlices to tf.Tensor", grad.name)
+            args = (tf.convert_to_tensor(grad),) + args[1:]
+        return grad_func(*args, **kwargs)
+    return wrapper
+
 class TensorQuantizer(tf.keras.layers.Layer, abc.ABC):
     """Tensor quantizer class containing the bare bones of a given Quantizer"""
 
@@ -421,13 +436,15 @@ def call_quantize_straight_through_estimator_grad(self, tensor: tf.Tensor):
         :param tensor: Tensor to quantize
         """
 
+        @_update_grad_to_tf_tensor_if_needed
         def grad(upstream: tf.Tensor, variables: List):
             """
             Straight through estimator grad function
             :param upstream: Gradient from child layers
             :param variables: Variables used in forward pass to return gradients for
             """
             assert len(variables) == 2, 'len variables is ' + str(len(variables))
+
             return qc_straight_through_estimator_grad(tensor, self._encoding_min, self._encoding_max,
                                                       self._quantizer_mode, upstream)
 
@@ -447,6 +464,7 @@ def call_quantsim_custom_grad_learned_grid(self, tensor: tf.Tensor):
         :param tensor: Tensor to quantize
         """
 
+        @_update_grad_to_tf_tensor_if_needed
         def grad(upstream: tf.Tensor, variables: List):
             """
             Range learning grad function