[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #6039: MXNet pre-quantized BERT

[GitBox <gi...@apache.org>]

anijain2305 commented on a change in pull request #6039:
URL: https://github.com/apache/incubator-tvm/pull/6039#discussion_r453445910



##########
File path: python/tvm/relay/frontend/nnvm_common.py
##########
@@ -57,9 +60,53 @@ def _impl(inputs, attrs):
 def _softmax_op(new_op):
     """softmax/log_softmax"""
     def _impl(inputs, attrs, _dtype='float32'):
-        # TODO(@icemelon9): currently ignore the 2nd input to softmax for mxnet 1.6
-        # assert len(inputs) == 1
         axis = attrs.get_int("axis", -1)
+        use_length = attrs.get_bool("use_length", False)
+        if use_length:
+            # The second arg is valid_length. We can use sequence mask to mask the input before
+            # computing softmax
+            assert len(inputs) == 2
+
+            data = inputs[0]
+            length = inputs[1]
+            data_shape = _infer_shape(data)
+            length_shape = _infer_shape(length)
+
+            if axis < 0:
+                axis = len(data_shape) + axis
+
+            data_ndims = len(data_shape)
+            length_ndims = len(length_shape)
+
+            # Sequence_mask supports axis = 0 and 1 and requires data to be in specific format.
+            if axis == data_ndims - 1 and data_ndims > 2 and length_ndims == 2:
+                new_batch_size = 1
+                for dim in range(length_ndims):
+                    assert data_shape[dim] == length_shape[dim]
+                    new_batch_size *= data_shape[dim]
+
+                # Reshape the data and length to satisfy sequence mask
+                data = _op.reshape(data, newshape=(new_batch_size, -1))
+                length = _op.reshape(length, newshape=(new_batch_size))
+
+                # Input data is now 2D, we can set the axis = 1
+                axis = 1
+            elif data_ndims > 2:
+                raise error.OpNotImplemented(\
+                        "Operator softmax with use_length=True is supported only for axis -1")
+
+            res = _op.sequence_mask(data=data,
+                                    valid_length=length,
+                                    mask_value=float(min_value("float").value),

Review comment:
       Done




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