icemelon9 commented on a change in pull request #6039:
URL: https://github.com/apache/incubator-tvm/pull/6039#discussion_r453364084
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File path: python/tvm/relay/frontend/nnvm_common.py
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@@ -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:
It'll be better to use the dtype of data instead of "float".
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