soiferj commented on a change in pull request #4825: [Frontend][ONNX] LSTM
Support
URL: https://github.com/apache/incubator-tvm/pull/4825#discussion_r376144976
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File path: python/tvm/relay/frontend/onnx.py
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@@ -1190,6 +1250,145 @@ def expand_shape(in_shape, shape):
return _op.broadcast_to(inputs[0], shape=tuple(shape))
+class LSTM(OnnxOpConverter):
+ """ Operator converter for LSTM.
+ """
+
+ @classmethod
+ def _activation_helper(cls, activation, alpha, beta):
+ convert_map = _get_convert_map(1)
+ attrs = {}
+ if alpha is not None:
+ attrs['alpha'] = alpha
+ if beta is not None:
+ attrs['beta'] = beta
+ return lambda x: convert_map[activation.decode("utf-8")]([x], attrs,
{})
+
+ @classmethod
+ def _activation_needs_alpha(cls, activation):
+ needs_alpha = [
+ "Affine",
+ "LeakyRelu",
+ "ThresholdedRelu",
+ "ScaledTanh",
+ "HardSigmoid",
+ "Elu",
+ ]
+ return activation.decode("utf-8") in needs_alpha
+
+ @classmethod
+ def _activation_needs_beta(cls, activation):
+ needs_beta = [
+ "Affine",
+ "ScaledTanh",
+ "HardSigmoid",
+ ]
+ return activation.decode("utf-8") in needs_beta
+
+ @classmethod
+ def _impl_v7(cls, inputs, attr, params):
+ # Unpack inputs, note that if optional and not provided then value
will be None.
+ X = inputs[0]
+ W = inputs[1]
Review comment:
Sounds good. Thanks a lot for the updates.
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