MargaretQian commented on code in PR #10949:
URL: https://github.com/apache/tvm/pull/10949#discussion_r846537572
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python/tvm/relay/frontend/onnx.py:
##########
@@ -836,6 +837,208 @@ def _impl_v1(cls, inputs, attr, params):
return Gelu._impl_v1([inp], attr, params)
+class EmbedLayerNormalization(OnnxOpConverter):
+ """Operator converter for EmbedLayerNormalization from Microsoft
onnxruntime contrib opset.
+
+ This layer embeds the input tokens, sums them, and applies layer
normalization.
+ """
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ input_ids = inputs[0]
+ segment_ids = inputs[1]
+ word_emb = inputs[2]
+ pos_emb = inputs[3]
+ segment_emb = inputs[4]
+ gamma = inputs[5]
+ beta = inputs[6]
+
+ mask = inputs[7]
+ pos_ids = inputs[8]
+
+ eps = attr.get("epsilon", 1e-12)
+
+ (batch_size, seq_len) = infer_shape(input_ids)
+
+ if segment_ids:
+ assert segment_emb
+
+ if pos_ids is None:
+ pos_ids = _op.const([list(range(seq_len))] * seq_len,
dtype="int64")
+
+ word_vec = _op.take(word_emb, input_ids, axis=0)
+ segment_vec = _op.take(segment_emb, segment_ids, axis=0)
+ pos_vec = _op.take(pos_emb, pos_ids, axis=0)
+
+ vec_sum = _op.add(word_vec, pos_vec)
+ if segment_ids:
+ vec_sum = _op.add(vec_sum, segment_vec)
+
+ eps_dtype = infer_type(word_emb).checked_type.dtype
+
+ u, s = _op.mean_variance(vec_sum, axis=-1, keepdims=True)
+ ln = _op.divide(
+ _op.subtract(vec_sum, u),
+ _op.sqrt(_op.add(s, _op.const(eps, dtype=eps_dtype))),
+ )
+ ln = _op.multiply(ln, gamma) + beta
+
+ mask_index = _op.const(np.zeros((batch_size,), dtype="int64"))
+ if mask:
+ # calculate number of words per sentence
+ mask_index = _op.sum(mask, axis=1)
+
+ return _expr.TupleWrapper(_expr.Tuple([ln, mask_index, vec_sum]), 3)
+
+
+class SkipLayerNormalization(OnnxOpConverter):
+ """Operator converter for SkipLayerNormalization from Microsoft
onnxruntime contrib opset.
+
+ This layer sums the two input tensors (along with optional bias), and
applies layer
+ normalization.
+ """
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ data = inputs[0]
+ skip = inputs[1]
+ gamma = inputs[2]
+ beta = inputs[3]
+ bias = inputs[4]
+
+ eps = attr.get("epsilon", 1e-12)
+
+ x = _op.add(data, skip)
+ if bias is not None:
+ x = _op.add(x, bias)
+
+ eps_dtype = infer_type(x).checked_type.dtype
+
+ u, s = _op.mean_variance(x, axis=-1, keepdims=True)
+ output = _op.divide(
+ _op.subtract(x, u),
+ _op.sqrt(_op.add(s, _op.const(eps, dtype=eps_dtype))),
+ )
+ output = _op.multiply(output, gamma)
Review Comment:
nit: this is basically same normalization calculation as 877-886 above
right? if it's easy, can we pull it out into a common helper function?
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