anwang2009 commented on code in PR #10949:
URL: https://github.com/apache/tvm/pull/10949#discussion_r847668535
##########
tests/python/frontend/onnx/test_forward.py:
##########
@@ -5433,6 +5433,218 @@ def verify_biasgelu(x, bias):
verify_biasgelu(x, bias)
[email protected]_targets
+def test_embedlayernormalization(target, dev):
+ def verify_embedlayernormalization(
+ input_ids,
+ segment_ids,
+ word_embedding,
+ position_embedding,
+ segment_embedding,
+ gamma,
+ beta,
+ ):
+ node = onnx.helper.make_node(
+ "EmbedLayerNormalization",
+ inputs=[
+ "input_ids",
+ "segment_ids",
+ "word_embedding",
+ "position_embedding",
+ "segment_embedding",
+ "gamma",
+ "beta",
+ ],
+ outputs=["output", "mask_index", "embedding_sum"],
+ domain="com.microsoft",
+ )
+
+ node.attribute.append(onnx.helper.make_attribute("epsilon", 1e-4))
+
+ graph = helper.make_graph(
+ [node],
+ "embedlayernormalization_test",
+ inputs=[
+ helper.make_tensor_value_info(
+ "input_ids", TensorProto.INT32, list(input_ids.shape)
+ ),
+ helper.make_tensor_value_info(
+ "segment_ids", TensorProto.INT32, list(segment_ids.shape)
+ ),
+ helper.make_tensor_value_info(
+ "word_embedding", TensorProto.FLOAT,
list(word_embedding.shape)
+ ),
+ helper.make_tensor_value_info(
+ "position_embedding", TensorProto.FLOAT,
list(position_embedding.shape)
+ ),
+ helper.make_tensor_value_info(
+ "segment_embedding", TensorProto.FLOAT,
list(segment_embedding.shape)
+ ),
+ helper.make_tensor_value_info("gamma", TensorProto.FLOAT,
list(gamma.shape)),
+ helper.make_tensor_value_info("beta", TensorProto.FLOAT,
list(beta.shape)),
+ ],
+ outputs=[
+ helper.make_tensor_value_info(
+ "output", TensorProto.FLOAT, list((batch_size,
sequence_length, hidden_size))
+ ),
+ helper.make_tensor_value_info("mask_index", TensorProto.INT32,
[batch_size]),
+ helper.make_tensor_value_info(
+ "embedding_sum",
+ TensorProto.FLOAT,
+ list((batch_size, sequence_length, hidden_size)),
+ ),
+ ],
+ )
+
+ model = helper.make_model(graph,
producer_name="embedlayernormalization_test")
+ verify_with_ort_with_inputs(
+ model,
+ [
+ input_ids,
+ segment_ids,
+ word_embedding,
+ position_embedding,
+ segment_embedding,
+ gamma,
+ beta,
+ ],
+ [
+ (batch_size, sequence_length, hidden_size),
+ batch_size,
+ (batch_size, sequence_length, hidden_size),
+ ],
+ target=target,
+ dev=dev,
+ rtol=1e-4,
+ atol=1e-4,
+ )
+
+ hidden_size = 384
+ batch_size = 4
+ sequence_length = 4
+ vocab_size = 5
+
+ input_ids = np.full((batch_size, sequence_length), 3).astype("int32")
+ segment_ids = np.zeros((batch_size, sequence_length)).astype("int32")
+ word_embedding = np.full((vocab_size, hidden_size), 1).astype("float32")
+ position_embedding = np.full((sequence_length, hidden_size),
2).astype("float32")
+ segment_embedding = np.full((vocab_size, hidden_size), 3).astype("float32")
+
+ gamma = np.random.uniform(0.5, 0.7, hidden_size).astype("float32")
Review Comment:
Done for EmbedLayerNormalization, added the remaining assert fails and TODOs
in the converters for uncovered optional cases.
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