mirror of
https://github.com/saymrwulf/onnxruntime.git
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b3cb775cf9
### Description <!-- Describe your changes. --> - allocation planner was breaking if graph had no nodes - in this particular model a branch of an If node returned an outer scope value directly. - if model used non-tensor types and sparse tensors are disabled the call to IsSpareTensor causes an exception when prematurely terminates the code. - it's perfectly fine to check if a value is a sparse tensor when support for them is disabled. we just can't do anything with that OrtValue which is what the current ifdef's after the call to IsSparseTensor handle. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Fix model execution failure for partner with model that uses sequences in a minimal build with sparse tensors disabled.
77 lines
3 KiB
Python
77 lines
3 KiB
Python
import numpy as np
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import onnx
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from onnx import TensorProto, helper, numpy_helper
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def order_repeated_field(repeated_proto, key_name, order):
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order = list(order)
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repeated_proto.sort(key=lambda x: order.index(getattr(x, key_name)))
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def make_node(op_type, inputs, outputs, name=None, doc_string=None, domain=None, **kwargs):
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node = helper.make_node(op_type, inputs, outputs, name, doc_string, domain, **kwargs)
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if doc_string == "":
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node.doc_string = ""
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order_repeated_field(node.attribute, "name", kwargs.keys())
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return node
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def make_graph(*args, doc_string=None, **kwargs):
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graph = helper.make_graph(*args, doc_string=doc_string, **kwargs)
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if doc_string == "":
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graph.doc_string = ""
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return graph
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test_graph = make_graph(
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name="test_graph",
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# model input of a sequence type to test IsSparseTensor issue
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inputs=[
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helper.make_tensor_sequence_value_info("seq_in", TensorProto.FLOAT, shape=None),
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],
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outputs=[
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helper.make_tensor_value_info("still_has_elements", TensorProto.BOOL, shape=[]),
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],
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initializer=[
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numpy_helper.from_array(np.array(0, dtype="int64"), name="i0"),
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],
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nodes=[
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make_node("SequenceLength", inputs=["seq_in"], outputs=["seq_len"], name="get_seq_len"),
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make_node("Greater", inputs=["seq_len", "i0"], outputs=["has_elements"], name="get_has_elements"),
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# If node with one branch that has no nodes to test the allocation planner issue
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# if sequence has elements:
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# remove one
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# output bool of whether it still has elements
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# else:
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# output false (gives us branch with no nodes)
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make_node(
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"If",
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name="test_if",
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inputs=["has_elements"],
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outputs=["still_has_elements"],
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then_branch=make_graph(
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name="then",
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inputs=[],
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outputs=[helper.make_tensor_value_info("then_bool_out", TensorProto.BOOL, shape=[])],
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nodes=[
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make_node("SequenceErase", inputs=["seq_in", "i0"], outputs=["seq_less_one"]),
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make_node("SequenceLength", inputs=["seq_less_one"], outputs=["new_seq_len"]),
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make_node("Greater", inputs=["new_seq_len", "i0"], outputs=["then_bool_out"]),
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],
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),
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else_branch=make_graph(
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name="else",
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initializer=[numpy_helper.from_array(np.array(False, dtype="bool"), name="else_bool_out")],
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inputs=[],
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outputs=[helper.make_tensor_value_info("else_bool_out", TensorProto.BOOL, shape=[])],
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nodes=[],
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),
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),
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],
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)
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# Graph with Sequence operations and an If node that has a subgraph with no nodes
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model = helper.make_model(opset_imports=[helper.make_operatorsetid("ai.onnx", 14)], ir_version=7, graph=test_graph)
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onnx.shape_inference.infer_shapes(model, strict_mode=True)
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onnx.save(model, "ort_github_issue_17000.onnx")
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