50 lines
2.0 KiB
Python
50 lines
2.0 KiB
Python
"""Compress stored ONNX weights without changing graph arithmetic."""
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import numpy as np
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from onnx import TensorProto, helper, numpy_helper
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def compress_weights(model):
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casts = []
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for initializer in model.graph.initializer:
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if initializer.data_type != TensorProto.FLOAT:
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continue
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name = initializer.name
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values = numpy_helper.to_array(initializer)
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if not np.isfinite(values).all() or np.max(np.abs(values), initial=0) > np.finfo(np.float16).max:
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continue
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array = values.astype(np.float16)
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stored_name = name + "__fp16_storage"
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initializer.CopyFrom(numpy_helper.from_array(array, stored_name))
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casts.append(helper.make_node(
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"Cast", [stored_name], [name], to=TensorProto.FLOAT,
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name=stored_name + "_restore",
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))
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nodes = list(model.graph.node)
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del model.graph.node[:]
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model.graph.node.extend(casts + nodes)
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def compress_exact_constants(model):
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"""Compress only constant tensors that round-trip without any value change."""
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nodes = []
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for node in model.graph.node:
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nodes.append(node)
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if node.op_type != "Constant":
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continue
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attribute = next((item for item in node.attribute if item.name == "value"), None)
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if attribute is None or attribute.t.data_type != TensorProto.FLOAT:
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continue
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values = numpy_helper.to_array(attribute.t)
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if not np.isfinite(values).all() or np.max(np.abs(values), initial=0) > np.finfo(np.float16).max:
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continue
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stored = values.astype(np.float16)
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if values.nbytes < 64 or not np.array_equal(values, stored.astype(np.float32)):
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continue
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name = node.output[0]
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node.output[0] = name + "__fp16_constant"
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attribute.t.CopyFrom(numpy_helper.from_array(stored))
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nodes.append(helper.make_node("Cast", [node.output[0]], [name], to=TensorProto.FLOAT))
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del model.graph.node[:]
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model.graph.node.extend(nodes)
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