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30 lines
894 B
Python
30 lines
894 B
Python
# Copyright (c) ONNX Project Contributors
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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from onnx.reference.op_run import OpRun
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class QLinearMatMul(OpRun):
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def _run(
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self, a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point
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):
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A = a.astype(np.int32)
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if a_zero_point is not None:
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A -= a_zero_point.astype(np.int32)
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B = b.astype(np.int32)
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if b_zero_point is not None:
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B -= b_zero_point.astype(np.int32)
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C = np.matmul(A, B)
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D = C * (a_scale * b_scale / y_scale)
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if y_zero_point is not None:
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D += y_zero_point
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dtype = y_zero_point.dtype
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else:
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dtype = a.dtype
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qmin, qmax = np.iinfo(dtype).min, np.iinfo(dtype).max
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return (np.clip(np.rint(D), qmin, qmax).astype(dtype),)
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