wehub-resource-sync
132 lines
4.6 KiB
Python
132 lines
4.6 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import tvm
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import tvm.testing
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from tvm import relax, tirx
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from tvm.ir import Op
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from tvm.script import relax as R
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def test_op_correctness():
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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dx = relax.Var("dx", R.Tensor((2, 3), "uint8"))
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s = relax.Var("s", R.Tensor([3], "float32"))
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zp = relax.Var("zp", R.Tensor([3], "int8"))
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assert relax.op.quantize(x, s, zp, 1, "int8").op == Op.get("relax.quantize")
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assert relax.op.dequantize(dx, s, zp, 1, "float32").op == Op.get("relax.dequantize")
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def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
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ret = bb.normalize(call)
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tvm.ir.assert_structural_equal(ret.ty, expected_ty)
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def test_qdq_op_infer_ty():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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dx = relax.Var("dx", R.Tensor((2, 3), "uint8"))
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s = relax.Var("s", R.Tensor([3], "float32"))
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zp = relax.Var("zp", R.Tensor([3], "int8"))
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_check_inference(bb, relax.op.quantize(x, s, zp, 1, "int8"), relax.TensorType((2, 3), "int8"))
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_check_inference(
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bb,
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relax.op.dequantize(dx, s, zp, 1, "float32"),
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relax.TensorType((2, 3), "float32"),
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)
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def test_qdq_op_infer_ty_unknown_dtype():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), dtype=None))
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dx = relax.Var("dx", R.Tensor((2, 3), dtype=None))
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s = relax.Var("s", R.Tensor([3], "float32"))
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s_unknown = relax.Var("s_unknown", R.Tensor([3], dtype=None))
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zp = relax.Var("zp", R.Tensor([3], "int8"))
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zp_unknown = relax.Var("zp_unknown", R.Tensor([3], dtype=None))
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_check_inference(
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bb, relax.op.quantize(x, s, zp, 1, "int8"), relax.TensorType((2, 3), dtype=None)
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)
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_check_inference(
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bb,
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relax.op.quantize(dx, s_unknown, zp, 1, "int8"),
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relax.TensorType((2, 3), dtype=None),
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)
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_check_inference(
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bb,
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relax.op.quantize(dx, s, zp_unknown, 1, "int8"),
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relax.TensorType((2, 3), dtype=None),
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)
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_check_inference(
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bb,
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relax.op.dequantize(dx, s, zp, 1, "float32"),
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relax.TensorType((2, 3), dtype=None),
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)
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def test_qdq_op_infer_ty_symbolic():
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bb = relax.BlockBuilder()
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n = tirx.Var("n", "int64")
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x = relax.Var("x", R.Tensor((n, 3), "float32"))
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dx = relax.Var("dx", R.Tensor((n, 3), "int8"))
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s = relax.Var("s", R.Tensor([3], "float32"))
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zp = relax.Var("zp", R.Tensor([3], "int8"))
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_check_inference(bb, relax.op.quantize(x, s, zp, 1, "int8"), relax.TensorType((n, 3), "int8"))
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_check_inference(
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bb,
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relax.op.dequantize(dx, s, zp, 1, "float32"),
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relax.TensorType((n, 3), "float32"),
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)
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def test_qdq_float8_e4m3fn_op_infer_ty_symbolic():
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bb = relax.BlockBuilder()
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n = tirx.Var("n", "int64")
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x = relax.Var("x", R.Tensor((n, 3), "float32"))
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dx = relax.Var("dx", R.Tensor((n, 3), "float8_e4m3fn"))
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s = relax.Var("s", R.Tensor([3], "float32"))
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zp = relax.Var("zp", R.Tensor([3], "float16"))
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_check_inference(
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bb,
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relax.op.quantize(x, s, zp, 1, "float8_e4m3fn"),
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relax.TensorType((n, 3), "float8_e4m3fn"),
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)
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_check_inference(
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bb,
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relax.op.dequantize(dx, s, zp, 1, "float32"),
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relax.TensorType((n, 3), "float32"),
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)
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def test_qdq_float8_e5m2_op_infer_ty_symbolic():
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dtype = "float8_e5m2"
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bb = relax.BlockBuilder()
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n = tirx.Var("n", "int64")
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x = relax.Var("x", R.Tensor((n, 3), "float32"))
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dx = relax.Var("dx", R.Tensor((n, 3), dtype))
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s = relax.Var("s", R.Tensor([3], "float32"))
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zp = relax.Var("zp", R.Tensor([3], "float16"))
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_check_inference(bb, relax.op.quantize(x, s, zp, 1, dtype), relax.TensorType((n, 3), dtype))
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_check_inference(
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bb,
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relax.op.dequantize(dx, s, zp, 1, "float32"),
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relax.TensorType((n, 3), "float32"),
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)
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if __name__ == "__main__":
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tvm.testing.main()
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