wehub-resource-sync
151 lines
4.5 KiB
Python
151 lines
4.5 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 numpy as np
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import pytest
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import tvm
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import tvm.script
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import tvm.testing
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from tvm import relax, tirx
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from tvm.script import relax as R
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param_specification = tvm.testing.parameter("by_string", "by_var")
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param_shape = tvm.testing.parameter("static_shape", "dynamic_shape", "ndim", "arbitrary")
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tensor_param_dtype = tvm.testing.parameter("float32", None)
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def test_bind_tensor_param(param_specification, param_shape, tensor_param_dtype):
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if param_shape == "static_shape":
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shape = [16]
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ndim = -1
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elif param_shape == "dynamic_shape":
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shape = [tirx.Var("N", "int64")]
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ndim = -1
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elif param_shape == "ndim":
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shape = None
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ndim = 1
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elif param_shape == "arbitrary":
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shape = None
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ndim = -1
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else:
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raise ValueError(f"Unknown param_shape: {param_shape}")
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@R.function
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def before(A: R.Tensor(shape, ndim=ndim, dtype=tensor_param_dtype)):
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R.func_attr({"global_symbol": "main"})
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B: R.Tensor(shape=shape, ndim=ndim, dtype=tensor_param_dtype) = A
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out = R.add(B, B)
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return out
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np_data = np.arange(16).astype("float32")
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inlined_relax_const = relax.const(np_data)
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@R.function
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def expected() -> R.Tensor([16], "float32"):
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R.func_attr({"global_symbol": "main"})
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B = inlined_relax_const
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out = R.add(B, B)
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return out
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if param_specification == "by_string":
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var = "A"
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elif param_specification == "by_var":
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var = before.params[0]
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else:
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raise ValueError("Unknown param_specification: {param_specification}")
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after = before.bind_params({var: np.arange(16).astype("float32")})
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tvm.ir.assert_structural_equal(expected, after)
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def test_bind_shape_param(param_shape):
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if param_shape == "static_shape":
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shape = [16]
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ndim = -1
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elif param_shape == "dynamic_shape":
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shape = [tirx.Var("N", "int64")]
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ndim = -1
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elif param_shape == "ndim":
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shape = None
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ndim = 1
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elif param_shape == "arbitrary":
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shape = None
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ndim = -1
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else:
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raise ValueError(f"Unknown param_shape: {param_shape}")
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@R.function
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def before(A: R.Shape(shape, ndim=ndim)):
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R.func_attr({"global_symbol": "main"})
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B: R.Shape(shape, ndim=ndim) = A
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return B
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@R.function
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def expected() -> R.Shape([16]):
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R.func_attr({"global_symbol": "main"})
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B = R.ShapeExpr([16])
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return B
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after = before.bind_params({"A": relax.ShapeExpr([16])})
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tvm.ir.assert_structural_equal(expected, after)
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prim_value_dtype = tvm.testing.parameter("int64", "int32", "float32")
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def test_bind_prim_value(prim_value_dtype):
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prim_type = tvm.ir.PrimType(prim_value_dtype)
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param = relax.Var("A", prim_type)
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before = relax.Function([param], param, prim_type).with_attr("global_symbol", "main")
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value = tirx.const(16, prim_value_dtype)
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after = before.bind_params({"A": value})
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assert not after.params
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tvm.ir.assert_structural_equal(after.ret_ty, prim_type)
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tvm.ir.assert_structural_equal(after.body.body, value)
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def test_error_on_unknown_var():
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@R.function
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def before(A: R.Tensor([16], dtype="float32")):
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R.func_attr({"global_symbol": "main"})
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return A
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unknown_var = relax.Var("unknown_var")
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with pytest.raises(RuntimeError):
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before.bind_params({unknown_var: np.arange(16).astype("float32")})
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def test_error_on_unknown_var_name():
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@R.function
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def before(A: R.Tensor([16], dtype="float32")):
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R.func_attr({"global_symbol": "main"})
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return A
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with pytest.raises(RuntimeError):
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before.bind_params({"unknown_var_name": np.arange(16).astype("float32")})
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if __name__ == "__main__":
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tvm.testing.main()
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