wehub-resource-sync
158 lines
6.3 KiB
Python
158 lines
6.3 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 pytest
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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, VDevice
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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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y = relax.Var("y", R.Tensor((2, 3), "float32"))
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z = relax.Var("z", R.Tensor((2, 3), "float32"))
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assert relax.op.ewise_fma(x, y, z).op == Op.get("relax.ewise_fma")
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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_ewise_fma_infer_ty():
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bb = relax.BlockBuilder()
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vdev0 = VDevice("llvm")
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor((2, 3)))
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x2 = relax.Var("x", R.Tensor((2, 3), "float32", vdev0))
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y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
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y1 = relax.Var("y", R.Tensor(dtype="float32", ndim=2))
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y2 = relax.Var("y", R.Tensor((2, 3), "float32", vdev0))
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z0 = relax.Var("z", R.Tensor((2, 3), "float32"))
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z1 = relax.Var("z", R.Tensor("float32"))
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z2 = relax.Var("z", R.Tensor((2, 3), "float32", vdev0))
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_check_inference(bb, relax.op.ewise_fma(x0, y0, z0), relax.TensorType((2, 3), "float32"))
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_check_inference(bb, relax.op.ewise_fma(x2, y2, z2), relax.TensorType((2, 3), "float32", vdev0))
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_check_inference(bb, relax.op.ewise_fma(x0, y1, z0), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, relax.op.ewise_fma(x0, y1, z1), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, relax.op.ewise_fma(x1, y0, z0), relax.TensorType((2, 3), dtype=""))
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def test_ewise_fma_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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y0 = relax.Var("y", R.Tensor((m, n), "float32"))
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y1 = relax.Var("y", R.Tensor(dtype="float32", ndim=2))
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z0 = relax.Var("z", R.Tensor((m, n), "float32"))
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_check_inference(bb, relax.op.ewise_fma(x0, y0, z0), relax.TensorType((m, n), "float32"))
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_check_inference(bb, relax.op.ewise_fma(x0, y1, z0), relax.TensorType(dtype="float32", ndim=2))
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def test_ewise_fma_infer_ty_shape_var():
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType(ndim=2))
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s1 = relax.Var("s", relax.ShapeType(ndim=2))
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s2 = relax.Var("s", relax.ShapeType())
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x0 = relax.Var("x", relax.TensorType(s0, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
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x2 = relax.Var("x", relax.TensorType(s2, "float32"))
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y = relax.Var("y", relax.TensorType(s0, "float32"))
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z = relax.Var("z", relax.TensorType(s0, "float32"))
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_check_inference(bb, relax.op.ewise_fma(x0, y, z), relax.TensorType(s0, "float32"))
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_check_inference(bb, relax.op.ewise_fma(x1, y, z), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, relax.op.ewise_fma(x2, y, z), relax.TensorType(dtype="float32", ndim=2))
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def test_ewise_fma_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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y0 = relax.Var("y", R.Tensor((2, 3), "float64"))
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z0 = relax.Var("z", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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y1 = relax.Var("y", R.Tensor((2, 3), "int8"))
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z1 = relax.Var("z", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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y2 = relax.Var("y", R.Tensor((2, 3), "int64"))
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z2 = relax.Var("z", R.Tensor((2, 3), "int64"))
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_check_inference(bb, relax.op.ewise_fma(x0, y0, z0), relax.TensorType((2, 3), "float64"))
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_check_inference(bb, relax.op.ewise_fma(x1, y1, z1), relax.TensorType((2, 3), "int8"))
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_check_inference(bb, relax.op.ewise_fma(x2, y2, z2), relax.TensorType((2, 3), "int64"))
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def test_ewise_fma_infer_ty_dtype_mismatch():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", R.Tensor((2, 3), "int32"))
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y1 = relax.Var("y", R.Tensor((2, 3), "float32"))
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z0 = relax.Var("z", R.Tensor((2, 3), "float32"))
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z1 = relax.Var("z", R.Tensor((2, 3), "int8"))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.ewise_fma(x, y0, z0))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.ewise_fma(x, y1, z1))
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def test_ewise_fma_infer_ty_ndim_mismatch():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
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y1 = relax.Var("y", R.Tensor((2, 3, 4), "float32"))
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z0 = relax.Var("z", R.Tensor((2, 3), "float32"))
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z1 = relax.Var("z", R.Tensor(dtype="float32", ndim=4))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.ewise_fma(x, y1, z0))
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with pytest.raises(ValueError):
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bb.normalize(relax.op.ewise_fma(x, y0, z1))
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def test_ewise_fma_wrong_input_number():
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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relax.op.ewise_fma(x)
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with pytest.raises(TypeError):
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relax.op.ewise_fma(x, x)
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with pytest.raises(TypeError):
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relax.op.ewise_fma(x, x, x, x)
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def test_ewise_fma_infer_ty_wrong_input_type():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", relax.ShapeType((2, 3)))
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y1 = relax.Var("y", relax.FuncType([], R.Tensor((2, 3), "float32")))
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z = relax.Var("z", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.ewise_fma(x, y0, z))
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with pytest.raises(TypeError):
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bb.normalize(relax.op.ewise_fma(x, y1, z))
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if __name__ == "__main__":
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tvm.testing.main()
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