wehub-resource-sync
96 lines
2.9 KiB
Python
96 lines
2.9 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.script
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import tvm.testing
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from tvm import IRModule, relax
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from tvm.script import relax as R
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def _check(
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parsed: relax.Function | IRModule,
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expect: relax.Function | IRModule | None,
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):
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test = parsed.script(show_meta=True)
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roundtrip_mod = tvm.script.from_source(test)
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tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
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if expect:
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tvm.ir.assert_structural_equal(parsed, expect)
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def test_matmul():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 4, 5), "float32"), y: R.Tensor((6, 2, 3, 5, 7), "float32")
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) -> R.Tensor((6, 2, 3, 4, 7), "float32"):
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gv: R.Tensor((6, 2, 3, 4, 7), "float32") = R.matmul(x, y)
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return gv
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x = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
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y = relax.Var("y", R.Tensor((6, 2, 3, 5, 7), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, y]):
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gv = bb.emit(relax.op.matmul(x, y))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_linear():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 4, 5), "float32"),
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w: R.Tensor((3, 5), "float32"),
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bias: R.Tensor((3,), "float32"),
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):
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gv = R.linear(x, w, bias)
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return gv
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x = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
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w = relax.Var("y", R.Tensor((3, 5), "float32"))
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bias = relax.Var("bias", R.Tensor((3,), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w, bias]):
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w_T = bb.emit(relax.op.permute_dims(w, axes=None))
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matmul = bb.emit(relax.op.matmul(x, w_T))
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out = matmul + bias
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bb.emit_func_output(out)
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_check(foo, bb.get()["foo"])
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def test_einsum():
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@R.function
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def foo(x: R.Tensor((1, 4), "float32"), y: R.Tensor((2, 4), "float32")):
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gv = R.einsum((x, y), "ij, ij -> i")
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return gv
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x = relax.Var("x", R.Tensor((1, 4), "float32"))
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y = relax.Var("y", R.Tensor((2, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, y]):
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gv = bb.emit(relax.op.einsum((x, y), "ij, ij -> i"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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if __name__ == "__main__":
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tvm.testing.main()
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