chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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_sum():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 3), "float32"):
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gv: R.Tensor((1, 3), "float32") = R.sum(x, axis=[1, 3])
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.sum(x, axis=[1, 3]))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_sum_without_specified_axis():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((), "float32"):
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gv: R.Tensor((), "float32") = R.sum(x)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.sum(x))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_sum_keep_dims():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 1, 3, 1), "float32"):
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gv: R.Tensor((1, 1, 3, 1), "float32") = R.sum(x, axis=[1, 3], keepdims=True)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.sum(x, axis=[1, 3], keepdims=True))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_mean():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 3), "float32"):
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gv: R.Tensor((1, 3), "float32") = R.mean(x, axis=[1, 3])
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.mean(x, axis=[1, 3]))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_median():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tuple(
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R.Tensor((1, 3, 4), "float32"), R.Tensor((1, 3, 4), "int64")
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):
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gv: R.Tuple(R.Tensor((1, 3, 4), "float32"), R.Tensor((1, 3, 4), "int64")) = R.median(
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x, axis=[1]
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)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.median(x, axis=[1]))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_variance():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1,), "float32"):
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gv: R.Tensor((1,), "float32") = R.variance(x, axis=[-1, -2, -3])
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.variance(x, axis=[-1, -2, -3]))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_max():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 1, 1, 1), "float32"):
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gv: R.Tensor((1, 1, 1, 1), "float32") = R.variance(x, axis=[-1, -2, -3], keepdims=True)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.variance(x, axis=[-1, -2, -3], keepdims=True))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_min():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 3, 4), "float32"):
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gv: R.Tensor((1, 3, 4), "float32") = R.min(x, axis=1)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.min(x, axis=1))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_prod():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 3, 4), "float32"):
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gv: R.Tensor((1, 3, 4), "float32") = R.prod(x, axis=1)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.prod(x, axis=1))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_std():
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@R.function
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def foo(x: R.Tensor((1, 2, 3, 4), "float32")) -> R.Tensor((1, 3, 4), "float32"):
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gv: R.Tensor((1, 3, 4), "float32") = R.std(x, axis=1)
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return gv
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x = relax.Var("x", R.Tensor((1, 2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.std(x, axis=1))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_scan():
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@R.function
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def foo(x: R.Tensor((2, 3, 4), "float32")):
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lv = R.cumsum(x, axis=1, dtype="int32")
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gv = R.cumprod(lv, axis=1, dtype="int32")
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return gv
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x = relax.Var("x", R.Tensor((2, 3, 4), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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lv = bb.emit(relax.op.cumsum(x, axis=1, dtype="int32"))
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gv = bb.emit(relax.op.cumprod(lv, axis=1, dtype="int32"))
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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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