wehub-resource-sync
142 lines
4.9 KiB
Python
142 lines
4.9 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 WA`RRANTIES 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 IRModule, relax
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from tvm.script.parser 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_nll_loss_backward():
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@R.function
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def foo(
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output_grad: R.Tensor((3, 10, 10), dtype="float32"),
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predictions: R.Tensor((3, 5, 10, 10), dtype="float32"),
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targets: R.Tensor((3, 10, 10), dtype="int64"),
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weights: R.Tensor((5,), dtype="float32"),
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) -> R.Tensor((3, 5, 10, 10), dtype="float32"):
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gv: R.Tensor((3, 5, 10, 10), dtype="float32") = R.grad.nll_loss_backward(
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output_grad, predictions, targets, weights, "mean", -1
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)
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return gv
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output_grad = relax.Var("output_grad", R.Tensor((3, 10, 10), "float32"))
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predictions = relax.Var("predictions", R.Tensor((3, 5, 10, 10), "float32"))
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targets = relax.Var("targets", R.Tensor((3, 10, 10), "int64"))
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weights = relax.Var("weights", R.Tensor((5,), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [output_grad, predictions, targets, weights]):
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gv = bb.emit(
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relax.op.grad.nll_loss_backward(
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output_grad, predictions, targets, weights, reduction="mean", ignore_index=-1
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)
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)
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_nll_loss_backward_no_weights():
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@R.function
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def foo(
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output_grad: R.Tensor((3, 10, 10), dtype="float32"),
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predictions: R.Tensor((3, 5, 10, 10), dtype="float32"),
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targets: R.Tensor((3, 10, 10), dtype="int64"),
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) -> R.Tensor((3, 5, 10, 10), dtype="float32"):
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gv: R.Tensor((3, 5, 10, 10), dtype="float32") = R.grad.nll_loss_backward(
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output_grad, predictions, targets, reduction="mean", ignore_index=-1
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)
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return gv
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output_grad = relax.Var("output_grad", R.Tensor((3, 10, 10), "float32"))
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predictions = relax.Var("predictions", R.Tensor((3, 5, 10, 10), "float32"))
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targets = relax.Var("targets", R.Tensor((3, 10, 10), "int64"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [output_grad, predictions, targets]):
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gv = bb.emit(
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relax.op.grad.nll_loss_backward(
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output_grad, predictions, targets, reduction="mean", ignore_index=-1
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)
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)
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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_pool2d_backward():
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@R.function
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def foo(
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output_grad: R.Tensor((3, 2, 6, 5), "float32"), data: R.Tensor((3, 2, 10, 10), "float32")
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):
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gv = R.grad.max_pool2d_backward(
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output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
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)
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return gv
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output_grad = relax.Var("output_grad", R.Tensor((3, 2, 6, 5), "float32"))
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data = relax.Var("data", R.Tensor((3, 2, 10, 10), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [output_grad, data]):
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gv = bb.emit(
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relax.op.grad.max_pool2d_backward(
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output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
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)
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)
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_avg_pool2d_backward():
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@R.function
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def foo(
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output_grad: R.Tensor((3, 2, 6, 5), "float32"), data: R.Tensor((3, 2, 10, 10), "float32")
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):
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gv = R.grad.avg_pool2d_backward(
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output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
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)
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return gv
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output_grad = relax.Var("output_grad", R.Tensor((3, 2, 6, 5), "float32"))
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data = relax.Var("data", R.Tensor((3, 2, 10, 10), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [output_grad, data]):
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gv = bb.emit(
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relax.op.grad.avg_pool2d_backward(
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output_grad, data, (5, 5), (2, 2), (2, 1, 2, 1), (1, 1), True
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)
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)
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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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