wehub-resource-sync
142 lines
5.2 KiB
Python
142 lines
5.2 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
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from tvm.ir import Op
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from tvm.script import relax as R
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def test_op_correctness():
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g = relax.Var("g", R.Tensor((3, 10, 10), "float32"))
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x = relax.Var("x", R.Tensor((3, 5, 10, 10), "float32"))
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y = relax.Var("y", R.Tensor((3, 10, 10), "int64"))
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w = relax.Var("w", R.Tensor((5,), "float32"))
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assert relax.op.grad.nll_loss_backward(g, x, y, w).op == Op.get("relax.grad.nll_loss_backward")
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g = relax.Var("g", R.Tensor((3, 3, 8, 8), "float32"))
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x = relax.Var("x", R.Tensor((3, 2, 10, 10), "float32"))
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assert relax.op.grad.max_pool2d_backward(g, x, (3, 3)).op == Op.get(
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"relax.grad.max_pool2d_backward"
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)
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assert relax.op.grad.avg_pool2d_backward(g, x, (3, 3)).op == Op.get(
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"relax.grad.avg_pool2d_backward"
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)
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g = relax.Var("g", R.Tensor((3, 2, 5), "float32"))
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x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
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indices = relax.Var("indices", R.Tensor((2,), "float32"))
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assert relax.op.grad.take_backward(g, x, indices, axis=1).op == Op.get(
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"relax.grad.take_backward"
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)
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assert relax.op.grad.no_grad(x).op == Op.get("relax.grad.no_grad")
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assert relax.op.grad.no_grad(x).args[0] == x
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assert relax.op.grad.start_checkpoint(x).op == Op.get("relax.grad.start_checkpoint")
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assert relax.op.grad.start_checkpoint(x).args[0] == x
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assert relax.op.grad.end_checkpoint(x).op == Op.get("relax.grad.end_checkpoint")
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assert relax.op.grad.end_checkpoint(x).args[0] == x
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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_start_checkpoint_input_not_var():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((3, 4), "float32"))
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y = relax.Var("y", R.Tensor((3, 4), "float32"))
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# ok because x + y will be normalized into a relax Var
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with bb.function("main", [x, y]):
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gv = bb.emit(relax.op.grad.start_checkpoint(x + y))
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bb.emit_func_output(gv)
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# wrong: tuple will not be normalized
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with pytest.raises(TypeError):
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bb.normalize(relax.op.grad.start_checkpoint((x, y)))
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# wrong: const will not be normalized
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with pytest.raises(TypeError):
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bb.normalize(relax.op.grad.start_checkpoint(relax.const(1, "float32")))
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def test_end_checkpoint_input_not_var():
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((3, 4), "float32"))
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y = relax.Var("y", R.Tensor((3, 4), "float32"))
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# ok because x + y will be normalized into a relax Var
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with bb.function("main", [x, y]):
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gv = bb.emit(relax.op.grad.end_checkpoint(x + y))
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bb.emit_func_output(gv)
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# wrong: tuple will not be normalized
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with pytest.raises(TypeError):
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bb.normalize(relax.op.grad.end_checkpoint((x, y)))
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# wrong: const will not be normalized
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with pytest.raises(TypeError):
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bb.normalize(relax.op.grad.end_checkpoint(relax.const(1, "float32")))
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def test_nll_loss_backward_infer_ty():
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bb = relax.BlockBuilder()
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g = relax.Var("g", R.Tensor((3, 10, 10)))
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x = relax.Var("x", R.Tensor((3, 5, 10, 10), "float32"))
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y = relax.Var("y", R.Tensor((3, 10, 10), "int64"))
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w = relax.Var("w", R.Tensor((5,), "float32"))
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_check_inference(bb, relax.op.grad.nll_loss_backward(g, x, y), x.ty)
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_check_inference(bb, relax.op.grad.nll_loss_backward(g, x, y, w), x.ty)
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def test_max_pool2d_backward_infer_ty():
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bb = relax.BlockBuilder()
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g = relax.Var("g", R.Tensor((3, 3, 8, 8), "float32"))
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x = relax.Var("x", R.Tensor((3, 2, 10, 10), "float32"))
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_check_inference(bb, relax.op.grad.max_pool2d_backward(g, x, (2, 2)), x.ty)
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_check_inference(bb, relax.op.grad.max_pool2d_backward(g, x, (3, 3)), x.ty)
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def test_avg_pool2d_backward_infer_ty():
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bb = relax.BlockBuilder()
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g = relax.Var("g", R.Tensor((3, 3, 8, 8), "float32"))
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x = relax.Var("x", R.Tensor((3, 2, 10, 10), "float32"))
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_check_inference(bb, relax.op.grad.avg_pool2d_backward(g, x, (2, 2)), x.ty)
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_check_inference(bb, relax.op.grad.avg_pool2d_backward(g, x, (3, 3)), x.ty)
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def test_take_backward_infer_ty():
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bb = relax.BlockBuilder()
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g = relax.Var("g", R.Tensor((3, 2, 5), "float32"))
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x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
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indices = relax.Var("indices", R.Tensor((2,), "float32"))
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_check_inference(bb, relax.op.grad.take_backward(g, x, indices, axis=1), x.ty)
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if __name__ == "__main__":
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tvm.testing.main()
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