wehub-resource-sync
269 lines
8.4 KiB
Python
269 lines
8.4 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.script
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import tvm.testing
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from tvm import relax
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from tvm.script import ir as I
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from tvm.script import relax as R
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from tvm.script import tirx as T
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def test_bind_tensors():
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"""Symbolic variables may occur in Tensor shapes"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main(
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x: R.Tensor(("batch", "m"), dtype="float32"),
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w0: R.Tensor(("m", "n"), dtype="float32"),
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w1: R.Tensor(("k", 10), dtype="float32"),
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) -> R.Tensor(("batch", "k"), dtype="float32"):
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batch = T.Var("batch", "int64")
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n = T.Var("n", "int64")
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k = T.Var("k", "int64")
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with R.dataflow():
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lv0 = R.call_dps_packed(
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"test0", (x, w0), out_ty=R.Tensor((batch, n), dtype="float32")
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)
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out = R.call_dps_packed(
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"test1", (lv0, w1), out_ty=R.Tensor((batch, k), dtype="float32")
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)
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R.output(out)
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return out
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symvar_map = {"batch": 1, "k": 3}
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target_func_name = "main"
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After = relax.transform.BindSymbolicVars(symvar_map, target_func_name)(Before)
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@I.ir_module
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class Expected:
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@R.function
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def main(
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x: R.Tensor((1, "m"), dtype="float32"),
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w0: R.Tensor(("m", "n"), dtype="float32"),
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w1: R.Tensor((3, 10), dtype="float32"),
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) -> R.Tensor((1, 3), dtype="float32"):
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n = T.int64()
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with R.dataflow():
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lv0 = R.call_dps_packed("test0", (x, w0), out_ty=R.Tensor((1, n), dtype="float32"))
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out = R.call_dps_packed(
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"test1", (lv0, w1), out_ty=R.Tensor((1, 3), dtype="float32")
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)
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R.output(out)
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return out
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tvm.ir.assert_structural_equal(Expected, After)
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def test_bind_shape():
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"""Symbolic variables may occur in ShapeExpr"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main(
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x: R.Shape(("batch", "m")),
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w0: R.Shape(("m", "n")),
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w1: R.Shape(("k", 10)),
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) -> R.Shape(("batch", "k")):
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batch = T.Var("batch", "int64")
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n = T.Var("n", "int64")
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k = T.Var("k", "int64")
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with R.dataflow():
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lv0 = R.call_dps_packed("test0", (x, w0), out_ty=R.Tensor((batch, n)))
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out = R.call_dps_packed("test1", (lv0, w1), out_ty=R.Tensor((batch, k)))
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R.output(out)
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return out
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symvar_map = {"batch": 1, "k": 3}
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target_func_name = "main"
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After = relax.transform.BindSymbolicVars(symvar_map, target_func_name)(Before)
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@I.ir_module
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class Expected:
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@R.function
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def main(x: R.Shape([1, "m"]), w0: R.Shape(["m", "n"]), w1: R.Shape([3, 10])) -> R.Shape(
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[1, 3]
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):
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n = T.int64()
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with R.dataflow():
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lv0 = R.call_dps_packed("test0", (x, w0), out_ty=R.Tensor((1, n)))
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out = R.call_dps_packed("test1", (lv0, w1), out_ty=R.Tensor((1, 3)))
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R.output(out)
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return out
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tvm.ir.assert_structural_equal(Expected, After)
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def test_arith():
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"""Symbolic shapes may use TIR arithmetic expressions"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main(
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x: R.Tensor(("batch", "m-1"), dtype="float32"),
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w0: R.Tensor(("m", "n"), dtype="float32"),
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w1: R.Tensor(("k", 10), dtype="float32"),
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) -> R.Tensor(("batch", "k*m"), dtype="float32"):
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batch = T.Var("batch", "int64")
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m = T.Var("m", "int64")
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n = T.Var("n", "int64")
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k = T.Var("k", "int64")
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with R.dataflow():
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lv0 = R.call_dps_packed(
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"test0",
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(x, w0),
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out_ty=R.Tensor((batch, m + n), dtype="float32"),
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)
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out = R.call_dps_packed(
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"test1",
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(lv0, w1),
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out_ty=R.Tensor((batch, k + n), dtype="float32"),
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)
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R.output(out)
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return out
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symvar_map = {"batch": 1, "k": 2, "m": 3}
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target_func_name = "main"
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After = relax.transform.BindSymbolicVars(symvar_map, target_func_name)(Before)
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@I.ir_module
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class Expected:
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@R.function
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def main(
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x: R.Tensor((1, 2), dtype="float32"),
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w0: R.Tensor((3, "n"), dtype="float32"),
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w1: R.Tensor((2, 10), dtype="float32"),
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) -> R.Tensor((1, 6), dtype="float32"):
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n = T.int64()
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with R.dataflow():
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lv0 = R.call_dps_packed(
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"test0", (x, w0), out_ty=R.Tensor((1, n + 3), dtype="float32")
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)
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out = R.call_dps_packed(
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"test1", (lv0, w1), out_ty=R.Tensor((1, n + 2), dtype="float32")
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)
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R.output(out)
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return out
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tvm.ir.assert_structural_equal(Expected, After)
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def test_bind_multiple_variables_by_name():
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"""String names may be used to replace across multiple functions"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main_1(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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@R.function
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def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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@tvm.script.ir_module
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class Expected:
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@R.function
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def main_1(x: R.Tensor(("m", 16), dtype="float32")):
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return x
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@R.function
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def main_2(x: R.Tensor(("m", 16), dtype="float32")):
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return x
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After = relax.transform.BindSymbolicVars({"n": 16})(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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def test_bind_single_variable_by_identity():
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"""TIR variables may be used to replace a specific var"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main_1(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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@R.function
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def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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@tvm.script.ir_module
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class Expected:
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@R.function
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def main_1(x: R.Tensor(("m", 16), dtype="float32")):
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return x
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@R.function
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def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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main_1_n = Before["main_1"].params[0].ty.shape[1]
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After = relax.transform.BindSymbolicVars({main_1_n: 16})(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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def test_bind_single_variable_by_function_name():
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"""Variable name and function name may be used to replace a specific var"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main_1(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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@R.function
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def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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@tvm.script.ir_module
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class Expected:
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@R.function
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def main_1(x: R.Tensor(("m", 16), dtype="float32")):
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return x
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@R.function
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def main_2(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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After = relax.transform.BindSymbolicVars({"n": 16}, "main_1")(Before)
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tvm.ir.assert_structural_equal(Expected, After)
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def test_error_for_unused_replacement():
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"""Each replacement must be used"""
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@tvm.script.ir_module
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class Before:
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@R.function
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def main(x: R.Tensor(("m", "n"), dtype="float32")):
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return x
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with pytest.raises(RuntimeError):
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relax.transform.BindSymbolicVars({"non_existing_var_name": 16})(Before)
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if __name__ == "__main__":
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tvm.testing.main()
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