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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/03/17 17:19:06 UTC
[GitHub] [tvm] comaniac commented on a change in pull request #7677: [Relay][Training][Pass] Factor out first-order AD to a module pass, and add ConcretizeLike pass
comaniac commented on a change in pull request #7677:
URL: https://github.com/apache/tvm/pull/7677#discussion_r596210282
##########
File path: src/relay/transforms/concretize_like.cc
##########
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file concretize_like.cc
+ * \brief Converts `*_like` operators to their explicit shape equivalent (e.g. `zeros_like(x, y)` to
+ * `zeros(x, y.shape)`), when the target shape is concrete. This removes unnecessary dependencies
+ * and can enable more opportunities for operator fusion.
+ */
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/transform.h>
+
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+class ConcretizeLikeRewrite {
+ public:
+ ConcretizeLikeRewrite() {
+ concrete_map_[Op::Get("reshape_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeReshape(data, shape);
+ };
+ concrete_map_[Op::Get("zeros_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeZeros(shape, dtype);
+ };
+ concrete_map_[Op::Get("ones_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeOnes(shape, dtype);
+ };
+ concrete_map_[Op::Get("collapse_sum_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ ICHECK_LE(shape.size(), std::numeric_limits<int64_t>::max());
+ static const Op& op = Op::Get("collapse_sum_to");
+ auto attrs = make_object<InitOpAttrs>();
+ auto cshape =
+ MakeConstantTensor(DataType::Int(32), {static_cast<int64_t>(shape.size())}, shape);
+ attrs->shape = shape;
+ return Call(op, {data, cshape}, Attrs(attrs));
+ };
+ concrete_map_[Op::Get("broadcast_to_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ return MakeBroadCastTo(data, shape);
+ };
+
+ for (const auto& pr : concrete_map_) {
+ if (!op_pat_.defined()) {
+ op_pat_ = IsExpr(pr.first);
+ } else {
+ op_pat_ = op_pat_ || IsExpr(pr.first);
+ }
+ }
Review comment:
1. Could you ellaborate what this loop does? It seems to me that it unions all the patterns that match ops in `concrete_map_`, but I didn't find `op_pat_` being used else where.
2. The construction of `concrete_map_` could be static, so we should be able to move it out of the constructor.
##########
File path: src/relay/transforms/concretize_like.cc
##########
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file concretize_like.cc
+ * \brief Converts `*_like` operators to their explicit shape equivalent (e.g. `zeros_like(x, y)` to
+ * `zeros(x, y.shape)`), when the target shape is concrete. This removes unnecessary dependencies
+ * and can enable more opportunities for operator fusion.
+ */
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/transform.h>
+
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+class ConcretizeLikeRewrite {
+ public:
+ ConcretizeLikeRewrite() {
+ concrete_map_[Op::Get("reshape_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeReshape(data, shape);
+ };
+ concrete_map_[Op::Get("zeros_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeZeros(shape, dtype);
+ };
+ concrete_map_[Op::Get("ones_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeOnes(shape, dtype);
+ };
+ concrete_map_[Op::Get("collapse_sum_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ ICHECK_LE(shape.size(), std::numeric_limits<int64_t>::max());
+ static const Op& op = Op::Get("collapse_sum_to");
+ auto attrs = make_object<InitOpAttrs>();
+ auto cshape =
+ MakeConstantTensor(DataType::Int(32), {static_cast<int64_t>(shape.size())}, shape);
+ attrs->shape = shape;
+ return Call(op, {data, cshape}, Attrs(attrs));
+ };
+ concrete_map_[Op::Get("broadcast_to_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ return MakeBroadCastTo(data, shape);
+ };
+
+ for (const auto& pr : concrete_map_) {
+ if (!op_pat_.defined()) {
+ op_pat_ = IsExpr(pr.first);
+ } else {
+ op_pat_ = op_pat_ || IsExpr(pr.first);
+ }
+ }
+
+ data_pat_ = IsWildcard();
+ like_pat_ = IsWildcard();
+ unary_like_pat_ = (IsOp("zeros_like") || IsOp("ones_like"))({like_pat_});
+ binary_like_pat_ = (IsOp("reshape_like") || IsOp("collapse_sum_like") ||
+ IsOp("broadcast_to_like"))({data_pat_, like_pat_});
+ }
+
+ Expr Callback(const Expr& pre, const Expr& post,
+ const Map<DFPattern, Array<Expr>>& node_map) const {
+ // we will rewrite iff the like argument has fully concrete shape
+ const CallNode* call_node = post.as<CallNode>();
+ ICHECK(call_node);
+ const OpNode* op_node = call_node->op.as<OpNode>();
+ ICHECK(op_node);
+ const Op op_ref = GetRef<Op>(op_node);
+ ICHECK(concrete_map_.count(op_ref) > 0);
+
+ Expr like = node_map[like_pat_][0];
+
+ if (!like->checked_type_.defined()) {
+ // TODO(@altanh): maybe because of the input being rewritten?
+ return post;
+ }
+
+ // skip trying to support this for now (ironic, as I was the one who added the feature)
+ if (const auto* attrs = call_node->attrs.as<ReshapeLikeAttrs>()) {
+ if (attrs->lhs_begin != 0 || attrs->rhs_begin != 0 || attrs->lhs_end.defined() ||
+ attrs->rhs_end.defined()) {
+ return post;
+ }
+ }
+
+ CHECK(like->checked_type_.defined())
+ << "ConcretizeLike requires checked types to be populated, please run type inference";
Review comment:
We already have L88-90 so this check seems useless.
##########
File path: src/relay/transforms/concretize_like.cc
##########
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file concretize_like.cc
+ * \brief Converts `*_like` operators to their explicit shape equivalent (e.g. `zeros_like(x, y)` to
+ * `zeros(x, y.shape)`), when the target shape is concrete. This removes unnecessary dependencies
+ * and can enable more opportunities for operator fusion.
+ */
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/transform.h>
+
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+class ConcretizeLikeRewrite {
+ public:
+ ConcretizeLikeRewrite() {
+ concrete_map_[Op::Get("reshape_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeReshape(data, shape);
+ };
+ concrete_map_[Op::Get("zeros_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeZeros(shape, dtype);
+ };
+ concrete_map_[Op::Get("ones_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeOnes(shape, dtype);
+ };
+ concrete_map_[Op::Get("collapse_sum_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ ICHECK_LE(shape.size(), std::numeric_limits<int64_t>::max());
+ static const Op& op = Op::Get("collapse_sum_to");
+ auto attrs = make_object<InitOpAttrs>();
+ auto cshape =
+ MakeConstantTensor(DataType::Int(32), {static_cast<int64_t>(shape.size())}, shape);
+ attrs->shape = shape;
+ return Call(op, {data, cshape}, Attrs(attrs));
+ };
+ concrete_map_[Op::Get("broadcast_to_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ return MakeBroadCastTo(data, shape);
+ };
+
+ for (const auto& pr : concrete_map_) {
+ if (!op_pat_.defined()) {
+ op_pat_ = IsExpr(pr.first);
+ } else {
+ op_pat_ = op_pat_ || IsExpr(pr.first);
+ }
+ }
+
+ data_pat_ = IsWildcard();
+ like_pat_ = IsWildcard();
+ unary_like_pat_ = (IsOp("zeros_like") || IsOp("ones_like"))({like_pat_});
+ binary_like_pat_ = (IsOp("reshape_like") || IsOp("collapse_sum_like") ||
+ IsOp("broadcast_to_like"))({data_pat_, like_pat_});
+ }
+
+ Expr Callback(const Expr& pre, const Expr& post,
+ const Map<DFPattern, Array<Expr>>& node_map) const {
+ // we will rewrite iff the like argument has fully concrete shape
+ const CallNode* call_node = post.as<CallNode>();
+ ICHECK(call_node);
+ const OpNode* op_node = call_node->op.as<OpNode>();
+ ICHECK(op_node);
+ const Op op_ref = GetRef<Op>(op_node);
+ ICHECK(concrete_map_.count(op_ref) > 0);
+
+ Expr like = node_map[like_pat_][0];
+
+ if (!like->checked_type_.defined()) {
+ // TODO(@altanh): maybe because of the input being rewritten?
+ return post;
+ }
+
+ // skip trying to support this for now (ironic, as I was the one who added the feature)
+ if (const auto* attrs = call_node->attrs.as<ReshapeLikeAttrs>()) {
+ if (attrs->lhs_begin != 0 || attrs->rhs_begin != 0 || attrs->lhs_end.defined() ||
+ attrs->rhs_end.defined()) {
+ return post;
+ }
+ }
+
+ CHECK(like->checked_type_.defined())
+ << "ConcretizeLike requires checked types to be populated, please run type inference";
+ const TensorTypeNode* like_ty = like->checked_type().as<TensorTypeNode>();
+ ICHECK(like_ty) << "got non-Tensor argument type " << PrettyPrint(like->checked_type());
+
+ Array<Integer> cshape;
+ for (const auto& dim : like_ty->shape) {
+ if (const auto* imm = dim.as<IntImmNode>()) {
+ cshape.push_back(Integer(GetRef<IntImm>(imm)));
+ continue;
+ }
+ return post;
+ }
+
+ if (call_node->args.size() == 2) {
+ return concrete_map_.at(op_ref)(node_map[data_pat_][0], cshape, like_ty->dtype);
+ }
+ return concrete_map_.at(op_ref)(Expr(), cshape, like_ty->dtype);
Review comment:
It might be better to refer to SimplifyExpr pass to separate unary and binary ops. Then maybe we could have a base struct to put the sharable logic.
##########
File path: src/relay/transforms/concretize_like.cc
##########
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file concretize_like.cc
+ * \brief Converts `*_like` operators to their explicit shape equivalent (e.g. `zeros_like(x, y)` to
+ * `zeros(x, y.shape)`), when the target shape is concrete. This removes unnecessary dependencies
+ * and can enable more opportunities for operator fusion.
+ */
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/transform.h>
+
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+class ConcretizeLikeRewrite {
+ public:
+ ConcretizeLikeRewrite() {
+ concrete_map_[Op::Get("reshape_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeReshape(data, shape);
+ };
+ concrete_map_[Op::Get("zeros_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeZeros(shape, dtype);
+ };
+ concrete_map_[Op::Get("ones_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeOnes(shape, dtype);
+ };
+ concrete_map_[Op::Get("collapse_sum_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ ICHECK_LE(shape.size(), std::numeric_limits<int64_t>::max());
+ static const Op& op = Op::Get("collapse_sum_to");
+ auto attrs = make_object<InitOpAttrs>();
+ auto cshape =
+ MakeConstantTensor(DataType::Int(32), {static_cast<int64_t>(shape.size())}, shape);
+ attrs->shape = shape;
+ return Call(op, {data, cshape}, Attrs(attrs));
+ };
+ concrete_map_[Op::Get("broadcast_to_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ return MakeBroadCastTo(data, shape);
+ };
+
+ for (const auto& pr : concrete_map_) {
+ if (!op_pat_.defined()) {
+ op_pat_ = IsExpr(pr.first);
+ } else {
+ op_pat_ = op_pat_ || IsExpr(pr.first);
+ }
+ }
+
+ data_pat_ = IsWildcard();
+ like_pat_ = IsWildcard();
+ unary_like_pat_ = (IsOp("zeros_like") || IsOp("ones_like"))({like_pat_});
+ binary_like_pat_ = (IsOp("reshape_like") || IsOp("collapse_sum_like") ||
+ IsOp("broadcast_to_like"))({data_pat_, like_pat_});
+ }
+
+ Expr Callback(const Expr& pre, const Expr& post,
+ const Map<DFPattern, Array<Expr>>& node_map) const {
+ // we will rewrite iff the like argument has fully concrete shape
+ const CallNode* call_node = post.as<CallNode>();
+ ICHECK(call_node);
+ const OpNode* op_node = call_node->op.as<OpNode>();
+ ICHECK(op_node);
+ const Op op_ref = GetRef<Op>(op_node);
+ ICHECK(concrete_map_.count(op_ref) > 0);
+
+ Expr like = node_map[like_pat_][0];
+
+ if (!like->checked_type_.defined()) {
+ // TODO(@altanh): maybe because of the input being rewritten?
+ return post;
+ }
+
+ // skip trying to support this for now (ironic, as I was the one who added the feature)
+ if (const auto* attrs = call_node->attrs.as<ReshapeLikeAttrs>()) {
+ if (attrs->lhs_begin != 0 || attrs->rhs_begin != 0 || attrs->lhs_end.defined() ||
+ attrs->rhs_end.defined()) {
+ return post;
+ }
+ }
Review comment:
This is too ad-hoc. It means we may not concretize *like ops in certain situations. Instead of hacking the unified callabck function, we should maintain this logic in the op specific function. I can think of two ways to achieve this:
1. Put the logic in the beginning of `concrete_map_` functions and return the same op if not applicable.
2. Construct another checker map that include checker functions of each op, and invoke the corresponding checker function here.
##########
File path: tests/python/relay/test_pass_concretize_like.py
##########
@@ -0,0 +1,108 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Tests for the ConcretizeLike pass."""
+import tvm
+import tvm.relay.testing
+from tvm import relay
+from tvm.relay.testing import run_infer_type
+
+
+def test_reshape_like():
+ data = relay.var("data", shape=(2, 3, 4), dtype="float32")
+ shape_like = relay.var("shape_like", shape=(6, 2, 2), dtype="float32")
+ f = relay.Function([data, shape_like], relay.reshape_like(data, shape_like))
+ f_expected = relay.Function([data, shape_like], relay.reshape(data, (6, 2, 2)))
Review comment:
This exposes a question of the current implementation: after the ConcretizeLike pass we expect unused arguments. If we don't need shape_like tensor anymore, we should remove it from the function.
##########
File path: src/relay/transforms/concretize_like.cc
##########
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file concretize_like.cc
+ * \brief Converts `*_like` operators to their explicit shape equivalent (e.g. `zeros_like(x, y)` to
+ * `zeros(x, y.shape)`), when the target shape is concrete. This removes unnecessary dependencies
+ * and can enable more opportunities for operator fusion.
+ */
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/transform.h>
+
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+class ConcretizeLikeRewrite {
+ public:
+ ConcretizeLikeRewrite() {
+ concrete_map_[Op::Get("reshape_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeReshape(data, shape);
+ };
+ concrete_map_[Op::Get("zeros_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeZeros(shape, dtype);
+ };
+ concrete_map_[Op::Get("ones_like")] = [](Expr data, Array<Integer> shape, DataType dtype) {
+ return MakeOnes(shape, dtype);
+ };
+ concrete_map_[Op::Get("collapse_sum_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ ICHECK_LE(shape.size(), std::numeric_limits<int64_t>::max());
+ static const Op& op = Op::Get("collapse_sum_to");
+ auto attrs = make_object<InitOpAttrs>();
+ auto cshape =
+ MakeConstantTensor(DataType::Int(32), {static_cast<int64_t>(shape.size())}, shape);
+ attrs->shape = shape;
+ return Call(op, {data, cshape}, Attrs(attrs));
+ };
+ concrete_map_[Op::Get("broadcast_to_like")] = [](Expr data, Array<Integer> shape,
+ DataType dtype) {
+ return MakeBroadCastTo(data, shape);
+ };
+
+ for (const auto& pr : concrete_map_) {
+ if (!op_pat_.defined()) {
+ op_pat_ = IsExpr(pr.first);
+ } else {
+ op_pat_ = op_pat_ || IsExpr(pr.first);
+ }
+ }
+
+ data_pat_ = IsWildcard();
+ like_pat_ = IsWildcard();
+ unary_like_pat_ = (IsOp("zeros_like") || IsOp("ones_like"))({like_pat_});
+ binary_like_pat_ = (IsOp("reshape_like") || IsOp("collapse_sum_like") ||
+ IsOp("broadcast_to_like"))({data_pat_, like_pat_});
Review comment:
1. These patterns can also be defined statically.
2. Now we have two places to specify the supported *like ops. I feel we should define a list of "unary like" and "binary like" ops once and used them both in `concrete_map_` and here. As a result, we could have the logic similar to L61-67 to construct the pattern.
##########
File path: tests/python/relay/test_pass_concretize_like.py
##########
@@ -0,0 +1,108 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Tests for the ConcretizeLike pass."""
+import tvm
+import tvm.relay.testing
+from tvm import relay
+from tvm.relay.testing import run_infer_type
+
+
+def test_reshape_like():
+ data = relay.var("data", shape=(2, 3, 4), dtype="float32")
+ shape_like = relay.var("shape_like", shape=(6, 2, 2), dtype="float32")
+ f = relay.Function([data, shape_like], relay.reshape_like(data, shape_like))
+ f_expected = relay.Function([data, shape_like], relay.reshape(data, (6, 2, 2)))
+ f_expected = run_infer_type(f_expected)
+
+ mod = tvm.IRModule.from_expr(f)
+ mod_concrete = relay.transform.ConcretizeLike()(mod)
+ assert tvm.ir.structural_equal(mod_concrete["main"], f_expected)
+
+
+def test_zeros_like():
+ dtype = "int32"
+ shape_like = relay.var("shape_like", shape=(3, 4, 5), dtype=dtype)
+ f = relay.Function([shape_like], relay.zeros_like(shape_like))
+ f_expected = relay.Function([shape_like], relay.zeros((3, 4, 5), dtype))
+ f_expected = run_infer_type(f_expected)
+
+ mod = tvm.IRModule.from_expr(f)
+ mod_concrete = relay.transform.ConcretizeLike()(mod)
+ assert tvm.ir.structural_equal(mod_concrete["main"], f_expected)
+
+
+def test_ones_like():
+ dtype = "int32"
+ shape_like = relay.var("shape_like", shape=(3, 4, 5), dtype=dtype)
+ f = relay.Function([shape_like], relay.ones_like(shape_like))
+ f_expected = relay.Function([shape_like], relay.ones((3, 4, 5), dtype))
+ f_expected = run_infer_type(f_expected)
+
+ mod = tvm.IRModule.from_expr(f)
+ mod_concrete = relay.transform.ConcretizeLike()(mod)
+ assert tvm.ir.structural_equal(mod_concrete["main"], f_expected)
+
+
+def test_collapse_sum_like():
+ data = relay.var("data", shape=(3, 3, 3), dtype="float32")
+ shape_like = relay.var("shape_like", shape=(3,), dtype="float32")
+ f = relay.Function([data, shape_like], relay.collapse_sum_like(data, shape_like))
+ f_expected = relay.Function([data, shape_like], relay.collapse_sum_to(data, (3,)))
+ f_expected = run_infer_type(f_expected)
+
+ mod = tvm.IRModule.from_expr(f)
+ mod_concrete = relay.transform.ConcretizeLike()(mod)
+ assert tvm.ir.structural_equal(mod_concrete["main"], f_expected)
+
+
+def test_broadcast_to_like():
+ data = relay.var("data", shape=(3,), dtype="float32")
+ shape_like = relay.var("shape_like", shape=(3, 3, 3), dtype="float32")
+ f = relay.Function([data, shape_like], relay.broadcast_to_like(data, shape_like))
+ f_expected = relay.Function([data, shape_like], relay.broadcast_to(data, (3, 3, 3)))
+ f_expected = run_infer_type(f_expected)
+
+ mod = tvm.IRModule.from_expr(f)
+ mod_concrete = relay.transform.ConcretizeLike()(mod)
+ assert tvm.ir.structural_equal(mod_concrete["main"], f_expected)
+
+
+def test_multiple():
+ x = relay.var("x", shape=(2, 3), dtype="float32")
+ y = relay.var("x", shape=(3,), dtype="float32")
+ l = x + y
+
+ dl = relay.ones_like(l)
+ dx = relay.zeros_like(x)
+ dy = relay.zeros_like(y)
+ dx = dx + relay.collapse_sum_like(dl, dx)
+ dy = dy + relay.collapse_sum_like(dl, dy)
+ ret = relay.Tuple([dx, dy])
+ f = relay.Function([x, y], ret)
+
+ dl_c = relay.ones((2, 3), "float32")
+ dx_c = relay.zeros((2, 3), "float32")
+ dy_c = relay.zeros((3,), "float32")
+ dx_c = dx_c + relay.collapse_sum_to(dl_c, (2, 3))
+ dy_c = dy_c + relay.collapse_sum_to(dl_c, (3,))
+ ret_c = relay.Tuple([dx_c, dy_c])
+ f_expected = relay.Function([x, y], ret_c)
+ f_expected = run_infer_type(f_expected)
+
+ mod = tvm.IRModule.from_expr(f)
+ mod_concrete = relay.transform.ConcretizeLike()(mod)
+ assert tvm.ir.structural_equal(mod_concrete["main"], f_expected)
Review comment:
Add `if __name__ == "__main__":`
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