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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/10/22 23:59:31 UTC
[GitHub] [tvm] anwang2009 opened a new pull request #9357: Draft: Add pass to fold type transformations into function signature
anwang2009 opened a new pull request #9357:
URL: https://github.com/apache/tvm/pull/9357
As title.
This transformer gives users more control over where preprocessing steps may be located, and what input types their relay program can accept
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[GitHub] [tvm] anwang2009 commented on a change in pull request #9357: Draft: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
anwang2009 commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r736991002
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
Review comment:
Multiple function is weird because you have stuff like
```
fn %f1(%in: Tensor(dtype=float32) {
cast(%in, dtype="float16")
...
}
fn %f2(%in: Tensor(dtype=float32) {
%0 = add(%in, %in)
f1(%0)
}
```
and then we need to translate the result to
```
fn %f1(%in: Tensor(dtype=float16) {
...
}
fn %f2(%in: Tensor(dtype=float32) {
%0 = add(%in, %in)
%1 = cast(%0)
f1(%1)
}
```
which is just a lot more steps.
Most quantized or to-mixed-precision passed models are single function so unless there is a greater need for multi function transformations I'm hoping to leave this as a simple transformation in the first iteration.
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
Review comment:
Ah, the message isn't about the function being called twice, it's about the param being directly fed into different operations, like
```
in
/ \
cast add
```
This is a case I didn't want to handle in the initial iteration because it's gnarly and uncommon. I just clarified the pass-level comment to this effect.
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "FoldTypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
Review comment:
It's required to remove the cast/quantize call, otherwise if the call is returned those operators are persisted in the graph.
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r737906572
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
Review comment:
OK, makes sense to me!
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[GitHub] [tvm] jroesch commented on pull request #9357: Draft: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
jroesch commented on pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#issuecomment-952352204
cc @electriclilies can you review this one?
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r738815257
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "LiftDtypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function parameter";
+ }
+ }
+ }
+
+ return Call(cur_op, post_call_node->args, pre_call_node->attrs, pre_call_node->type_args,
+ pre_call_node->span);
+ }
+
+ Expr VisitExpr_(const FunctionNode* node) {
+ function_count_++;
+ if (function_count_ > 1) {
+ CHECK(false) << "LiftDtypeTransformation is supported for only single-function graphs";
+ }
+
+ for (auto param : node->params) {
+ input_transform_map_.insert(std::pair<Var, const CallNode*>(param, NULL));
+ }
+ auto body = this->Mutate(node->body);
+
+ return Function(node->params, body, node->ret_type, node->type_params, node->attrs, node->span);
+ }
+
+ const Op cast_op_ = Op::Get("cast");
+ const Op quantize_op_ = Op::Get("qnn.quantize");
+
+ private:
+ // Maps function parameter to the first-encountered call node within
+ // the function that takes in that input.
+ std::map<Var, const CallNode*> input_transform_map_;
+
+ // Tracks number of functions in this program.
+ int function_count_;
+};
+
+Expr LiftDtypeTransformation(const Expr& expr, const IRModule& mod) {
+ return LiftDtypeTransformationRewriter().Mutate(expr);
+}
+
+namespace transform {
+
+Pass LiftDtypeTransformation() {
+ runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)> pass_func =
+ [=](Function f, IRModule m, PassContext pc) {
+ return Downcast<Function>(LiftDtypeTransformation(f, m));
+ };
+ return CreateFunctionPass(pass_func, 0, "LiftDtypeTransformation", {});
Review comment:
I think you probably want the opt-level to be higher here, probably 4, so that the user needs to specify it before it is run.
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[GitHub] [tvm] anwang2009 commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
anwang2009 commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r738822877
##########
File path: tests/python/relay/test_fold_type_transformation.py
##########
@@ -0,0 +1,51 @@
+# 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.
+import tvm
+from tvm import relay
+
+
+def test_simple_cast_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.cast(data, "float16")
+ out = relay.add(out, out)
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="float16")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
+
+
+def test_simple_quantize_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.qnn.op.quantize(data, relay.const(2.0), relay.const(0), out_dtype="uint8")
+ out = relay.add(out, out)
+
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="uint8")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
Review comment:
Turns out the irmod creation automatically wraps the expressions in a function, resolved
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r737910807
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "LiftDtypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function parameter";
+ }
Review comment:
Do the arguments to the call and the function parameters need to be the same global vars?
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "LiftDtypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function parameter";
+ }
+ }
+ }
+
+ return Call(cur_op, post_call_node->args, pre_call_node->attrs, pre_call_node->type_args,
+ pre_call_node->span);
+ }
+
+ Expr VisitExpr_(const FunctionNode* node) {
+ function_count_++;
+ if (function_count_ > 1) {
+ CHECK(false) << "LiftDtypeTransformation is supported for only single-function graphs";
+ }
+
+ for (auto param : node->params) {
+ input_transform_map_.insert(std::pair<Var, const CallNode*>(param, NULL));
+ }
Review comment:
Do you need to rewrite the function parameters here?
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r737898055
##########
File path: tests/python/relay/test_fold_type_transformation.py
##########
@@ -0,0 +1,51 @@
+# 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.
+import tvm
+from tvm import relay
+
+
+def test_simple_cast_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.cast(data, "float16")
+ out = relay.add(out, out)
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="float16")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
+
+
+def test_simple_quantize_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.qnn.op.quantize(data, relay.const(2.0), relay.const(0), out_dtype="uint8")
+ out = relay.add(out, out)
+
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="uint8")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
Review comment:
Yeah, right now your tests don't actually wrap the dataflow in a function
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r737901548
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "FoldTypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
Review comment:
Oh, got it, misread what was going on here!
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r737909747
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
Review comment:
I think this is actually an argument to the call, not a parameter in this case.
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "LiftDtypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function parameter";
+ }
Review comment:
Do the arguments to the call and the function parameters need to be the same global vars?
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "LiftDtypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function parameter";
+ }
+ }
+ }
+
+ return Call(cur_op, post_call_node->args, pre_call_node->attrs, pre_call_node->type_args,
+ pre_call_node->span);
+ }
+
+ Expr VisitExpr_(const FunctionNode* node) {
+ function_count_++;
+ if (function_count_ > 1) {
+ CHECK(false) << "LiftDtypeTransformation is supported for only single-function graphs";
+ }
+
+ for (auto param : node->params) {
+ input_transform_map_.insert(std::pair<Var, const CallNode*>(param, NULL));
+ }
Review comment:
Do you need to rewrite the function parameters here?
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[GitHub] [tvm] AndrewZhaoLuo commented on pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
AndrewZhaoLuo commented on pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#issuecomment-1027116065
@anwang2009 do you still have interest in merging this PR?
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[GitHub] [tvm] electriclilies commented on pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#issuecomment-953379326
After taking another look, I'm a bit confused how you're doing the rewrite of the function parameters, added some questions related to that
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[GitHub] [tvm] masahi commented on pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
masahi commented on pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#issuecomment-1008444181
@anwang2009 Please take a look at the CI problem.
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Draft: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r736950950
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
Review comment:
Not sure what this means, can you clarify? (Is it that you shouldn't run the pass on a function multiple times?)
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "FoldTypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function-level input";
+ }
+ }
+ }
+
+ return Call(cur_op, post_call_node->args, pre_call_node->attrs, pre_call_node->type_args,
+ pre_call_node->span);
+ }
+
+ Expr VisitExpr_(const FunctionNode* node) {
+ function_count_++;
+ if (function_count_ > 1) {
+ CHECK(false) << "FoldTypeTransformation is supported for only single-function graphs";
+ }
+
+ for (auto param : node->params) {
+ input_transform_map_.insert(std::pair<Var, const CallNode*>(param, NULL));
+ }
+ auto body = this->Mutate(node->body);
+
+ return Function(node->params, body, node->ret_type, node->type_params, node->attrs, node->span);
+ }
+
+ const Op cast_op_ = Op::Get("cast");
+ const Op quantize_op_ = Op::Get("qnn.quantize");
+
+ private:
+ // Maps function-level input to the first-encountered call node within
+ // the function that takes in that input.
+ std::map<Var, const CallNode*> input_transform_map_;
+
+ // Tracks number of functions in this program.
+ int function_count_;
+};
+
+Expr FoldTypeTransformation(const Expr& expr, const IRModule& mod) {
Review comment:
I think technically you are lifting the cast and quantize ops out of the function -- something like "LiftDtypeTransformation" might be a better name
##########
File path: tests/python/relay/test_fold_type_transformation.py
##########
@@ -0,0 +1,51 @@
+# 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.
+import tvm
+from tvm import relay
+
+
+def test_simple_cast_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.cast(data, "float16")
+ out = relay.add(out, out)
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="float16")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
+
+
+def test_simple_quantize_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.qnn.op.quantize(data, relay.const(2.0), relay.const(0), out_dtype="uint8")
+ out = relay.add(out, out)
+
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="uint8")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
Review comment:
Could you add a test where the call's op is a relay function, not just an op?
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "FoldTypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
Review comment:
correct me if I'm wrong, but it seems like you would want to return a call from this rewrite function in all cases? I think you don't need this return at all.
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
Review comment:
nit: it seems like you are using "function-level input" to refer to function arguments and parameters, I think it would help readability to change it to use those terms instead.
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function-level input var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function input with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
Review comment:
Why can't we do the transform if the function is called twice? Could you just turn the map into a counter and not transform the arguments if you've already seen them?
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
Review comment:
Why can this pass only run on modules containing a single function? It would be great if it could generalize to modules containing more than one function.
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type of each function-level input is transformed only once
+ * per program.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ */
+class FoldTypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ CHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
Review comment:
I think this should be an ICHECK, not a CHECK since it is an internal error, not a user facing one.
##########
File path: python/tvm/relay/transform/transform.py
##########
@@ -1248,3 +1248,10 @@ def SplitArgs(max_function_args):
The registered pass for constant folding.
"""
return _ffi_api.SplitArgs(max_function_args)
+
+
+def FoldTypeTransformation():
+ """
+ Automatic function signature transformation
Review comment:
Can you add detail about what the transformation does
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[GitHub] [tvm] anwang2009 commented on a change in pull request #9357: Draft: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
anwang2009 commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r736991389
##########
File path: tests/python/relay/test_fold_type_transformation.py
##########
@@ -0,0 +1,51 @@
+# 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.
+import tvm
+from tvm import relay
+
+
+def test_simple_cast_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.cast(data, "float16")
+ out = relay.add(out, out)
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="float16")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
+
+
+def test_simple_quantize_fold():
+ data = relay.var("data", shape=[1, 3, 224, 224], dtype="float32")
+ out = relay.qnn.op.quantize(data, relay.const(2.0), relay.const(0), out_dtype="uint8")
+ out = relay.add(out, out)
+
+ mod = tvm.IRModule.from_expr(out)
+ mod = tvm.relay.transform.InferType()(mod)
+ mod = tvm.relay.transform.FoldTypeTransformation()(mod)
+
+ data_fp16 = relay.var("data", shape=[1, 3, 224, 224], dtype="uint8")
+ out = relay.add(data_fp16, data_fp16)
+ expected_mod = tvm.IRModule.from_expr(out)
+ expected_mod = tvm.relay.transform.InferType()(expected_mod)
+
+ assert tvm.ir.structural_equal(mod, expected_mod)
Review comment:
Do you mean like
```
fn(in) {
%0 = cast(in)
%1 = other_fn(%0)
}
```
or something else?
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r738815257
##########
File path: src/relay/transforms/lift_dtype_transformation.cc
##########
@@ -0,0 +1,167 @@
+/*
+ * 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 src/relay/transforms/lift_dtype_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function parameter is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function parameter is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
+ * - The type transformation operation must be either a "cast"
+ * or "qnn.quantize".
+ * - Each function parameter is used only once
+ * per program. There should be no structure that looks like:
+ *
+ * in in
+ * / \ but the following is ok: |
+ * cast add cast
+ */
+class LiftDtypeTransformationRewriter : public MixedModeMutator {
+ protected:
+ Expr Rewrite_(const CallNode* pre_call_node, const Expr& post) final {
+ const CallNode* post_call_node = post.as<CallNode>();
+ ICHECK(post_call_node) << "Expected a CallNode, but got " << post;
+
+ Expr cur_op = pre_call_node->op;
+ for (auto arg : pre_call_node->args) {
+ auto maybe_var_node = arg.as<VarNode>();
+ if (maybe_var_node) {
+ auto var = Downcast<Var>(arg);
+ auto it = input_transform_map_.find(var);
+ if (it != input_transform_map_.end()) {
+ // Checks that the function parameter var hasn't been an arg
+ // to a CallNode yet.
+ CHECK(!it->second) << "Function param with name '" << var->name_hint()
+ << "' is fed into more than one call; "
+ << "aborting transformation";
+
+ it->second = pre_call_node;
+
+ // Get the type to transform the function signature to
+ DataType out_dtype;
+ if (cur_op == cast_op_) {
+ auto attrs = pre_call_node->attrs.as<CastAttrs>();
+ out_dtype = attrs->dtype;
+ } else if (cur_op == quantize_op_) {
+ auto attrs = pre_call_node->attrs.as<qnn::QuantizeAttrs>();
+ out_dtype = attrs->out_dtype;
+ } else {
+ CHECK(false) << "LiftDtypeTransformation will only fold cast and "
+ << "quantize type transformations";
+ }
+
+ // Mutate the var node type
+ VarNode* var_node = const_cast<VarNode*>(maybe_var_node);
+ const TensorTypeNode* anno = var_node->type_annotation.as<TensorTypeNode>();
+ auto mut_anno = const_cast<TensorTypeNode*>(anno);
+ auto shape = anno->shape;
+ mut_anno->dtype = out_dtype;
+
+ return GetRef<Expr>(var_node);
+ } else {
+ LOG(WARNING) << "Variable '" << var->name_hint() << "' encountered"
+ << " but wasn't registered as a function parameter";
+ }
+ }
+ }
+
+ return Call(cur_op, post_call_node->args, pre_call_node->attrs, pre_call_node->type_args,
+ pre_call_node->span);
+ }
+
+ Expr VisitExpr_(const FunctionNode* node) {
+ function_count_++;
+ if (function_count_ > 1) {
+ CHECK(false) << "LiftDtypeTransformation is supported for only single-function graphs";
+ }
+
+ for (auto param : node->params) {
+ input_transform_map_.insert(std::pair<Var, const CallNode*>(param, NULL));
+ }
+ auto body = this->Mutate(node->body);
+
+ return Function(node->params, body, node->ret_type, node->type_params, node->attrs, node->span);
+ }
+
+ const Op cast_op_ = Op::Get("cast");
+ const Op quantize_op_ = Op::Get("qnn.quantize");
+
+ private:
+ // Maps function parameter to the first-encountered call node within
+ // the function that takes in that input.
+ std::map<Var, const CallNode*> input_transform_map_;
+
+ // Tracks number of functions in this program.
+ int function_count_;
+};
+
+Expr LiftDtypeTransformation(const Expr& expr, const IRModule& mod) {
+ return LiftDtypeTransformationRewriter().Mutate(expr);
+}
+
+namespace transform {
+
+Pass LiftDtypeTransformation() {
+ runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)> pass_func =
+ [=](Function f, IRModule m, PassContext pc) {
+ return Downcast<Function>(LiftDtypeTransformation(f, m));
+ };
+ return CreateFunctionPass(pass_func, 0, "LiftDtypeTransformation", {});
Review comment:
I think you probably want the opt-level to be higher here, probably 3 or 4, so that the user needs to specify it before it is run.
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[GitHub] [tvm] electriclilies commented on a change in pull request #9357: Add pass to fold type transformations into function signature
Posted by GitBox <gi...@apache.org>.
electriclilies commented on a change in pull request #9357:
URL: https://github.com/apache/tvm/pull/9357#discussion_r737907476
##########
File path: src/relay/transforms/fold_type_transformation.cc
##########
@@ -0,0 +1,163 @@
+/*
+ * 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 src/relay/transforms/fold_type_transformation.cc
+ * \brief A pass for transforming relay graph function
+ * signatures such that when a function-level inputs is
+ * transformed by a subsequent cast or quantize operation,
+ * that operation is folded into the signature itself.
+ */
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/qnn/attrs.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+
+/*! \brief This class transforms a relay module's function signature
+ * such that when a function-level input is transformed by a subsequent
+ * "cast" or "qnn.quantize" operation, that operation is folded into
+ * the signature itself. For example,
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), float32]) {
+ * %0 = qnn.quantize(%data, 2f, 0, out_dtype="uint8");
+ * add(%0, %0)
+ * }
+ *
+ * would be transformed to
+ *
+ * def @main(%data: Tensor[(1, 3, 224, 224), uint8]) {
+ * add(%0, %0)
+ * }
+ *
+ * Note that now it is the user's responsibility to modify their
+ * input pre-processing pipeline to satisfy the new signature's
+ * constraints.
+ *
+ * For this pass to fold a type transformation, the following conditions
+ * must be met:
+ * - The relay module must contain only a single function.
Review comment:
Alright, this is OK for the first iteration
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