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new 0dbe70c [Relay] Added Merge Composite pass (#4771)
0dbe70c is described below
commit 0dbe70c16dd6d8a2f7596a175544589e6b05e711
Author: mbarrett97 <[email protected]>
AuthorDate: Mon Feb 10 19:39:20 2020 +0000
[Relay] Added Merge Composite pass (#4771)
* [Relay] Added MergeComposite pass
This pass allows for patterns to be wrapped
in a function marked with 'Composite' and a
composite function name. This is intended to be
used with the external codegen for the cases where
an external operator maps to multiple Relay
operators. In that case, the mapping can be expressed
as a pattern and assigned a name.
For more information on this pass and its motivation,
see the RFC:
https://discuss.tvm.ai/t/rfc-external-codegen-defining-composite-relay-operators/5470
Change-Id: Icb1b803a9f0ac57c529143200228f3bb5793afc0
* [Relay] Merge composite tests
Added tests for the merge_composite pass.
Change-Id: I1728b4a05b0c1c36140a40f1afe028fde62185dd
* Merge composite additional test
Change-Id: I9bc7d6053c575e9468ac5abc31214c6ad8507e46
* Support priority order in merge_composite
The order in which the patterns are matched
was currently random as an unordered_map was
used to store the pattern table. This uses
arrays instead so that a distinct priority
order of matching can be defined. Additional
tests have also been added to verify this
behaviour.
Change-Id: Ief347df4262639138d5d9d7c8cee7ef233af7b56
* Improved merge composite docs
Change-Id: Ie3a72045ecc3f13ad3c302fbdf192b7296a306a8
* Removed unused variable
Change-Id: I7814d5fde368ffaf1b3d6d806060c774c7720364
* Remove unnecessary op check
Change-Id: I38e78d2acd5b86cb8e837be72ff9d72cd10bcf33
* Improve styling on composite function creation
Change-Id: I37add1c3134e0b5d5085fe1eb9daf8e06890fa8c
* Comment reword
Change-Id: Ie05872dcbbe0c3e1190b0597083b9a64e6b66c66
* Stylistic changes to avoid std::move
Change-Id: I43a93995bbf10530399900c992aa99dd4ae4575f
* Relax a check in ExtractPattern
Change-Id: I0faef77a66c55f83f09e6e47c561ffaea63dedfa
* Remove new line
Change-Id: Ifdd02c12087a7e1a0a9b54825669bc0de8f13c3d
* Removed MatchPattern from MergeComposite
This is not necessary now that ExtractPattern
can fulfill the same purpose.
Change-Id: I14dc020afa8e50f2df4c0a2efb88a011987f8196
* Removed a new line
Change-Id: I8b50f0c9069aa1bcaccbe68eb421031f01a64842
* Improved docs for merge composite
Change-Id: Ib1959a35c856e7ea5639de2e4ef314a54f44caf5
* Fixed free vars in test
Change-Id: I2b7f273db275964ec0e9820560663f0808adee79
* Handle case where root arg might not be a call
Change-Id: I4eeea3ce723d3ba337d110dcc690377daebe8626
* Removed blank line
Change-Id: I07f5392c0e95cfe3cfa5c333703cc6f82d6034fb
* Change to CHECK_EQ
Change-Id: I5c5d62d3cd57f72508b30b926f72091ae6f0d1cc
* Revised a conditional
Change-Id: I23a7897ca15a7cd076db5039dc653a4b8c27e803
* Improved doc styling
Change-Id: I377f0a1c1ac70f3b8d7584b0c49bddc8c6c134ef
* Fail extraction if vars conflict
Change-Id: I78e36d805e8ed6b55e61d490212a967c857554a4
* Added further merge composite tests
Change-Id: Ib1d800409fca4c1834c7fe0cab5a26ab99a26820
Co-authored-by: lhutton1 <[email protected]>
---
include/tvm/relay/expr.h | 2 +
python/tvm/relay/transform.py | 25 +
src/relay/pass/merge_composite.cc | 218 +++++++++
tests/python/relay/test_pass_merge_composite.py | 609 ++++++++++++++++++++++++
4 files changed, 854 insertions(+)
diff --git a/include/tvm/relay/expr.h b/include/tvm/relay/expr.h
index 64f2278..1dcf957 100644
--- a/include/tvm/relay/expr.h
+++ b/include/tvm/relay/expr.h
@@ -561,6 +561,8 @@ constexpr const char* kParams = "__params__";
constexpr const char* kExternalSymbol = "ExternalSymbol";
/*! \brief Mark if the function should be avoided being optimized. */
constexpr const char* kSkipOptimization = "SkipOptimization";
+/*! \brief Treat the function as a composite operator. */
+constexpr const char* kComposite = "Composite";
} // namespace attr
} // namespace relay
diff --git a/python/tvm/relay/transform.py b/python/tvm/relay/transform.py
index 26b20e0..cfca4a6 100644
--- a/python/tvm/relay/transform.py
+++ b/python/tvm/relay/transform.py
@@ -513,6 +513,31 @@ def Legalize(legalize_map_attr_name="FTVMLegalize"):
return _transform.Legalize(legalize_map_attr_name)
+def MergeComposite(pattern_table):
+ """Merge multiple operators into a single composite relay function.
+
+ Parameters
+ ----------
+ pattern_table : list(tuple)
+ A list of (pattern_name, pattern) tuples.
+ The order of the patterns in the list will determine the order
+ of priority in which they are matched.
+
+ Returns
+ -------
+ ret : tvm.relay.Pass
+ The registered pass that merges operators into a single composite
+ relay function.
+ """
+ pattern_names = []
+ patterns = []
+ for pattern_name, pattern in pattern_table:
+ pattern_names.append(pattern_name)
+ patterns.append(pattern)
+
+ return _transform.MergeComposite(pattern_names, patterns)
+
+
def RewriteAnnotatedOps(fallback_device):
"""Rewrite the annotated program where annotation operators, e.g.
`on_deivce`, mark which device an expression should be scheduled to.
diff --git a/src/relay/pass/merge_composite.cc
b/src/relay/pass/merge_composite.cc
new file mode 100644
index 0000000..28bf8fa
--- /dev/null
+++ b/src/relay/pass/merge_composite.cc
@@ -0,0 +1,218 @@
+/*
+ * 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/pass/merge_composite.cc
+ * \brief Merges expressions matching patterns into functions marked
+ * as 'composite'. This is primarily intended to be used alongside the
+ * external codegen infrastructure to support the case where multiple
+ * Relay operators map to a single external operator.
+ */
+
+#include <tvm/te/operation.h>
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/op_attr_types.h>
+#include <tvm/relay/transform.h>
+
+namespace tvm {
+namespace relay {
+namespace merge_composite {
+
+class MergeCompositeWrapper : public ExprMutator {
+ public:
+ explicit MergeCompositeWrapper(const std::string& pattern_name, const Expr&
pattern)
+ : pattern_name_(pattern_name), pattern_(pattern) {}
+
+ Expr ExtractPattern(const Var& pattern, const Expr& root,
+ Map<std::string, Array<Expr>>* var_map) {
+ if (var_map->find(pattern->name_hint()) == var_map->end()) {
+ // if we haven't encountered this var yet, make a new free var and
associate
+ // it with the value at 'root'
+ auto free_var = VarNode::make(pattern->name_hint(), Type());
+ var_map->Set(pattern->name_hint(), Array<Expr>({free_var, root}));
+ return std::move(free_var);
+ } else {
+ // if we have encountered this var already, return the free var that was
created
+ auto vars = (*var_map)[pattern->name_hint()];
+ auto free_var = vars[0];
+ auto graph_expr = vars[1];
+ // make sure to first check they both map to the same node in the graph
+ if (graph_expr != root) {
+ return Expr();
+ }
+ return (*var_map)[pattern->name_hint()][0];
+ }
+ }
+
+ Expr ExtractPattern(const Constant& pattern, const Expr& root,
+ Map<std::string, Array<Expr>>* var_map) {
+ return root;
+ }
+
+ /*!
+ * \brief Try and extract a given pattern from a graph as a subgraph.
+ * \param pattern The pattern to extract.
+ * \param root The graph to extract from.
+ * \param var_map A map between free vars in the subgraph and nodes in the
graph.
+ * \return The extracted subgraph.
+ *
+ * \note How does this work?
+ *
+ * A pattern consists of Relay expression containing only operator call
nodes, constants
+ * and free variables. The free variables indicate where the pattern can
'attach' in your
+ * graph. This function takes the final call node of the pattern and the
call node currently
+ * being traversed in the Relay graph. It traverses through the pattern in
lockstep with call node
+ * from the graph (referred to as the 'root' node here) to check they're
identical. If at any point
+ * they differ, an empty expression is returned to signify the extract
failed. If a free var is
+ * reached in the pattern, the corresponding value in the root is associated
with the name of the
+ * free var (via the var_map) so that when we construct the composite
function, the inputs match
+ * up correctly with the rest of the graph. The return value of this
function when successful is
+ * a new Relay expression ready to be wrapped into a composite function.
+ */
+ Expr ExtractPattern(const Call& pattern, const Call& root,
+ Map<std::string, Array<Expr>>* var_map) {
+ // check to make sure both calls are to operators (not functions)
+ if (!pattern->op->IsInstance<OpNode>() || !root->op->IsInstance<OpNode>())
+ return Expr();
+ if (pattern->op.as<OpNode>()->name != root->op.as<OpNode>()->name)
+ return Expr();
+
+ unsigned int i = 0;
+ Array<Expr> new_args;
+ for (const auto& arg : pattern->args) {
+ Expr new_arg;
+ if (arg->IsInstance<CallNode>()) {
+ // fail if the root argument is not also a call node
+ if (!root->args[i]->IsInstance<CallNode>()) {
+ return Expr();
+ }
+ // if it's a call node, recursively call this function
+ new_arg = ExtractPattern(Downcast<Call>(arg),
+ Downcast<Call>(root->args[i]),
+ var_map);
+ } else if (arg->IsInstance<VarNode>()) {
+ // if there's a var in the pattern, it must be a free var
+ // so call the function to update the var_map
+ new_arg = ExtractPattern(Downcast<Var>(arg),
+ root->args[i],
+ var_map);
+ } else if (arg->IsInstance<ConstantNode>()) {
+ // if there's a constant, simply get the corresponding
+ // value of the constant from the root
+ new_arg = ExtractPattern(Downcast<Constant>(arg),
+ root->args[i],
+ var_map);
+ }
+ if (!new_arg.defined()) {
+ return Expr();
+ }
+ new_args.push_back(new_arg);
+ i++;
+ }
+ return CallNode::make(root->op, new_args, root->attrs);
+ }
+
+ Expr VisitExpr_(const CallNode* cn) {
+ Call call = GetRef<Call>(cn);
+ if (call->op->IsInstance<FunctionNode>()) {
+ Function func = Downcast<Function>(call->op);
+ CHECK(func.defined());
+ const auto name_node = FunctionGetAttr(func,
attr::kComposite).as<tir::StringImmNode>();
+ // don't step into existing composite functions
+ if (name_node && name_node->value != "") {
+ tvm::Array<tvm::relay::Expr> new_args;
+ for (const auto& arg : call->args) {
+ auto new_e = this->Mutate(arg);
+ new_args.push_back(new_e);
+ }
+ return CallNode::make(call->op, new_args, call->attrs);
+ }
+ }
+
+ Expr expr = ExprMutator::VisitExpr_(cn);
+ call = Downcast<Call>(expr);
+ if (!call->op->IsInstance<OpNode>())
+ return std::move(call);
+
+ // only call patterns are supported
+ Call pattern = Downcast<Call>(pattern_);
+ CHECK(pattern.defined());
+ Map<std::string, Array<Expr>> args_map;
+ auto extract = ExtractPattern(pattern, call, &args_map);
+ if (extract.defined()) {
+ auto free_vars = FreeVars(extract);
+ // make the composite function
+ auto f = FunctionNode::make(free_vars, extract, call->checked_type_, {},
Attrs());
+ f = FunctionSetAttr(f, attr::kComposite,
tir::StringImmNode::make(pattern_name_));
+ f = FunctionSetAttr(f, attr::kPrimitive, tvm::Integer(1));
+ // find the expressions associated with the free vars using the args_map
+ // this tells us which expressions should be given as inputs to the
composite function
+ Array<Expr> args;
+ for (const auto& free_var : free_vars) {
+ args.push_back(args_map[free_var->name_hint()][1]);
+ }
+ auto new_call = CallNode::make(f, args);
+ return std::move(new_call);
+ }
+ return std::move(call);
+ }
+
+ private:
+ /*! \brief The name of the pattern to match */
+ std::string pattern_name_;
+ /*! \brief The pattern to match */
+ Expr pattern_;
+};
+
+Expr MergeComposite(const Expr& expr,
+ const Array<tir::StringImm>& pattern_names, const Array<Expr>& patterns) {
+ CHECK_EQ(pattern_names.size(), patterns.size());
+ Expr merged_expr = expr;
+ // merge the patterns one-by-one in order
+ for (size_t i = 0; i < patterns.size(); i++) {
+ std::string pattern_name = pattern_names[i]->value;
+ Expr pattern = patterns[i];
+ merged_expr = MergeCompositeWrapper(pattern_name,
pattern).Mutate(merged_expr);
+ }
+ return merged_expr;
+}
+
+} // namespace merge_composite
+
+namespace transform {
+
+Pass MergeComposite(const tvm::Array<tir::StringImm>& pattern_names,
+ const tvm::Array<Expr>& patterns) {
+ runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)>
pass_func =
+ [=](Function f, IRModule m, PassContext pc) {
+ return Downcast<Function>(
+ relay::merge_composite::MergeComposite(f, pattern_names,
patterns));
+ };
+ auto func_pass = CreateFunctionPass(pass_func, 0, "MergeComposite", {});
+ return func_pass;
+}
+
+TVM_REGISTER_GLOBAL("relay._transform.MergeComposite")
+.set_body_typed(MergeComposite);
+
+} // namespace transform
+
+} // namespace relay
+} // namespace tvm
diff --git a/tests/python/relay/test_pass_merge_composite.py
b/tests/python/relay/test_pass_merge_composite.py
new file mode 100644
index 0000000..4f785d7
--- /dev/null
+++ b/tests/python/relay/test_pass_merge_composite.py
@@ -0,0 +1,609 @@
+# 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.
+"""Unit tests for merge composite."""
+from tvm import expr
+from tvm import relay
+from tvm.relay.testing import run_opt_pass
+
+"""
+The merge composite pass is designed to merge multiple relay operators, that
+match a given pattern, and combine them into a single relay function.
+
+For example suppose we have the graph:
+
+ conv2d
+ | (merge composite pass)
+ bias_add ====> conv2d_bias_relu
+ | (our target)
+ relu
+
+Our Relay IR before the pass:
+ fn (%data: Tensor[(1, 512, 28, 28), float32], %kernel: Tensor[(256, 512,
1, 1), float32],
+ %bias: Tensor[(256), float32]) -> Tensor[(1, 256, 28, 28),
float32] {
+ %0 = nn.conv2d(%data, %kernel, kernel_size=[1, 1])
+ /* ty=Tensor[(1, 256, 28, 28), float32] */;
+ %1 = nn.bias_add(%0, %bias) /* ty=Tensor[(1, 256, 28, 28), float32] */;
+ nn.relu(%1) /* ty=Tensor[(1, 256, 28, 28), float32] */
+ }
+
+Our Relay IR after the pass:
+ fn (%data: Tensor[(1, 512, 28, 28), float32], %kernel: Tensor[(256, 512,
1, 1), float32],
+ %bias: Tensor[(256), float32]) -> Tensor[(1, 256, 28, 28),
float32] {
+ %2 = fn (%x: Tensor[(1, 512, 28, 28), float32], %y: Tensor[(256, 512, 1,
1), float32],
+ %z: Tensor[(256), float32], Primitive=1,
Composite="conv2d_bias_relu") ->
+ Tensor[(1, 256, 28, 28), float32] {
+ %0 = nn.conv2d(%x, %y, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 28,
28), float32] */;
+ %1 = nn.bias_add(%0, %z) /* ty=Tensor[(1, 256, 28, 28), float32] */;
+ nn.relu(%1) /* ty=Tensor[(1, 256, 28, 28), float32] */
+ };
+ %2(%data, %kernel, %bias) /* ty=Tensor[(1, 256, 28, 28), float32] */
+ }
+
+As you can see in the second relay example, the pattern we specified has been
wrapped
+in a function. The function is then called, producing the same result as the
first relay
+example.
+
+One convenient use for this pass is to offload multiple operators to a single
external
+codegen function.
+"""
+
+
+def make_add_sub_mul_pattern():
+ """Create a pattern to match the following graph.
+
+ add sub
+ \ /
+ \ /
+ mul
+ """
+ x = relay.var('x')
+ y = relay.var('y')
+ add_node = relay.add(x, y)
+ sub_node = relay.subtract(x, y)
+ mul_node = relay.multiply(add_node, sub_node)
+ return mul_node
+
+
+def make_add_relu_pattern():
+ """Create a pattern to match the following graph.
+
+ add
+ |
+ relu
+ """
+ x = relay.var('x')
+ y = relay.var('y')
+ add_node = relay.add(x, y)
+ r = relay.nn.relu(add_node)
+ return r
+
+
+def make_conv_bias_relu_pattern():
+ """Create a pattern to match the following graph.
+
+ conv2d
+ |
+ bias_add
+ |
+ relu
+ """
+ x = relay.var('x')
+ y = relay.var('y')
+ z = relay.var('z')
+ conv_node = relay.nn.conv2d(x, y)
+ bias_node = relay.nn.bias_add(conv_node, z)
+ r = relay.nn.relu(bias_node)
+ return r
+
+
+def test_simple_merge():
+ """Test composite function is correctly produced from simple graph.
+
+ We could expect the pattern `make_add_relu_pattern` to be merged
+ into a single op `add_relu`.
+
+ a b
+ \ / a b
+ add ====> \ /
+ | add_relu
+ relu
+
+ """
+ pattern_table = [
+ ("add_relu", make_add_relu_pattern())
+ ]
+
+ def before():
+ a = relay.var('a', shape=(10, 10))
+ b = relay.var('b', shape=(10, 10))
+ add_node = relay.add(a, b)
+ r = relay.nn.relu(add_node)
+ return relay.Function([a, b], r)
+
+ def expected():
+ a = relay.var('a', shape=(10, 10))
+ b = relay.var('b', shape=(10, 10))
+
+ # add_relu function
+ in_1 = relay.var('in_1', shape=(10, 10))
+ in_2 = relay.var('in_2', shape=(10, 10))
+ add_node = relay.add(in_1, in_2)
+ relu_node = relay.nn.relu(add_node)
+ add_relu = relay.Function([in_1, in_2], relu_node)
+
+ # merged function
+ r = relay.Call(add_relu, [a, b])
+ return relay.Function([a, b], r)
+
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(expected(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+
+def test_branch_merge():
+ """Test composite function is correctly produced from branching graph.
+
+ We would expect the pattern `make_add_sub_mul_pattern` to be merged
+ into a single op `add_sub_mul`.
+
+ a b a b
+ \/ \/
+ add sub a b
+ \ / \/
+ \ / add_sub_mul
+ mul c |
+ / \ \ |
+ c / c | ====> add_sub_mul
+ \/ \/ |
+ add sub |
+ \ / relu
+ \ /
+ mul
+ |
+ |
+ relu
+ """
+
+ pattern_table = [
+ ("add_sub_mul", make_add_sub_mul_pattern())
+ ]
+
+ def before():
+ a = relay.var('a', shape=(10, 10))
+ b = relay.var('b', shape=(10, 10))
+ c = relay.var('c', shape=(10, 10))
+ add_node = relay.add(a, b)
+ sub_node = relay.subtract(a, b)
+ mul_node = relay.multiply(add_node, sub_node)
+ add_node_2 = relay.add(c, mul_node)
+ sub_node_2 = relay.subtract(c, mul_node)
+ mul_node_2 = relay.multiply(add_node_2, sub_node_2)
+ r = relay.nn.relu(mul_node_2)
+ return relay.Function([a, b, c], r)
+
+ def expected():
+ a = relay.var('a', shape=(10, 10))
+ b = relay.var('b', shape=(10, 10))
+ c = relay.var('c', shape=(10, 10))
+
+ # add_sub_mul function
+ in_1 = relay.var('in_1', shape=(10, 10))
+ in_2 = relay.var('in_2', shape=(10, 10))
+ add_node = relay.add(in_1, in_2)
+ sub_node = relay.subtract(in_1, in_2)
+ mul_node = relay.multiply(add_node, sub_node)
+ add_sub_mul = relay.Function([in_1, in_2], mul_node)
+
+ # merged function
+ add_sub_mul_1 = relay.Call(add_sub_mul, [a, b])
+ add_sub_mul_2 = relay.Call(add_sub_mul, [c, add_sub_mul_1])
+ r = relay.nn.relu(add_sub_mul_2)
+ return relay.Function([a, b, c], r)
+
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(expected(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+
+def test_multiple_patterns():
+ """Test different patterns are merged correctly in the graph.
+
+ We would expect the pattern `make_conv_bias_relu_pattern` to be merged
+ into a single op `conv_bias_relu`. We would also expect
`make_add_relu_pattern`
+ to be merged into a single op `add_relu`.
+
+ data kernel
+ \ /
+ \ /
+ conv2d data kernel bias
+ | \ | /
+ | bias conv2d_bias_relu
+ | / |
+ bias_add ====> | a
+ | | /
+ relu a add_relu
+ \ / |
+ add | b
+ | | /
+ relu b mul
+ | /
+ mul
+ """
+ pattern_table = [
+ ("conv2d_bias_relu", make_conv_bias_relu_pattern()),
+ ("add_relu", make_add_relu_pattern())
+ ]
+
+ def before():
+ data = relay.var('data', shape=(1, 512, 28, 28))
+ kernel = relay.var('kernel', shape=(256, 512, 1, 1))
+ bias = relay.var('bias', shape=(256,))
+ a = relay.var('a', shape=(1, 256, 28, 28))
+ b = relay.var('b', shape=(1, 256, 28, 28))
+
+ conv_node = relay.nn.conv2d(data,
+ kernel,
+ kernel_size=(1, 1),
+ padding=(0, 0),
+ strides=(1, 1))
+
+ bias_node = relay.nn.bias_add(conv_node, bias)
+ relu_node = relay.nn.relu(bias_node)
+ add_node = relay.add(relu_node, a)
+ relu_node_2 = relay.nn.relu(add_node)
+ r = relay.multiply(relu_node_2, b)
+ return relay.Function([data, kernel, bias, a, b], r)
+
+ def expected():
+ data = relay.var('data', shape=(1, 512, 28, 28))
+ kernel = relay.var('kernel', shape=(256, 512, 1, 1))
+ bias = relay.var('bias', shape=(256,))
+ a = relay.var('a', shape=(1, 256, 28, 28))
+ b = relay.var('b', shape=(1, 256, 28, 28))
+
+ # conv_bias_relu function
+ in_1 = relay.var('in_1', shape=(1, 512, 28, 28))
+ in_2 = relay.var('in_2', shape=(256, 512, 1, 1))
+ in_3 = relay.var('in_3', shape=(256,))
+
+ conv_node = relay.nn.conv2d(in_1,
+ in_2,
+ kernel_size=(1, 1),
+ padding=(0, 0),
+ strides=(1, 1))
+
+ bias_node = relay.nn.bias_add(conv_node, in_3)
+ r = relay.nn.relu(bias_node)
+ conv_bias_add_relu = relay.Function([in_1, in_2, in_3], r)
+
+ # add_relu function
+ in_4 = relay.var('in_4', shape=(1, 256, 28, 28))
+ in_5 = relay.var('in_5', shape=(1, 256, 28, 28))
+ add_node = relay.add(in_4, in_5)
+ r = relay.nn.relu(add_node)
+ add_relu = relay.Function([in_4, in_5], r)
+
+ # merged function
+ conv_bias_add_relu_1 = relay.Call(conv_bias_add_relu, [data, kernel,
bias])
+ add_relu_1 = relay.Call(add_relu, [conv_bias_add_relu_1, a])
+ r = relay.multiply(add_relu_1, b)
+ return relay.Function([data, kernel, bias, a, b], r)
+
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(expected(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+
+def test_merge_order():
+ """Test that patterns are merged in the order they exist in the pattern
table.
+
+ There can be cases where one pattern is a subgraph of another, in which
case
+ it is not clear which match should take priority. The priority should come
+ from the order in which the patterns are declared in the pattern table. The
+ first patterns will be merged with highest priority and the last with
lowest.
+
+ A: B: C:
+ add add abs
+ | | |
+ abs abs relu
+ |
+ relu
+
+ """
+
+ def pattern_A():
+ x = relay.var('x')
+ y = relay.var('y')
+ out = relay.add(x, y)
+ out = relay.abs(out)
+ out = relay.nn.relu(out)
+ return out
+
+ def pattern_B():
+ x = relay.var('x')
+ y = relay.var('y')
+ out = relay.add(x, y)
+ out = relay.abs(out)
+ return out
+
+ def pattern_C():
+ x = relay.var('x')
+ out = relay.abs(x)
+ out = relay.nn.relu(x)
+ return out
+
+ def before():
+ input_1 = relay.var('input_1', shape=(10, 10))
+ input_2 = relay.var('input_2', shape=(10, 10))
+ out = relay.add(input_1, input_2)
+ out = relay.abs(out)
+ out = relay.nn.relu(out)
+ return relay.Function([input_1, input_2], out)
+
+ def after_A_priority():
+ input_1 = relay.var('input_1', shape=(10, 10))
+ input_2 = relay.var('input_2', shape=(10, 10))
+ x = relay.var('x')
+ y = relay.var('y')
+ out = relay.add(x, y)
+ out = relay.abs(out)
+ out = relay.nn.relu(out)
+ merged_func = relay.Function([x, y], out)
+ merged_func = merged_func.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ merged_func = merged_func.set_attribute('Composite',
expr.StringImm('A'))
+ ret = relay.Call(merged_func, [input_1, input_2])
+ return relay.Function([input_1, input_2], ret)
+
+ def after_B_priority():
+ input_1 = relay.var('input_1', shape=(10, 10))
+ input_2 = relay.var('input_2', shape=(10, 10))
+ x = relay.var('x')
+ y = relay.var('y')
+ out = relay.add(x, y)
+ out = relay.abs(out)
+ merged_func = relay.Function([x, y], out)
+ merged_func = merged_func.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ merged_func = merged_func.set_attribute('Composite',
expr.StringImm('B'))
+ merged_call = relay.Call(merged_func, [input_1, input_2])
+ ret = relay.nn.relu(merged_call)
+ return relay.Function([input_1, input_2], ret)
+
+ def after_C_priority():
+ input_1 = relay.var('input_1', shape=(10, 10))
+ input_2 = relay.var('input_2', shape=(10, 10))
+ add = relay.add(input_1, input_2)
+ x = relay.var('x')
+ out = relay.abs(x)
+ out = relay.nn.relu(out)
+ merged_func = relay.Function([x], out)
+ merged_func = merged_func.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ merged_func = merged_func.set_attribute('Composite',
expr.StringImm('C'))
+ ret = relay.Call(merged_func, [add])
+ return relay.Function([input_1, input_2], ret)
+
+ # check A highest priority
+ pattern_table = [
+ ("A", pattern_A()),
+ ("B", pattern_B()),
+ ("C", pattern_C()),
+ ]
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(after_A_priority(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+ # check B highest priority
+ pattern_table = [
+ ("B", pattern_A()),
+ ("C", pattern_B()),
+ ("A", pattern_C()),
+ ]
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(after_A_priority(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+ # check C highest priority
+ pattern_table = [
+ ("C", pattern_A()),
+ ("A", pattern_B()),
+ ("B", pattern_C()),
+ ]
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(after_A_priority(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+
+def test_parallel_merge():
+ """Tests that parallel patterns relying on the same inputs are correctly
merged.
+
+ The test graph is difficult to draw out as ascii art. It is essentially
two parallel
+ add-sub-mul units which both consume input_1 and input_2 with their
results being multiplied
+ to give the output. We expect both parallel branches should get merged and
both should still
+ consume the same input variables, input_1 and input_2."""
+
+ def before():
+ input_1 = relay.var('input_1', shape=(10, 10))
+ input_2 = relay.var('input_2', shape=(10, 10))
+ branch_1_add = relay.add(input_1, input_2)
+ branch_1_sub = relay.subtract(input_1, input_2)
+ branch_1 = relay.multiply(branch_1_add, branch_1_sub)
+ branch_2_add = relay.add(input_1, input_2)
+ branch_2_sub = relay.subtract(input_1, input_2)
+ branch_2 = relay.multiply(branch_2_add, branch_2_sub)
+ out = relay.multiply(branch_1, branch_2)
+ return relay.Function([input_1, input_2], out)
+
+ def after():
+ input_1 = relay.var('input_1', shape=(10, 10))
+ input_2 = relay.var('input_2', shape=(10, 10))
+ x = relay.var('x')
+ y = relay.var('y')
+ branch_1 = relay.multiply(relay.add(x, y), relay.subtract(x, y))
+ func_1 = relay.Function([x, y], branch_1)
+ call_1 = relay.Call(func_1, [input_1, input_2])
+ x1 = relay.var('x1')
+ y1 = relay.var('y1')
+ branch_2 = relay.multiply(relay.add(x1, y1), relay.subtract(x1, y1))
+ func_2 = relay.Function([x1, y1], branch_2)
+ call_2 = relay.Call(func_2, [input_1, input_2])
+ out = relay.multiply(call_1, call_2)
+ return relay.Function([input_1, input_2], out)
+
+ pattern_table = [
+ ("add_sub_mul", make_add_sub_mul_pattern())
+ ]
+ result = run_opt_pass(before(),
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(after(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+
+def test_multiple_input_subgraphs():
+ """Test the case when multiple input subgraphs feed into another subgraph.
+
+ (1) (2) (3) (4)
+ add add add add
+ | | | |
+ relu relu relu relu
+ \ / \ /
+ \ / \ /
+ add sub
+ \ /
+ \ /
+ \ /
+ mul
+
+ ----> When 1=3 and 2=4 (Case 'A')
+
+ add_relu add_relu
+ \ /
+ \ /
+ add_sub_mul
+
+ ----> When 1!=3 and 2!=4 (Case 'B')
+
+ add_relu add_relu add_relu add_relu
+ \ / \ /
+ \ / \ /
+ add sub
+ \ /
+ -------- -----
+ \ /
+ mul
+
+ The difference in behaviour comes from the fact that add_sub_mul expects
that the
+ inputs to add and sub are identical (the same two relay expressions). So
when you
+ have 4 independent inputs, the pattern should not be merged.
+ """
+
+ def before():
+ before_funcs = {}
+ inputs = [relay.var('input_' + str(i), shape=(10, 10)) for i in
range(8)]
+ add_relu_1 = relay.add(inputs[0], inputs[1])
+ add_relu_1 = relay.nn.relu(add_relu_1)
+ add_relu_2 = relay.add(inputs[2], inputs[3])
+ add_relu_2 = relay.nn.relu(add_relu_2)
+ add_relu_3 = relay.add(inputs[4], inputs[5])
+ add_relu_3 = relay.nn.relu(add_relu_3)
+ add_relu_4 = relay.add(inputs[6], inputs[7])
+ add_relu_4 = relay.nn.relu(add_relu_4)
+ add = relay.add(add_relu_1, add_relu_2)
+ sub = relay.subtract(add_relu_3, add_relu_4)
+ out = relay.multiply(add, sub)
+ before_funcs['B'] = relay.Function(inputs, out)
+ sub = relay.subtract(add_relu_1, add_relu_2)
+ out = relay.multiply(add, sub)
+ before_funcs['A'] = relay.Function(inputs[:4], out)
+ return before_funcs
+
+ def after_A():
+ inputs = [relay.var('input_' + str(i), shape=(10, 10)) for i in
range(4)]
+ x = relay.var('x')
+ y = relay.var('y')
+ add_relu_1 = relay.add(x, y)
+ add_relu_1 = relay.nn.relu(add_relu_1)
+ add_relu_1 = relay.Function([x, y], add_relu_1)
+ add_relu_1 = add_relu_1.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ add_relu_1 = add_relu_1.set_attribute('Composite',
expr.StringImm('add_relu'))
+ add_relu_call_1 = relay.Call(add_relu_1, [inputs[0], inputs[1]])
+ x1 = relay.var('x1')
+ y1 = relay.var('y1')
+ add_relu_2 = relay.add(x1, y1)
+ add_relu_2 = relay.nn.relu(add_relu_2)
+ add_relu_2 = relay.Function([x1, y1], add_relu_2)
+ add_relu_2 = add_relu_2.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ add_relu_2 = add_relu_2.set_attribute('Composite',
expr.StringImm('add_relu'))
+ add_relu_call_2 = relay.Call(add_relu_2, [inputs[2], inputs[3]])
+ x2 = relay.var('x2')
+ y2 = relay.var('y2')
+ add = relay.add(x2, y2)
+ sub = relay.subtract(x2, y2)
+ add_sub_mul = relay.multiply(add, sub)
+ add_sub_mul = relay.Function([x2, y2], add_sub_mul)
+ add_sub_mul = add_sub_mul.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ add_sub_mul = add_sub_mul.set_attribute('Composite',
expr.StringImm('add_sub_mul'))
+ add_sub_mul_call = relay.Call(add_sub_mul, [add_relu_call_1,
add_relu_call_2])
+ return relay.Function(inputs, add_sub_mul_call)
+
+ def after_B():
+ inputs = [relay.var('input_' + str(i), shape=(10, 10)) for i in
range(8)]
+ add_relu_calls = []
+ for i in range(4):
+ x = relay.var('x' + str(i))
+ y = relay.var('x' + str(i))
+ add_relu = relay.add(x, y)
+ add_relu = relay.nn.relu(add_relu)
+ add_relu = relay.Function([x, y], add_relu)
+ add_relu = add_relu.set_attribute('Primitive',
expr.IntImm('int32', 1))
+ add_relu = add_relu.set_attribute('Composite',
expr.StringImm('add_relu'))
+ add_relu_call = relay.Call(add_relu, [inputs[i*2], inputs[i*2+1]])
+ add_relu_calls.append(add_relu_call)
+
+ add = relay.add(add_relu_calls[0], add_relu_calls[1])
+ sub = relay.subtract(add_relu_calls[2], add_relu_calls[3])
+ out = relay.multiply(add, sub)
+ return relay.Function(inputs, out)
+
+ pattern_table = [
+ ("add_sub_mul", make_add_sub_mul_pattern()),
+ ("add_relu", make_add_relu_pattern())
+ ]
+ # check case 'A'
+ result = run_opt_pass(before()['A'],
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(after_A(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+ # check case 'B'
+ result = run_opt_pass(before()['B'],
relay.transform.MergeComposite(pattern_table))
+ assert not relay.analysis.free_vars(result)
+ expected = run_opt_pass(after_B(), relay.transform.InferType())
+ assert relay.analysis.alpha_equal(result, expected)
+
+
+if __name__ == "__main__":
+ test_simple_merge()
+ test_branch_merge()
+ test_multiple_patterns()
+ test_merge_order()
+ test_parallel_merge()
+ test_multiple_input_subgraphs()
\ No newline at end of file
