[cherry-pick]Verify the correctness of graph rewrited by GeneratePass (#36453)

* [WIP]Verify the correctness of graph rewrited by GeneratePass, test=develop

* add delete subgraph and unittest, test=develop

* check simple pass, test=develop

* fix coverage, test=develop

* limit with input_spec via Paddle API, test=develop

paddle/fluid/framework/ir/generate_pass.cc

@@ -21,6 +21,16 @@ namespace ir {
 
 void InitGeneratePattern(const proto::PassDesc& pass_desc, PDPattern* pattern) {
   const proto::BlockDesc& block = pass_desc.pattern().blocks(0);
+  for (const proto::VarDesc& var : block.vars()) {
+    PDNode* var_pdnode = pattern->NewNode(var.name())->AsInput();
+    var_pdnode->assert_is_var();
+    var_pdnode->assert_more([&](Node* x) {
+      if (VarDesc(var).GetShape() == x->Var()->GetShape()) {
+        return true;
+      }
+      return false;
+    });
+  }
   // Traverse all operators to create subgraph.
   for (int index = 0; index < block.ops_size(); ++index) {
     const proto::OpDesc& op = block.ops(index);
@@ -31,15 +41,32 @@ void InitGeneratePattern(const proto::PassDesc& pass_desc, PDPattern* pattern) {
         pattern->NewNode(std::to_string(index))->assert_is_op(op.type());
     // Create PDNodes for inputs of current operator.
     for (const proto::OpDesc::Var& var : op.inputs()) {
-      for (const std::string& argument : var.arguments()) {
+      for (int n = 0; n < var.arguments_size(); ++n) {
+        const std::string& argument = var.arguments(n);
         // The input may be the output of other operator.
         PDNode* var_pdnode = pattern->RetrieveNode(argument);
         if (nullptr == var_pdnode) {
           var_pdnode = pattern->NewNode(argument)->AsInput();
+          var_pdnode->assert_is_var();
         } else if (var_pdnode->IsOutput()) {
           var_pdnode->AsIntermediate();
         }
-        var_pdnode->assert_is_op_input(op.type());
+        var_pdnode->assert_more([&](Node* x) {
+          for (auto* out : x->outputs) {
+            if (out->IsOp() && out->Op()->Type() == op.type()) {
+              const auto& inputs = out->Op()->Inputs();
+              const auto& iter = inputs.find(var.parameter());
+              if (inputs.end() != iter) {
+                if (iter->second.end() != std::find(iter->second.begin(),
+                                                    iter->second.end(),
+                                                    x->Name())) {
+                  return true;
+                }
+              }
+            }
+          }
+          return false;
+        });
         pattern->AddEdge(var_pdnode, op_pdnode);
       }
     }
@@ -50,6 +77,24 @@ void InitGeneratePattern(const proto::PassDesc& pass_desc, PDPattern* pattern) {
         PDNode* var_pdnode = pattern->RetrieveNode(argument);
         if (nullptr == var_pdnode) {
           var_pdnode = pattern->NewNode(argument)->AsOutput();
+          var_pdnode->assert_is_var();
+          var_pdnode->assert_more([&](Node* x) {
+            for (Node* input : x->inputs) {
+              if (input && input->IsOp() && input->Op() &&
+                  input->Op()->Type() == op.type()) {
+                const auto& outputs = input->Op()->Outputs();
+                const auto& iter = outputs.find(var.parameter());
+                if (outputs.end() != iter) {
+                  if (iter->second.end() != std::find(iter->second.begin(),
+                                                      iter->second.end(),
+                                                      x->Name())) {
+                    return true;
+                  }
+                }
+              }
+            }
+            return false;
+          });
         } else if (var_pdnode->IsInput()) {
           var_pdnode->AsIntermediate();
         }
@@ -73,17 +118,63 @@ void InitGeneratePattern(const proto::PassDesc& pass_desc, PDPattern* pattern) {
   }
 }
 
-GraphPatternDetector::handle_t GetGenerateRewrite(
-    const PDPattern& pattern, const proto::PassDesc& pass_desc) {
-  GraphPatternDetector::handle_t handler = [&](
-      const GraphPatternDetector::subgraph_t subgraph, Graph* graph) {
-    // There are some duplicate patterns.
+// There are some duplicate patterns.
+bool IsDuplicatePattern(const GraphPatternDetector::subgraph_t& subgraph,
+                        Graph* graph) {
   for (auto iter : subgraph) {
     if (nullptr == graph->RetrieveNode(iter.second->id())) {
       VLOG(3) << "Node [" << iter.second->Name()
               << "] of subgraph has been removed. So skip this optimize.";
+      return true;
+    }
+  }
+  return false;
+}
+
+GraphPatternDetector::handle_t GetGenerateDelete(
+    const PDPattern& pattern, const proto::PassDesc& pass_desc) {
+  GraphPatternDetector::handle_t handler = [&](
+      const GraphPatternDetector::subgraph_t& subgraph, Graph* graph) {
+    if (IsDuplicatePattern(subgraph, graph)) {
       return;
     }
+    // `var_node_maps` record the mapping of variable to the pattern subgraph.
+    std::map<std::string, Node*> var_node_maps;
+    for (const proto::PassDesc::VarMap& var_map : pass_desc.var_maps()) {
+      Node* node = subgraph.at(pattern.RetrieveNode(var_map.pattern_var()));
+      const auto& iter = var_node_maps.find(var_map.replace_var());
+      if (var_node_maps.end() == iter) {
+        // first node is input
+        var_node_maps.insert({var_map.replace_var(), node});
+      } else {
+        // output node
+        for (Node* s_node : node->outputs) {
+          iter->second->outputs.push_back(s_node);
+          std::replace(s_node->inputs.begin(), s_node->inputs.end(), node,
+                       iter->second);
+          s_node->Op()->RenameInput(node->Name(), iter->second->Name());
+        }
+      }
+    }
+    // Remove nodes that are intermediate.
+    std::unordered_set<const Node*> remove_nodes;
+    for (const std::unique_ptr<PDNode>& pdnode : pattern.nodes()) {
+      remove_nodes.emplace(subgraph.at(pdnode.get()));
+    }
+    for (auto iter : var_node_maps) {
+      remove_nodes.erase(iter.second);
+    }
+    GraphSafeRemoveNodes(graph, remove_nodes);
+  };
+  return handler;
+}
+
+GraphPatternDetector::handle_t GetGenerateRewrite(
+    const PDPattern& pattern, const proto::PassDesc& pass_desc) {
+  GraphPatternDetector::handle_t handler = [&](
+      const GraphPatternDetector::subgraph_t& subgraph, Graph* graph) {
+    if (IsDuplicatePattern(subgraph, graph)) {
+      return;
     }
     const proto::BlockDesc& block = pass_desc.replace().blocks(0);
     // `var_node_maps` record the mapping of variable to the pattern subgraph.
@@ -175,7 +266,11 @@ void GeneratePass::ApplyImpl(Graph* graph) const {
   for (const proto::PassDesc& pass_desc : multi_pass_desc_.pass_descs()) {
     GraphPatternDetector detector;
     InitGeneratePattern(pass_desc, detector.mutable_pattern());
+    if (pass_desc.replace().blocks(0).ops_size() == 0) {
+      detector(graph, GetGenerateDelete(detector.pattern(), pass_desc));
+    } else {
       detector(graph, GetGenerateRewrite(detector.pattern(), pass_desc));
+    }
     // The rewrited graph needs to be verified. Current Pass should be skipped
     // if validation failed. Rewrite based on the original graph cannot
     // implement rollback operation.

python/paddle/fluid/ir.py

@@ -127,11 +127,13 @@ def apply_build_strategy(main_program, startup_program, build_strategy,
 
 
 class RegisterPassHelper(object):
+    _register_helpers = list()
+
     def __init__(self, pass_pairs, pass_type=str(), input_specs=dict()):
         self._pass_type = pass_type
         self._pass_pairs = pass_pairs
-        if isinstance(input_specs, dict):
         self._input_specs = input_specs
+        RegisterPassHelper._register_helpers.append(self)
 
     def _get_args_from_func(self, func):
         args = list()
@@ -148,6 +150,35 @@ class RegisterPassHelper(object):
                 args.append(paddle.static.data(arg_name, [-1]))
         return args
 
+    def _prune_program_desc(self, program_desc):
+        block_desc = program_desc.blocks[0]
+        # block_desc.ClearField("vars")
+        for var in [
+                var for var in block_desc.vars
+                if var.name not in self._input_specs
+        ]:
+            block_desc.vars.remove(var)
+        for op_desc in block_desc.ops:
+            default_attrs = core.get_op_attrs_default_value(
+                paddle.compat.to_bytes(op_desc.type))
+            remove_attrs = list()
+            for attr in op_desc.attrs:
+                # attr must not in 
+                if attr.name not in [
+                        "op_namescope", "op_callstack", "op_device"
+                ]:
+                    attr_list_fields = attr.ListFields()
+                    # attr format must be: name, type, value
+                    if len(attr_list_fields) == 3:
+                        attr_value = attr.ListFields()[-1][-1]
+                        default_attr_value = default_attrs.get(attr.name)
+                        # value must not default
+                        if default_attr_value != attr_value:
+                            continue
+                remove_attrs.append(attr)
+            for attr in remove_attrs:
+                op_desc.attrs.remove(attr)
+
     def _func_to_program_desc(self, func, program_desc, is_replace=False):
         vars = list()
         program = paddle.static.Program()
@@ -166,6 +197,7 @@ class RegisterPassHelper(object):
                 elif isinstance(out, paddle.fluid.framework.Variable):
                     vars.append(out.name)
         program_desc.ParseFromString(program.desc.serialize_to_string())
+        self._prune_program_desc(program_desc)
         if is_replace:
             attrs = list()
             for op in program.current_block().ops:
@@ -296,7 +328,7 @@ class PassDesc(object):
     OP = OpHelper()
 
 
-def RegisterPass(function=None, input_specs=None):
+def RegisterPass(function=None, input_specs=dict()):
     """
     The function decorator of Register Pass. Decorator @RegisterPass handles
     the function and register it into a core.Pass instance. Use name of function
@@ -305,11 +337,11 @@ def RegisterPass(function=None, input_specs=None):
     Args:
         function (callable): The function with return of callable pair(s) that
             represents the pattern subgraph and the replace subgraph.
-        input_specs (dict[str, InputSpec]|None): Dict of InputSpec to specific the shape/dtype
+        input_specs (dict[str, InputSpec]): Dict of InputSpec to specific the shape/dtype
             information of Tensor. Some operators limit the shape and dtype of datas when
             create subgraph with Paddle APIs. So user need specify InputSpec of data to
             ensure create a correctly subgraph. Of course, this argument is not limited to
-            matching subgraph. The default is None.
+            matching subgraph. The default is dict().
 
     Returns:
         callables: Callable pair(s).
@@ -351,6 +383,7 @@ def RegisterPass(function=None, input_specs=None):
                     "Return value of Pass function must be (callable, callable)."
                 )
             helper = RegisterPassHelper(pass_pairs, pass_type, input_specs)
+            core.register_pass(pass_type, helper.SerializeMultiPassDesc)
         return python_func
 
     if inspect.isfunction(function):

python/paddle/fluid/tests/unittests/ir/test_ir_generate_pass.py

@@ -15,7 +15,7 @@
 import unittest
 import paddle
 from paddle.static import InputSpec
-from paddle.fluid import ir
+from paddle.fluid import core, ir
 import numpy as np
 
 
@@ -45,23 +45,37 @@ def generate_fc_fuse():
     return list(map(create_pass_pair, [True, False]))
 
 
-# add(X=add(x, y), Y=z)z => add_n(X=[x, y, z])
+# add(X=add(X=x, Y=y), Y=z) => sum(X=[x, y, z])
 @ir.RegisterPass
-def generate_add_n():
+def multi_add_to_sum_v1():
+    pattern = lambda x, y, z: paddle.add(paddle.add(x, y), z)
+    replace = lambda x, y, z: paddle.add_n([x, y, z])
+    return pattern, replace
+
+
+@ir.RegisterPass
+def multi_add_to_sum_v2():
     def pattern(x, y, z):
-        return paddle.add(paddle.add(x, y), z)
+        ewadd1 = ir.PassDesc.OP.elementwise_add(X=x, Y=y)
+        ewadd2 = ir.PassDesc.OP.elementwise_add(X=ewadd1, Y=z)
+        return ewadd2
+
+    replace = lambda x, y, z: ir.PassDesc.OP.sum(X=[x, y, z])
+    return pattern, replace
 
-    def replace(x, y, z):
-        return paddle.add_n([x, y, z])
 
+@ir.RegisterPass
+def multi_add_to_sum_v3():
+    pattern = lambda x, y, z: paddle.add(paddle.add(x, y), z)
+    replace = lambda x, y, z: ir.PassDesc.OP.sum(X=[x, y, z])
     return pattern, replace
 
 
 # mul(x, y1), mul(x, y2) => slice(mul(x, concat(y1, y2)))
 @ir.RegisterPass(input_specs={
-    'x': InputSpec([1, 1]),
-    'y1': InputSpec([1, 1]),
-    'y2': InputSpec([1, 1])
+    'x': InputSpec([16, 32]),
+    'y1': InputSpec([32, 12]),
+    'y2': InputSpec([32, 48])
 })
 def generate_combine_mul_v1():
     def pattern(x, y1, y2):
@@ -72,8 +86,8 @@ def generate_combine_mul_v1():
     def replace(x, y1, y2):
         concat_out = paddle.concat([y1, y2], axis=-1)
         mul_out = paddle.matmul(x, concat_out)
-        out1 = paddle.slice(mul_out, axes=[1], starts=[0], ends=[1])
-        out2 = paddle.slice(mul_out, axes=[1], starts=[1], ends=[2])
+        out1 = paddle.slice(mul_out, axes=[1], starts=[0], ends=[12])
+        out2 = paddle.slice(mul_out, axes=[1], starts=[12], ends=[60])
         return out1, out2
 
     return pattern, replace
@@ -97,11 +111,22 @@ def generate_combine_mul_v2():
 
 
 # reshape(reshape(x)) => x
-@ir.RegisterPass(input_specs={'x': InputSpec([-1, 16, 16, 16])})
-def generate_simplify_inference():
+@ir.RegisterPass(input_specs={'x': InputSpec([10, 16, 16])})
+def generate_simplify_inference_v1():
+    def pattern(x):
+        transpose = paddle.transpose(x, [0, 2, 1])
+        return paddle.transpose(transpose, [0, 2, 1])
+
+    return pattern, lambda x: x
+
+
+@ir.RegisterPass
+def generate_simplify_inference_v2():
     def pattern(x):
-        transpose = paddle.transpose(x, [0, 3, 1, 2])
-        return paddle.transpose(transpose, [0, 3, 1, 2])
+        op1 = ir.PassDesc.OP.transpose2
+        op2 = ir.PassDesc.OP.transpose2
+        # op2.Attr("axis").EQ(op1.Attr("axis"))
+        return op2(X=op1(X=x))
 
     return pattern, lambda x: x
 
@@ -153,46 +178,73 @@ class TestGeneratePass(unittest.TestCase):
         _check_fc_fuse_pass(multi_pass_desc.pass_descs[0], True)
         _check_fc_fuse_pass(multi_pass_desc.pass_descs[1], False)
 
-    def test_generate_add_n(self):
-        helper = ir.RegisterPassHelper([generate_add_n()])
-        s = helper.SerializeMultiPassDesc()
-        multi_pass_desc = get_multi_pass_desc_from_str(s)
-        self.assertEqual(len(multi_pass_desc.pass_descs), 1)
-        pass_desc = multi_pass_desc.pass_descs[0]
-        self.assertEqual(len(pass_desc.var_maps), 4)
-        self.assertEqual(len(pass_desc.attr_maps), 0)
-        self.assertEqual(len(pass_desc.pattern.blocks[0].ops), 2)
-        self.assertEqual(len(pass_desc.replace.blocks[0].ops), 1)
-        pattern_op_dicts = self.convert_ops_to_op_dicts(
-            pass_desc.pattern.blocks[0].ops)
-        replace_op_dicts = self.convert_ops_to_op_dicts(
-            pass_desc.replace.blocks[0].ops)
-        self.assertEqual(len(pattern_op_dicts.get("elementwise_add", [])), 2)
-        self.assertEqual(len(replace_op_dicts.get("sum", [])), 1)
+    def check_multi_add_to_sum(self, pass_type):
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(program, startup_program):
+            x = paddle.static.data("x", [10, 10, 10], "float32")
+            y = paddle.static.data("y", [10, 10, 10], "float32")
+            z = paddle.static.data("z", [10, 10, 10], "float32")
+            add_1 = paddle.add(paddle.add(x, y), z)
+            matmul_1 = paddle.matmul(add_1, z)
+            add_tmp = paddle.add(x, y)
+            add_2 = paddle.add(add_tmp, z)
+            matmul_2 = paddle.matmul(add_2, add_tmp)
+            out = paddle.add(matmul_1, matmul_2)
+        graph = core.Graph(program.desc)
+        before_node_nums = len(graph.nodes())
+        core.get_pass(pass_type).apply(graph)
+        after_node_nums = len(graph.nodes())
+        self.assertEqual(after_node_nums, before_node_nums - 2)
+        after_program = paddle.fluid.framework.IrGraph(graph).to_program()
+        executor = paddle.static.Executor(paddle.CPUPlace())
+        executor.run(startup_program)
+        feed = {
+            "x": np.random.random([10, 10, 10]).astype("float32"),
+            "y": np.random.random([10, 10, 10]).astype("float32"),
+            "z": np.random.random([10, 10, 10]).astype("float32")
+        }
+        before_out = executor.run(program, feed=feed, fetch_list=[out.name])
+        after_out = executor.run(after_program,
+                                 feed=feed,
+                                 fetch_list=[out.name])
+        self.assertTrue(np.allclose(before_out, after_out))
+
+    def test_multi_add_to_sum(self):
+        paddle.enable_static()
+        self.check_multi_add_to_sum("multi_add_to_sum_v1")
+        self.check_multi_add_to_sum("multi_add_to_sum_v2")
+        self.check_multi_add_to_sum("multi_add_to_sum_v3")
 
     def test_generate_combine_mul_v1(self):
-        input_specs = {
-            'x': InputSpec([1, 1]),
-            'y1': InputSpec([1, 1]),
-            'y2': InputSpec([1, 1])
+        paddle.enable_static()
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(program, startup_program):
+            x = paddle.static.data("x", [16, 32])
+            y = paddle.static.data("y", [32, 12])
+            z = paddle.static.data("z", [32, 48])
+            out1 = paddle.matmul(x, y)
+            out2 = paddle.matmul(x, z)
+        graph = core.Graph(program.desc)
+        before_node_nums = len(graph.nodes())
+        core.get_pass("generate_combine_mul_v1").apply(graph)
+        after_node_nums = len(graph.nodes())
+        self.assertEqual(after_node_nums, before_node_nums + 4)
+        after_program = paddle.fluid.framework.IrGraph(graph).to_program()
+        executor = paddle.static.Executor(paddle.CPUPlace())
+        executor.run(startup_program)
+        feed = {
+            "x": np.random.random([16, 32]).astype("float32"),
+            "y": np.random.random([32, 12]).astype("float32"),
+            "z": np.random.random([32, 48]).astype("float32")
         }
-        helper = ir.RegisterPassHelper(
-            [generate_combine_mul_v1()], input_specs=input_specs)
-        s = helper.SerializeMultiPassDesc()
-        multi_pass_desc = get_multi_pass_desc_from_str(s)
-        self.assertEqual(len(multi_pass_desc.pass_descs), 1)
-        pass_desc = multi_pass_desc.pass_descs[0]
-        self.assertEqual(len(pass_desc.var_maps), 5)
-        self.assertEqual(len(pass_desc.pattern.blocks[0].ops), 2)
-        self.assertEqual(len(pass_desc.replace.blocks[0].ops), 4)
-        pattern_op_dicts = self.convert_ops_to_op_dicts(
-            pass_desc.pattern.blocks[0].ops)
-        replace_op_dicts = self.convert_ops_to_op_dicts(
-            pass_desc.replace.blocks[0].ops)
-        self.assertEqual(len(pattern_op_dicts.get("matmul_v2", [])), 2)
-        self.assertEqual(len(replace_op_dicts.get("concat", [])), 1)
-        self.assertEqual(len(replace_op_dicts.get("matmul_v2", [])), 1)
-        self.assertEqual(len(replace_op_dicts.get("slice", [])), 2)
+        before_out1, before_out2 = executor.run(
+            program, feed=feed, fetch_list=[out1.name, out2.name])
+        after_out1, after_out2 = executor.run(
+            after_program, feed=feed, fetch_list=[out1.name, out2.name])
+        self.assertTrue(np.allclose(before_out1, after_out1))
+        self.assertTrue(np.allclose(before_out2, after_out2))
 
     def test_generate_combine_mul_v2(self):
         helper = ir.RegisterPassHelper([generate_combine_mul_v2()])
@@ -212,17 +264,31 @@ class TestGeneratePass(unittest.TestCase):
         self.assertEqual(len(replace_op_dicts.get("matmul_v2", [])), 1)
         self.assertEqual(len(replace_op_dicts.get("slice", [])), 2)
 
+    def check_generate_simplify_inference(self, pass_type):
+        paddle.enable_static()
+        program = paddle.static.Program()
+        startup_program = paddle.static.Program()
+        with paddle.static.program_guard(program, startup_program):
+            x = paddle.static.data("x", [10, 16, 16], "float32")
+            x1 = paddle.transpose(paddle.transpose(x, [0, 2, 1]), [0, 2, 1])
+            tmp = paddle.transpose(x, [0, 2, 1])
+            x2 = paddle.transpose(tmp, [0, 2, 1])
+            out = paddle.add(x1, paddle.matmul(x2, tmp))
+        graph = core.Graph(program.desc)
+        before_node_nums = len(graph.nodes())
+        core.get_pass(pass_type).apply(graph)
+        after_node_nums = len(graph.nodes())
+        self.assertEqual(after_node_nums, before_node_nums - 6)
+        after_program = paddle.fluid.framework.IrGraph(graph).to_program()
+        executor = paddle.static.Executor(paddle.CPUPlace())
+        executor.run(startup_program)
+        feed = {"x": np.random.random([10, 16, 16]).astype("float32")}
+        before_out = executor.run(program, feed=feed, fetch_list=[out.name])
+        after_out = executor.run(after_program,
+                                 feed=feed,
+                                 fetch_list=[out.name])
+        self.assertTrue(np.allclose(before_out, after_out))
+
     def test_generate_simplify_inference(self):
-        input_specs = {'x': InputSpec([-1, 16, 16, 16])}
-        helper = ir.RegisterPassHelper(
-            [generate_simplify_inference()], input_specs=input_specs)
-        s = helper.SerializeMultiPassDesc()
-        multi_pass_desc = get_multi_pass_desc_from_str(s)
-        self.assertEqual(len(multi_pass_desc.pass_descs), 1)
-        pass_desc = multi_pass_desc.pass_descs[0]
-        self.assertEqual(len(pass_desc.var_maps), 2)
-        self.assertEqual(len(pass_desc.pattern.blocks[0].ops), 2)
-        self.assertEqual(len(pass_desc.replace.blocks[0].ops), 0)
-        pattern_op_dicts = self.convert_ops_to_op_dicts(
-            pass_desc.pattern.blocks[0].ops)
-        self.assertEqual(len(pattern_op_dicts.get("transpose2", [])), 2)
+        self.check_generate_simplify_inference("generate_simplify_inference_v1")
+        self.check_generate_simplify_inference("generate_simplify_inference_v2")