Fuse multi transformer layer pass (#47541) (#47830)

* add fuse_multi_transformer_layer_pass

Author: RichardWooSJTU

paddle/fluid/framework/ir/CMakeLists.txt

@@ -154,6 +154,7 @@ pass_library(skip_layernorm_fuse_pass base)
 pass_library(multihead_matmul_fuse_pass inference)
 pass_library(fused_multi_transformer_encoder_pass inference)
 pass_library(fused_multi_transformer_decoder_pass inference)
+pass_library(fuse_multi_transformer_layer_pass inference)
 pass_library(adaptive_pool2d_convert_global_pass inference)
 pass_library(unsqueeze2_eltwise_fuse_pass inference)
 pass_library(yolo_box_fuse_pass inference)
@@ -368,6 +369,10 @@ cc_test(
   test_fused_multi_transformer_decoder_pass
   SRCS fused_multi_transformer_decoder_pass_tester.cc
   DEPS fused_multi_transformer_decoder_pass)
+cc_test(
+  test_fuse_multi_transformer_layer_pass
+  SRCS fuse_multi_transformer_layer_pass_tester.cc
+  DEPS fuse_multi_transformer_layer_pass)
 cc_test(
   test_conv_bn_fuse_pass_cc
   SRCS conv_bn_fuse_pass_tester.cc

paddle/fluid/framework/ir/fuse_multi_transformer_layer_pass.cc

@@ -0,0 +1,325 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed 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.
+
+#include "paddle/fluid/framework/ir/fuse_multi_transformer_layer_pass.h"
+
+#include <string>
+
+#include "paddle/fluid/framework/convert_utils.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+
+namespace paddle {
+namespace framework {
+class Scope;
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace framework {
+namespace ir {
+namespace patterns {
+
+std::unordered_map<std::string, std::string>
+MultiTransformerLayerPattern::operator()(bool enable_int8,
+                                         int num_fused_op,
+                                         bool is_decoder) {
+  std::string fused_multi_transformer_name =
+      enable_int8 ? "fused_multi_transformer_int8" : "fused_multi_transformer";
+
+  std::unordered_map<std::string, std::string> node_reprs;
+
+  // x0 and src_mask is unqiue input of subgraph
+  auto* x0 = pattern->NewNode(x0_repr());
+  x0->assert_is_op_input(fused_multi_transformer_name, "X")->AsInput();
+  auto* src_mask = pattern->NewNode(src_mask_repr());
+  src_mask->assert_is_op_input(fused_multi_transformer_name, "SrcMask")
+      ->AsInput();
+
+  for (int i = 0; i < num_fused_op; ++i) {
+    auto fuse_op_repr =
+        PDNodeName(name_scope_, repr_, id_, "fuse_op_" + std::to_string(i));
+    node_reprs["fuse_op_" + std::to_string(i)] = fuse_op_repr;
+    auto* fused_multi_transformer =
+        pattern->NewNode(fuse_op_repr)
+            ->assert_is_op(fused_multi_transformer_name);
+
+    auto out_repr =
+        PDNodeName(name_scope_, repr_, id_, "out_" + std::to_string(i));
+    node_reprs["out_" + std::to_string(i)] = out_repr;
+    auto* out = pattern->NewNode(out_repr)->assert_is_op_output(
+        fused_multi_transformer_name, "Out");
+
+    if (is_decoder) {
+      auto shape_repr =
+          PDNodeName(name_scope_, repr_, id_, "shape_" + std::to_string(i));
+      node_reprs["shape_" + std::to_string(i)] = shape_repr;
+      auto* shape = pattern->NewNode(shape_repr)->assert_is_op("shape");
+
+      auto shape_out_repr =
+          PDNodeName(name_scope_, repr_, id_, "shape_out_" + std::to_string(i));
+      node_reprs["shape_out_" + std::to_string(i)] = shape_out_repr;
+      auto* shape_out =
+          pattern->NewNode(shape_out_repr)->assert_is_op_output("shape", "Out");
+
+      shape->LinksFrom({src_mask}).LinksTo({shape_out});
+
+      auto slice_repr =
+          PDNodeName(name_scope_, repr_, id_, "slice_" + std::to_string(i));
+      node_reprs["slice_" + std::to_string(i)] = slice_repr;
+      auto* slice = pattern->NewNode(slice_repr)->assert_is_op("slice");
+
+      auto slice_out_repr =
+          PDNodeName(name_scope_, repr_, id_, "slice_out_" + std::to_string(i));
+      node_reprs["slice_out_" + std::to_string(i)] = slice_out_repr;
+      auto* slice_out =
+          pattern->NewNode(slice_out_repr)->assert_is_op_output("slice", "Out");
+
+      slice->LinksFrom({shape_out}).LinksTo({slice_out});
+
+      fused_multi_transformer->LinksFrom({x0, src_mask, slice_out})
+          .LinksTo({out});
+    } else {
+      auto cache_kv_repr =
+          PDNodeName(name_scope_, repr_, id_, "cache_kv_" + std::to_string(i));
+      node_reprs["cache_kv_" + std::to_string(i)] = cache_kv_repr;
+      auto* cache_kv = pattern->NewNode(cache_kv_repr);
+      cache_kv->assert_is_op_input(fused_multi_transformer_name, "CacheKV");
+      cache_kv->AsInput();
+
+      auto fill_const_op_repr =
+          PDNodeName(name_scope_, repr_, id_, "fill_op_" + std::to_string(i));
+      node_reprs["fill_op_" + std::to_string(i)] = fill_const_op_repr;
+      auto fill_const_op = pattern->NewNode(fill_const_op_repr)
+                               ->assert_is_op("fill_constant_batch_size_like");
+
+      fused_multi_transformer->LinksFrom({x0, src_mask, cache_kv})
+          .LinksTo({out});
+      fill_const_op->LinksFrom({x0}).LinksTo({cache_kv});
+    }
+    x0 = out;
+  }
+  x0->AsOutput();
+  return node_reprs;
+}
+}  // namespace patterns
+
+inline void MergeInput(OpDesc* op,
+                       const std::vector<VariableNameMap>& input_name_maps,
+                       const std::string& input_name) {
+  std::vector<std::string> tmp = input_name_maps[0].at(input_name);
+  for (size_t i = 1; i < input_name_maps.size(); ++i) {
+    tmp.insert(tmp.end(),
+               input_name_maps[i].at(input_name).begin(),
+               input_name_maps[i].at(input_name).end());
+  }
+  op->SetInput(input_name, tmp);
+}
+
+template <typename T>
+inline void MergeAttrs(const std::vector<OpDesc*>& ops,
+                       const std::string& attr_name) {
+  std::vector<T> res;
+  for (size_t i = 0; i < ops.size(); ++i) {
+    auto scale_vec =
+        PADDLE_GET_CONST(std::vector<T>, ops[i]->GetAttr(attr_name));
+    res.insert(res.end(), scale_vec.begin(), scale_vec.end());
+  }
+  ops[0]->SetAttr(attr_name, res);
+}
+
+int FuseMultiTransformerLayerPass::BuildFusion(Graph* graph,
+                                               const std::string& name_scope,
+                                               Scope* scope) const {
+  GraphPatternDetector gpd;
+  auto* pattern = gpd.mutable_pattern();
+
+  // TODO(wufeisheng): Get enable_int8 attr from graph after
+  // fused_multi_transformer pass with int8 merged
+  bool enable_int8 = false;
+
+  int num_fuse_op = 0;
+  bool is_decoder = false;
+
+  if (graph->Has(kFusedMultiTransformerEncoderFusionCount)) {
+    num_fuse_op = graph->Get<int>(kFusedMultiTransformerEncoderFusionCount);
+    is_decoder = false;
+  } else if (graph->Has(kFusedMultiTransformerDecoderFusionCount)) {
+    num_fuse_op = graph->Get<int>(kFusedMultiTransformerDecoderFusionCount);
+    is_decoder = true;
+  }
+  if (num_fuse_op == 0) {
+    VLOG(4) << "fuse_multi_transformer_layer_pass will be skipped "
+               "cause num_fuse_op is not been set or set to 0";
+    return 0;
+  }
+  if (!is_decoder) {
+    VLOG(4) << "fuse_multi_transformer_layer_pass will match encoder pattern";
+  } else {
+    VLOG(4) << "fuse_multi_transformer_layer_pass will match decoder pattern";
+  }
+
+  patterns::MultiTransformerLayerPattern multi_layer_pattern(pattern,
+                                                             name_scope);
+  auto node_reprs = multi_layer_pattern(enable_int8, num_fuse_op, is_decoder);
+
+  int fusion_count{0};
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* graph) {
+    ///////////////////
+    //// Get nodes ////
+    ///////////////////
+
+    GET_IR_NODE_FROM_SUBGRAPH(src_mask, src_mask, multi_layer_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(x0, x0, multi_layer_pattern);
+
+    std::vector<Node*> fuse_op_nodes;
+    std::vector<Node*> out_nodes;
+
+    std::vector<std::string> unused_node_prefixes = {
+        "shape_", "shape_out_", "slice_", "slice_out_"};
+    std::vector<Node*> unused_nodes;
+
+    std::vector<OpDesc*> fuse_op_descs;
+    std::vector<VariableNameMap> fuse_op_input_var_name_maps;
+    std::vector<VariableNameMap> fuse_op_output_var_name_maps;
+
+    for (int i = 0; i < num_fuse_op; ++i) {
+      PDNode* fuse_op_pdnode =
+          multi_layer_pattern.PatternBase::pattern->RetrieveNode(
+              node_reprs["fuse_op_" + std::to_string(i)]);
+      Node* fuse_op_node = subgraph.at(fuse_op_pdnode);
+      fuse_op_nodes.push_back(fuse_op_node);
+      fuse_op_descs.push_back(fuse_op_node->Op());
+      fuse_op_input_var_name_maps.emplace_back(fuse_op_node->Op()->Inputs());
+      fuse_op_output_var_name_maps.emplace_back(fuse_op_node->Op()->Outputs());
+
+      PDNode* out_pdnode =
+          multi_layer_pattern.PatternBase::pattern->RetrieveNode(
+              node_reprs["out_" + std::to_string(i)]);
+      out_nodes.push_back(subgraph.at(out_pdnode));
+
+      // fill_const op use x0 as input
+      if (!is_decoder && i != 0) {
+        PDNode* fill_op_pdnode =
+            multi_layer_pattern.PatternBase::pattern->RetrieveNode(
+                node_reprs["fill_op_" + std::to_string(i)]);
+        Node* fill_op_node = subgraph.at(fill_op_pdnode);
+        fill_op_node->Op()->SetInput("Input", {x0->Name()});
+        IR_NODE_UNLINK(out_nodes[i - 1], fill_op_node);
+        IR_NODE_LINK_TO(x0, fill_op_node);
+      } else if (is_decoder && i != 0) {
+        for (const auto& unused_node_prefix : unused_node_prefixes) {
+          PDNode* unused_pdnode =
+              multi_layer_pattern.PatternBase::pattern->RetrieveNode(
+                  node_reprs[unused_node_prefix + std::to_string(i)]);
+          Node* unused_node = subgraph.at(unused_pdnode);
+          unused_nodes.push_back(unused_node);
+        }
+      }
+    }
+
+    ///////////////
+    //// Merge ////
+    ///////////////
+
+    // Merge inputs
+    std::vector<std::string> inputs_names = {"CacheKV",
+                                             "FFN1Bias",
+                                             "FFN1Weight",
+                                             "FFN2Bias",
+                                             "FFN2Weight",
+                                             "FFNLnBias",
+                                             "FFNLnScale",
+                                             "LnBias",
+                                             "LnScale",
+                                             "OutLinearBias",
+                                             "OutLinearW",
+                                             "QKVBias",
+                                             "QKVW"};
+
+    for (const auto& input_name : inputs_names) {
+      MergeInput(fuse_op_descs[0], fuse_op_input_var_name_maps, input_name);
+    }
+
+    // Merge outputs
+    fuse_op_descs[0]->SetOutput(
+        "Out", fuse_op_output_var_name_maps[num_fuse_op - 1]["Out"]);
+    auto& merged_cache_kv_out_names =
+        fuse_op_output_var_name_maps[0]["CacheKVOut"];
+    for (int i = 1; i < num_fuse_op; ++i) {
+      const auto& out_var_names = fuse_op_output_var_name_maps[i]["CacheKVOut"];
+      merged_cache_kv_out_names.insert(merged_cache_kv_out_names.end(),
+                                       out_var_names.begin(),
+                                       out_var_names.end());
+    }
+    fuse_op_descs[0]->SetOutput("CacheKVOut", merged_cache_kv_out_names);
+
+    ////////////////
+    //// ReLink ////
+    ////////////////
+    // Before relink, out nodes (0 -> num_layer-1) should be removed
+    std::unordered_set<const Node*> marked_out_nodes(out_nodes.begin(),
+                                                     out_nodes.end() - 1);
+    GraphSafeRemoveNodes(graph, marked_out_nodes);
+
+    // Relink all input nodes of fused_multi_transformer ops to the first op
+    auto& merged_inputs = fuse_op_nodes[0]->inputs;
+    for (int i = 1; i < num_fuse_op; ++i) {
+      merged_inputs.insert(merged_inputs.end(),
+                           fuse_op_nodes[i]->inputs.begin(),
+                           fuse_op_nodes[i]->inputs.end());
+    }
+
+    // Relink fuse op -> out
+    IR_NODE_UNLINK(fuse_op_nodes[num_fuse_op - 1], out_nodes[num_fuse_op - 1]);
+    IR_NODE_LINK_TO(fuse_op_nodes[0], out_nodes[num_fuse_op - 1]);
+
+    /////////////////////////////
+    //// Delete unused nodes ////
+    /////////////////////////////
+    // Delete fused_multi_transformer op expect for the first one
+    std::unordered_set<const Node*> marked_fuse_op_nodes(
+        fuse_op_nodes.begin() + 1, fuse_op_nodes.end());
+
+    if (is_decoder) {
+      marked_fuse_op_nodes.insert(unused_nodes.begin(), unused_nodes.end());
+    }
+
+    GraphSafeRemoveNodes(graph, marked_fuse_op_nodes);
+    ++fusion_count;
+  };
+
+  gpd(graph, handler);
+  return fusion_count;
+}
+
+void FuseMultiTransformerLayerPass::ApplyImpl(Graph* graph) const {
+  FusePassBase::Init(name_scope_, graph);
+  auto* scope = param_scope();
+  PADDLE_ENFORCE_NOT_NULL(
+      scope,
+      platform::errors::Fatal("During the fuse_multi_transformer_layer pass, "
+                              "The scope should not be null."));
+  int fusion_count = BuildFusion(graph, name_scope_, scope);
+
+  AddStatis(fusion_count);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(fuse_multi_transformer_layer_pass,
+              paddle::framework::ir::FuseMultiTransformerLayerPass);