transform cachekv datalayout of fused_multi_transformer_xpu (#53144)

paddle/fluid/framework/ir/CMakeLists.txt

@@ -245,6 +245,8 @@ if(WITH_XPU)
   pass_library(fused_multi_transformer_xpu_quant_pass inference DIR xpu DEPS
                ${XPU_PASS_DEPS})
   pass_library(stack_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
+  pass_library(fused_multi_transformer_cachekv_layout_trans_pass inference DIR
+               xpu DEPS ${XPU_PASS_DEPS})
 endif()
 
 cc_library(
@@ -528,4 +530,8 @@ if(WITH_XPU)
     test_stack_fuse_pass
     SRCS xpu/stack_fuse_pass_test.cc
     DEPS stack_fuse_pass)
+  cc_test(
+    test_fused_multi_transformer_cachekv_layout_trans_pass
+    SRCS xpu/fused_multi_transformer_cachekv_layout_trans_pass_test.cc
+    DEPS fused_multi_transformer_cachekv_layout_trans_pass)
 endif()

paddle/fluid/framework/ir/pass.cc

@@ -62,6 +62,7 @@ static const std::vector<std::string> xpu_support_subgraph_passes = {
     "embedding_with_eltwise_add_xpu_fuse_pass",
     "multi_encoder_xpu_fuse_pass",
     "multi_encoder_xpu_slice_fuse_pass",
+    "fused_multi_transformer_cachekv_layout_trans_pass",
     "one_beam_size_fuse_pass",
     "stack_fuse_pass",
     "fused_multi_transformer_xpu_quant_pass",

paddle/fluid/framework/ir/xpu/fused_multi_transformer_cachekv_layout_trans_pass.cc

+// Copyright (c) 2023 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/xpu/fused_multi_transformer_cachekv_layout_trans_pass.h"
+#include <string>
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/framework/ir/xpu/pass_utils.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+namespace patterns {
+
+struct FusedMultiTransformerFillConstantPattern : public PatternBase {
+  FusedMultiTransformerFillConstantPattern(PDPattern* pattern,
+                                           const std::string& name_scope);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(fill_constant);
+  PATTERN_DECL_NODE(fused_multi_transformer);
+  // declare variable node's name
+  PATTERN_DECL_NODE(fill_constant_out);
+};  // struct FusedMultiTransformerFillConstantPattern
+
+FusedMultiTransformerFillConstantPattern::
+    FusedMultiTransformerFillConstantPattern(PDPattern* pattern,
+                                             const std::string& name_scope)
+    : PatternBase(pattern, name_scope, name_scope) {
+  auto* fill_constant = pattern->NewNode(fill_constant_repr())
+                            ->assert_is_op("fill_constant")
+                            ->assert_has_n_inputs(5)
+                            ->assert_more([](Node* node) {
+                              return node->Op()->GetAttrIfExists<std::string>(
+                                         "friendly_device_type") != "xpu";
+                            });
+  auto* fill_constant_out = pattern->NewNode(fill_constant_out_repr())
+                                ->assert_is_op_output("fill_constant", "Out");
+  auto* fused_multi_transformer =
+      pattern->NewNode(fused_multi_transformer_repr())
+          ->assert_is_op("fused_multi_transformer");
+
+  fill_constant->LinksTo({fill_constant_out});
+  fused_multi_transformer->LinksFrom({fill_constant_out});
+}
+
+struct FusedMultiTransformerGatherPattern : public PatternBase {
+  FusedMultiTransformerGatherPattern(PDPattern* pattern,
+                                     const std::string& name_scope);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(fused_multi_transformer);
+  PATTERN_DECL_NODE(gather);
+  // declare variable node's name
+  PATTERN_DECL_NODE(gather_in);
+  PATTERN_DECL_NODE(gather_out);
+};  // struct FusedMultiTransformerGatherPattern
+
+FusedMultiTransformerGatherPattern::FusedMultiTransformerGatherPattern(
+    PDPattern* pattern, const std::string& name_scope)
+    : PatternBase(pattern, name_scope, name_scope) {
+  auto* gather_in =
+      pattern->NewNode(gather_in_repr())->assert_is_op_input("gather", "X");
+  auto* gather = pattern->NewNode(gather_repr())
+                     ->assert_is_op("gather")
+                     ->assert_more([](Node* node) {
+                       return node->Op()->GetAttrIfExists<int>("axis") == 1;
+                     });
+  auto* gather_out =
+      pattern->NewNode(gather_out_repr())->assert_is_op_output("gather", "Out");
+  auto* fused_multi_transformer =
+      pattern->NewNode(fused_multi_transformer_repr())
+          ->assert_is_op("fused_multi_transformer");
+
+  gather->LinksFrom({gather_in}).LinksTo({gather_out});
+  fused_multi_transformer->LinksFrom({gather_out});
+}
+}  // namespace patterns
+
+void FusedMultiTransformerCacheKVLayoutTransPass::FillConstantReshapePass(
+    ir::Graph* graph) const {
+  PADDLE_ENFORCE_NOT_NULL(
+      graph, platform::errors::PreconditionNotMet("graph should not be null."));
+  GraphPatternDetector gpd;
+  patterns::FusedMultiTransformerFillConstantPattern pattern(
+      gpd.mutable_pattern(), name_scope_);
+
+  int found_subgraph_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* graph) {
+    VLOG(4) << "handle FillConstantReshapePass";
+    GET_IR_NODE(fused_multi_transformer);
+    GET_IR_NODE(fill_constant);
+    GET_IR_NODE(fill_constant_out);
+    auto cachekv_names = fused_multi_transformer->Op()->Input("CacheKV");
+    if (std::count(cachekv_names.begin(),
+                   cachekv_names.end(),
+                   fill_constant_out->Name()) == 0)
+      return;
+
+    auto fill_constant_input_names =
+        fill_constant->Op()->Input("ShapeTensorList");
+    auto fill_constant_trans_input_names =
+        std::vector<std::string>{fill_constant_input_names[0],
+                                 fill_constant_input_names[3],
+                                 fill_constant_input_names[1],
+                                 fill_constant_input_names[2],
+                                 fill_constant_input_names[4]};
+    fill_constant->Op()->SetInput("ShapeTensorList",
+                                  fill_constant_trans_input_names);
+
+    auto fill_constant_output_shape = fill_constant_out->Var()->GetShape();
+    fill_constant_out->Var()->SetShape({fill_constant_output_shape[0],
+                                        fill_constant_output_shape[3],
+                                        fill_constant_output_shape[1],
+                                        fill_constant_output_shape[2],
+                                        fill_constant_output_shape[4]});
+
+    fused_multi_transformer->Op()->SetAttr("friendly_device_type",
+                                           std::string("xpu"));
+    fill_constant->Op()->SetAttr("friendly_device_type", std::string("xpu"));
+    found_subgraph_count++;
+  };
+
+  gpd(graph, handler);
+  AddStatis(found_subgraph_count);
+}
+
+void FusedMultiTransformerCacheKVLayoutTransPass::GatherReshapePass(
+    ir::Graph* graph) const {
+  PADDLE_ENFORCE_NOT_NULL(
+      graph, platform::errors::PreconditionNotMet("graph should not be null."));
+  GraphPatternDetector gpd;
+  patterns::FusedMultiTransformerGatherPattern pattern(gpd.mutable_pattern(),
+                                                       name_scope_);
+
+  int found_subgraph_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* graph) {
+    VLOG(4) << "handle GatherReshapePass";
+    GET_IR_NODE(gather);
+    GET_IR_NODE(fused_multi_transformer);
+    GET_IR_NODE(gather_in);
+    GET_IR_NODE(gather_out);
+    auto cachekv_names = fused_multi_transformer->Op()->Input("CacheKV");
+    if (std::count(cachekv_names.begin(),
+                   cachekv_names.end(),
+                   gather_out->Name()) == 0)
+      return;
+
+    auto gather_in_shape = gather_in->Var()->GetShape();
+    auto gather_out_shape = gather_out->Var()->GetShape();
+    gather_in->Var()->SetShape({gather_in_shape[0],
+                                gather_in_shape[3],
+                                gather_in_shape[1],
+                                gather_in_shape[2],
+                                gather_in_shape[4]});
+    gather_out->Var()->SetShape({gather_out_shape[0],
+                                 gather_out_shape[3],
+                                 gather_out_shape[1],
+                                 gather_out_shape[2],
+                                 gather_out_shape[4]});
+    gather->Op()->SetAttr("axis", 2);
+    fused_multi_transformer->Op()->SetAttr("friendly_device_type",
+                                           std::string("xpu"));
+
+    found_subgraph_count++;
+  };
+
+  gpd(graph, handler);
+  AddStatis(found_subgraph_count);
+}
+
+void FusedMultiTransformerCacheKVLayoutTransPass::ApplyImpl(
+    ir::Graph* graph) const {
+  PADDLE_ENFORCE_NOT_NULL(
+      graph, platform::errors::PreconditionNotMet("graph should not be null."));
+  Init(name_scope_, graph);
+
+  FillConstantReshapePass(graph);
+  GatherReshapePass(graph);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(
+    fused_multi_transformer_cachekv_layout_trans_pass,
+    paddle::framework::ir::FusedMultiTransformerCacheKVLayoutTransPass);

paddle/fluid/framework/ir/xpu/fused_multi_transformer_cachekv_layout_trans_pass.h

+// Copyright (c) 2023 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.
+
+#pragma once
+#include <string>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace phi {
+class DenseTensor;
+}  // namespace phi
+
+namespace paddle {
+namespace framework {
+class Scope;
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class FusedMultiTransformerCacheKVLayoutTransPass : public FusePassBase {
+ protected:
+  void ApplyImpl(ir::Graph* graph) const override;
+
+ private:
+  /*
+   Origin subgraph:
+        (ShapeTensorList: [d0,d1,d2,d3,d4])
+                    |
+              fill_constant
+                    |
+            fused_multi_transformer
+
+   Fused subgraph:
+        (ShapeTensorList: [d0,d3,d1,d2,d4])
+                    |
+              fill_constant
+                    |
+            fused_multi_transformer
+  */
+  void FillConstantReshapePass(ir::Graph* graph) const;
+
+  /*
+   Origin subgraph:
+        (gather_x: [d0,d1,d2,d3,d4])
+                    |
+              gather(axis=1)
+                    |
+            fused_multi_transformer
+
+   Fused subgraph:
+        (gather_x: [d0,d3,d1,d2,d4])
+                    |
+              gather(axis=2)
+                    |
+            fused_multi_transformer
+  */
+  void GatherReshapePass(ir::Graph* graph) const;
+
+  const std::string name_scope_{
+      "fused_multi_transformer_cachekv_layout_trans_pass"};
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/xpu/fused_multi_transformer_cachekv_layout_trans_pass_test.cc

+// Copyright (c) 2023 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 <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/framework/ir/pass_tester_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+VarDesc* Data(paddle::framework::BlockDesc* block,
+              std::string name,
+              std::vector<int64_t> shape = {},
+              bool is_persistable = false,
+              proto::VarType::Type data_type = proto::VarType::FP32) {
+  auto* var = block->Var(name);
+  var->SetType(proto::VarType::LOD_TENSOR);
+  var->SetDataType(data_type);
+  var->SetShape(shape);
+  var->SetPersistable(is_persistable);
+  return var;
+}
+
+VarDesc* fill_constant(BlockDesc* block, std::vector<VarDesc*> shapes) {
+  VarDesc* out = Data(block, shapes[0]->Name() + "_out");
+  OpDesc* op = block->AppendOp();
+  op->SetType("fill_constant");
+  std::vector<std::string> shape_names;
+  for (auto shape : shapes) {
+    shape_names.push_back(shape->Name());
+  }
+  op->SetInput("ShapeTensorList", {shape_names});
+  op->SetOutput("Out", {out->Name()});
+  return out;
+}
+
+TEST(FillConstantReshapePass, basic) {
+  paddle::framework::ProgramDesc program;
+  auto* block = program.MutableBlock(0);
+  auto* shape0 = Data(block, "shape0");
+  auto* shape1 = Data(block, "shape1");
+  auto* shape2 = Data(block, "shape2");
+  auto* shape3 = Data(block, "shape3");
+  auto* shape4 = Data(block, "shape4");
+  auto* shape5 = Data(block, "shape5");
+  auto* shape6 = Data(block, "shape6");
+  auto* shape7 = Data(block, "shape7");
+  auto* shape8 = Data(block, "shape8");
+  auto* shape9 = Data(block, "shape9");
+  auto* fill0 = fill_constant(block, {shape0, shape1, shape2, shape3, shape4});
+  fill0->SetShape({1, 2, 3, 4, 5});
+  auto* fill1 = fill_constant(block, {shape5, shape6, shape7, shape8, shape9});
+  fill1->SetShape({1, 2, 3, 4, 5});
+  OpDesc* fused_multi_transformer = block->AppendOp();
+  fused_multi_transformer->SetType("fused_multi_transformer");
+  fused_multi_transformer->SetInput("CacheKV", {fill0->Name(), fill1->Name()});
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(program));
+  auto pass = PassRegistry::Instance().Get(
+      "fused_multi_transformer_cachekv_layout_trans_pass");
+  pass->Apply(graph.get());
+  auto fills = GetOpNodes(graph, "fill_constant");
+  auto fill0_in_names = fills[0]->Op()->Input("ShapeTensorList");
+  std::vector<std::string> expect_fill0_in_names{
+      "shape0", "shape3", "shape1", "shape2", "shape4"};
+  PADDLE_ENFORCE_EQ(fill0_in_names,
+                    expect_fill0_in_names,
+                    platform::errors::PreconditionNotMet(
+                        "fill_constant name should be updated."));
+  auto fill1_in_names = fills[1]->Op()->Input("ShapeTensorList");
+  std::vector<std::string> expect_fill1_in_names{
+      "shape5", "shape8", "shape6", "shape7", "shape9"};
+  PADDLE_ENFORCE_EQ(fill1_in_names,
+                    expect_fill1_in_names,
+                    platform::errors::PreconditionNotMet(
+                        "fill_constant name should be updated."));
+}
+
+TEST(GatherReshapePass, basic) {
+  Layers layers;
+  auto* gather0_x = layers.data("gather0_x", {2, 1, 24, 512, 64});
+  auto* gather0_index = layers.data("gather0_index", {1});
+  auto* gather0_out = layers.gather(gather0_x, gather0_index, 1);
+  gather0_out->SetShape({2, 1, 24, 512, 64});
+  auto* gather1_x = layers.data("gather1_x", {2, 1, 24, 512, 64});
+  auto* gather1_index = layers.data("gather1_index", {1});
+  auto* gather1_out = layers.gather(gather1_x, gather1_index, 1);
+  gather1_out->SetShape({2, 1, 24, 512, 64});
+  auto* block = layers.Block();
+  OpDesc* fused_multi_transformer = block->AppendOp();
+  fused_multi_transformer->SetType("fused_multi_transformer");
+  fused_multi_transformer->SetInput("CacheKV",
+                                    {gather0_out->Name(), gather1_out->Name()});
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
+  auto pass = PassRegistry::Instance().Get(
+      "fused_multi_transformer_cachekv_layout_trans_pass");
+  pass->Apply(graph.get());
+  auto gathers = GetOpNodes(graph, "gather");
+  for (auto* gather : gathers) {
+    PADDLE_ENFORCE_EQ(
+        gather->Op()->GetAttrIfExists<int>("axis"),
+        2,
+        platform::errors::PreconditionNotMet(
+            "gather's axis attr should be updated to 2 by pass."));
+  }
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(fused_multi_transformer_cachekv_layout_trans_pass);

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -519,6 +519,7 @@ XpuPassStrategy::XpuPassStrategy() : PassStrategy({}) {
       "embedding_with_eltwise_add_xpu_fuse_pass",
       "multi_encoder_xpu_fuse_pass",
       "multi_encoder_xpu_slice_fuse_pass",
+      "fused_multi_transformer_cachekv_layout_trans_pass",
       "one_beam_size_fuse_pass",
       "delete_cast_op_pass",
       "stack_fuse_pass",

paddle/phi/infermeta/fusion.cc

@@ -291,31 +291,26 @@ void FusedMultiTransformerXpuInferMeta(
     std::vector<MetaTensor*> cache_kv_out) {
   auto x_dim = x.dims();
   auto y_dim = qkvw[0]->dims();
-  PADDLE_ENFORCE_EQ(
-      x_dim.size(),
-      3,
-      phi::errors::InvalidArgument("The dimensions of x must be 3"
-                                   "(batch_size, seq_len, dim_embed),"
-                                   "but received dimensions of"
-                                   "Input is [%d]",
-                                   x_dim.size()));
+  PADDLE_ENFORCE_EQ(x_dim.size(),
+                    3,
+                    phi::errors::InvalidArgument(
+                        "The dimensions of x must be 3(batch_size, seq_len, "
+                        "dim_embed), but received dimensions of Input is [%d]",
+                        x_dim.size()));
   PADDLE_ENFORCE_EQ(
       y_dim.size(),
       4,
-      phi::errors::InvalidArgument("The dimensions of qkv_weight must be 4"
-                                   "(3, num_head, dim_head, dim_embed),"
-                                   "but received dimensions of"
-                                   "Input is [%d]",
-                                   y_dim.size()));
+      phi::errors::InvalidArgument(
+          "The dimensions of qkv_weight must be 4(3, num_head, dim_head, "
+          "dim_embed), but received dimensions of qkv_weight is [%d]",
+          y_dim.size()));
   PADDLE_ENFORCE_EQ(
       x_dim[2],
       trans_qkvw ? y_dim[3] : y_dim[0],
       phi::errors::InvalidArgument(
-          "ShapeError: the dimension of x_dim[2] and y_dim[3](trans_qkvw is "
-          "true) or y_dim[0](trans_qkvw is false)"
-          "must be equal. But received: the shape "
-          "of input x = [%s], and the shape of "
-          "input qkv_weight = [%s]",
+          "The dimension of x_dim[2] and y_dim[3](trans_qkvw is  true) or "
+          "y_dim[0](trans_qkvw is false) must be equal, but received: the "
+          "shape of input x = [%s], and the shape of input qkv_weight = [%s]",
           x_dim,
           y_dim));
   if (cache_kv.size() > 0) {
@@ -330,27 +325,27 @@ void FusedMultiTransformerXpuInferMeta(
                       phi::errors::InvalidArgument(
                           "The first dim of CacheKV must be 2, but got %d",
                           c_dim[0]));  // 2
-    PADDLE_ENFORCE_EQ(c_dim[1],
-                      x_dim[0],
-                      phi::errors::InvalidArgument(
-                          "The second dim of CacheKV must be equal with "
-                          "batch size %d, but got %d",
-                          x_dim[0],
-                          c_dim[1]));  // batch_size
-    PADDLE_ENFORCE_EQ(c_dim[2],
-                      trans_qkvw ? y_dim[1] : y_dim[2],
-                      phi::errors::InvalidArgument(
-                          "The third dim of CacheKV must be equal with num "
-                          "head %d, but got %d",
-                          trans_qkvw ? y_dim[1] : y_dim[2],
-                          c_dim[2]));  // num_head
-    PADDLE_ENFORCE_EQ(c_dim[4],
-                      trans_qkvw ? y_dim[2] : y_dim[3],
-                      phi::errors::InvalidArgument(
-                          "The fifth dim of CacheKV must be equal with head "
-                          "size %d, but got %d",
-                          trans_qkvw ? y_dim[2] : y_dim[3],
-                          c_dim[4]));  // head_size
+    PADDLE_ENFORCE_EQ(
+        c_dim[2],
+        x_dim[0],
+        phi::errors::InvalidArgument("The third dim of CacheKV must be equal "
+                                     "with batch size %d, but got %d",
+                                     x_dim[0],
+                                     c_dim[2]));  // batch_size
+    PADDLE_ENFORCE_EQ(
+        c_dim[3],
+        trans_qkvw ? y_dim[1] : y_dim[2],
+        phi::errors::InvalidArgument("The fourth dim of CacheKV must be equal "
+                                     "with num head %d, but got %d",
+                                     trans_qkvw ? y_dim[1] : y_dim[2],
+                                     c_dim[3]));  // num_head
+    PADDLE_ENFORCE_EQ(
+        c_dim[4],
+        trans_qkvw ? y_dim[2] : y_dim[3],
+        phi::errors::InvalidArgument("The fifth dim of CacheKV must be equal "
+                                     "with head size %d, but got %d",
+                                     trans_qkvw ? y_dim[2] : y_dim[3],
+                                     c_dim[4]));  // head_size
   }
 
   out->set_dims(x_dim);