PaddlePaddle
diff --git a/‎paddle/fluid/framework/CMakeLists.txt
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+2-2Lines changed: 2 additions & 2 deletions b/‎paddle/fluid/framework/CMakeLists.txt
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+2-2Lines changed: 2 additions & 2 deletions
diff --git a/‎paddle/fluid/framework/ir/CMakeLists.txt
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+11-1Lines changed: 11 additions & 1 deletion b/‎paddle/fluid/framework/ir/CMakeLists.txt
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+11-1Lines changed: 11 additions & 1 deletion
diff --git a/‎paddle/fluid/framework/ir/auto_mixed_precision_pass.cc
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+30-2Lines changed: 30 additions & 2 deletions b/‎paddle/fluid/framework/ir/auto_mixed_precision_pass.cc
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+30-2Lines changed: 30 additions & 2 deletions
diff --git a/‎paddle/fluid/framework/ir/delete_quant_dequant_linear_op_pass.cc
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+11-1Lines changed: 11 additions & 1 deletion b/‎paddle/fluid/framework/ir/delete_quant_dequant_linear_op_pass.cc
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+11-1Lines changed: 11 additions & 1 deletion
diff --git a/‎paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.cc
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+37-9Lines changed: 37 additions & 9 deletions b/‎paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.cc
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+37-9Lines changed: 37 additions & 9 deletions
diff --git a/‎paddle/fluid/framework/ir/graph.h
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+14Lines changed: 14 additions & 0 deletions b/‎paddle/fluid/framework/ir/graph.h
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+14Lines changed: 14 additions & 0 deletions
diff --git a/‎paddle/fluid/framework/ir/quantize_helper.cc
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+79Lines changed: 79 additions & 0 deletions b/‎paddle/fluid/framework/ir/quantize_helper.cc
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+79Lines changed: 79 additions & 0 deletions
@@ -55,13 +55,13 @@ function(pass_library TARGET DEST)
       ${TARGET}
       SRCS ${pass_library_DIR}/${TARGET}.cc
       DEPS graph_pattern_detector pass fuse_pass_base op_version_registry
-           ${pass_library_DEPS})
+           quantize_helper ${pass_library_DEPS})
   else()
     cc_library(
       ${TARGET}
       SRCS ${TARGET}.cc
       DEPS graph_pattern_detector pass fuse_pass_base op_version_registry
-           ${pass_library_DEPS})
+           quantize_helper ${pass_library_DEPS})
   endif()
 
   # add more DEST here, such as train, dist and collect USE_PASS into a file automatically.
 
@@ -59,6 +59,10 @@ cc_library(
   placement_pass_base
   SRCS placement_pass_base.cc
   DEPS pass)
+cc_library(
+  quantize_helper
+  SRCS quantize_helper.cc
+  DEPS graph graph_helper)
 
 cc_library(
   coalesce_grad_tensor_pass
@@ -237,7 +241,11 @@ if(WITH_XPU)
     xpu_pass_utils
     SRCS xpu/pass_utils.cc
     DEPS pass xpu_quant_utils)
-  set(XPU_PASS_DEPS xpu_quant_utils xpu_pass_utils)
+  cc_library(
+    xpu_graph_pattern_detector
+    SRCS xpu/xpu_graph_pattern_detector.cc
+    DEPS graph_pattern_detector)
+  set(XPU_PASS_DEPS xpu_quant_utils xpu_pass_utils xpu_graph_pattern_detector)
   pass_library(cast_mixed_precision_op_fuse_pass inference DIR xpu DEPS
                ${XPU_PASS_DEPS})
   pass_library(yolo_box_xpu_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
@@ -247,6 +255,8 @@ if(WITH_XPU)
   # pass_library(conv1d_xpu_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
   pass_library(conv2d_xpu_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
   pass_library(conv2d_bias_fuse_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
+  pass_library(xpu_quantize_op_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
+  pass_library(xpu_quantize_squash_pass inference DIR xpu DEPS ${XPU_PASS_DEPS})
   pass_library(redundant_unsqueeze_squeeze_elimination_pass inference DIR xpu
                DEPS ${XPU_PASS_DEPS})
   pass_library(redundant_squeeze_unsqueeze_elimination_pass inference DIR xpu
 
@@ -523,7 +523,6 @@ void AutoMixedPrecisionPass::UpdateOpPrecision() const {
             vars_should_not_low_precision.insert(in_var_node->Var()->Name());
           }
         }
-
         // when op_1 only support cpu kernel. if op_2's intput var is op_1's
         // output var, then op_2 should not run at low precision.
         if (GetOpOriginalType(op_type) != "feed" &&
@@ -687,6 +686,16 @@ bool AutoMixedPrecisionPass::InputVarsNotConvert(
       if (std::find(vecs.begin(), vecs.end(), var_name) != vecs.end()) {
         return true;
       }
+    } else if (GetOpOriginalType(op_desc->Type()) == "quantize_linear" ||
+               GetOpOriginalType(op_desc->Type()) == "dequantize_linear") {
+      auto vecs = op_desc->Input("Scale");
+      if (std::find(vecs.begin(), vecs.end(), var_name) != vecs.end()) {
+        return true;
+      }
+      vecs = op_desc->Input("ZeroPoint");
+      if (std::find(vecs.begin(), vecs.end(), var_name) != vecs.end()) {
+        return true;
+      }
     }
   }
 
@@ -733,6 +742,11 @@ bool AutoMixedPrecisionPass::OutputVarsNotConvert(
 }
 
 void AutoMixedPrecisionPass::SetVarPrecision() const {
+  auto* scope = param_scope();
+  PADDLE_ENFORCE_NOT_NULL(scope,
+                          platform::errors::PreconditionNotMet(
+                              "During the auto_mixed_precision_pass, the scope "
+                              "should not be null."));
   for (const auto& nodes : all_op_nodes_) {
     for (auto* op_node : nodes) {
       if (op_run_low_precision_.count(op_node->Op()->Type()) == 0) {
@@ -749,7 +763,21 @@ void AutoMixedPrecisionPass::SetVarPrecision() const {
           if (!IsFP32AndFP64(real_in_var_node->Var()->GetDataType())) continue;
           if (!VarNodeHasDtype(real_in_var_node)) continue;
           if (InputVarsNotConvert(op_node, in_var_name)) continue;
-
+          // Judge the real tensor is same to variable, Paddle-Slim weight use
+          // fp32 variable to save int8 tensor.
+          if (real_in_var_node->Var()->Persistable()) {
+            auto* tensor = scope->Var(real_in_var_node->Name())
+                               ->GetMutable<phi::DenseTensor>();
+            if (framework::TransToProtoVarType(tensor->type()) !=
+                real_in_var_node->Var()->GetDataType()) {
+              VLOG(3) << "[AutoMixedPrecisionPass] variable "
+                      << real_in_var_node->Name() << "'s proto data type "
+                      << real_in_var_node->Var()->GetDataType()
+                      << " is different from real dense tensor "
+                      << framework::TransToProtoVarType(tensor->type());
+              continue;
+            }
+          }
           if (real_in_var_node->Var()->Persistable()) {
             real_in_var_node->Var()->SetDataType(
                 framework::TransToProtoVarType(low_precision_));
 
@@ -19,6 +19,7 @@
 #include <string>
 #include <unordered_set>
 #include <vector>
+#include "paddle/fluid/framework/ir/quantize_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -94,6 +95,8 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
       scope,
       platform::errors::InvalidArgument(
           "Scope in DeleteQuantDequantLinearOpPass should not be null."));
+  std::unordered_map<std::string, std::vector<float>> var_quant_scales{};
+
   // Create pattern
   patterns::DeleteQuantDequantLinearOpPattern pattern(gpd.mutable_pattern(),
                                                       pattern_name);
@@ -141,7 +144,11 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
       auto* any_op_desc = dequantize_linear_op_out->outputs[i]->Op();
       any_op_desc->SetAttr("Input_scale_" + quantize_linear_op_x->Var()->Name(),
                            input_scale);
-
+      if (!var_quant_scales.count(quantize_linear_op_x->Var()->Name())) {
+        var_quant_scales.insert(
+            std::make_pair(quantize_linear_op_x->Var()->Name(),
+                           std::vector<float>({input_scale})));
+      }
       // link x to any_op2
       any_op_desc->RenameInput(dequantize_linear_op_out->Var()->Name(),
                                quantize_linear_op_x->Var()->Name());
@@ -161,6 +168,9 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
   };
   gpd(graph, handler);
   AddStatis(found_count);
+
+  SaveQuantInfoInTheGraph(
+      graph, "has_quant_info", "var_quant_scales", var_quant_scales);
 }
 
 }  // namespace ir
 
@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/ir/delete_weight_dequant_linear_op_pass.h"
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/quantize_helper.h"
 
 #include "glog/logging.h"
 
@@ -35,18 +36,20 @@ void DeleteWeightDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
                     true,
                     platform::errors::InvalidArgument(
                         "Graph must have kParamScopeAttr attribute."));
-
+  VLOG(3) << "Handle delete weight dequant linear op pass ...";
   auto& scope = graph->Get<framework::Scope>(kParamScopeAttr);
   bool is_int8 = false;
 
   std::unordered_set<const Node*> nodes2rm;
+  std::unordered_map<std::string, std::vector<float>> var_quant_scales{};
 
   for (const Node* n : graph->Nodes()) {
     if (n->IsOp()) {
       auto* op = n->Op();
       if (op->Type() == "dequantize_linear") {
-        Node *weight_var_node = nullptr, *calcu_op_node = nullptr,
-             *while_op_node = nullptr;
+        Node* weight_var_node = nullptr;
+        Node* calcu_op_node = nullptr;
+        Node* while_op_node = nullptr;
         Node *dequantized_weight_var_node = nullptr, *scale_var_node = nullptr;
         // 1. Judge whether for dequant weight and find
         // weight_var_node/scale_var_node
@@ -59,9 +62,12 @@ void DeleteWeightDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
               scale_var_node = input_node;
             }
           } else {
-            return;
+            break;
           }
         }
+        if (weight_var_node == nullptr || scale_var_node == nullptr) {
+          continue;
+        }
         // 2. Find next_op_node
         // For while op: delete its input which is related to dequantized
         // For calculation op: set weight scale as their attributes
@@ -106,7 +112,7 @@ void DeleteWeightDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
                     }
                   } else {
                     PADDLE_THROW(platform::errors::Unimplemented(
-                        "The dtype of quantization scale must be FP32/16, "
+                        "The dtype of quantization scale must be FP32/FP16, "
                         "but received %d, which is not supported.",
                         weight_scale_tensor->dtype()));
                   }
@@ -125,14 +131,34 @@ void DeleteWeightDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
 
                     calcu_op_desc->SetAttr("weight_scale", weight_scale[0]);
                   } else {
-                    PADDLE_THROW(platform::errors::Unimplemented(
-                        "Delete Weight Dequant Linear Op Pass is not supported "
-                        "for "
-                        "per-channel quantization"));
+                    std::vector<int64_t> weights_shape =
+                        weight_var_node->Var()->GetShape();
+                    quant_axis = quant_axis >= 0
+                                     ? quant_axis
+                                     : quant_axis + weights_shape.size();
+                    PADDLE_ENFORCE_EQ(
+                        weight_scale_nums,
+                        weights_shape[quant_axis],
+                        platform::errors::InvalidArgument(
+                            "When quant_axis != -1, it means using per_channel "
+                            "dequantization. In this situation, the number of "
+                            "weight_scale should be equal with "
+                            "weights_shape[quant_axis=%d]=%ld , but received "
+                            "%d.",
+                            quant_axis,
+                            weights_shape[quant_axis],
+                            weight_scale_nums));
+                    calcu_op_desc->SetAttr("weight_scale", weight_scale);
                   }
+                  if (!var_quant_scales.count(weight_var_node->Var()->Name())) {
+                    var_quant_scales.insert(std::make_pair(
+                        weight_var_node->Var()->Name(), weight_scale));
+                  }
+
                   calcu_op_desc->RenameInput(
                       dequantized_weight_var_node->Var()->Name(),
                       weight_var_node->Var()->Name());
+                  calcu_op_desc->Flush();
                 }
               }
             }
@@ -153,6 +179,8 @@ void DeleteWeightDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
   }
 
   GraphSafeRemoveNodes(graph, nodes2rm);
+  SaveQuantInfoInTheGraph(
+      graph, "has_quant_info", "var_quant_scales", var_quant_scales);
   graph->Set("enable_int8", new bool(is_int8));
 }
 }  // namespace ir
 
@@ -412,6 +412,20 @@ class Graph {
     return sub_graphs_.size();
   }
 
+  std::vector<std::string> AttrNames() const {
+    if (FLAGS_convert_all_blocks) {
+      if (IsMainGraph()) {
+        return GetSubGraph(0)->AttrNames();
+      }
+    }
+    std::vector<std::string> res;
+    res.reserve(attrs_.size());
+    for (auto &attr : attrs_) {
+      res.push_back(attr.first);
+    }
+    return res;
+  }
+
  private:
   // TODO(levi): delete this interface after when we can convert all
   // blocks into sub_graphs.
 
@@ -0,0 +1,79 @@
+// 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
+//
+//     https://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/quantize_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void SaveQuantInfoInTheGraph(
+    ir::Graph* graph,
+    const std::string& flag,
+    const std::string& key_suffix,
+    const std::unordered_map<std::string, std::vector<float>>& info_map) {
+  const std::string suffix = "_" + key_suffix + "_" + flag;
+  if (!graph->Has(flag)) {
+    graph->Set(flag, new bool(true));
+  }
+  for (auto iter = info_map.begin(); iter != info_map.end(); ++iter) {
+    graph->Set(iter->first + suffix, new std::vector<float>(iter->second));
+  }
+}
+
+std::unordered_map<std::string, std::vector<float>> GetQuantInfoFromTheGraph(
+    ir::Graph* graph, const std::string& flag, const std::string& key_suffix) {
+  std::unordered_map<std::string, std::vector<float>> info_map;
+  const std::string suffix = "_" + key_suffix + "_" + flag;
+  if (graph->Has(flag)) {
+    std::vector<std::string> attr_names = graph->AttrNames();
+    for (auto fake_name : attr_names) {
+      size_t pos = fake_name.find(suffix);
+      if (pos != std::string::npos) {
+        std::string name = fake_name.substr(0, pos);
+        auto scales_vector = graph->Get<std::vector<float>>(fake_name);
+        info_map.insert(std::make_pair(name, scales_vector));
+      }
+    }
+  }
+  return info_map;
+}
+
+bool AreScalesPresentForNodes(
+    std::unordered_map<std::string, std::vector<float>>* var_quant_scales,
+    std::initializer_list<Node*> nodes) {
+  bool present = true;
+  for (auto node : nodes) {
+    if (var_quant_scales->count(node->Name()) == 0) {
+      present = false;
+    }
+  }
+  return present;
+}
+
+float GetScaleValueForNode(
+    std::unordered_map<std::string, std::vector<float>>* var_quant_scales,
+    Node* node) {
+  return var_quant_scales->at(node->Name())[0];
+}
+
+std::vector<float> GetScaleVecValueForNode(
+    std::unordered_map<std::string, std::vector<float>>* var_quant_scales,
+    Node* node) {
+  return var_quant_scales->at(node->Name());
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle