[Paddle Inference] fix bugs in quant_conv2d_dequant_fuse_pass when weight is...

[Paddle Inference] fix bugs in quant_conv2d_dequant_fuse_pass when weight is shared  between ops (#45719)

* fix_old_format

* fix bug in quant_conv2d_dequant

* fix bug in quant_conv2d_dequant

paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc

@@ -440,7 +440,8 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
   // Create pattern
   patterns::DequantOpFuse pattern(gpd.mutable_pattern(), pattern_name);
   pattern(quantized_op_input, quantized_op_type, dequant_type, weight_name);
-
+  // Record whether quantized_op_weight_node has been dealt with
+  std::unordered_set<Node*> quantized_op_weight_node_set;
   // Create new op desc
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
@@ -507,6 +508,10 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
     const auto& w_dims = weight_tensor->dims();
     float* quantized_weight_data =
         weight_tensor->mutable_data<float>(platform::CPUPlace());
+    //  Determine whether this weight tensor has been re-writed, avoiding
+    //  re-write it again when this weight tensor is shared among many ops.
+    if (!quantized_op_weight_node_set.count(quantized_op_weight_node)) {
+      quantized_op_weight_node_set.insert(quantized_op_weight_node);
       // If quantized op is fc, weight scale size = 1;
       // If quantized op is conv2d, weight scale size = weight dims[0]
       // If quantized op is conv2d_transpose, weight scale size = weight dims[1]
@@ -537,13 +542,14 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
                     "the received is %d",
                     quant_axis));
           }
-        PADDLE_ENFORCE_EQ(
-            weight_scale.size(),
+          PADDLE_ENFORCE_EQ(weight_scale.size(),
                             static_cast<size_t>(w_dims[1]),
                             platform::errors::InvalidArgument(
                                 "mul/matmul/matmul_v2 op weight dequantized by "
-                "[fake_channel_wise_dequantize_max_abs] requires weight scale "
-                "size = 2nd dim of mul/matmul/matmul_v2's weight, which is %d, "
+                                "[fake_channel_wise_dequantize_max_abs] "
+                                "requires weight scale "
+                                "size = 2nd dim of mul/matmul/matmul_v2's "
+                                "weight, which is %d, "
                                 "but got "
                                 "%d.",
                                 static_cast<size_t>(w_dims[1]),
@@ -562,7 +568,8 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
                 "[fake_channel_wise_dequantize_max_abs], but got %s. "
                 "If you uses PaddleSlim to generate the quantized "
                 "model, please set the 'weight_quantize_type' params as "
-              "'channel_wise_abs_max' and generate the quantized model again.",
+                "'channel_wise_abs_max' and generate the quantized model "
+                "again.",
                 dequant_type));
         if (quant_axis == 0) {
         } else {
@@ -570,7 +577,8 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
               quant_axis == 0,
               true,
               platform::errors::InvalidArgument(
-                "'quant_axis' of conv2d/depthwise_conv2d op weight dequantized "
+                  "'quant_axis' of conv2d/depthwise_conv2d op weight "
+                  "dequantized "
                   "by [fake_channel_wise_dequantize_max_abs]should be 0, but "
                   "the received is %d",
                   quant_axis));
@@ -616,13 +624,14 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
                 weight_scale.size()));
         for (int j = 0; j < weight_tensor->numel(); j++) {
           int inner_size = w_dims[2] * w_dims[3];
-        quantized_weight_data[j] *= weight_scale[(j / inner_size) % w_dims[1]];
+          quantized_weight_data[j] *=
+              weight_scale[(j / inner_size) % w_dims[1]];
         }
       } else {
         PADDLE_THROW(platform::errors::InvalidArgument(
             "Unsupported quantized op type: %s", quantized_op_type));
       }
-
+    }
     // create new op_desc
     auto base_op_desc = *quantized_op_node->Op()->Proto();
     std::string new_input = quantized_op_input_node->Name();