[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) {
@@ -507,32 +508,108 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
     const auto& w_dims = weight_tensor->dims();
     float* quantized_weight_data =
         weight_tensor->mutable_data<float>(platform::CPUPlace());
-    // 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]
-    if (quantized_op_type == "mul" || quantized_op_type == "matmul" ||
-        quantized_op_type == "matmul_v2" || quantized_op_type == "fc") {
-      if (dequant_type == "fake_dequantize_max_abs") {
-        PADDLE_ENFORCE_EQ(weight_scale.size(),
-                          1,
-                          platform::errors::InvalidArgument(
-                              "mul/matmul/matmul_v2 op weight dequantized by "
-                              "[fake_dequantize_max_abs] "
-                              "requires weight scale size = 1, but got %d.",
-                              weight_scale.size()));
+    //  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]
+      if (quantized_op_type == "mul" || quantized_op_type == "matmul" ||
+          quantized_op_type == "matmul_v2" || quantized_op_type == "fc") {
+        if (dequant_type == "fake_dequantize_max_abs") {
+          PADDLE_ENFORCE_EQ(weight_scale.size(),
+                            1,
+                            platform::errors::InvalidArgument(
+                                "mul/matmul/matmul_v2 op weight dequantized by "
+                                "[fake_dequantize_max_abs] "
+                                "requires weight scale size = 1, but got %d.",
+                                weight_scale.size()));
+          for (int j = 0; j < weight_tensor->numel(); j++) {
+            quantized_weight_data[j] *= weight_scale[0];
+          }
+        }
+        if (dequant_type == "fake_channel_wise_dequantize_max_abs") {
+          if (quant_axis == 0) {
+          } else {
+            PADDLE_ENFORCE_EQ(
+                quant_axis == 1,
+                true,
+                platform::errors::InvalidArgument(
+                    "'quant_axis' of mul/matmul/fc/matmul_v2 op weight "
+                    "dequantized by "
+                    "[fake_channel_wise_dequantize_max_abs]should be 1, but "
+                    "the received is %d",
+                    quant_axis));
+          }
+          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, "
+                                "but got "
+                                "%d.",
+                                static_cast<size_t>(w_dims[1]),
+                                weight_scale.size()));
+          for (int j = 0; j < weight_tensor->numel(); j++) {
+            quantized_weight_data[j] *= weight_scale[j % w_dims[1]];
+          }
+        }
+      } else if (quantized_op_type == "conv2d" ||
+                 quantized_op_type == "depthwise_conv2d") {
+        PADDLE_ENFORCE_EQ(
+            dequant_type,
+            "fake_channel_wise_dequantize_max_abs",
+            platform::errors::InvalidArgument(
+                "conv2d op must be dequantized by "
+                "[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.",
+                dequant_type));
+        if (quant_axis == 0) {
+        } else {
+          PADDLE_ENFORCE_EQ(
+              quant_axis == 0,
+              true,
+              platform::errors::InvalidArgument(
+                  "'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));
+        }
+        PADDLE_ENFORCE_EQ(
+            weight_scale.size(),
+            static_cast<size_t>(w_dims[0]),
+            platform::errors::InvalidArgument(
+                "conv2d op requires weight scale size = channel size of the "
+                "weight, which is %d, but got %d.",
+                static_cast<size_t>(w_dims[0]),
+                weight_scale.size()));
         for (int j = 0; j < weight_tensor->numel(); j++) {
-          quantized_weight_data[j] *= weight_scale[0];
+          int inner_size = w_dims[1] * w_dims[2] * w_dims[3];
+          quantized_weight_data[j] *= weight_scale[j / inner_size];
         }
-      }
-      if (dequant_type == "fake_channel_wise_dequantize_max_abs") {
+      } else if (quantized_op_type == "conv2d_transpose") {
+        PADDLE_ENFORCE_EQ(
+            dequant_type,
+            "fake_channel_wise_dequantize_max_abs",
+            platform::errors::InvalidArgument(
+                "conv2d_transpose must be dequantized by "
+                "[fake_channel_wise_dequantize_max_abs], but got %s",
+                dequant_type));
         if (quant_axis == 0) {
         } else {
           PADDLE_ENFORCE_EQ(
               quant_axis == 1,
               true,
               platform::errors::InvalidArgument(
-                  "'quant_axis' of mul/matmul/fc/matmul_v2 op weight "
-                  "dequantized by "
+                  "'quant_axis' of conv2d_transpose op weight dequantized by "
                   "[fake_channel_wise_dequantize_max_abs]should be 1, but "
                   "the received is %d",
                   quant_axis));
@@ -541,88 +618,20 @@ void QuantDequantFusePass::FuseDequant(ir::Graph* graph,
             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, "
-                "but got "
-                "%d.",
+                "conv2d_transpose op requires weight scale size = channel size "
+                "of the weight, which is %d, but got %d.",
                 static_cast<size_t>(w_dims[1]),
                 weight_scale.size()));
         for (int j = 0; j < weight_tensor->numel(); j++) {
-          quantized_weight_data[j] *= weight_scale[j % w_dims[1]];
+          int inner_size = w_dims[2] * w_dims[3];
+          quantized_weight_data[j] *=
+              weight_scale[(j / inner_size) % w_dims[1]];
         }
-      }
-    } else if (quantized_op_type == "conv2d" ||
-               quantized_op_type == "depthwise_conv2d") {
-      PADDLE_ENFORCE_EQ(
-          dequant_type,
-          "fake_channel_wise_dequantize_max_abs",
-          platform::errors::InvalidArgument(
-              "conv2d op must be dequantized by "
-              "[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.",
-              dequant_type));
-      if (quant_axis == 0) {
       } else {
-        PADDLE_ENFORCE_EQ(
-            quant_axis == 0,
-            true,
-            platform::errors::InvalidArgument(
-                "'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));
+        PADDLE_THROW(platform::errors::InvalidArgument(
+            "Unsupported quantized op type: %s", quantized_op_type));
       }
-      PADDLE_ENFORCE_EQ(
-          weight_scale.size(),
-          static_cast<size_t>(w_dims[0]),
-          platform::errors::InvalidArgument(
-              "conv2d op requires weight scale size = channel size of the "
-              "weight, which is %d, but got %d.",
-              static_cast<size_t>(w_dims[0]),
-              weight_scale.size()));
-      for (int j = 0; j < weight_tensor->numel(); j++) {
-        int inner_size = w_dims[1] * w_dims[2] * w_dims[3];
-        quantized_weight_data[j] *= weight_scale[j / inner_size];
-      }
-    } else if (quantized_op_type == "conv2d_transpose") {
-      PADDLE_ENFORCE_EQ(
-          dequant_type,
-          "fake_channel_wise_dequantize_max_abs",
-          platform::errors::InvalidArgument(
-              "conv2d_transpose must be dequantized by "
-              "[fake_channel_wise_dequantize_max_abs], but got %s",
-              dequant_type));
-      if (quant_axis == 0) {
-      } else {
-        PADDLE_ENFORCE_EQ(
-            quant_axis == 1,
-            true,
-            platform::errors::InvalidArgument(
-                "'quant_axis' of conv2d_transpose op weight dequantized by "
-                "[fake_channel_wise_dequantize_max_abs]should be 1, but "
-                "the received is %d",
-                quant_axis));
-      }
-      PADDLE_ENFORCE_EQ(
-          weight_scale.size(),
-          static_cast<size_t>(w_dims[1]),
-          platform::errors::InvalidArgument(
-              "conv2d_transpose op requires weight scale size = channel size "
-              "of the weight, which is %d, but got %d.",
-              static_cast<size_t>(w_dims[1]),
-              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]];
-      }
-    } 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();