Paddle-TRT int8 support mul op channelwise quant (#28422)

* paddle-trt support mul channelwise quant

* add support for depthwise_conv2d

* add errmsg for unsupported op type

paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc

@@ -195,32 +195,73 @@ void FuseDequant(ir::Graph* graph, Scope* scope,
     auto* weight_tensor =
         scope->Var(quantized_op_weight_node->Name())->GetMutable<LoDTensor>();
     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]
-    bool valid_scale_size =
-        (weight_scale.size() == 1 ||
-         weight_scale.size() == static_cast<size_t>(w_dims[0]) ||
-         weight_scale.size() == static_cast<size_t>(w_dims[1]));
-    PADDLE_ENFORCE_EQ(
-        valid_scale_size, true,
-        platform::errors::InvalidArgument(
-            "TRT int8 quant: invalid scale size(%d).", weight_scale.size()));
-    float* quantized_weight_data =
-        weight_tensor->mutable_data<float>(platform::CPUPlace());
-    for (int j = 0; j < weight_tensor->numel(); j++) {
-      if (weight_scale.size() == 1) {
-        quantized_weight_data[j] *= weight_scale[0];
-      } else {
-        if (quantized_op_type == "conv2d_transpose") {
-          int inner_size = w_dims[2] * w_dims[3];
-          quantized_weight_data[j] *=
-              weight_scale[(j / inner_size) % w_dims[1]];
-        } else {
-          int inner_size = w_dims[1] * w_dims[2] * w_dims[3];
-          quantized_weight_data[j] *= weight_scale[j / inner_size];
+    if (quantized_op_type == "mul" || quantized_op_type == "fc") {
+      if (dequant_type == "fake_dequantize_max_abs") {
+        PADDLE_ENFORCE_EQ(
+            weight_scale.size(), 1,
+            platform::errors::InvalidArgument(
+                "mul 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") {
+        PADDLE_ENFORCE_EQ(
+            weight_scale.size(), static_cast<size_t>(w_dims[1]),
+            platform::errors::InvalidArgument(
+                "mul op weight dequantized by "
+                "[fake_channel_wise_dequantize_max_abs] requires weight scale "
+                "size = 2nd dim of mul'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",
+                                            dequant_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));
+      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