quantization: add quant for conv+add fusion (#1309) (#1318)

Author: jiayisunx

intel_extension_for_pytorch/quantization/_recipe.py

@@ -16,6 +16,9 @@
 conv_gemm_ops = [str(F.conv2d), str(nn.Conv2d), str(F.conv3d), str(nn.Conv3d), str(torch.conv2d), str(torch.conv3d), \
     str(F.conv_transpose2d), str(torch.nn.ConvTranspose2d), str(F.conv_transpose3d), str(torch.nn.ConvTranspose3d),
     str(torch.conv_transpose2d), str(torch.conv_transpose2d), str(F.linear), str(nn.Linear), str(torch.matmul), str(torch.Tensor.matmul)]
+conv_ops = [str(F.conv2d), str(nn.Conv2d), str(F.conv3d), str(nn.Conv3d), str(torch.conv2d), str(torch.conv3d), \
+    str(F.conv_transpose2d), str(torch.nn.ConvTranspose2d), str(F.conv_transpose3d), str(torch.nn.ConvTranspose3d),
+    str(torch.conv_transpose2d), str(torch.conv_transpose2d)]
 rnn_ops = [str(torch.nn.LSTM)]
 
 # Those ops only support s8->s8 path, and also require the qscheme is per_tensor_symmetric.
@@ -233,6 +236,7 @@ def reset_input_inf_dtype_to_orig_dtype(node, input_idx):
                 node.input_tensor_force_inf_dtype[input_idx] = node.input_tensor_infos[input_idx].inf_dtype
 
     conv_gemm_node = _find_fused_node_with_cur_add(node, conv_gemm_ops)
+    conv_node = _find_fused_node_with_cur_add(node, conv_ops)
     if conv_gemm_node is None:
         #  If pre_nodes don't have gemm node, need to check whether have quantizable node before it,
         #  if does't have quantizable node before it, we will not insert fake quant before add.
@@ -255,13 +259,17 @@ def reset_input_inf_dtype_to_orig_dtype(node, input_idx):
         if node.input_tensor_infos[0] is not None and node.input_tensor_infos[0] in conv_gemm_node.output_tensor_infos:
             node.input_tensor_infos[0].inf_dtype = node.input_tensor_infos[0].orig_dtype
             node.input_tensor_force_inf_dtype[0] = node.input_tensor_infos[0].inf_dtype
-            # set another input's dtype, if another's input is from non-quantizable op, we can remove the fake quant.
-            reset_input_inf_dtype_to_orig_dtype(node, 1)
+            # TODO: set another input's dtype for conv nodes when oneDNN is ready.
+            if conv_node is None:
+                # set another input's dtype, if another's input is from non-quantizable op, we can remove the fake quant.
+                reset_input_inf_dtype_to_orig_dtype(node, 1)
         elif node.input_tensor_infos[1] is not None and node.input_tensor_infos[1] in conv_gemm_node.output_tensor_infos:
             node.input_tensor_infos[1].inf_dtype = node.input_tensor_infos[1].orig_dtype
             node.input_tensor_force_inf_dtype[1] = node.input_tensor_infos[1].inf_dtype
-            # set another input's dtype, if another's input is from non-quantizable op, we can remove the fake quant.
-            reset_input_inf_dtype_to_orig_dtype(node, 0)
+            # TODO: set another input's dtype for conv nodes when oneDNN is ready.
+            if conv_node is None:
+                # set another input's dtype, if another's input is from non-quantizable op, we can remove the fake quant.
+                reset_input_inf_dtype_to_orig_dtype(node, 0)
 
 # get a default recipe
 def get_default_recipe(nodes):