add 16a8w matmul custom annotation (#5864)

Summary:
Pull Request resolved: #5864

As title, separate the 16a8w matmul custom annotation from the large chunk of PR.

Reviewed By: kirklandsign

Differential Revision: D63876215

fbshipit-source-id: 484fd93c31cda7b755e5e343fdc2513a29749304

Author: cccclai

backends/qualcomm/quantizer/custom_annotation.py

@@ -118,3 +118,29 @@ def annotate_matmul_input1(node: Node, quantization_config: QuantizationConfig):
                 if "SDPA" in full_qualified_name:
                     annotate_matmul(node, quantization_config_16a8w)
                     annotate_matmul_input1(node.args[1], quantization_config_8a8w)
+
+
+def custom_annotate_matmul_16a8w(gm: torch.fx.GraphModule):
+    """
+    Annotate matmul op with 16a8w quantization config
+    """
+
+    def annotate_matmul(node: Node, quantization_config: QuantizationConfig):
+        input_qspec_map = {}
+        input_act = node.args[0]
+        input_spec = quantization_config.input_activation
+        input_qspec_map[input_act] = input_spec
+        input_act1 = node.args[1]
+        input_spec1 = quantization_config.weight
+        input_qspec_map[input_act1] = input_spec1
+        node.meta[QUANT_ANNOTATION_KEY] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=quantization_config.output_activation,
+            _annotated=True,
+        )
+
+    # Annotate 16a8w for matmul op to get better performance
+    quantization_config_16a8w = get_16a8w_qnn_ptq_config()
+    for node in gm.graph.nodes:
+        if node.op == "call_function" and node.target == torch.ops.aten.matmul.default:
+            annotate_matmul(node, quantization_config_16a8w)