Qualcomm AI Engine Direct - Add rewrite function of observer

- Add function to rewrite observer after prepare_pt2e
- Add corresponding test case

Author: Chun-I Tsai

backends/qualcomm/tests/models.py

@@ -1269,6 +1269,14 @@ def forward(self, x):
         return x.repeat(1, 2, 3, 4)
 
 
+class ReWriteObs(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return torch.nn.functional.relu(x).expand(3, 4)
+
+
 class Reshape(torch.nn.Module):
     def __init__(self):
         super().__init__()

backends/qualcomm/tests/test_qnn_delegate.py

@@ -49,6 +49,7 @@
     generate_qnn_executorch_compiler_spec,
     PyQnnManagerAdaptor,
     QnnPartitioner,
+    rewrite_prepared_observer,
     skip_annotation,
     to_edge_transform_and_lower_to_qnn,
     update_spill_fill_size,
@@ -3058,6 +3059,37 @@ def test_qnn_backend_dynamic_shape(self):
                     check_io_shape=True,
                 )
 
+    def test_qnn_backend_rewrite_prepared_observer(self):
+        from torch.ao.quantization import observer
+        from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
+
+        module = ReWriteObs()  # noqa: F405
+        sample_input = (torch.randn([3, 1]),)
+        module = torch.export.export(module, sample_input, strict=True).module()
+
+        quantizer = make_quantizer()
+
+        prepared = prepare_pt2e(module, quantizer)
+        prepared(*sample_input)
+
+        new_obs = observer.FixedQParamsObserver(
+            scale=0.004,
+            zero_point=0,
+            dtype=torch.uint8,
+            quant_min=0,
+            quant_max=255,
+            qscheme=torch.per_tensor_affine,
+        )
+
+        rewrite_prepared_observer(prepared, {"activation_post_process_2": new_obs})
+        self.assertTrue(
+            prepared.activation_post_process_1
+            == prepared.activation_post_process_2
+            == new_obs
+        )
+        quantized_module = convert_pt2e(prepared)
+        self.lower_module_and_test_output(quantized_module, sample_input)
+
     def test_qnn_backend_skip_node_id_partitioner(self):
         module = SimpleModel()  # noqa: F405
         sample_input = (torch.ones(1, 32, 28, 28), torch.ones(1, 32, 28, 28))

backends/qualcomm/utils/utils.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 import operator
 import warnings
-from collections import OrderedDict
+from collections import defaultdict, OrderedDict
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import executorch.backends.qualcomm.python.PyQnnManagerAdaptor as PyQnnManagerAdaptor
@@ -1038,3 +1038,53 @@ def tag_quant_io(gm: torch.fx.GraphModule, get_quant_io_dtype_fn: Callable):
     for node in gm.graph.nodes:
         if dtype := get_quant_io_dtype_fn(node):
             node.meta[QCOM_QUANTIZED_IO] = dtype
+
+
+def rewrite_prepared_observer(
+    graph_module: torch.fx.GraphModule, name_obs_dict: Dict[str, torch.nn.Module]
+):
+    """
+    Rewrite the observer of the specified observer module name in the graph_module.
+
+    Example:
+    Consider the following graph_module after prepare_pt2e:
+    gm = prepare_pt2e(gm)
+    print(gm)
+
+    GraphModule(
+      (activation_post_process_0): MinMaxObserver(min_val=inf, max_val=-inf)
+      (activation_post_process_1): MinMaxObserver(min_val=inf, max_val=-inf)
+      (activation_post_process_2): MinMaxObserver(min_val=inf, max_val=-inf)
+      (activation_post_process_3): MinMaxObserver(min_val=inf, max_val=-inf)
+    )
+
+    new_observer = observer.FixedQParamsObserver(
+        scale=0.125,
+        zero_point=42,
+        dtype=torch.uint8,
+        quant_min=0,
+        quant_max=255,
+        qscheme=torch.per_tensor_affine,
+    )
+
+    Calling rewrite_prepared_observer(gm, {"activation_post_process_0": new_observer})
+    is equivalent to:
+    gm.activation_post_process_0 = new_observer
+
+    Note:
+    If the rewritten observer is a SharedQuantizationSpec, all other shared observers will also be rewritten.
+    """
+    module_name_list = defaultdict(list)
+    for name, module in graph_module.named_modules(remove_duplicate=False):
+        module_name_list[module].append(name)
+
+    for name, new_observer in name_obs_dict.items():
+        old_module = getattr(graph_module, name, None)
+
+        if not old_module:
+            print(
+                f"[WARNING], No observer named as {name} found, please check the moudle name"
+            )
+            continue
+        for target_name in module_name_list[old_module]:
+            setattr(graph_module, target_name, new_observer)