Add prepare_obs_or_fq_callback to quantizer (pytorch#140863)

Test Plan: CI.

Differential Revision: D65982003

Pull Request resolved: pytorch#140863
Approved by: https://github.com/jerryzh168

Author: sxu

test/quantization/pt2e/test_quantize_pt2e.py

@@ -1,5 +1,5 @@
 # Owner(s): ["oncall: quantization"]
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 import torch
 from torch import Tensor
@@ -18,6 +18,7 @@
 )
 from torch.ao.quantization.quantizer import (
     DerivedQuantizationSpec,
+    EdgeOrNode,
     FixedQParamsQuantizationSpec,
     QuantizationAnnotation,
     QuantizationSpec,
@@ -2339,6 +2340,76 @@ def forward(self, x):
         m = convert_pt2e(m)
         m(*example_inputs)
 
+    def test_prepare_obs_or_fq_callback(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                x = torch.nn.functional.max_pool2d(x, 2, 2)
+                x = torch.nn.functional.pixel_shuffle(x, 2)
+                return x.permute(0, 2, 3, 1)
+
+        class BackendAQuantizer(Quantizer):
+            def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+                act_qspec = QuantizationSpec(
+                    dtype=torch.uint8,
+                    quant_min=0,
+                    quant_max=255,
+                    qscheme=torch.per_tensor_affine,
+                    is_dynamic=False,
+                    observer_or_fake_quant_ctr=observer.default_observer,
+                )
+                for node in model.graph.nodes:
+                    if node.op == "call_function" and node.target in (
+                        torch.ops.aten.max_pool2d.default,
+                        torch.ops.aten.permute.default,
+                        torch.ops.aten.pixel_shuffle.default,
+                    ):
+                        node.meta["quantization_annotation"] = QuantizationAnnotation(
+                            input_qspec_map={
+                                node.args[0]: act_qspec,
+                            },
+                            output_qspec=SharedQuantizationSpec((node.args[0], node)),
+                            _annotated=True,
+                        )
+
+            def validate(self, model: torch.fx.GraphModule) -> None:
+                pass
+
+            def prepare_obs_or_fq_callback(
+                self,
+                model: torch.fx.GraphModule,
+                edge_or_node_to_obs_or_fq: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+            ) -> None:
+                # hard code output quant by updating entire sharing group
+                output_node = next(n for n in model.graph.nodes if n.op == "output")
+                output_value = output_node.args[0][0]
+                old_observer = edge_or_node_to_obs_or_fq[output_value]
+                sharing_group = [
+                    k for k, v in edge_or_node_to_obs_or_fq.items() if v is old_observer
+                ]
+                new_observer = observer.FixedQParamsObserver(
+                    scale=0.125,
+                    zero_point=42,
+                    dtype=torch.uint8,
+                    quant_min=0,
+                    quant_max=255,
+                    qscheme=torch.per_tensor_affine,
+                )
+                for x in sharing_group:
+                    edge_or_node_to_obs_or_fq[x] = new_observer
+
+        example_inputs = (torch.rand(1, 32, 16, 16),)
+        gm = export_for_training(Model().eval(), example_inputs).module()
+        gm = prepare_pt2e(gm, BackendAQuantizer())
+        gm = convert_pt2e(gm)
+        for n in gm.graph.nodes:
+            if n.op == "call_function" and n.target in (
+                torch.ops.quantized_decomposed.quantize_per_tensor.default,
+                torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+            ):
+                # Entire graph share the same qspec which was overriden by FixedQParamsObserver
+                self.assertEqual(n.args[1], 0.125)
+                self.assertEqual(n.args[2], 42)
+
 
 instantiate_parametrized_tests(TestQuantizePT2E)
 

torch/ao/quantization/pt2e/prepare.py

@@ -535,6 +535,7 @@ def prepare(
     model: GraphModule,
     node_name_to_scope: Dict[str, Tuple[str, type]],
     is_qat: bool,
+    obs_or_fq_callback=None,
 ) -> GraphModule:
     # Since we are mutating the graph as we go, we iterate over the original
     # nodes before observer insertion, instead of model.graph.nodes.
@@ -549,6 +550,8 @@ def prepare(
     obs_or_fq_map = _get_obs_or_fq_map(
         edge_or_node_to_group_id, edge_or_node_to_qspec, is_qat
     )
+    if obs_or_fq_callback:
+        obs_or_fq_callback(model, obs_or_fq_map)
 
     for node in nodes_before_observation:
         # TODO: simplify logic for inserting observers

torch/ao/quantization/quantize_pt2e.py

@@ -99,7 +99,12 @@ def calibrate(model, data_loader):
     model = quantizer.transform_for_annotation(model)
     quantizer.annotate(model)
     quantizer.validate(model)
-    model = prepare(model, node_name_to_scope, is_qat=False)
+    model = prepare(
+        model,
+        node_name_to_scope,
+        is_qat=False,
+        obs_or_fq_callback=quantizer.prepare_obs_or_fq_callback,
+    )
     model.meta.update(original_graph_meta)
     model = _disallow_eval_train(model)
     return model
@@ -172,7 +177,12 @@ def train_loop(model, train_data):
     # subgraph that don't need to be quantized
     # TODO: only fuse if conv and bn are both configured to be quantized
     _fuse_conv_bn_qat(model)
-    model = prepare(model, node_name_to_scope, is_qat=True)
+    model = prepare(
+        model,
+        node_name_to_scope,
+        is_qat=True,
+        obs_or_fq_callback=quantizer.prepare_obs_or_fq_callback,
+    )
     model.meta.update(original_graph_meta)
     model = _disallow_eval_train(model)
     return model

torch/ao/quantization/quantizer/quantizer.py

@@ -159,3 +159,23 @@ def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
     @abstractmethod
     def validate(self, model: torch.fx.GraphModule) -> None:
         pass
+
+    def prepare_obs_or_fq_callback(
+        self,
+        model: torch.fx.GraphModule,
+        edge_or_node_to_obs_or_fq: Dict[EdgeOrNode, ObserverOrFakeQuantize],
+    ) -> None:
+        """A callback that will be called after the observers or fake quants are created
+        for each sharing group, but before they are inserted into the graph. The
+        callback can be used to make final quantization adjustments, such as enforcing
+        specific scale and zero point on model input or output.
+
+        Args:
+          * `model`: the graph module being prepared.
+          * `edge_or_node_to_obs_or_fq`: a dictionary mapping each annotated edge and
+            node to the corresponding observer or fake quant object. Note that multiple
+            edges and/or nodes can map to the same observer / fake quant instance if
+            they were annotated with SharedQuantizationSpec. This dictionary can be
+            modified by the callback.
+        """
+        return