update QDQ config for P1 models (#204)

* update for p1

* updated

* update test

* fix

* update test

---------

Co-authored-by: hualxie <hualxie@microsoft.com>

Author: xieofxie

src/winml/modelkit/analyze/runtime_checker/result_processor.py

@@ -267,7 +267,7 @@ def check_df_consistent(
     for group_key, group_df in grouped:
         eval_df = group_df
         if placeholder_col in group_df.columns:
-            eval_df = group_df[not group_df[placeholder_col]]
+            eval_df = group_df[group_df[placeholder_col].isna() | (group_df[placeholder_col] == "")]
 
         # If all rows are placeholders, this group should not trigger conflicts.
         if eval_df.empty:

src/winml/modelkit/pattern/op_input_gen/conv_input_generator.py

@@ -259,6 +259,16 @@ def get_infinite_property_names(self) -> list[str]:
             "B_shape",
         ]
 
+    def get_qdq_config(self):
+        """Return QDQ configuration for Conv operator inputs."""
+        # B/Y can be non-QDQ from P1 models
+        return {
+            "X": QDQParameterConfig(support_activation=True),
+            "W": QDQParameterConfig(support_weight=True),
+            "B": QDQParameterConfig(support_non_qdq=True, qdq_types=[SupportedONNXType.INT32]),
+            "Y": QDQParameterConfig(support_non_qdq=True, support_activation=True),
+        }
+
 
 @register_runtime_checker_op
 class ConvTransposeInputGenerator(ConvInputGenerator):

src/winml/modelkit/pattern/op_input_gen/matmul_input_generator.py

@@ -304,5 +304,5 @@ def get_qdq_config(self):
         return {
             "A": QDQParameterConfig(support_activation=True),
             "B": QDQParameterConfig(support_weight=True),
-            "C": QDQParameterConfig(qdq_types=[SupportedONNXType.INT32]),
+            "C": QDQParameterConfig(support_non_qdq=True, qdq_types=[SupportedONNXType.INT32]),
         }

src/winml/modelkit/pattern/op_input_gen/resize_input_generator.py

@@ -95,6 +95,22 @@ def get_input_and_infinite_attribute_combinations(self) -> list[dict[str, InputC
                 }
             )
 
+            # p1 model - segmentation
+            combinations.append(
+                {
+                    "coordinate_transformation_mode": "pytorch_half_pixel",
+                    "cubic_coeff_a": -0.75,
+                    "mode": "linear",
+                    "nearest_mode": "floor",
+                    "X": InputShapeConstraint(x_shape),
+                    # Explicit roi to avoid empty-optional rejection on older schemas
+                    "roi": InputValueConstraint(np.zeros(2 * ndim, dtype=np.float32)),
+                    "scales": InputValueConstraint(scales_up),
+                    "extrapolation_value": 0.0,
+                    "axes": list(range(ndim)),  # All axes
+                }
+            )
+
             # Combination using scales (downsample) - empty sizes
             combinations.append(
                 {

src/winml/modelkit/pattern/op_input_gen/transpose_input_generator.py

@@ -126,5 +126,5 @@ def get_infinite_property_names(self) -> list[str]:
     def get_qdq_config(self):
         """Return QDQ configuration for Transpose operator inputs."""
         return {
-            "data": QDQParameterConfig(support_activation=True),
+            "data": QDQParameterConfig(support_non_qdq=True, support_activation=True),
         }

tests/unit/analyze/core/test_qdq.py

@@ -827,8 +827,9 @@ class TestIterQDQCombinationsTagSchema:
     - Optional QDQ input not provided: present in 'qdq_types' as ''
 
     Note: When an operator has pass-through inputs, 'input_is_constant' contains only
-    those pass-through inputs (Gather). When no pass-through inputs exist, all inputs
-    are in 'input_is_constant' from the outer constant-combination loop (Gemm).
+    those pass-through inputs (Gather). When an optional input supports both QDQ and
+    non-QDQ (pass-through) modes (Gemm C), 'input_is_constant' contains that input only
+    in the non-QDQ combination; pure QDQ inputs (A, B) never appear in 'input_is_constant'.
     """
 
     @pytest.fixture
@@ -970,29 +971,41 @@ def test_gemm_weight_b_present_in_qdq_types(self, gemm_gen) -> None:
             assert final_tags["qdq_types"]["B"] != ""
 
     def test_gemm_qdq_inputs_not_in_input_is_constant(self, gemm_gen) -> None:
-        """A (activation) and B (weight) not exist in input_is_constant.
+        """A (activation) and B (weight) never appear in input_is_constant.
 
-        Gemm has no pass-through inputs, so input_is_constant does not exist.
+        C may appear in input_is_constant for its non-QDQ (pass-through) combination,
+        but pure QDQ inputs A and B are never pass-through.
         """
         gen = gemm_gen
         kwargs, tags = self._gemm_float_c_provided_kwargs_tags(gen)
         results = list(gen.iter_const_and_dynamic_models(kwargs, tags))
         assert len(results) > 0
         for _, final_tags in results:
-            assert "input_is_constant" not in final_tags
+            ic = final_tags.get("input_is_constant", {})
+            assert "A" not in ic
+            assert "B" not in ic
 
     # ---- Gemm: optional QDQ input (C) ----
 
     def test_gemm_optional_c_provided_has_int32_type(self, gemm_gen) -> None:
-        """When optional C is provided as constant, qdq_types['C'] is INT32 annotation."""
+        """When optional C is provided and quantized, qdq_types['C'] is INT32 annotation.
+
+        C supports both QDQ (INT32) and non-QDQ (pass-through) modes. In the non-QDQ
+        combination qdq_types['C'] is None; when quantized it must be INT32.
+        """
         gen = gemm_gen
         int32_ann = dtypes.SupportedONNXType.INT32.annotation
         kwargs, tags = self._gemm_float_c_provided_kwargs_tags(gen)
         results = list(gen.iter_const_and_dynamic_models(kwargs, tags))
         assert len(results) > 0
+        int32_seen = False
         for _, final_tags in results:
             assert "C" in final_tags["qdq_types"]
-            assert final_tags["qdq_types"]["C"] == int32_ann
+            c_type = final_tags["qdq_types"]["C"]
+            if c_type is not None:
+                assert c_type == int32_ann
+                int32_seen = True
+        assert int32_seen, "Expected at least one result with C quantized as INT32"
 
     def test_gemm_optional_c_not_provided_recorded_as_empty_in_qdq_types(self, gemm_gen) -> None:
         """When optional C is not provided (None), qdq_types['C'] is '' (not omitted)."""
@@ -1045,8 +1058,8 @@ class TestIterQDQCombinations:
             ("Concat", 240),  # 15 base shapes/axes * 4 variadic counts * 4 activation types
             (
                 "Conv",
-                1536,
-            ),  # shape 3 * auto_pad 4 * group_opts 2 * kernel shape 2 * optional b 2 * 16 = 1536
+                1536 * 4,
+            ),  # shape 3 * attrs 4 * 2 * kernel shape 2 * opt B 2 * 16 * B/Y non qdq 4
             (
                 "ConvTranspose",
                 3072,
@@ -1071,8 +1084,8 @@ class TestIterQDQCombinations:
             ("Gelu", unary_input_shapes * 4 * 2),  # 64
             (
                 "Gemm",
-                2304,
-            ),  # attributes 2 * 2 * 3 * 3 * C dim 4 * 16 = 2304
+                36 * 16 * (4 + 3 * 2),
+            ),  # attributes (2 * 2 * 3 * 3) * QDQ * C (qdq + non-qdq * opt)
             ("GlobalAveragePool", 3 * 4),  # 12
             ("InstanceNormalization", 3 * 16),  # 48
             ("LayerNormalization", 5 * 2 * 2 * 16),  # 320
@@ -1094,9 +1107,9 @@ class TestIterQDQCombinations:
             ("Reshape", 36 * 4 * 2 * 2),  # allowzero 2 * is_constant 2
             (
                 "Resize",
-                2880,
+                3456,
             ),  # shape 4 * T2 3 * QDQ 4 * antialias 2
-            # * attribute 5 * (optional input 4 + 2)
+            # * attribute 6 * (optional input 4 + 2)
             (
                 "ScatterND",
                 1680,
@@ -1112,7 +1125,7 @@ class TestIterQDQCombinations:
             # All unary use this and it is enough
             ("Tanh", unary_input_shapes * 4),  # 32
             ("TopK", 768),  # QDQ 4 * example 12 * k is_constant 2 * parameter 8
-            ("Transpose", 11 * 4 * 2),  # 88
+            ("Transpose", 11 * 4 * 2 * 2),  # cases * QDQ * opt perm * non_qdq data
             ("Unsqueeze", 208),  # 26 * 4 QDQ types * 2 is_constant axes
             (
                 "Where",