Add discrepancies when comparing the execution of two models (#79)

* update requirements

* add discrepancies figures

* fix command line

* doc

CHANGELOGS.rst

@@ -5,7 +5,7 @@ Change Logs
 +++++
 
 * :pr:`77`: supports ConcatOfShape and Slice with the light API
-* :pr:`76`: add a mode to compare models without execution
+* :pr:`76`, :pr:`79`: add a mode to compare models without execution
 * :pr:`75`: add QuickGelu to ExtendedReferenceEvaluator
 * :pr:`71`: adds tools to compare two onnx graphs
 * :pr:`61`: adds function to plot onnx model as graphs

_unittests/ut_reference/test_evaluator_yield.py

@@ -462,6 +462,31 @@ def test_compare_execution(self):
         self.assertIn("CAAA Constant", text)
         self.assertEqual(len(align), 5)
 
+    def test_compare_execution_discrepancies(self):
+        m1 = parse_model(
+            """
+            <ir_version: 8, opset_import: [ "": 18]>
+            agraph (float[N] x) => (float[N] z) {
+                two = Constant <value_float=2.0> ()
+                four = Add(two, two)
+                z = Mul(x, x)
+            }"""
+        )
+        m2 = parse_model(
+            """
+            <ir_version: 8, opset_import: [ "": 18]>
+            agraph (float[N] x) => (float[N] z) {
+                two = Constant <value_float=2.0> ()
+                z = Mul(x, x)
+            }"""
+        )
+        res1, res2, align, dc = compare_onnx_execution(m1, m2, keep_tensor=True)
+        text = dc.to_str(res1, res2, align)
+        print(text)
+        self.assertIn("CAAA Constant", text)
+        self.assertIn("| a=", text)
+        self.assertIn(" r=", text)
+
     def test_no_execution(self):
         model = make_model(
             make_graph(

onnx_array_api/_command_lines_parser.py

@@ -106,9 +106,15 @@ def get_parser_compare() -> ArgumentParser:
     parser.add_argument(
         "-c",
         "--column-size",
-        default=50,
+        default=60,
         help="column size when displaying the results",
     )
+    parser.add_argument(
+        "-d",
+        "--discrepancies",
+        default=0,
+        help="show precise discrepancies when mode is execution",
+    )
     return parser
 
 
@@ -120,7 +126,11 @@ def _cmd_compare(argv: List[Any]):
     onx1 = onnx.load(args.model1)
     onx2 = onnx.load(args.model2)
     res1, res2, align, dc = compare_onnx_execution(
-        onx1, onx2, verbose=args.verbose, mode=args.mode
+        onx1,
+        onx2,
+        verbose=args.verbose,
+        mode=args.mode,
+        keep_tensor=args.discrepancies in (1, "1", "True", True),
     )
     text = dc.to_str(res1, res2, align, column_size=int(args.column_size))
     print(text)

onnx_array_api/reference/evaluator_yield.py

@@ -57,6 +57,7 @@ class ResultExecution:
     summary: str
     op_type: str
     name: str
+    value: Optional[Any] = None
 
     def __len__(self) -> int:
         return 6
@@ -122,9 +123,11 @@ def make_summary(value: Any, length: int = 4, modulo: int = 26) -> str:
         else:
             value2 = value.flatten().astype(np.float64)
         value4 = value2.reshape((4, -1)).sum(axis=1)
-    value4i = value4.astype(np.int64) % modulo
-    s = "".join([chr(65 + i) for i in value4i])
-    return s
+    value4 = np.where(np.abs(value4) < 1e10, value4, np.nan)
+    s = []
+    for v in value4:
+        s.append("?" if np.isnan(v) else (chr(65 + int(v) % modulo)))
+    return "".join(s)
 
 
 class YieldEvaluator:
@@ -228,6 +231,7 @@ def enumerate_summarized(
         output_names: Optional[List[str]] = None,
         feed_inputs: Optional[Dict[str, Any]] = None,
         raise_exc: bool = True,
+        keep_tensor: bool = False,
     ) -> Iterator[ResultExecution]:
         """
         Executes the onnx model and enumerate intermediate results without their names.
@@ -236,17 +240,40 @@ def enumerate_summarized(
         :param feed_inputs: dictionary `{ input name: input value }`
         :param raise_exc: raises an exception if the execution fails or stop
             where it is
+        :param keep_tensor:keep the tensor in order to compute precise distances
         :return: iterator on ResultExecution
         """
         for kind, name, value, op_type in self.enumerate_results(
             output_names, feed_inputs, raise_exc=raise_exc
         ):
             summary = make_summary(value)
             yield ResultExecution(
-                kind, value.dtype, value.shape, summary, op_type, name
+                kind,
+                value.dtype,
+                value.shape,
+                summary,
+                op_type,
+                name,
+                value=value if keep_tensor else None,
             )
 
 
+def discrepancies(
+    expected: np.ndarray, value: np.ndarray, eps: float = 1e-7
+) -> Dict[str, float]:
+    """
+    Computes absolute error and relative error between two matrices.
+    """
+    assert (
+        expected.size == value.size
+    ), f"Incompatible shapes v1.shape={expected.shape}, v2.shape={value.shape}"
+    expected = expected.ravel().astype(np.float32)
+    value = value.ravel().astype(np.float32)
+    diff = np.abs(expected - value)
+    rel = diff / (np.abs(expected) + eps)
+    return dict(aerr=float(diff.max()), rerr=float(rel.max()))
+
+
 class DistanceExecution:
     """
     Computes a distance between two results.
@@ -403,6 +430,14 @@ def to_str(
                 d = self.distance_pair(d1, d2)
                 symbol = "=" if d == 0 else "~"
                 line = f"{symbol} | {_align(str(d1), column_size)} | {_align(str(d2), column_size)}"
+                if (
+                    d1.value is not None
+                    and d2.value is not None
+                    and d1.value.size == d2.value.size
+                ):
+                    disc = discrepancies(d1.value, d2.value)
+                    a, r = disc["aerr"], disc["rerr"]
+                    line += f" | a={a:.3f} r={r:.3f}"
             elif i == last[0]:
                 d2 = s2[j]
                 line = (
@@ -551,6 +586,7 @@ def compare_onnx_execution(
     verbose: int = 0,
     raise_exc: bool = True,
     mode: str = "execute",
+    keep_tensor: bool = False,
 ) -> Tuple[List[ResultExecution], List[ResultExecution], List[Tuple[int, int]]]:
     """
     Compares the execution of two onnx models.
@@ -566,6 +602,7 @@ def compare_onnx_execution(
     :param raise_exc: raise exception if the execution fails or stop at the error
     :param mode: the model should be executed but the function can be executed
         but the comparison may append on nodes only
+    :param keep_tensor: keeps the tensor in order to compute a precise distance
     :return: four results, a sequence of results for the first model and the second model,
         the alignment between the two, DistanceExecution
     """
@@ -589,15 +626,15 @@ def compare_onnx_execution(
             print("[compare_onnx_execution] execute first model")
         res1 = list(
             YieldEvaluator(model1).enumerate_summarized(
-                None, feeds1, raise_exc=raise_exc
+                None, feeds1, raise_exc=raise_exc, keep_tensor=keep_tensor
             )
         )
         if verbose:
             print(f"[compare_onnx_execution] got {len(res1)} results")
             print("[compare_onnx_execution] execute second model")
         res2 = list(
             YieldEvaluator(model2).enumerate_summarized(
-                None, feeds2, raise_exc=raise_exc
+                None, feeds2, raise_exc=raise_exc, keep_tensor=keep_tensor
             )
         )
     elif mode == "nodes":