Treat arg-cache length mismatch as a cache miss in ChunkSizeTuner

openfold3/core/utils/chunk_utils.py

@@ -388,6 +388,9 @@ def test_chunk_size(chunk_size):
         return candidates[lo]
 
     def _compare_arg_caches(self, ac1, ac2):
+        # When recursing this tests that tensors have the same rank
+        if len(ac1) != len(ac2):
+            return False
         consistent = True
         for a1, a2 in zip(ac1, ac2, strict=True):
             assert type(a1) is type(a2)
@@ -412,12 +415,11 @@ def tune_chunk_size(
         max_chunk_size=DEFAULT_MAX_CHUNK_SIZE,
     ) -> int:
         def remove_tensors(a):
-            return a.shape if type(a) is torch.Tensor else a
+            return (a.shape, a.dtype.itemsize) if type(a) is torch.Tensor else a
 
         arg_data = tree_map(remove_tensors, args, object)
         if self.cached_arg_data is not None:
             # If args have changed shape/value, we need to re-tune
-            assert len(self.cached_arg_data) == len(arg_data)
             consistent = self._compare_arg_caches(self.cached_arg_data, arg_data)
         else:
             # Otherwise, we can reuse the precomputed value

openfold3/tests/utils/test_utils.py

@@ -244,3 +244,78 @@ def fn(arg, chunk_size, _max=max_viable):
                     fn, args=(None,), min_chunk_size=4, max_chunk_size=1024
                 )
                 self.assertEqual(result, expected)
+
+    def test_chunk_size_tuner_handles_arg_rank_change(self):
+        tuner = ChunkSizeTuner()
+
+        def fn(t, chunk_size):
+            if chunk_size > 2 ** t.dim() * t.dtype.itemsize:
+                raise RuntimeError("Chunk size too large")
+            return t
+
+        first = tuner.tune_chunk_size(
+            representative_fn=fn,
+            args=(torch.zeros(2, 3, 4, 5),),
+            min_chunk_size=4,
+            max_chunk_size=256,
+        )
+        second = tuner.tune_chunk_size(
+            representative_fn=fn,
+            args=(torch.zeros(2, 3, 4, 5, 6),),
+            min_chunk_size=4,
+            max_chunk_size=256,
+        )
+
+        self.assertNotEqual(
+            first, second, "Chunk size should have been re-tuned for new arg rank"
+        )
+
+    def test_chunk_size_tuner_handles_dtype_bytes_change(self):
+        tuner = ChunkSizeTuner()
+
+        def fn(t, chunk_size):
+            if chunk_size > 2 ** t.dim() * t.dtype.itemsize:
+                raise RuntimeError("Chunk size too large")
+            return t
+
+        first = tuner.tune_chunk_size(
+            representative_fn=fn,
+            args=(torch.zeros(2, 3, 4, 5, dtype=torch.float32),),
+            min_chunk_size=4,
+            max_chunk_size=256,
+        )
+        second = tuner.tune_chunk_size(
+            representative_fn=fn,
+            args=(torch.zeros(2, 3, 4, 5, dtype=torch.bfloat16),),
+            min_chunk_size=4,
+            max_chunk_size=256,
+        )
+
+        self.assertNotEqual(
+            first, second, "Chunk size should have been re-tuned for new dtype bytes"
+        )
+
+    def test_chunk_size_tuner_handles_arg_count_change(self):
+        tuner = ChunkSizeTuner()
+
+        def fn(*args, chunk_size):
+            if chunk_size > 2 ** len(args):
+                raise RuntimeError("Chunk size too large")
+            return args
+
+        first = tuner.tune_chunk_size(
+            representative_fn=fn,
+            args=(1, 2, 3, 4, 5),
+            min_chunk_size=4,
+            max_chunk_size=256,
+        )
+        second = tuner.tune_chunk_size(
+            representative_fn=fn,
+            args=(1, 2, 3, 4, 5, 6),
+            min_chunk_size=4,
+            max_chunk_size=256,
+        )
+
+        self.assertNotEqual(
+            first, second, "Chunk size should have been re-tuned for new arg count"
+        )