example

Author: xadupre

_doc/recipes/plot_dynamic_shapes_max.py

@@ -0,0 +1,184 @@
+"""
+Cannot export ``torch.sym_max(x.shape[0], y.shape[0])``
+=======================================================
+
+This is related to the following issues:
+`Cannot export torch.sym_max(x.shape[0], y.shape[0])
+<https://github.com/pytorch/pytorch/issues/150851>`_.
+
+The algorithm trying to automatically infer shapes after every operator
+in the exported program is something very aggreessive. Here is a case where
+it takes a wrong decision and how to get around it.
+
+Wrong Model
++++++++++++
+"""
+
+import torch
+from onnx_diagnostic import doc
+
+
+class Model(torch.nn.Module):
+    def forward(self, x, y, fact):
+        s1 = max(x.shape[0], y.shape[0])
+        s2 = max(x.shape[1], y.shape[1])
+        # Shapes cannot be known here.
+        z = torch.zeros((s1, s2), dtype=x.dtype)
+        z[: x.shape[0], : x.shape[1]] = x
+        z[: y.shape[0], : y.shape[1]] += y
+        return z * fact
+
+
+model = Model()
+x = torch.arange(6).reshape((2, 3))
+y = torch.arange(6).reshape((3, 2)) * 10
+fact = torch.tensor([[1, 2, 3]], dtype=x.dtype)
+z = model(x, y, fact)
+print(f"x.shape={x.shape}, y.shape={y.shape}, z.shape={z.shape}")
+
+# %%
+# Export
+# ++++++
+DYN = torch.export.Dim.DYNAMIC
+
+ep = torch.export.export(
+    model, (x, y, fact), dynamic_shapes=({0: DYN, 1: DYN}, {0: DYN, 1: DYN}, {1: DYN})
+)
+print(ep)
+
+# %%
+# But does it really work? Let's print the shapes.
+model_ep = ep.module()
+ez = model_ep(x, y, fact)
+print("case 1:", z.shape, ez.shape)
+
+# %%
+# Case with different shapes.
+
+x = torch.arange(4).reshape((2, 2))
+y = torch.arange(9).reshape((3, 3))
+try:
+    ez = model_ep(x, y, fact)
+    print("case 2:", model(x, y, fact).shape, ez.shape)
+except Exception as e:
+    print("case 2 failed:", e)
+
+# %%
+# It does not even compute. The exported program does not get the correct shape.
+#
+# Rewritten Model
+# +++++++++++++++
+#
+# ``max`` does not get captured, :func:`torch.sym_max` is no better,
+# :func:`torch.max` only works on tensors. Nothing really works.
+# We use a trick to introduce new shape the shape inference algorithm
+# cannot know. This requires to hide the failing logic in a custom operator.
+
+
+def make_undefined_dimension(i: int) -> torch.SymInt:
+    """
+    Uses for a custom op when a new dimension must be introduced to bypass
+    some verficiation. The following function creates a dummy output
+    with a dimension based on the content.
+
+    .. code-block:: python
+
+        def symbolic_shape(x, y):
+            return torch.empty(
+                x.shape[0],
+                make_undefined_dimension(min(x.shape[1], y[0])),
+            )
+    """
+    t = torch.ones((i * 2,))
+    t[:i] = 0
+    res = torch.nonzero(t).shape[0]
+    return res
+
+
+def copy_max_dimensions(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    shape = torch.max(torch.tensor(x.shape), torch.tensor(y.shape))
+    z = torch.zeros(tuple(shape), dtype=x.dtype)
+    z[0 : x.shape[0], 0 : x.shape[1]] = x[0 : x.shape[0], 0 : x.shape[1]]
+    z[0 : y.shape[0], 0 : y.shape[1]] += y[0 : y.shape[0], 0 : y.shape[1]]
+    return z
+
+
+def symbolic_shape(x, y):
+    return torch.empty(
+        tuple(
+            make_undefined_dimension(max(x.shape[i], y.shape[i])) for i in range(len(x.shape))
+        ),
+        dtype=x.dtype,
+    )
+
+
+def register(fct, fct_shape, namespace, fname):
+    schema_str = torch.library.infer_schema(fct, mutates_args=())
+    custom_def = torch.library.CustomOpDef(namespace, fname, schema_str, fct)
+    custom_def.register_kernel("cpu")(fct)
+    custom_def._abstract_fn = fct_shape
+
+
+register(
+    copy_max_dimensions, lambda x, y: symbolic_shape(x, y), "mylib", "copy_max_dimensions"
+)
+
+# %%
+# Now everything is registered. Let's rewrite the model.
+
+class RewrittenModel(torch.nn.Module):
+    def forward(self, x, y, fact):
+        z = torch.ops.mylib.copy_max_dimensions(x, y)
+        return z * fact
+
+# %%
+# And check it works.
+
+rewritten_model = RewrittenModel()
+x = torch.arange(6).reshape((2, 3))
+y = torch.arange(6).reshape((3, 2)) * 10
+z = rewritten_model(x, y, fact)
+print(f"x.shape={x.shape}, y.shape={y.shape}, z.shape={z.shape}")
+
+# %%
+# Export again
+# ++++++++++++
+
+ep = torch.export.export(
+    rewritten_model,
+    (x, y, fact),
+    dynamic_shapes=({0: DYN, 1: DYN}, {0: DYN, 1: DYN}, {1: DYN}),
+)
+print(ep)
+
+# %%
+# We check it works.
+
+model_ep = ep.module()
+ez = model_ep(x, y, fact)
+print("case 1:", z.shape, ez.shape)
+
+x = torch.arange(4).reshape((2, 2))
+y = torch.arange(9).reshape((3, 3))
+try:
+    ez = model_ep(x, y, fact)
+    print("case 2:", rewritten_model(x, y, fact).shape, ez.shape)
+except Exception as e:
+    print("case 2 failed:", e)
+
+# %%
+# Final Check on very different dimension
+# +++++++++++++++++++++++++++++++++++++++
+
+x = torch.arange(6 * 8).reshape((6, 8))
+y = torch.arange(10 * 4).reshape((10, 4)) * 10
+fact = torch.arange(8).reshape((1, -1))
+
+print("final case:", rewritten_model(x, y, fact).shape, model_ep(x, y, fact).shape)
+
+# %%
+# This is not perfect as we get an exported program but some logic
+# is hidden in a custom operator.
+
+
+doc.plot_legend("dynamic shapes\nworkaround\nmax(d1, d2)", "dynamic shapes", "yellow")

_doc/recipes/plot_dynamic_shapes_python_int.py

@@ -1,5 +1,5 @@
 """
-Do not use python int with dynamic shape
+Do not use python int with dynamic shapes
 =========================================
 
 :func:`torch.export.export` uses :class:`torch.SymInt` to operate on shapes and