Merge branch 'main' of https://github.com/sdpython/onnx-diagnostic into speedup

xadupre · xadupre · commit fafb38af54bb · 2025-06-19T08:44:24.000+02:00
diff --git a/_doc/conf.py b/_doc/conf.py
@@ -233,6 +233,7 @@ def linkcode_resolve(domain, info):
     "onnx-script": "https://github.com/microsoft/onnxscript",
     "onnxscript": "https://github.com/microsoft/onnxscript",
     "onnxscript Tutorial": "https://microsoft.github.io/onnxscript/tutorial/index.html",
+    "optree": "https://github.com/metaopt/optree",
     "Pattern-based Rewrite Using Rules With onnxscript": "https://microsoft.github.io/onnxscript/tutorial/rewriter/rewrite_patterns.html",
     "opsets": "https://onnx.ai/onnx/intro/concepts.html#what-is-an-opset-version",
     "pyinstrument": "https://pyinstrument.readthedocs.io/en/latest/",
diff --git a/_doc/recipes/plot_dynamic_shapes_json.py b/_doc/recipes/plot_dynamic_shapes_json.py
@@ -0,0 +1,113 @@
+"""
+JSON returns list when the original dynamic shapes are list or tuple
+====================================================================
+
+Dynamic shapes given to :func:`torch.export.export` must follow the
+same semantic. What if we confuse tuple and list when defining the dynamic shapes,
+how to restore the expected type assuming we know the inputs?
+Not often useful but maybe we will learn more about
+:epkg:`optree`.
+
+Dynamic Shapes After JSON
++++++++++++++++++++++++++
+
+JSON format does not make the difference between a list and a tuple.
+So after serializing to json and restoring, both of them become lists.
+"""
+
+import json
+import pprint
+import torch
+from onnx_diagnostic import doc
+from onnx_diagnostic.helpers import string_type
+from onnx_diagnostic.helpers.cache_helper import make_dynamic_cache
+from onnx_diagnostic.export.shape_helper import all_dynamic_shape_from_inputs
+
+bsize, nheads, slen, dim = 2, 1, 30, 96
+
+inputs = dict(
+    input_mask_position=(
+        torch.randint(15, size=(2, 3), dtype=torch.int64),
+        torch.randint(1, size=(2, 33), dtype=torch.int64),
+        torch.arange(3, dtype=torch.int64),
+    ),
+    past_key_values=make_dynamic_cache(
+        [(torch.randn(bsize, nheads, slen, dim), torch.randn(bsize, nheads, slen, dim))]
+    ),
+)
+
+print(string_type(inputs, with_shape=True))
+
+# %%
+# Function :func:`onnx_diagnostic.export.shape_helper.all_dynamic_shape_from_inputs`
+# produces the corresponding dynamic shapes assuming they are all dynamic.
+ds = all_dynamic_shape_from_inputs(inputs)
+pprint.pprint(ds)
+
+# %%
+# Converted into JSON.
+
+json_str = json.dumps(ds, indent=2, ensure_ascii=False)
+print(json_str)
+
+# %%
+# Restoration.
+ds2 = json.loads(json_str)
+pprint.pprint(ds2)
+
+# %%
+# tuple are replaced by list.
+
+# The trick to restore tuple when expected
+# ++++++++++++++++++++++++++++++++++++++++
+
+
+def flatten_unflatten_like_dynamic_shapes(obj):
+    if isinstance(obj, torch.Tensor):
+        return obj
+    flat, spec = torch.utils._pytree.tree_flatten(obj)
+    start = 0
+    end = 0
+    subtrees = []
+    for subspec in spec.children_specs:
+        end += subspec.num_leaves
+        value = subspec.unflatten(flat[start:end])
+        value = flatten_unflatten_like_dynamic_shapes(value)
+        subtrees.append(value)
+        start = end
+    if spec.type is dict or spec.context:
+        return dict(zip(spec.context, subtrees))
+    if spec.type is tuple:
+        return tuple(subtrees)
+    return subtrees
+
+
+def _align(inputs, ds):
+    if isinstance(inputs, torch.Tensor):
+        return ds
+    if isinstance(inputs, tuple):
+        return tuple(_align(o, d) for o, d in zip(inputs, ds))
+    if isinstance(inputs, list):
+        return [_align(o, d) for o, d in zip(inputs, ds)]
+    if isinstance(inputs, dict):
+        return {k: _align(inputs[k], d) for k, d in ds.items()}
+    raise TypeError(f"Unexpected types inputs is {type(inputs)}, ds is {type(ds)}")
+
+
+def fix_dynamic_shapes(inputs, dynamic_shapes):
+    flat_unflat_inputs = flatten_unflatten_like_dynamic_shapes(inputs)
+    return _align(flat_unflat_inputs, dynamic_shapes)
+
+
+fixed_ds = fix_dynamic_shapes(inputs, ds2)
+pprint.pprint(fixed_ds)
+
+# %%
+# The code changed tuple into list as expected.
+assert isinstance(ds2["input_mask_position"], list)
+assert isinstance(fixed_ds["input_mask_position"], tuple)
+
+
+# %%
+
+doc.plot_legend("dynamic shapes\nto json\nfrom json", "torch.export.export", "green")
diff --git a/_unittests/ut_export/test_shape_helper.py b/_unittests/ut_export/test_shape_helper.py
@@ -1,7 +1,10 @@
 import unittest
 import torch
 from onnx_diagnostic.ext_test_case import ExtTestCase, requires_transformers, requires_torch
-from onnx_diagnostic.export.shape_helper import all_dynamic_shape_from_inputs
+from onnx_diagnostic.export.shape_helper import (
+    all_dynamic_shape_from_inputs,
+    guess_dynamic_shapes_from_inputs,
+)
 from onnx_diagnostic.torch_models.hghub import get_untrained_model_with_inputs
 
 
@@ -10,23 +13,24 @@ class TestShapeHelper(ExtTestCase):
     @requires_torch("2.7.99")
     def test_all_dynamic_shape_from_inputs(self):
         ds = all_dynamic_shape_from_inputs((torch.randn((5, 6)), torch.randn((1, 6))))
+        self.assertEqual(({0: "d_0_0", 1: "d_0_1"}, {0: "d_1_0", 1: "d_1_1"}), ds)
+        ds = all_dynamic_shape_from_inputs([torch.randn((5, 6)), torch.randn((1, 6))])
         self.assertEqual([{0: "d_0_0", 1: "d_0_1"}, {0: "d_1_0", 1: "d_1_1"}], ds)
         ds = all_dynamic_shape_from_inputs(
             (torch.randn((5, 6)), torch.randn((1, 6))), dim_prefix=torch.export.Dim.AUTO
         )
         self.assertEqual(
-            [
+            (
                 {0: torch.export.Dim.AUTO, 1: torch.export.Dim.AUTO},
                 {0: torch.export.Dim.AUTO, 1: torch.export.Dim.AUTO},
-            ],
+            ),
             ds,
         )
 
     @requires_transformers("4.52")
     @requires_torch("2.7.99")
     def test_all_dynamic_shape_from_inputs_dynamic_cache(self):
         data = get_untrained_model_with_inputs("arnir0/Tiny-LLM")
-        print(self.string_type(data["inputs"], with_shape=True))
         ds = all_dynamic_shape_from_inputs(data["inputs"])
         self.assertEqual(
             {
@@ -41,6 +45,29 @@ def test_all_dynamic_shape_from_inputs_dynamic_cache(self):
             ds,
         )
 
+    @requires_transformers("4.52")
+    @requires_torch("2.7.99")
+    def test_guess_dynamic_shapes_from_inputs(self):
+        data = get_untrained_model_with_inputs("arnir0/Tiny-LLM", add_second_input=True)
+        guessed = guess_dynamic_shapes_from_inputs(
+            [data["inputs"], data["inputs2"]], auto="dd"
+        )
+        self.assertEqual(
+            (
+                (),
+                {
+                    "attention_mask": {0: "dd_0I0", 1: "dd_0I1"},
+                    "input_ids": {0: "dd_1I0", 1: "dd_1I1"},
+                    "past_key_values": [
+                        [{0: "dd_2I_0o_0l0", 2: "dd_2I_0o_0l2"}],
+                        [{0: "dd_2I_1o_0l0", 2: "dd_2I_1o_0l2"}],
+                    ],
+                    "position_ids": {0: "dd_3I0", 1: "dd_3I1"},
+                },
+            ),
+            guessed,
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)
diff --git a/onnx_diagnostic/export/dynamic_shapes.py b/onnx_diagnostic/export/dynamic_shapes.py
@@ -630,9 +630,12 @@ def __init__(
         method_name: str = "forward",
         name: str = "main",
     ):
-        assert isinstance(model, torch.nn.Module) or inspect.ismodule(
-            model
-        ), f"unexpected type for model={type(model)}, it must be a torch.nn.Module"
+        assert (
+            model is None or isinstance(model, torch.nn.Module) or inspect.ismodule(model)
+        ), (
+            f"unexpected type for model={type(model)}, "
+            f"it must be a torch.nn.Module or None"
+        )
         assert name, (
             f"name={name!r} cannot be empty this string is used to "
             f"display meaningful error messages"
@@ -641,26 +644,42 @@ def __init__(
         self.model = model
         self.level = level
         self.method_name = method_name
-        self.forward = getattr(model, method_name)
-        self.signature = inspect.signature(self.forward)
+        self.forward = getattr(model, method_name) if model is not None else None
+        self.signature = inspect.signature(self.forward) if self.forward else None
 
         # information about the signature
-        self.forward_parameter_names = set(
-            p.name
-            for p in self.signature.parameters.values()
-            if p.kind not in {p.VAR_POSITIONAL, p.VAR_KEYWORD}
+        self.forward_parameter_names = (
+            set(
+                p.name
+                for p in self.signature.parameters.values()
+                if p.kind not in {p.VAR_POSITIONAL, p.VAR_KEYWORD}
+            )
+            if self.signature
+            else None
+        )
+        self.forward_ordered_parameter_names = (
+            list(self.signature.parameters) if self.signature else None
+        )
+        self.forward_positioned_parameter_names = (
+            [
+                p.name
+                for p in self.signature.parameters.values()
+                if p.kind in (p.VAR_POSITIONAL, p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD)
+            ]
+            if self.signature
+            else None
+        )
+        names = (
+            [p.name for p in self.signature.parameters.values() if p.kind == p.VAR_POSITIONAL]
+            if self.signature
+            else None
         )
-        self.forward_ordered_parameter_names = list(self.signature.parameters)
-        self.forward_positioned_parameter_names = [
-            p.name
-            for p in self.signature.parameters.values()
-            if p.kind in (p.VAR_POSITIONAL, p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD)
-        ]
-        names = [
-            p.name for p in self.signature.parameters.values() if p.kind == p.VAR_POSITIONAL
-        ]
         self.forward_args = names[0] if names else None
-        names = [p.name for p in self.signature.parameters.values() if p.kind == p.VAR_KEYWORD]
+        names = (
+            [p.name for p in self.signature.parameters.values() if p.kind == p.VAR_KEYWORD]
+            if self.signature
+            else None
+        )
         self.forward_kwargs = names[0] if names else None
         self.forward_custom_op_schema = None
         self.forward_need_serialization = False
@@ -711,6 +730,7 @@ def process_inputs(
     @property
     def true_model_name(self) -> str:
         "Returns class name or module name."
+        assert self.model is not None, "model was None when the class was initialized."
         return (
             self.model.__class__.__name__
             if isinstance(self.model, torch.nn.Module)
@@ -942,7 +962,7 @@ def guess_dynamic_shapes(self, auto: Union[bool, str] = False) -> DYNAMIC_SHAPES
                 )
             )
         names = s2.pop()
-        for name in names:
+        for i, name in enumerate(names):
             assert name not in {"_diag", "verbose"}, (
                 f"{self.full_name}: unexpected parameter {name!r}, names={names}"
                 f"\ninputs[0]={string_type(self.inputs[0], with_shape=True)}"
@@ -968,6 +988,14 @@ def move_to_kwargs(
         with the corresponding dynamic shapes.
         *kwargs*, *dynamic_shapes* are modified inplace.
         """
+        assert (
+            self.signature is not None
+            and self.forward_parameter_names is not None
+            and self.forward_ordered_parameter_names is not None
+        ), (
+            "model was None when the class was initialized, "
+            "cannot move args to kwargs without the signature."
+        )
         sig = self.signature
         arg_dyn, kw_dyn = dynamic_shapes
         for i, p in enumerate(sig.parameters):
diff --git a/onnx_diagnostic/export/shape_helper.py b/onnx_diagnostic/export/shape_helper.py
@@ -1,5 +1,6 @@
-from typing import Any, Set
+from typing import Any, Dict, List, Set, Tuple, Union
 from ..helpers.cache_helper import flatten_unflatten_for_dynamic_shapes
+from .dynamic_shapes import ModelInputs
 
 
 def all_dynamic_shape_from_inputs(inputs: Any, dim_prefix: Any = "d") -> Any:
@@ -47,3 +48,79 @@ def tensor_to_shape(tensor):
     return flatten_unflatten_for_dynamic_shapes(
         inputs, change_function=tensor_to_shape, use_dict=True
     )
+
+
+def guess_dynamic_shapes_from_inputs(
+    inputs: List[Any], auto: Union[bool, str] = False
+) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+    """
+    Guesses which dimension is dimension from a set of inputs.
+    Every dimension having different values over multiple sets
+    of inputs. Every dimension not changing remains static.
+
+    :param inputs: a list of input sets
+    :param auto: True for ``torch.export.Dim.AUTO``,
+        False for ``torch.export.Dim.DYNAMIC``,
+        a string to get a unique string for every dynamic dimension
+    :return: args and kwargs
+
+    .. runpython::
+        :showcode:
+
+        import pprint
+        import torch
+        from onnx_diagnostic.helpers.cache_helper import make_dynamic_cache
+        from onnx_diagnostic.export.shape_helper import guess_dynamic_shapes_from_inputs
+
+        bsize, nheads, slen, dim = 2, 1, 30, 96
+        inputs1 = dict(
+            input_ids=torch.randint(15, size=(2, 3), dtype=torch.int64),
+            attention_mask=torch.randint(1, size=(2, 33), dtype=torch.int64),
+            position_ids=torch.arange(3, dtype=torch.int64),
+            past_key_values=make_dynamic_cache(
+                [
+                    (
+                        torch.randn(bsize, nheads, slen, dim),
+                        torch.randn(bsize, nheads, slen, dim),
+                    ),
+                ]
+            ),
+        )
+        bsize, nheads, slen, dim = 3, 1, 33, 96
+        inputs2 = dict(
+            input_ids=torch.randint(15, size=(3, 4), dtype=torch.int64),
+            attention_mask=torch.randint(1, size=(3, 34), dtype=torch.int64),
+            position_ids=torch.arange(4, dtype=torch.int64),
+            past_key_values=make_dynamic_cache(
+                [
+                    (
+                        torch.randn(bsize, nheads, slen, dim),
+                        torch.randn(bsize, nheads, slen, dim),
+                    ),
+                ]
+            ),
+        )
+        ds = guess_dynamic_shapes_from_inputs([inputs1, inputs2], auto="d")
+        pprint.pprint(ds)
+
+    This function returns something equivalent to function
+    :class:`torch.export.dynamic_shapes.AdditionalInputs` but this
+    one needs a model.
+
+    .. runpython::
+        :showcode:
+
+        import pprint
+        import torch
+        from onnx_diagnostic.helpers.cache_helper import make_dynamic_cache
+        from onnx_diagnostic.export.shape_helper import guess_dynamic_shapes_from_inputs
+        from onnx_diagnostic.torch_models.hghub import get_untrained_model_with_inputs
+
+        data = get_untrained_model_with_inputs("arnir0/Tiny-LLM", add_second_input=True)
+        ds = torch.export.dynamic_shapes.AdditionalInputs()
+        ds.add((), data["inputs"])
+        ds.add((), data["inputs2"])
+        pprint.pprint(ds.dynamic_shapes(data["model"], (), data["inputs"]))
+    """
+    mi = ModelInputs(None, inputs)
+    return mi.guess_dynamic_shapes(auto=auto)
diff --git a/onnx_diagnostic/helpers/cache_helper.py b/onnx_diagnostic/helpers/cache_helper.py
