fix script

Author: xadupre

_unittests/ut_export/test_dynamic_shapes.py

@@ -679,6 +679,42 @@ def test_couple_input_ds_replace_string(self):
             ).replace_string_by(value="DYN"),
         )
 
+    def test_couple_input_ds_replace_by_string(self):
+        T3x1 = torch.rand((3, 1))
+        T3x4 = torch.rand((3, 4))
+        T5x1 = torch.rand((5, 1))
+        args = (T5x1,)
+        kwargs = {"A": T3x4, "B": (T3x1, T3x1)}
+        ds_batch = {0: "batch"}
+        ds_batch_seq = {0: "batch", 1: "seq"}
+        ds = {"X": ds_batch, "A": ds_batch, "B": (ds_batch, ds_batch)}
+        Cls = CoupleInputsDynamicShapes
+        res = Cls(
+            args,
+            kwargs,
+            ds,
+            args_names=["X"],
+        ).replace_by_string()
+        self.assertEqual(ds, res)
+
+        ds_batch = {0: torch.export.Dim("batch")}
+        ds_batch_seq = {0: torch.export.Dim("batch"), 1: torch.export.Dim.DYNAMIC}
+        ds = {"X": ds_batch, "A": ds_batch_seq, "B": (ds_batch_seq, ds_batch_seq)}
+        res = Cls(
+            args,
+            kwargs,
+            ds,
+            args_names=["X"],
+        ).replace_by_string()
+        self.assertEqual(
+            {
+                "X": {0: "batch"},
+                "A": {0: "batch", 1: "Dim1"},
+                "B": ({0: "batch", 1: "Dim1"}, {0: "batch", 1: "Dim1"}),
+            },
+            res,
+        )
+
     def test_couple_input_ds_change_dynamic_dimensions(self):
         T257 = torch.arange(2 * 5 * 7).reshape((2, 5, 7))
         T29 = torch.arange(2 * 9).reshape((2, 9))
@@ -703,6 +739,37 @@ def test_couple_input_ds_change_dynamic_dimensions_fixed(self):
         self.assertEqual((1, 5, 8), new_input["A"].shape)
         self.assertEqual((1, 50), new_input["B"].shape)
 
+    def test_dynamic_cache_replace_by_string(self):
+        n_layers = 2
+        bsize, nheads, slen, dim = 2, 4, 3, 7
+        cache = make_dynamic_cache(
+            [
+                (torch.randn(bsize, nheads, slen, dim), torch.randn(bsize, nheads, slen, dim))
+                for i in range(n_layers)
+            ]
+        )
+
+        DYN = torch.export.Dim.DYNAMIC
+        ds = {
+            "cache": [
+                [{0: DYN, 1: DYN}, {0: DYN, 1: DYN}],
+                [{0: DYN, 1: DYN}, {0: DYN, 1: DYN}],
+            ]
+        }
+        inst = CoupleInputsDynamicShapes((), dict(cache=cache), ds)
+        as_string = inst.replace_by_string()
+        self.assertEqual(
+            {
+                "cache": [
+                    {0: "Dim0", 1: "Dim1"},
+                    {0: "Dim2", 1: "Dim3"},
+                    {0: "Dim4", 1: "Dim5"},
+                    {0: "Dim6", 1: "Dim7"},
+                ]
+            },
+            as_string,
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

onnx_diagnostic/_command_lines_parser.py

@@ -291,7 +291,8 @@ def get_parser_validate() -> ArgumentParser:
         "--ortfusiontype",
         required=False,
         help="applies onnxruntime fusion, this parameter should contain the "
-        "model type or multiple values separated by |",
+        "model type or multiple values separated by `|`. `ALL` can be used "
+        "to run them all",
     )
     parser.add_argument("-v", "--verbose", default=0, type=int, help="verbosity")
     parser.add_argument("--dtype", help="changes dtype if necessary")

onnx_diagnostic/export/dynamic_shapes.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 import numpy as np
 import torch
 from ..helpers import string_type
@@ -8,6 +8,30 @@
 DYNAMIC_SHAPES = Tuple[Tuple[Any, ...], Dict[str, Any]]
 
 
+def flatten_dynamic_shapes(ds: Any) -> Any:
+    """Flattens the dynamic shapes."""
+    if isinstance(ds, list):
+        return _flat_list([flatten_dynamic_shapes(t) for t in ds])
+    if isinstance(ds, tuple):
+        return tuple(_flat_list([flatten_dynamic_shapes(t) for t in ds]))
+    if isinstance(ds, dict):
+        if all(isinstance(i, int) for i in ds):
+            # That's a dynamic shape
+            return ds
+        return _flat_list([flatten_dynamic_shapes(t) for t in ds.values()])
+    raise AssertionError(f"Not implemented for {type(ds)}: {ds}")
+
+
+def _flat_list(li: List[Any]) -> List[Dict[int, str]]:
+    res = []
+    for t in li:
+        if isinstance(t, dict):
+            res.append(t)
+        else:
+            res.extend(t)
+    return res
+
+
 class CoupleInputsDynamicShapes:
     """
     Pair inputs / dynamic shapes.
@@ -76,7 +100,7 @@ def _replace_string_dim_tensor(cls, inputs, ds, value=None):
         assert isinstance(inputs, torch.Tensor), f"unexpected type for inputs {type(inputs)}"
         assert isinstance(ds, dict) and all(isinstance(s, int) for s in ds), (
             f"Unexpected types, inputs is a Tensor but ds is {ds}, "
-            f"a dictionary is expected to specify a dimension dimension"
+            f"a dictionary is expected to specify a dimension"
         )
         if value is None:
             value = torch.export.Dim.DYNAMIC
@@ -86,6 +110,57 @@ def _replace_string_dim_tensor(cls, inputs, ds, value=None):
                 new_ds[i] = value
         return new_ds
 
+    def replace_by_string(self):
+        """
+        Replaces dimensions by strings.
+
+        Example:
+
+        .. runpython::
+            :showcode:
+
+            import torch
+            from onnx_diagnostic.export.dynamic_shapes import CoupleInputsDynamicShapes
+
+            Dim = torch.export.Dim
+            T3x1 = torch.rand((3, 1))
+            T3x4 = torch.rand((3, 4))
+            ds_batch = {0: Dim("batch")}
+            ds_batch_seq = {0: Dim("batch"), 1: Dim("seq")}
+            kwargs = {"A": T3x4, "B": (T3x1, T3x1)}
+            ds = {"A": ds_batch, "B": (ds_batch, ds_batch_seq)}
+            print(CoupleInputsDynamicShapes((), kwargs, ds).replace_by_string())
+        """
+        unique = set()
+        return self._generic_walker(
+            lambda inputs, ds, unique=unique: self._replace_dim_tensor_by_string(
+                inputs, ds, unique=unique
+            )
+        )
+
+    @classmethod
+    def _replace_dim_tensor_by_string(cls, inputs, ds, unique: Set[str]):
+        assert isinstance(inputs, torch.Tensor), f"unexpected type for inputs {type(inputs)}"
+        assert isinstance(ds, dict) and all(isinstance(s, int) for s in ds), (
+            f"Unexpected types, inputs is a Tensor but ds is {ds}, "
+            f"a dictionary is expected to specify a dimension"
+        )
+        new_ds = ds.copy()
+        for i, v in ds.items():
+            if isinstance(v, str):
+                assert v not in unique, f"Dimension {v!r} is already defined in {unique}"
+                unique.add(v)
+                new_ds[i] = v
+            elif v in (torch.export.Dim.DYNAMIC, torch.export.Dim.AUTO):
+                name = f"Dim{len(unique)}"
+                new_ds[i] = name
+                unique.add(name)
+            else:
+                name = v.__name__
+                unique.add(name)
+                new_ds[i] = name
+        return new_ds
+
     def invalid_dimensions_for_export(self):
         """
         Tells if the inputs are valid based on the dynamic shapes definition.
@@ -252,6 +327,9 @@ def _generic_walker_step(cls, processor: Callable, inputs, ds):
             f"map this class with the given dynamic shapes."
         )
         flat, _spec = torch.utils._pytree.tree_flatten(inputs)
+        if all(isinstance(t, torch.Tensor) for t in flat):
+            # We need to flatten dynamic shapes as well
+            ds = flatten_dynamic_shapes(ds)
         return cls._generic_walker_step(processor, flat, ds)
 
     class ChangeDimensionProcessor:

onnx_diagnostic/torch_models/test_helper.py

@@ -5,6 +5,7 @@
 import time
 import onnx
 import torch
+from ..export import CoupleInputsDynamicShapes
 from ..helpers import max_diff, string_type, string_diff
 from ..helpers.helper import flatten_object
 from ..helpers.rt_helper import make_feeds
@@ -506,7 +507,12 @@ def validate_model(
         ), f"Missing attribute num_attention_heads in configuration {config}"
         num_attention_heads = config.num_attention_heads
 
-        model_types = ortfusiontype.split("|")
+        if ortfusiontype == "ALL":
+            from onnxruntime.transformers.optimizer import MODEL_TYPES
+
+            model_types = sorted(MODEL_TYPES)
+        else:
+            model_types = ortfusiontype.split("|")
         for model_type in model_types:
             flavour = f"ort{model_type}"
             summary[f"version_{flavour}_hidden_size"] = hidden_size
@@ -517,13 +523,15 @@ def validate_model(
                 print(f"[validate_model] run onnxruntime fusion for {model_type!r}")
             input_filename = data["onnx_filename"]
             output_path = f"{os.path.splitext(input_filename)[0]}.ort.{model_type}.onnx"
-            run_ort_fusion(
+            ort_sum, ort_data = run_ort_fusion(
                 input_filename,
                 output_path,
                 model_type=model_type,
                 num_attention_heads=num_attention_heads,
                 hidden_size=hidden_size,
             )
+            summary.update(ort_sum)
+            data.update(ort_data)
             data[f"onnx_filename_{flavour}"] = output_path
             duration = time.perf_counter() - begin
             summary[f"time_ortfusion_{flavour}"] = duration
@@ -590,7 +598,7 @@ def call_exporter(
             optimization=optimization,
         )
         return summary, data
-    if exporter.startswith("custom-"):
+    if exporter == "custom" or exporter.startswith("custom"):
         # torch export
         summary, data = call_torch_export_custom(
             exporter=exporter,
@@ -758,6 +766,9 @@ def _mk(key):
 
     if input_data_key in data:
         source = data[input_data_key]
+        if not os.path.exists(source):
+            summary[_mk("ERR_onnx_missing")] = f"FileNotFoundError({source!r})"
+            return summary, data
         summary[input_data_key] = source
         summary[_mk("onnx_size")] = os.stat(source).st_size
     else:
@@ -866,7 +877,7 @@ def call_torch_export_onnx(
     assert "model" in data, f"model is missing from data: {sorted(data)}"
     assert "inputs_export" in data, f"inputs_export is missing from data: {sorted(data)}"
     summary: Dict[str, Union[str, int, float]] = {}
-    dynamo = "nostrict" not in exporter
+    dynamo = "dynamo" in exporter
     args, kwargs = split_args_kwargs(data["inputs_export"])
     ds = data.get("dynamic_shapes", None)
     if verbose:
@@ -884,15 +895,23 @@ def call_torch_export_onnx(
     summary["export_args"] = string_type(args, with_shape=True)
     summary["export_kwargs"] = string_type(kwargs, with_shape=True)
 
+    export_export_kwargs = (
+        dict(dynamo=True, dynamic_shapes=ds)
+        if dynamo
+        else dict(
+            dynamo=False,
+            dynamic_axes=CoupleInputsDynamicShapes(args, kwargs, ds).replace_by_string(),
+        )
+    )
+
     begin = time.perf_counter()
     if quiet:
         try:
             epo = torch.onnx.export(
                 data["model"],
                 args,
                 kwargs=kwargs,
-                dynamic_shapes=ds,
-                dynamo=dynamo,
+                **export_export_kwargs,
             )
         except Exception as e:
             summary["ERR_export_export"] = str(e)
@@ -904,8 +923,7 @@ def call_torch_export_onnx(
             data["model"],
             args,
             kwargs=kwargs,
-            dynamic_shapes=ds,
-            dynamo=dynamo,
+            **export_export_kwargs,
         )
 
     summary["time_export_export"] = time.perf_counter() - begin
@@ -966,6 +984,7 @@ def call_torch_export_custom(
         None,
     }, f"unexpected value for optimization={optimization}"
     assert exporter in {
+        "custom",
         "custom-strict",
         "custom-strict-dec",
         "custom-strict-all",
@@ -1155,14 +1174,24 @@ def run_ort_fusion(
             f"[run_ort_fusion] starts optimization for "
             f"model_type={model_type!r} with {n_nodes} nodes"
         )
-    new_onx = optimize_by_fusion(
-        onx,
-        model_type=model_type,
-        num_heads=num_attention_heads,
-        hidden_size=hidden_size,
-        optimization_options=opts,
-    )
-    duration = {time.perf_counter() - begin}
+    try:
+        new_onx = optimize_by_fusion(
+            onx,
+            model_type=model_type,
+            num_heads=num_attention_heads,
+            hidden_size=hidden_size,
+            optimization_options=opts,
+        )
+    except Exception as e:
+        duration = {time.perf_counter() - begin}
+        if verbose:
+            print(f"[run_ort_fusion] failed in {duration} for model_type={model_type!r}")
+        return {
+            f"ERR_opt_ort_{model_type}": str(e),
+            f"opt_ort_{model_type}_duration": duration,
+        }, {}
+
+    duration = time.perf_counter() - begin
     delta = len(new_onx.model.graph.node)
     if verbose:
         print(f"[run_ort_fusion] done in {duration} with {delta} nodes")
@@ -1175,6 +1204,7 @@ def run_ort_fusion(
     return {
         f"opt_ort_{model_type}_n_nodes1": n_nodes,
         f"opt_ort_{model_type}_n_nodes2": delta,
+        f"opt_ort_{model_type}_delta_node": delta - n_nodes,
         f"opt_ort_{model_type}_duration": duration,
         f"opt_ort_{model_type}_duration_save": d,
     }, {f"opt_ort_{model_type}": output_path}