Adds replay functionality in side-by-side

CHANGELOGS.rst

@@ -8,7 +8,7 @@ Change Logs
 * :pr:`311`: use custom and local function to use PackedMultiHeadAttention from onnxruntime
 * :pr:`310`: splits patches into multiple files 
 * :pr:`308`: add option --save_ep to dump the exported program as well as torch input
-* :pr:`304`, :pr:`306`, :pr:`316`, :pr:`317`: improves side-by-side comparison, creates command line sbs
+* :pr:`304`, :pr:`306`, :pr:`316`, :pr:`317`, :pr:`318`: improves side-by-side comparison, creates command line sbs
 
 0.8.2
 +++++

_unittests/ut_helpers/test_onnx_helper.py

@@ -19,6 +19,8 @@
     enumerate_results,
     shadowing_names,
     onnx_dtype_name,
+    extract_subset_of_nodes,
+    make_submodel,
 )
 
 
@@ -476,6 +478,58 @@ def test_onnx_dtype_name(self):
         self.assertRaise(lambda: onnx_dtype_name(1000), ValueError)
         self.assertEqual(onnx_dtype_name(1000, exc=False), "UNEXPECTED")
 
+    def test_extract_subset_of_nodes(self):
+        model = oh.make_model(
+            oh.make_graph(
+                [
+                    oh.make_node("Unsqueeze", ["X", "zero"], ["xu1"]),
+                    oh.make_node("Unsqueeze", ["xu1", "un"], ["xu2"]),
+                    oh.make_node("Reshape", ["xu2", "shape1"], ["xm1"]),
+                    oh.make_node("Reshape", ["Y", "shape2"], ["xm2c"]),
+                    oh.make_node("Cast", ["xm2c"], ["xm2"], to=1),
+                    oh.make_node("MatMul", ["xm1", "xm2"], ["xm"]),
+                    oh.make_node("Reshape", ["xm", "shape3"], ["Z"]),
+                ],
+                "dummy",
+                [oh.make_tensor_value_info("X", TFLOAT, [320, 1280])],
+                [oh.make_tensor_value_info("Z", TFLOAT, [3, 5, 320, 640])],
+                [
+                    onh.from_array(
+                        np.random.rand(3, 5, 1280, 640).astype(np.float32), name="Y"
+                    ),
+                    onh.from_array(np.array([0], dtype=np.int64), name="zero"),
+                    onh.from_array(np.array([1], dtype=np.int64), name="un"),
+                    onh.from_array(np.array([1, 320, 1280], dtype=np.int64), name="shape1"),
+                    onh.from_array(np.array([15, 1280, 640], dtype=np.int64), name="shape2"),
+                    onh.from_array(np.array([3, 5, 320, 640], dtype=np.int64), name="shape3"),
+                ],
+            ),
+            opset_imports=[oh.make_opsetid("", 18)],
+            ir_version=9,
+        )
+        submodel = extract_subset_of_nodes(model, "xm", cut_points={"Y", "xu2", "xm1"})
+        op_types = [n.op_type for n in submodel]
+        self.assertEqual(["Reshape", "Cast", "MatMul"], op_types)
+
+        def _type_rank_fn(name):
+            if name in {"Y", "xu2"}:
+                return TensorProto.FLOAT, 4
+            if name in {"xm1", "xm"}:
+                return TensorProto.FLOAT, 3
+            if name == "shape2":
+                return TensorProto.INT64, 1
+            raise AssertionError(f"unexpected name={name!r}")
+
+        new_model = make_submodel(
+            submodel,
+            ir_version=model.ir_version,
+            opset_imports=model.opset_import,
+            type_rank_fn=_type_rank_fn,
+            output_names=["xm"],
+        )
+        check_model(new_model)
+        self.check_ort(new_model)
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

_unittests/ut_torch_onnx/test_sbs.py

@@ -10,7 +10,7 @@
 )
 from onnx_diagnostic.reference import ExtendedReferenceEvaluator, OnnxruntimeEvaluator
 from onnx_diagnostic.torch_export_patches.patch_inputs import use_dyn_not_str
-from onnx_diagnostic.torch_onnx.sbs import run_aligned, RunAlignedRecord
+from onnx_diagnostic.torch_onnx.sbs import run_aligned, RunAlignedRecord, ReplayConfiguration
 from onnx_diagnostic.export.api import to_onnx
 
 
@@ -23,7 +23,7 @@ def setUpClass(cls):
 
     def test_run_aligned_record(self):
         r = RunAlignedRecord(
-            ep_id_node=-1,
+            ep_id_node=1,
             onnx_id_node=-1,
             ep_name="A",
             onnx_name="B",
@@ -512,6 +512,56 @@ def forward(self, x):
         self.assertEqual(onnx_op_type.count("reset"), 1)
         self.clean_dump()
 
+    @hide_stdout()
+    @ignore_warnings((DeprecationWarning, FutureWarning, UserWarning))
+    def test_sbs_replay(self):
+        torch = self.torch
+
+        class Model(self.torch.nn.Module):
+            def __init__(self):
+                super(Model, self).__init__()
+                self.fc1 = torch.nn.Linear(10, 3200)  # input size 10 → hidden size 32
+                self.relu = torch.nn.ReLU()
+                self.fc2 = torch.nn.Linear(3200, 1)  # hidden → output
+                with torch.no_grad():
+                    self.fc2.bias += 1999
+                    self.fc1.bias += 999
+
+            def forward(self, x):
+                x = self.relu(self.fc1(x))
+                x = self.fc2(x)
+                return x
+
+        inputs = dict(x=self.torch.randn((5, 10), dtype=torch.float16))
+        ds = dict(x={0: "batch"})
+        model = Model()
+        model = model.to(torch.float16)
+        model(**inputs)
+        ep = self.torch.export.export(
+            model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds)
+        )
+        filename = self.get_dump_file("test_sbs_replay.onnx")
+        dump_folder = self.get_dump_folder("test_sbs_replay_linear")
+        to_onnx(ep, exporter="custom", filename=filename)
+        onx = onnx.load(filename)
+        results = list(
+            run_aligned(
+                ep,
+                onx,
+                kwargs=inputs,
+                run_cls=OnnxruntimeEvaluator,
+                verbose=11,
+                use_tensor=True,
+                replay_configuration=ReplayConfiguration(
+                    dump_folder=dump_folder, selected_op_types={"Gemm"}
+                ),
+            ),
+        )
+        df = pandas.DataFrame(list(results))
+        df.to_excel(self.get_dump_file("test_sbs_replay.xlsx"))
+        print(df)
+        # self.clean_dump()
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)

_unittests/ut_xrun_doc/test_command_lines_exe.py

@@ -112,6 +112,7 @@ def forward(self, x):
         input_file = self.get_dump_file("test_h_parser_sbs.inputs.pt")
         ep_file = self.get_dump_file("test_h_parser_sbs.ep")
         onnx_file = self.get_dump_file("test_h_parser_sbs.model.onnx")
+        replay_foler = self.get_dump_folder("test_h_parser_sbs.replay")
         torch.save(inputs, input_file)
         to_onnx(
             Model(),
@@ -139,6 +140,10 @@ def forward(self, x):
                     output,
                     "-m",
                     onnx_file,
+                    "-t",
+                    "Gemm",
+                    "-f",
+                    replay_foler,
                 ]
             )
         text = st.getvalue()

onnx_diagnostic/_command_lines_parser.py

@@ -1140,6 +1140,14 @@ def get_parser_sbs() -> ArgumentParser:
             - torch.export.save(ep: torch.export.ExportedProgram)
             - torch.save(**inputs)
             - onnx.save(...)
+
+            The Replay functionality is just a way to investigates a part of a model.
+            It saves torch and onnx inputs, the torch outputs, and the minimal onnx model
+            which shares its inputs with the exported program.
+            This is used to investigate the discrepancies between the torch
+            model (through the exported program) and its onnx conversion.
+            This functionality dumps everything it can to disk
+            so that it be replayed in a separate process.
             """
         ),
     )
@@ -1222,10 +1230,33 @@ def get_parser_sbs() -> ArgumentParser:
         ),
     )
     parser.add_argument(
-        "--gemmlinear",
-        action=BooleanOptionalAction,
-        default=False,
-        help="Replaces Gemm(A,X.T,B) by torch...linear(A,X,B) on onnx side",
+        "-s",
+        "--replay-threshold",
+        type=float,
+        required=False,
+        default=1e6,
+        help="Triggers the replay if the discrepancies are higher than this value.",
+    )
+    parser.add_argument(
+        "-n",
+        "--replay-names",
+        required=False,
+        default="",
+        help="Triggers the replay if a result name is in this set of values (comma separated)",
+    )
+    parser.add_argument(
+        "-t",
+        "--replay-op-types",
+        required=False,
+        default="",
+        help="Triggers the replay if an onnx type is in this set of values (comma separated)",
+    )
+    parser.add_argument(
+        "-f",
+        "--replay-folder",
+        required=False,
+        default="replay",
+        help="If the replay is triggered, this defines the folder where everything is dumped.",
     )
 
     return parser
@@ -1235,7 +1266,7 @@ def _cmd_sbs(argv: List[Any]):
     import pandas
     import torch
     from .helpers import flatten_object, max_diff, string_diff, string_type
-    from .torch_onnx.sbs import run_aligned
+    from .torch_onnx.sbs import run_aligned, ReplayConfiguration
     from .reference import OnnxruntimeEvaluator
 
     parser = get_parser_sbs()
@@ -1306,6 +1337,17 @@ def _size(name):
     onx = onnx.load(args.onnx)
     print(f"-- done in {time.perf_counter() - begin:1.1f}s")
 
+    replay_configuration = None
+    if args.replay_threshold < 1e6 or args.replay_names or args.replay_op_types:
+        replay_configuration = ReplayConfiguration(
+            threshold=args.replay_threshold,
+            selected_names=set(args.replay_names.split(",")) if args.replay_names else None,
+            selected_op_types=(
+                set(args.replay_op_types.split(",")) if args.replay_op_types else None
+            ),
+            dump_folder=args.replay_folder,
+        )
+
     print("-- starts side-by-side")
     ratio = int(args.ratio)
     data = []
@@ -1319,9 +1361,9 @@ def _size(name):
         args=margs,
         kwargs=mkwargs,
         use_tensor=True,
-        gemmlinear=args.gemmlinear,
         reset_names=args.reset.split(","),
         exc=False,
+        replay_configuration=replay_configuration,
     ):
         data.append(obs)
         if (

onnx_diagnostic/helpers/onnx_helper.py

@@ -3,7 +3,7 @@
 import os
 import sys
 import warnings
-from typing import Any, Dict, Iterator, List, Optional, Sequence, Set, Tuple, Union
+from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Set, Tuple, Union
 import numpy as np
 import numpy.typing as npt
 import onnx
@@ -15,6 +15,7 @@
     GraphProto,
     ModelProto,
     NodeProto,
+    OperatorSetIdProto,
     TensorProto,
     ValueInfoProto,
     load as onnx_load,
@@ -1195,3 +1196,104 @@ def shadowing_names(
         existing |= not_empty
         created |= not_empty
     return shadow, post_shadow, created
+
+
+def extract_subset_of_nodes(
+    model: ModelProto,
+    name: str,
+    node_index: Optional[int] = None,
+    cut_points: Optional[Set[str]] = None,
+) -> List[NodeProto]:
+    """
+    Extracts the minimal subgraphs which can produce the output ``name``
+    knowing ``cut_points``.
+
+    :param model: original model
+    :param name: result name
+    :param node_index: if the node index is known, otherwise searches for it
+    :param cut_points: the known results or input name otherwise
+    :return: minimal list of nodes
+    """
+    if node_index is None:
+        for i, node in enumerate(model.graph.node):
+            if name in node.output:
+                node_index = i
+                break
+    assert (
+        node_index is not None
+        and node_index < len(model.graph.node)
+        and name in model.graph.node[node_index].output
+    ), f"node_index is still empty or wrong for result {name!r}"
+    if cut_points is None:
+        cut_points = {n.name for n in model.graph.input} | {
+            n.name for n in model.graph.initializer
+        }
+    elif model.graph.initializer:
+        cut_points = cut_points | {n.name for n in model.graph.initializer}
+
+    node = model.graph.node[node_index]
+    selected = {node_index}
+    current_node_index = node_index
+    current_input_index = 0
+    intermediate = {name}
+    inputs = set(k for k in node.input if k)
+    while not (inputs <= cut_points) and current_node_index >= 0:
+        node = model.graph.node[current_node_index]
+        if current_input_index == 0:
+            needs = [o for o in node.output if o in intermediate and o not in cut_points]
+            if needs:
+                selected.add(current_node_index)
+            else:
+                current_node_index -= 1
+                continue
+        res = node.input[current_input_index]
+        if res not in cut_points:
+            intermediate.add(res)
+        current_input_index += 1
+        if current_input_index >= len(node.input):
+            current_node_index -= 1
+            current_input_index = 0
+
+    return [model.graph.node[i] for i in sorted(selected)]
+
+
+def make_submodel(
+    nodes: List[NodeProto],
+    ir_version: int,
+    opset_imports: List[OperatorSetIdProto],
+    output_names: List[str],
+    type_rank_fn: Callable[[str], Tuple[int, int]],
+) -> ModelProto:
+    """
+    Creates a model with the given list of nodes.
+    It computes the minimum list of inputs needed for this model.
+    The function assumes the nodes are sorted.
+    It does not handle yet subgraphs.
+
+    :param nodes: list of nodes
+    :param ir_version: ir version
+    :param opset_imports: opset import
+    :param output_names: desired outputs
+    :param function: function returning the type and the rank of a result
+    :return: model proto
+    """
+
+    def _mkv_(name, itype, irank):
+        return oh.make_tensor_value_info(name, itype, [f"{name}_d{i}" for i in range(irank)])
+
+    not_known: Set[str] = set()
+    for node in nodes[::-1]:
+        not_known -= set(node.output)
+        not_known |= set(node.input)
+
+    model = oh.make_model(
+        oh.make_graph(
+            nodes,
+            "submodel",
+            [_mkv_(n, *type_rank_fn(n)) for n in sorted(not_known)],
+            [_mkv_(n, *type_rank_fn(n)) for n in sorted(output_names)],
+        ),
+        ir_version=ir_version,
+        opset_imports=opset_imports,
+    )
+    return model