File: fx_onnx_interpreter.py

package info (click to toggle)
pytorch-cuda 2.6.0%2Bdfsg-7
  • links: PTS, VCS
  • area: contrib
  • in suites: forky, sid, trixie
  • size: 161,620 kB
  • sloc: python: 1,278,832; cpp: 900,322; ansic: 82,710; asm: 7,754; java: 3,363; sh: 2,811; javascript: 2,443; makefile: 597; ruby: 195; xml: 84; objc: 68
file content (799 lines) | stat: -rw-r--r-- 34,260 bytes parent folder | download | duplicates (3)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
# mypy: allow-untyped-defs
from __future__ import annotations

import inspect
import logging
import operator
import re
from typing import Callable, Sequence

import onnxscript
from onnxscript.function_libs.torch_lib import (
    graph_building as onnxscript_graph_building,
)

import torch
import torch.fx
from torch.onnx import _type_utils as jit_type_utils
from torch.onnx._internal._lazy_import import onnxscript_apis
from torch.onnx._internal.fx import (
    _pass,
    diagnostics,
    onnxfunction_dispatcher,
    type_utils as fx_type_utils,
)
from torch.utils import _pytree


def _fx_node_to_onnx_message_formatter(
    fn: Callable,
    self,
    node: torch.fx.Node,
    *args,
    **kwargs,
) -> str:
    return f"FX Node: {node.op}:{node.target}[name={node.name}]. "


def _fx_graph_to_onnx_message_formatter(
    fn: Callable,
    self,
    fx_graph_module: torch.fx.GraphModule,
    *args,
    **kwargs,
) -> str:
    return f"FX Graph: {fx_graph_module._get_name()}. "


def _location_from_fx_stack_trace(
    node_stack_trace: str,
) -> diagnostics.infra.Location | None:
    """Extract location from FX node stack trace.

    TODO(bowbao): Create fx utils module and move this function there.

    Args:
        node_stack_trace: The stack trace of the FX node. Example:

            File "path/file.py", line 311, in <function>
                <code>
            |   File "path/file2.py", line 389, in <function>
                <code>

    Returns:
        location: The location of the FX node.
    """
    if "File" not in node_stack_trace:
        return None

    lines = node_stack_trace.strip().split("\n")
    idx = 0
    while idx < len(lines) and "File" not in lines[idx]:
        idx += 1
    if idx + 1 >= len(lines):
        return None

    pattern = re.compile(r"^File \"(.+)\", line (\d+), in (.+)$")
    matches = pattern.match(lines[idx].strip())
    if matches:
        uri = matches.group(1)
        line_number = int(matches.group(2))
        snippet = lines[idx + 1].strip()
        return diagnostics.infra.Location(uri=uri, line=line_number, snippet=snippet)
    return None


def _retrieve_or_adapt_input_to_graph_set(
    fx_node_arg: fx_type_utils.Argument,
    fx_name_to_onnxscript_value: dict[
        str,
        onnxscript_graph_building.TorchScriptTensor
        | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
    ],
    tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
):
    """Map FX value to TorchScript value.

    When creating TorchScript graph from FX graph, we need a mapping from FX variable
    to TorchScript variable. This function maps FX variable, fx_node_arg, to torch.jit.Value.
    """

    onnx_tensor = fx_node_arg
    if isinstance(onnx_tensor, torch.fx.Node):
        # 1. fx_node_arg is a torch.fx.Node, which means
        #    fx_node_arg stands for the output of that torch.fx.Node.
        # 2. fx_node_arg (variable in torch.fx.Graph) is be mapped to
        #    torch.jit.Value, fx_name_to_onnxscript_value[fx_node_arg.name],
        #    in TorchScript graph.
        return fx_name_to_onnxscript_value[onnx_tensor.name]
    elif isinstance(onnx_tensor, (tuple, list)) and any(
        isinstance(node, torch.fx.Node)
        and fx_type_utils.is_torch_symbolic_type(node.meta.get("val"))
        for node in onnx_tensor
    ):
        # This intends to handle dynamic axes. for example, if the input size of op.Expand
        # is dynamic, each dimension would be variable (i.e., sym variable in Pytorch
        # FX graph. Note that sym variable is mapped to tensor in ONNX Script world)
        # calculated by other operators.
        sequence_mixed_elements: list[
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
            | list[int]
        ] = []
        # onnx_tensor contains a list of scalars which could be one of
        #   - tensor with empty shape,
        #   - tensor with tensor with shape (1,),
        #   - torch.SymInt,
        #   - int
        #   - ...
        # They should all be promoted to tensor with shape (1,)
        # in order to call ONNX's Concat.
        for tensor in onnx_tensor:
            # Prepare `tensor` as input of ONNX's Concat.

            if isinstance(
                tensor, torch.fx.Node
            ) and fx_type_utils.is_torch_symbolic_type(tensor.meta.get("val")):
                # In this case, tensor is a torch.SymInt from Dynamo's perspective.
                # It might be mapped to tensor with shape () or (1,) in ONNX.
                element_value = fx_name_to_onnxscript_value[tensor.name]
                if isinstance(
                    element_value, onnxscript_graph_building.TorchScriptTensor
                ):
                    # All elements sequence_mixed_elements will be send to onnx's Concat
                    # as inputs. Therefore, they are required to have the same rank.
                    # Since tensors with rank=0 (i.e., scalar) cannot be concated, all
                    # scalars are promoted to tensors with shape (1,).
                    with onnxscript.evaluator.default_as(tracer):
                        element_value = onnxscript_apis.torchlib_opset().Reshape(
                            element_value, [1]
                        )  # type: ignore[arg-type, type-var]
                sequence_mixed_elements.append(element_value)
            elif isinstance(tensor, int):
                # NOTE: op.Concat doesn't support scalar, so we need to wrap it with
                # dim, and onnx-script will promote it to tensor(int64)
                sequence_mixed_elements.append([tensor])
            else:
                raise RuntimeError(
                    f"Unsupported type in sequence_mixed_elements: {type(tensor)}"
                )
        # Concat all the elements in the sequence.
        # shapes are mapped to tensors in ONNX graph (TorchScriptGraph),
        # so list of sym_ints is concatenated to a tensor before calling ONNX op.

        # For example:
        #    inputs: [[2], [4], fx.Node(SymIntA), [1], fx.Node(SymIntB)]
        #    outputs: op.Concat([op.Constant(2), op.Constant(4), TorchScriptTensor(A), op.Constant(1), TorchScriptTensor(B)])

        # onnx-script auto wraps python number with op.Constants,
        # so we don't need to specifically process them.
        with onnxscript.evaluator.default_as(tracer):
            output = onnxscript_apis.torchlib_opset().Concat(
                *sequence_mixed_elements, axis=0
            )  # type: ignore[type-var]
        output.dtype = torch.int64  # type: ignore[union-attr]
        output.shape = [len(sequence_mixed_elements)]  # type: ignore[union-attr]
        return output
    elif isinstance(onnx_tensor, (tuple, list)) and all(
        isinstance(node, torch.fx.Node) or node is None for node in onnx_tensor
    ):
        sequence_elements: list[
            onnxscript_graph_building.TorchScriptTensor
            | None
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
        ] = []
        for tensor in onnx_tensor:
            sequence_elements.append(
                fx_name_to_onnxscript_value[tensor.name] if tensor is not None else None  # type: ignore[index, union-attr]
            )
        return sequence_elements
    if isinstance(onnx_tensor, torch.dtype):
        onnx_tensor = int(  # type: ignore[call-overload]
            jit_type_utils.JitScalarType.from_dtype(onnx_tensor).onnx_type()
        )
    # NOTE: if device is specified in kwargs (not consumed), it's free to ignored. But
    # if it's in args, we need to set it to string for dispatcher to match schema.
    if isinstance(onnx_tensor, torch.device):
        # torch.device is not supported by onnxscript (no op). We turn it into
        # a string.
        return str(onnx_tensor)
    # all other cases, we do nothing.
    return onnx_tensor


def filter_incompatible_and_dtype_convert_kwargs(kwargs):
    """Filter out kwargs that are not supported by onnxscript."""
    filtered = {}
    for key, value in kwargs.items():
        if key in {
            "layout",
            "device",
            "requires_grad",
            "pin_memory",
            "memory_format",
            "implicit",
        }:
            continue
        if key == "dtype":
            if value is None:
                # We omit if dtype is not provided, because onnxscript handles the
                # default case.
                continue
            else:
                value = int(jit_type_utils.JitScalarType.from_dtype(value).onnx_type())  # type: ignore[call-overload]
        filtered[key] = value
    return filtered


def _fill_tensor_shape_type(
    onnxscript_values: onnxscript_graph_building.TorchScriptTensor
    | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
    name: str,
    expected_values: fx_type_utils.META_VALUE_TYPE
    | list[fx_type_utils.META_VALUE_TYPE]
    | tuple[fx_type_utils.META_VALUE_TYPE | None, ...],
):
    """Fill the meta information of onnxscript_values with that from the fx FakeTensor."""

    if isinstance(expected_values, (list, tuple)) and not isinstance(
        onnxscript_values, (list, tuple)
    ):
        # ex: aten::split - in onnx_dtype: seq(tensor)
        # onnxscript_values is a single tensor, but expected_values is a list of tensors.
        return

    flat_onnxscript_values, _ = _pytree.tree_flatten(onnxscript_values)
    flat_expected_values, _ = _pytree.tree_flatten(expected_values)
    for i, (onnxscript_value, expected_value) in enumerate(
        zip(flat_onnxscript_values, flat_expected_values)
    ):
        if expected_value is None:
            # There is no shape/type from None.
            # NOTE: according to https://github.com/pytorch/pytorch/blob/main/torch/_meta_registrations.py,
            # None could be a valid value for return type, so we need to handle it.
            # e.g. the function: meta__scaled_dot_product_flash() in cpu mode.
            continue
        elif fx_type_utils.is_torch_symbolic_type(expected_value):
            # aten::sym_size output is a int, not a tensor, which stands
            # for the size of one dim. We treat it as 1-D tensor.
            onnxscript_value.dtype = fx_type_utils.from_sym_value_to_torch_dtype(
                expected_value
            )
            onnxscript_value.shape = torch.Size([1])
        elif isinstance(expected_value, (int, float, bool)):
            onnxscript_value.dtype = fx_type_utils.from_scalar_type_to_torch_dtype(
                type(expected_value)
            )
            onnxscript_value.shape = torch.Size([])
        elif isinstance(expected_value, complex):
            # From complex scalar to real representation
            onnxscript_value_to_torch_dtype = (
                fx_type_utils.from_scalar_type_to_torch_dtype(type(expected_value))
            )
            onnxscript_value.dtype = (
                fx_type_utils.from_complex_to_float(onnxscript_value_to_torch_dtype)
                if onnxscript_value_to_torch_dtype is not None
                else None
            )
            onnxscript_value.shape = torch.Size([2])
        elif fx_type_utils.is_torch_complex_dtype(expected_value.dtype):
            # Like torch.view_as_real, we flatten complex tensors to real tensors with
            # additional last dimension of 2
            onnxscript_value.shape = torch.Size((*expected_value.size(), 2))
            # complex64 -> float32, complex128 -> float64, etc.
            onnxscript_value.dtype = fx_type_utils.from_complex_to_float(
                expected_value.dtype
            )
            # Dispatcher needs to know the value is complex
            onnxscript_value.is_complex = True
        else:
            # We set node output sizes to be dynamic to continue the model conversion,
            # and inputs are also set to be dynamic in add_input().
            onnxscript_value.shape = expected_value.size()
            onnxscript_value.dtype = expected_value.dtype

        # naming
        if i > 0:
            onnxscript_value.name = f"{name}_{i}"
        else:
            onnxscript_value.name = name


def _fill_in_default_kwargs(
    node: torch.fx.Node,
) -> tuple[list[fx_type_utils.Argument], dict[str, fx_type_utils.Argument]]:
    """Find and Fill in the not provided kwargs with default values."""

    # TODO: aten::sym_size has overload, but fx graph is using
    # overloadpacket for some reasons.
    # https://github.com/pytorch/pytorch/issues/97201
    # We manually assigned overload for aten::sym_size.
    if hasattr(node.target, "_schema"):
        node_schema = node.target._schema  # type: ignore[union-attr]
    else:
        node_schema = torch.ops.aten.sym_size.int._schema  # type: ignore[union-attr]

    # This function assumes the order of arguments in FX op is the
    # same as the order of arguments in TorchScript op.
    complete_args: list[fx_type_utils.Argument] = []
    complete_kwargs: dict[str, fx_type_utils.Argument] = {}

    if inspect.isbuiltin(node.target):
        complete_args = list(node.args)
    else:
        for i, expected_arg in enumerate(node_schema.arguments):
            if i < len(node.args):
                complete_args.append(node.args[i])
            elif expected_arg.name in node.kwargs:
                complete_kwargs[expected_arg.name] = node.kwargs[expected_arg.name]
            else:
                # Get default from schema.
                complete_kwargs[expected_arg.name] = expected_arg.default_value

    return complete_args, complete_kwargs


def _wrap_fx_args_as_onnxscript_args(
    complete_args: list[fx_type_utils.Argument],
    complete_kwargs: dict[str, fx_type_utils.Argument],
    fx_name_to_onnxscript_value: dict[
        str,
        onnxscript_graph_building.TorchScriptTensor
        | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
    ],
    tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
) -> tuple[
    Sequence[
        onnxscript_graph_building.TorchScriptTensor
        | str
        | int
        | float
        | bool
        | list
        | complex
        | None
    ],
    dict[str, fx_type_utils.Argument],
]:
    """Map all FX arguments of a node to arguments in TorchScript graph."""

    onnxscript_args = tuple(
        _retrieve_or_adapt_input_to_graph_set(arg, fx_name_to_onnxscript_value, tracer)
        for arg in complete_args
    )
    onnxscript_kwargs = filter_incompatible_and_dtype_convert_kwargs(complete_kwargs)

    return onnxscript_args, onnxscript_kwargs


class FxOnnxInterpreter:
    """Stateless class to process FX graph Nodes and translate them into their ONNX counterparts.

    All FX nodes described by [FX Graph](https://pytorch.org/docs/stable/fx.html#torch.fx.Graph) are supported.
    Similarly to [FX Interpreter pattern](https://pytorch.org/docs/stable/fx.html#torch.fx.Interpreter), each FX node
    must be implemented on its own method in this class.

    Each operator's implementation returns either an `onnxscript.OnnxFunction` or
    `onnxscript.TracedOnnxFunction` instance based on the dispatch algorithm. They can
    also raise RuntimeError: If there are no overloaded functions available for the given FX node.

    TODO: Convert methods to @staticmethod when the diagnostic system supports it
          DO NOT ADD NEW ATTRIBUTES TO THIS CLASS!
    """

    def __init__(
        self,
        diagnostic_context: diagnostics.DiagnosticContext,
    ):
        # THIS SHOULD BE THE ONLY STATE IN THIS CLASS (constraint from diagnosticS API)
        # TODO: Diagnostics API should be revised to get rid of this attribute.
        # DO NOT add other class-level attributes.
        self.diagnostic_context = diagnostic_context

    @diagnostics.diagnose_call(
        diagnostics.rules.fx_node_to_onnx,
        diagnostic_message_formatter=_fx_node_to_onnx_message_formatter,
    )
    def run_node(
        self,
        node,
        fx_graph_module: torch.fx.GraphModule,
        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
        onnxscript_tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ],
    ):
        """Execute a single FX node to produce its ONNX counterpart.

        Args:
            node: The FX node to be translated.
            fx_graph_module: The FX graph module containing the node.
            onnxfunction_dispatcher: The dispatcher to find the best matched ONNX op.
            onnxscript_graph: The ONNX graph to be populated.
            onnxscript_tracer: The tracer to trace the ONNX graph.
            fx_name_to_onnxscript_value: The mapping from FX node name to ONNX Script value.

        Raises:
            RuntimeError: When a node.op is not supported.
        """
        # Record stack trace of node in diagnostic.
        node_stack_trace = node.stack_trace
        if node_stack_trace:
            diagnostic = self.diagnostic_context.inflight_diagnostic(
                rule=diagnostics.rules.fx_node_to_onnx
            )
            with diagnostic.log_section(logging.INFO, "PyTorch source information"):
                diagnostic.info("```\n%s\n```", node_stack_trace)
            location = _location_from_fx_stack_trace(node_stack_trace)
            if location is not None:
                diagnostic.with_location(location)

        if node.op == "placeholder":
            self.placeholder(node, onnxscript_graph, fx_name_to_onnxscript_value)
        elif node.op == "get_attr":
            self.get_attr(
                node,
                onnxscript_graph,
                fx_name_to_onnxscript_value,
                fx_graph_module,
            )
        elif node.op == "call_function":
            self.call_function(
                node,
                onnxscript_tracer,
                fx_name_to_onnxscript_value,
                onnxfunction_dispatcher,
                fx_graph_module,
            )
        elif node.op == "call_method":
            self.call_method(node)
        elif node.op == "call_module":
            self.call_module(
                node,
                onnxscript_graph,
                fx_name_to_onnxscript_value,
                onnxscript_tracer,
                fx_graph_module,
                onnxfunction_dispatcher,
            )
        elif node.op == "output":
            self.output(node, onnxscript_graph, fx_name_to_onnxscript_value)
        else:
            raise RuntimeError(f"Found node type not defined in torch.fx: {node.op}")

    @diagnostics.diagnose_call(
        diagnostics.rules.fx_graph_to_onnx,
        diagnostic_message_formatter=_fx_graph_to_onnx_message_formatter,
    )
    def run(
        self,
        fx_graph_module: torch.fx.GraphModule,
        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
        parent_onnxscript_graph: onnxscript_graph_building.TorchScriptGraph
        | None = None,
    ) -> onnxscript_graph_building.TorchScriptGraph:
        """Analyze all FX nodes and trigger their ONNX translation.

        Args:
            fx_graph_module: FX graph module to be translated.
            onnxfunction_dispatcher: ONNX function dispatcher.
            parent_onnxscript_graph: The parent TorchScript graph. Must be provided if
                `fx_graph_module` is a submodule. If not provided,
                `fx_graph_module` is assumed to be the root module.
        """
        diagnostic = self.diagnostic_context.inflight_diagnostic()
        with diagnostic.log_section(logging.DEBUG, "FX Graph:"):
            diagnostic.debug(
                "```\n%s\n```",
                diagnostics.LazyString(fx_graph_module.print_readable, False),
            )

        if parent_onnxscript_graph is not None:
            # If parent_onnxscript_graph is provided, we assume fx_graph_module is a
            # submodule representing a forward call of an nn.Module.
            # Compose package and version where the nn.Module is defined as domain name
            # for the local function.

            onnx_meta: _pass.GraphModuleOnnxMeta | None = fx_graph_module.meta.get(
                "onnx"
            )
            if onnx_meta is None:
                raise RuntimeError(
                    f"ONNX meta is not found in submodule {fx_graph_module._get_name()}. "
                    f"Only submodules produced by `Modularize` pass is supported in ONNX export."
                )

            onnx_domain = onnx_meta.package_info.to_onnx_domain_string()
        else:
            # Leave as default domain name for the root module.
            onnx_domain = None

        onnxscript_graph = onnxscript_graph_building.TorchScriptGraph(
            parent_onnxscript_graph, domain_name=onnx_domain
        )
        onnxscript_tracer = onnxscript_graph_building.TorchScriptTracingEvaluator(
            onnxscript_graph
        )
        # In the following loop, a TorchScript graph is created to
        # represent the input FX graph with ONNX symbols (e.g., onnx::add).
        # To connect the values to nodes in the TorchScript graph, we maintain
        # fx_name_to_onnxscript_value. Basically, we want to translate
        #   fx_tensor_x (type: torch.fx.Node) -> fx_node_1 -> fx_tensor_y (type: torch.fx.Node)
        # to
        #   fx_name_to_onnxscript_value[fx_tensor_x.name] -> onnx_node_1 -> fx_name_to_onnxscript_value[fx_tensor_y.name]
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ] = {}

        # TODO: Fix FakeTensorMode limitation asap
        # We want to pass list of ints and floats to TorchScript graph correctly
        # in _export_fx_to_ts, so we must disable FakeTensorMode. Otherwise, graph may
        # receive FakeTensor and results runtime error. In addition, TorchScript-based
        # ONNX exporter used in _ts_graph_to_onnx_model_in_protobuf is not compatible
        # with FakeTensorMode.
        with torch.utils._mode_utils.no_dispatch():
            for node in fx_graph_module.graph.nodes:
                self.run_node(
                    node,
                    fx_graph_module,
                    onnxfunction_dispatcher,
                    onnxscript_graph,
                    onnxscript_tracer,
                    fx_name_to_onnxscript_value,
                )

        with diagnostic.log_section(logging.DEBUG, "ONNX Graph:"):
            diagnostic.debug("```\n%s\n```", onnxscript_graph.torch_graph)  # type: ignore[attr-defined]

        return onnxscript_graph

    def placeholder(
        self,
        node: torch.fx.Node,
        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ],
    ):
        # Input of graph.
        # The node.meta["val"] is generated by FakeTensorProp.
        # NOTE: add_input() intends to create nodes with shape/type
        fake_tensor = node.meta.get("val", None)
        # NOTE: During the tracing, when inputs are constants, they are represented
        # by nodes with node.meta['val'] being None (nn.Module to dynamo_export)
        # or nodes with node.meta['val'] being a builtin value (ExportedProgram to dynamo_export).
        # Nonethless, the nodes are not consumed by others, so we don't need to
        # create a TorchScriptTensor for them.
        if fake_tensor is None or isinstance(fake_tensor, (int, float, bool, str)):
            output = onnxscript_graph.add_input(
                input_name=None,
            )
        elif isinstance(fake_tensor, torch.Tensor):
            # NOTE: ONNX doesn't support tensor of complex64/complex128, so we
            # convert them to float32/float64 with real representation.
            if fx_type_utils.is_torch_complex_dtype(fake_tensor.dtype):
                fake_tensor = torch.view_as_real(fake_tensor.resolve_conj())
            output = onnxscript_graph.add_input(
                input_name=node.name,
                shape=fake_tensor.shape,
                dtype=fake_tensor.dtype,
            )

        elif fx_type_utils.is_torch_symbolic_type(fake_tensor):
            output = onnxscript_graph.add_input(
                input_name=node.name,
                shape=torch.Size([]),
                dtype=fx_type_utils.from_sym_value_to_torch_dtype(fake_tensor),
            )
        else:
            raise RuntimeError(
                f"Unsupported type(node.meta['val']) for placeholder: {type(fake_tensor)}"
            )
        assert (
            output is not None
        ), f"Node creates None with target={node.target} and name={node.name}"

        assert isinstance(output, onnxscript_graph_building.TorchScriptTensor)
        assert isinstance(output, onnxscript.tensor.Tensor)

        fx_name_to_onnxscript_value[node.name] = output

    def call_function(
        self,
        node: torch.fx.Node,
        onnxscript_tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ],
        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
        fx_graph_module: torch.fx.GraphModule,
    ):
        # aten ops and other stateless functions.
        if node.target == operator.getitem and isinstance(
            fx_name_to_onnxscript_value[node.args[0].name],  # type: ignore[union-attr,index]
            tuple,
        ):
            onnx_tensor_tuple = fx_name_to_onnxscript_value[node.args[0].name]  # type: ignore[union-attr,index]
            index = node.args[1]
            value = onnx_tensor_tuple[index]  # type: ignore[index]
            assert (
                value is not None
            ), f"Node creates None with target={node.target} and name={node.name}"
            assert isinstance(
                value, (onnxscript_graph_building.TorchScriptTensor, tuple)
            ), type(value)

            fx_name_to_onnxscript_value[node.name] = value
            return

        # Map FX inputs to ONNX inputs and fill optional inputs with default values.
        # torch_args and torch_kwargs are for op-level validation
        fx_args, fx_kwargs = _fill_in_default_kwargs(node)

        onnx_args, onnx_kwargs = _wrap_fx_args_as_onnxscript_args(
            fx_args,
            fx_kwargs,
            fx_name_to_onnxscript_value,
            onnxscript_tracer,
        )
        # Dispatch to ONNX op through OpShema. The input argument dtypes are compared to
        # function signature in OpSchema, and find the best matched overload.
        symbolic_fn = onnxfunction_dispatcher.dispatch(
            node=node,
            onnx_args=onnx_args,  # type: ignore[arg-type]
            onnx_kwargs=onnx_kwargs,
            diagnostic_context=self.diagnostic_context,
        )
        with onnxscript.evaluator.default_as(onnxscript_tracer):
            output: (
                onnxscript_graph_building.TorchScriptTensor
                | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
            ) = symbolic_fn(*onnx_args, **onnx_kwargs)
        assert (
            output is not None
        ), f"Node creates None with target={node.target}, name={node.name}, args={onnx_args}, kwargs={onnx_kwargs}"
        # Assign type and shape from fx graph.
        _fill_tensor_shape_type(output, node.name, node.meta["val"])
        # One fx node could produce multiple outputs (e.g., tuple of tensors); in
        # that case, v is a tuple of TorchScriptTensors.
        assert isinstance(
            output, (onnxscript_graph_building.TorchScriptTensor, tuple)
        ), type(output)
        fx_name_to_onnxscript_value[node.name] = output

    def output(
        self,
        node: torch.fx.Node,
        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ],
    ):
        if isinstance(node.args[0], torch.fx.Node):
            onnx_tensor_or_tensor_tuple = fx_name_to_onnxscript_value[node.args[0].name]
            onnxscript_graph.register_outputs(onnx_tensor_or_tensor_tuple)
        else:
            # ONNX can't represent collection types (e.g., dictionary, tuple of tuple of
            # tensor, etc), we flatten the collection and register each element as output.
            flat_args, _ = _pytree.tree_flatten(node.args[0])
            for arg in flat_args:
                assert isinstance(
                    arg, torch.fx.Node
                ), f"arg must be a torch.fx.Node, not {type(arg)}"
                onnx_tensor_or_tensor_tuple = fx_name_to_onnxscript_value[arg.name]
                onnxscript_graph.register_outputs(onnx_tensor_or_tensor_tuple)

    def call_method(self, node: torch.fx.Node):
        # TODO(wechi): Support call_method.
        raise RuntimeError("call_method is not supported yet.")

    def call_module(
        self,
        node: torch.fx.Node,
        parent_onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ],
        tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,
        root_fx_graph_module: torch.fx.GraphModule,
        onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,
    ) -> None:
        """Export a fx.GraphModule submodule to ONNXScript graph.

        The export process specifically targets `call_module` nodes that are created by
        the exporter's `Modularize` pass. Each `call_module` node has an associated fx.GraphModule
        by `node.target` underneath the root fx.GraphModule. These `call_module` nodes are exported as ONNX
        function nodes. The related `sub_module` is then exported as an ONNX model local function,
        which is represented by another `TorchScriptGraph`. This `TorchScriptGraph` sets the current
        `onnxscript_graph` as its parent.

        Args:
            node: The call_module node in the FX graph that represents the submodule call.
            parent_onnxscript_graph: The parent ONNXScript graph to which the ONNX function and
                function node belong.
            fx_name_to_onnxscript_value: The mapping from FX node name to ONNXScript value.
            tracer: The tracer used to trace the ONNXScript graph.
            root_fx_graph_module: The root FX module.
            onnxfunction_dispatcher: The dispatcher.
        """
        assert isinstance(
            node.target, str
        ), f"node.target must be a str, not {type(node.target)} for node {node}."

        sub_module = root_fx_graph_module.get_submodule(node.target)

        assert isinstance(
            sub_module, torch.fx.GraphModule
        ), f"sub_module must be a torch.fx.GraphModule, not {type(sub_module)} for node {node}."

        sub_onnxscript_graph = self.run(
            sub_module, onnxfunction_dispatcher, parent_onnxscript_graph
        )

        onnx_args, _ = _wrap_fx_args_as_onnxscript_args(
            list(node.args), {}, fx_name_to_onnxscript_value, tracer
        )

        # TODO: We may want to consider other naming styles. The goal is to be stable and
        # unique such that it can be easily identified in case of kernel substitution.
        # Example for current style is combination of qualified module class name and
        # module attribute name: `torch_nn_modules_conv_Conv2d_conv1`.
        # Other naming styles such as qualified module class name made unique can also
        # be considered.
        unique_module_name = f"{sub_module._get_name()}_{node.target}"

        outputs: (
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...]
        ) = parent_onnxscript_graph.add_module_call(  # type: ignore[assignment]
            unique_module_name, sub_onnxscript_graph, onnx_args
        )

        assert isinstance(
            outputs, (onnxscript_graph_building.TorchScriptTensor, tuple)
        ), f"Unexpected outputs type {type(outputs)} for node {node}."

        _fill_tensor_shape_type(outputs, node.name, node.meta["val"])
        fx_name_to_onnxscript_value[node.name] = outputs

        # Skip op_level_validation for call_module. Subgraph nodes are validated individually.

    def get_attr(
        self,
        node: torch.fx.Node,
        onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,
        fx_name_to_onnxscript_value: dict[
            str,
            onnxscript_graph_building.TorchScriptTensor
            | tuple[onnxscript_graph_building.TorchScriptTensor, ...],
        ],
        fx_graph_module: torch.fx.GraphModule,
    ):
        assert isinstance(node.target, str), f"node.target {node.target} is not a str."
        attr_tensor = getattr(fx_graph_module, node.target)
        assert isinstance(attr_tensor, torch.Tensor), f"{attr_tensor} is not a tensor."

        # Parameter/buffer name cannot contain "."
        # Revert from "/" to restore namespace formatting.
        input_ = onnxscript_graph.add_initializer(
            name=node.target.replace("/", "."),
            value=attr_tensor,
        )

        assert isinstance(input_, onnxscript_graph_building.TorchScriptTensor)
        assert isinstance(input_, onnxscript.tensor.Tensor)
        fx_name_to_onnxscript_value[node.name] = input_