mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
d7f23f6826
Summary:
This PR restores original names to placeholder nodes, replacing the default names arg0_1, arg1_1, and so on.
User inputs now follow the signature of mod.forward(), for example forward(x, y) produces nodes x, y. If the tensors are nested in dictionaries, lists, tuples, or dataclasses, the names are a concatenation of the path to the tensor, e.g. x = {'a': torch.randn(4), 'b': [torch.randn(4), torch.randn(4)]} produces nodes x_a, x_b_0, x_b_1.
Parameters, buffers, constants, and custom objects follow the FQN of the object, prefixed by "p", "b", "c", and "obj" respectively. For example, self.bar.l0.weight gets you p_bar_l0_weight.
Effect tokens are named token_1, token_2, and so on, since they are not grounded in model inputs or named attributes.
note: breaking the original diff into 3 parts (top-level renaming, higher-order-op subgraphs, constant input de/serialization) because of its size.
Examples:
```python
# params, buffers, constants, inputs, torch.cond
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_l0_weight: "f32[4, 4]", p_l0_bias: "f32[4]", c_alpha: "f32[4]", b_beta: "f32[4]", x_0_a: "f32[4, 4]", y: "f32[4, 4]"):
# No stacktrace found for following nodes
mul: "f32[4, 4]" = torch.ops.aten.mul.Tensor(x_0_a, x_0_a)
t: "f32[4, 4]" = torch.ops.aten.t.default(p_l0_weight); p_l0_weight = None
addmm: "f32[4, 4]" = torch.ops.aten.addmm.default(p_l0_bias, y, t); p_l0_bias = y = t = None
return addmm
# model code
class Bar(torch.nn.Module):
def forward(self, x):
return x * x
class Foo(torch.nn.Module):
def __init__(self):
super().__init__()
self.bar = Bar()
self.l0 = torch.nn.Linear(4, 4)
self.alpha = torch.randn(4)
self.register_buffer('beta', torch.randn(4))
def forward(self, x, y):
x = x[0]['a']
mul = self.bar(x)
z1 = self.l0(y)
return z1
# custom objects, dataclasses, tokens, constant inputs
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, token_1: "f32[0]", obj_attr, data_x: "f32[4, 4]", data_y: "f32[4, 4]", mode):
# No stacktrace found for following nodes
mul: "f32[4, 4]" = torch.ops.aten.mul.Scalar(data_x, 30); data_x = None
div: "f32[4, 4]" = torch.ops.aten.div.Tensor_mode(data_y, 1.0, rounding_mode = 'floor'); data_y = None
add: "f32[4, 4]" = torch.ops.aten.add.Tensor(mul, div); mul = div = None
with_effects = torch._higher_order_ops.effects.with_effects(token_1, torch.ops._TorchScriptTesting.takes_foo.default, obj_attr, add); token_1 = obj_attr = add = None
getitem: "f32[0]" = with_effects[0]
getitem_1: "f32[4, 4]" = with_effects[1]; with_effects = None
return (getitem, getitem_1)
# model code
class Foo(torch.nn.Module):
def __init__(self):
super().__init__()
self.attr = torch.classes._TorchScriptTesting._Foo(10, 20)
def forward(self, data, a=1.0, mode="floor"):
x = self.attr.add_tensor(data.x) + torch.div(data.y, a, rounding_mode=mode)
x = torch.ops._TorchScriptTesting.takes_foo(self.attr, x)
return x
dataclass
class DataClass:
x: Tensor
y: Tensor
register_dataclass_as_pytree_node(
DataClass,
serialized_type_name="test.DataClass"
)
args = (DataClass(x=torch.randn(4, 4), y=torch.randn(4, 4)), )
kwargs = {'mode': 'floor'}
ep = torch.export.export(Foo(), args, kwargs, strict=False)
```
Test Plan: verification checks on placeholder names for all export() calls, unit test in test/export/test_export.py
Differential Revision: D55456418
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122904
Approved by: https://github.com/angelayi, https://github.com/thiagocrepaldi
566 lines
21 KiB
Python
566 lines
21 KiB
Python
import dataclasses
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import inspect
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import math
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import operator
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import re
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
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import torch
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from torch._subclasses.fake_tensor import FakeTensor
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from torch.export import ExportedProgram
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from torch.export.exported_program import _rename_without_collisions
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from torch.export.graph_signature import ConstantArgument, InputKind, OutputKind
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from torch.utils._pytree import (
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_register_pytree_node,
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Context,
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FlattenFunc,
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FromDumpableContextFn,
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GetAttrKey,
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KeyPath,
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keystr,
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MappingKey,
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SequenceKey,
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ToDumpableContextFn,
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tree_flatten_with_path,
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UnflattenFunc,
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)
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placeholder_prefixes = {
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InputKind.USER_INPUT: "",
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InputKind.PARAMETER: "p_",
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InputKind.BUFFER: "b_",
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InputKind.CONSTANT_TENSOR: "c_",
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InputKind.CUSTOM_OBJ: "obj_",
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InputKind.TOKEN: "token",
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}
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def _check_input_constraints_for_graph(
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input_placeholders: List[torch.fx.Node], flat_args_with_path, range_constraints
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):
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def get_keystr(key_path: KeyPath) -> str:
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"""For a given index into the flat_args, return a human readable string
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describing how to access it, e.g. "*args["foo"][0].bar"
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"""
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# Prefix the keypath with "*args" or "**kwargs" to make it clearer where
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# the arguments come from. Ultimately we ought to serialize the
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# original arg names for the best error message here.
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args_kwargs_key_path = key_path[0]
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assert isinstance(args_kwargs_key_path, SequenceKey)
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if args_kwargs_key_path.idx == 0:
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return f"*args{keystr(key_path[1:])}"
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else:
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kwarg_key = key_path[1]
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assert isinstance(kwarg_key, MappingKey)
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name = str(kwarg_key)[1:-1] # get rid of the enclosed []
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return f"{name}{keystr(key_path[2:])}"
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import sympy
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from torch._export.passes.add_runtime_assertions_for_constraints_pass import (
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_convert_range_to_int,
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)
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from torch.utils._sympy.solve import try_solve
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if len(flat_args_with_path) != len(input_placeholders):
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raise RuntimeError(
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"Unexpected number of inputs "
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f"(expected {len(input_placeholders)}, got {len(flat_args_with_path)})"
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)
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# NOTE: export already guarantees that the same symbol is used in metadata
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# for all InputDims related by equality constraints, so we can just unify
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# symbols with given input dimension values to check equality constraints.
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unification_map: "Dict[sympy.Symbol, Any]" = {}
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for (key_path, arg), node in zip(flat_args_with_path, input_placeholders):
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node_val = node.meta.get("val")
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if isinstance(node_val, FakeTensor):
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if not isinstance(arg, torch.Tensor):
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)} to be a tensor, but got {type(arg)}",
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)
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if len(node_val.shape) != len(arg.shape):
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raise RuntimeError(
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f"Unexpected number of dimensions in input at {get_keystr(key_path)}.shape "
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f"(expected {node_val.shape}, got {arg.shape})"
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)
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for j, (arg_dim, node_dim) in enumerate(zip(arg.shape, node_val.shape)):
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# TODO(avik): Assert the following property in the IR verifier:
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# node_dim is either an int or a SymInt containing an int or a unary sympy.Expr
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if (
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isinstance(node_dim, torch.SymInt)
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and len(node_dim.node.expr.free_symbols) == 1
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):
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symbol = next(iter(node_dim.node.expr.free_symbols))
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if symbol in unification_map:
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existing_dim = node_dim.node.expr.subs(unification_map)
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if arg_dim != existing_dim:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
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f"{existing_dim}, but got {arg_dim}",
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)
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else:
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if (
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isinstance(arg_dim, torch.SymInt)
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and not arg_dim.node.expr.is_number
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):
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# This can happen when, say, arg is a fake tensor.
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# We do not run checks on symbolic shapes of fake inputs as
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# such checks can affect the shape env.
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pass
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else:
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solution = try_solve(
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sympy.Eq(node_dim.node.expr, arg_dim), symbol
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)
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if solution is None:
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raise RuntimeError( # noqa: TRY200
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f"Expected input {node.name}.shape[{j}] = {arg_dim} to be "
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f"of the form {node_dim.node.expr}, where {symbol} is an integer"
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)
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else:
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unification_map[symbol] = int(solution[1])
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if node_dim.node.expr in range_constraints:
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min_val, max_val = _convert_range_to_int(
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range_constraints[node_dim.node.expr]
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)
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# NOTE: we allow dimensions to be 0/1 at runtime
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if min_val > 2:
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if arg_dim < min_val:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be >= "
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f"{min_val}, but got {arg_dim}",
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)
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if max_val < math.inf:
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if arg_dim > max_val:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be <= "
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f"{max_val}, but got {arg_dim}",
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)
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else:
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if arg_dim != node_dim:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
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f"{node_dim}, but got {arg_dim}",
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)
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elif isinstance(node_val, (int, float, str)):
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if type(arg) != type(node_val) or arg != node_val:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)} to be equal to {node_val}, but got {arg}",
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)
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def register_dataclass_as_pytree_node(
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cls: Type[Any],
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flatten_fn: Optional[FlattenFunc] = None,
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unflatten_fn: Optional[UnflattenFunc] = None,
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*,
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serialized_type_name: Optional[str] = None,
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to_dumpable_context: Optional[ToDumpableContextFn] = None,
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from_dumpable_context: Optional[FromDumpableContextFn] = None,
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return_none_fields: bool = False,
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) -> None:
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assert dataclasses.is_dataclass(
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cls
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), f"Only dataclasses can be registered with this function: {cls}"
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def default_flatten_fn(obj: Any) -> Tuple[List[Any], Context]:
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flattened = []
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flat_names = []
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none_names = []
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for f in dataclasses.fields(obj):
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name, val = f.name, getattr(obj, f.name)
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if val is not None or return_none_fields:
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flattened.append(val)
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flat_names.append(name)
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else:
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none_names.append(name)
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return flattened, [flat_names, none_names]
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def default_unflatten_fn(values: Iterable[Any], context: Context) -> Any:
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flat_names, none_names = context
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return cls(**dict(zip(flat_names, values)), **dict.fromkeys(none_names))
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def default_flatten_fn_with_keys(obj: Any) -> Tuple[List[Any], Context]:
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flattened, (flat_names, none_names) = flatten_fn(obj) # type: ignore[misc]
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return [(MappingKey(k), v) for k, v in zip(flat_names, flattened)], flat_names
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flatten_fn = flatten_fn if flatten_fn is not None else default_flatten_fn
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unflatten_fn = unflatten_fn if unflatten_fn is not None else default_unflatten_fn
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if (to_dumpable_context is None) ^ (from_dumpable_context is None):
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raise ValueError(
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f"Both to_dumpable_context and from_dumpable_context for {cls} must "
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"be None or registered."
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)
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_register_pytree_node(
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cls,
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flatten_fn,
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unflatten_fn,
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serialized_type_name=serialized_type_name,
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flatten_with_keys_fn=default_flatten_fn_with_keys,
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to_dumpable_context=to_dumpable_context,
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from_dumpable_context=from_dumpable_context,
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)
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def is_param(program: ExportedProgram, node: torch.fx.Node) -> bool:
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"""
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Checks if the given node is a parameter within the exported program
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"""
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return node.name in program.graph_signature.inputs_to_parameters
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def get_param(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> Optional[torch.nn.Parameter]:
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"""
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Returns the parameter associated with the given node in the exported program.
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Returns None if the node is not a parameter within the exported program
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"""
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if is_param(program, node):
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parameter_name = program.graph_signature.inputs_to_parameters[node.name]
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return program.state_dict[parameter_name]
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return None
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def is_buffer(program: ExportedProgram, node: torch.fx.Node) -> bool:
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"""
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Checks if the given node is a buffer within the exported program
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"""
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return node.name in program.graph_signature.inputs_to_buffers
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def get_buffer(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> Optional[torch.Tensor]:
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"""
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Returns the buffer associated with the given node in the exported program.
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Returns None if the node is not a buffer within the exported program
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"""
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if is_buffer(program, node):
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buffer_name = program.graph_signature.inputs_to_buffers[node.name]
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if buffer_name in program.graph_signature.non_persistent_buffers:
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return program.constants[buffer_name]
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else:
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return program.state_dict[buffer_name]
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return None
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def is_lifted_tensor_constant(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> bool:
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"""
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Checks if the given node is a lifted tensor constant within the exported program
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"""
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return node.name in program.graph_signature.inputs_to_lifted_tensor_constants
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def get_lifted_tensor_constant(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> Optional[torch.Tensor]:
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"""
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Returns the lifted tensor constant associated with the given node in the exported program.
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Returns None if the node is not a lifted tensor constant within the exported program
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"""
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if is_lifted_tensor_constant(program, node):
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lifted_tensor_name = program.graph_signature.inputs_to_lifted_tensor_constants[
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node.name
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]
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return program.constants[lifted_tensor_name]
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return None
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def sequential_split(gm: torch.fx.GraphModule, node_call_back) -> torch.fx.GraphModule:
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"""
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Splits the graph module into multiple submodules based on the node_call_back.
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The node_call_back should return True if the node is a delimiter. Delimiter will be
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the first node in the next submodule.
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"""
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from torch.fx.passes.split_module import split_module
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split_map = {}
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split_id = 0
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for node in gm.graph.nodes:
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if node_call_back(node):
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split_id += 1
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split_map[node] = split_id
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new_gm = split_module(
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gm,
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gm,
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lambda node: split_map[node],
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keep_original_order=True,
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keep_original_node_name=True,
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)
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# Keep the codegen from original graph module to preserve e.g. pytree info.
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new_gm.graph._codegen = gm.graph._codegen
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new_gm.recompile()
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return new_gm
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def nodes_filter(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
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"""Returns the nodes that match the node_call_back as a list."""
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return [node for node in nodes if node_call_back(node)]
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def nodes_first(
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nodes: List[torch.fx.Node], node_call_back=None
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) -> Optional[torch.fx.Node]:
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"""
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Returns the first node that matches the node_call_back. If no node matches, returns None.
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When node_call_back is None, returns the first node in the node list.
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"""
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ret = nodes_filter(nodes, node_call_back if node_call_back else lambda node: True)
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if len(ret) > 0:
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return ret[0]
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return None
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def nodes_count(nodes: List[torch.fx.Node], node_call_back) -> int:
|
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"""Returns the number of nodes that match the node_call_back."""
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return len(nodes_filter(nodes, node_call_back))
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def nodes_map(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
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"""
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Sequentially visit the nodes list and invoke node_call_back on each element.
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Returns the nodes list after the node_call_back is invoked on each element.
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"""
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for node in nodes:
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node_call_back(node)
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return nodes
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def node_replace_(
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old_node: torch.fx.Node, new_node: torch.fx.Node, delete_old: bool = False
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) -> None:
|
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"""
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Replace all uses of old_node with new_node.
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"""
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old_node.replace_all_uses_with(new_node)
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if delete_old:
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old_node.users.clear()
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old_node.graph.erase_node(old_node)
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def node_inline_(call_mod_node: torch.fx.Node) -> None:
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"""
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Inline the submodule of the given node into the parent module.
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Note: we only support the case where submodule takes tensors inputs.
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"""
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assert call_mod_node.op == "call_module"
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gm = call_mod_node.graph.owning_module
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assert isinstance(call_mod_node.target, str)
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sub_gm = getattr(gm, call_mod_node.target)
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phs = (node for node in sub_gm.graph.nodes if node.op == "placeholder")
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body = (
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node for node in sub_gm.graph.nodes if node.op not in ("placeholder", "output")
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)
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output = [node for node in sub_gm.graph.nodes if node.op == "output"]
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for ph, arg in zip(phs, call_mod_node.args):
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assert isinstance(arg, torch.fx.Node)
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node_replace_(ph, arg, delete_old=True)
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with gm.graph.inserting_before(call_mod_node):
|
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for node in body:
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new_node = gm.graph.node_copy(node)
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node_replace_(node, new_node, delete_old=True)
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|
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if len(output) > 0:
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assert len(output) == 1 and len(output[0].args) == 1
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new_output = output[0].args[0]
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|
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if isinstance(new_output, torch.fx.Node):
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node_replace_(call_mod_node, new_output, delete_old=True)
|
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elif isinstance(new_output, (list, tuple)):
|
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# Inline the get_item calls for the output node.
|
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get_item_users = nodes_filter(
|
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list(call_mod_node.users.keys()),
|
|
lambda node: node.op == "call_function"
|
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and node.target == operator.getitem,
|
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)
|
|
# get_item_node.args[1] is the idx referring to new_output[idx]
|
|
nodes_map(
|
|
get_item_users,
|
|
lambda get_item_node: node_replace_(
|
|
get_item_node,
|
|
new_output[get_item_node.args[1]],
|
|
delete_old=True,
|
|
),
|
|
)
|
|
call_mod_node.graph.erase_node(call_mod_node)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Unsupported output type {type(new_output)}. Expect it to be a Node or a list/tuple of Nodes."
|
|
)
|
|
else:
|
|
call_mod_node.graph.erase_node(call_mod_node)
|
|
|
|
gm.delete_all_unused_submodules()
|
|
gm.recompile()
|
|
return gm
|
|
|
|
|
|
def placeholder_naming_pass(
|
|
gm: torch.fx.GraphModule,
|
|
export_graph_signature: torch.export.ExportGraphSignature,
|
|
mod: torch.nn.Module,
|
|
fake_args,
|
|
fake_kwargs,
|
|
fake_params_buffers,
|
|
constants: Dict[str, Any],
|
|
) -> None:
|
|
"""
|
|
This pass is run at the end of _export_non_strict() to assign better placeholder node names:
|
|
- User inputs:
|
|
These follow the signature of mod.forward(), e.g. forward(x, y) produces nodes x, y.
|
|
For nested inputs from dictionaries, lists, tuples, or dataclasses,
|
|
the names are a concatenation of the path to the tensor.
|
|
e.g. x = {
|
|
'a': torch.randn(),
|
|
'b': [torch.randn(), torch.randn()]
|
|
}
|
|
produces nodes x_a, x_b_0, x_b_1.
|
|
- Parameters/buffers/constants/custom objects:
|
|
These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively.
|
|
e.g. self.bar.l0.weight produces "p_bar_l0_weight".
|
|
- Effect tokens:
|
|
These are named token, token_1, ...
|
|
"""
|
|
|
|
def _strip_name(x):
|
|
if x.startswith("L__self___"):
|
|
x = x[len("L__self___") :]
|
|
return x.replace(".", "_")
|
|
|
|
def _extract_pytree_key(x):
|
|
if isinstance(x, MappingKey):
|
|
return str(x.key).replace(".", "_")
|
|
elif isinstance(x, SequenceKey):
|
|
return str(x.idx)
|
|
elif isinstance(x, GetAttrKey):
|
|
return x.name
|
|
else:
|
|
raise RuntimeError(f"Pytree key of type {type(x)} not handled for {x}")
|
|
|
|
name_map: Dict[str, str] = {}
|
|
|
|
# map user input names with mod.forward() signature
|
|
combined_args = (
|
|
inspect.signature(mod.forward).bind(*fake_args, **fake_kwargs).arguments
|
|
)
|
|
flat_args_with_path, _ = tree_flatten_with_path(combined_args)
|
|
user_input_names = [
|
|
(None if isinstance(spec.arg, ConstantArgument) else spec.arg.name)
|
|
for spec in export_graph_signature.input_specs
|
|
if spec.kind == InputKind.USER_INPUT
|
|
]
|
|
|
|
# use pytree path to name nested user inputs
|
|
for (arg_path, arg), user_input_name in zip(flat_args_with_path, user_input_names):
|
|
if user_input_name:
|
|
_rename_without_collisions(
|
|
name_map,
|
|
user_input_name,
|
|
placeholder_prefixes[InputKind.USER_INPUT]
|
|
+ "_".join(_extract_pytree_key(x).lower() for x in arg_path),
|
|
is_placeholder=True,
|
|
)
|
|
|
|
# use graph signature input specs to map param/buffer/constant names
|
|
# name effect tokens as token, token_1, ... (these aren't visible to user)
|
|
for spec in export_graph_signature.input_specs:
|
|
if spec.kind == InputKind.USER_INPUT:
|
|
continue
|
|
# this should never be ConstantArgument, but avoid lint issue
|
|
if isinstance(spec.arg, ConstantArgument):
|
|
continue
|
|
if spec.kind == InputKind.TOKEN:
|
|
base_name = ""
|
|
else:
|
|
base_name = _strip_name(spec.target).lower()
|
|
_rename_without_collisions(
|
|
name_map,
|
|
spec.arg.name,
|
|
placeholder_prefixes[spec.kind] + base_name,
|
|
is_placeholder=True,
|
|
)
|
|
|
|
# handle naming collisions with call_function/get_attr inputs.
|
|
# here, we want to prioritize user input names over call_function names
|
|
# e.g. not have forward(self, mul): lead to a placeholder node called mul_13,
|
|
# so we increment the suffix of call_function nodes as needed
|
|
for node in gm.graph.nodes:
|
|
if node.op == "placeholder":
|
|
continue
|
|
_rename_without_collisions(name_map, node.name, node.name)
|
|
|
|
# assign new node names
|
|
for node in gm.graph.nodes:
|
|
if node.op == "placeholder":
|
|
if node.name in name_map: # skip constant inputs
|
|
node.name = node.target = name_map[node.name]
|
|
elif node.name in name_map:
|
|
node.name = name_map[node.name]
|
|
|
|
# TODO(pianpwk), in immediate follow-up PR
|
|
# propagate names to higher order op subgraphs
|
|
# name_hoo_subgraph_placeholders(gm)
|
|
|
|
# re-generate graph module code
|
|
gm.recompile()
|
|
|
|
# modify graph signature (input specs, output specs, user input mutations)
|
|
for spec in export_graph_signature.input_specs:
|
|
if isinstance(spec.arg, ConstantArgument):
|
|
continue
|
|
assert spec.arg.name in name_map
|
|
spec.arg.name = name_map[spec.arg.name]
|
|
if ( # handle targets for custom objects
|
|
spec.kind == InputKind.CUSTOM_OBJ and spec.target in name_map
|
|
):
|
|
spec.target = name_map[spec.target][4:] # strip obj_ prefix
|
|
|
|
for spec in export_graph_signature.output_specs:
|
|
if isinstance(spec.arg, ConstantArgument):
|
|
continue
|
|
if spec.arg.name in name_map:
|
|
spec.arg.name = name_map[spec.arg.name]
|
|
if spec.kind == OutputKind.USER_INPUT_MUTATION and spec.target in name_map:
|
|
spec.target = name_map[spec.target]
|
|
|
|
# rename keys in constants dict for custom objects
|
|
for name in list(constants.keys()):
|
|
constant = constants[name]
|
|
if name in name_map and not isinstance(
|
|
constant, torch.Tensor
|
|
): # rename custom objects with generic names
|
|
new_name = name_map[name]
|
|
if (
|
|
new_name != name
|
|
and re.match(r"arg(\d+)_1", name)
|
|
and new_name != placeholder_prefixes[InputKind.CUSTOM_OBJ] + name
|
|
):
|
|
constants[new_name] = constant
|
|
del constants[name]
|