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import hashlib
import torch
import torch.fx
from typing import Any, Dict, Optional, TYPE_CHECKING
from torch.fx.node import _get_qualified_name, _format_arg
from torch.fx.graph import _parse_stack_trace
from torch.fx.passes.shape_prop import TensorMetadata
from torch.fx._compatibility import compatibility
from itertools import chain
__all__ = ['FxGraphDrawer']
try:
import pydot
HAS_PYDOT = True
except ImportError:
HAS_PYDOT = False
_COLOR_MAP = {
"placeholder": '"AliceBlue"',
"call_module": "LemonChiffon1",
"get_param": "Yellow2",
"get_attr": "LightGrey",
"output": "PowderBlue",
}
_HASH_COLOR_MAP = [
"CadetBlue1",
"Coral",
"DarkOliveGreen1",
"DarkSeaGreen1",
"GhostWhite",
"Khaki1",
"LavenderBlush1",
"LightSkyBlue",
"MistyRose1",
"MistyRose2",
"PaleTurquoise2",
"PeachPuff1",
"Salmon",
"Thistle1",
"Thistle3",
"Wheat1",
]
_WEIGHT_TEMPLATE = {
"fillcolor": "Salmon",
"style": '"filled,rounded"',
"fontcolor": "#000000",
}
if HAS_PYDOT:
@compatibility(is_backward_compatible=False)
class FxGraphDrawer:
"""
Visualize a torch.fx.Graph with graphviz
Basic usage:
g = FxGraphDrawer(symbolic_traced, "resnet18")
g.get_dot_graph().write_svg("a.svg")
"""
def __init__(
self,
graph_module: torch.fx.GraphModule,
name: str,
ignore_getattr: bool = False,
ignore_parameters_and_buffers: bool = False,
skip_node_names_in_args: bool = True,
parse_stack_trace: bool = False,
dot_graph_shape: Optional[str] = None,
):
self._name = name
self.dot_graph_shape = (
dot_graph_shape if dot_graph_shape is not None else "record"
)
_WEIGHT_TEMPLATE["shape"] = self.dot_graph_shape
self._dot_graphs = {
name: self._to_dot(
graph_module, name, ignore_getattr, ignore_parameters_and_buffers, skip_node_names_in_args, parse_stack_trace
)
}
for node in graph_module.graph.nodes:
if node.op != "call_module":
continue
leaf_node = self._get_leaf_node(graph_module, node)
if not isinstance(leaf_node, torch.fx.GraphModule):
continue
self._dot_graphs[f"{name}_{node.target}"] = self._to_dot(
leaf_node,
f"{name}_{node.target}",
ignore_getattr,
ignore_parameters_and_buffers,
skip_node_names_in_args,
parse_stack_trace,
)
def get_dot_graph(self, submod_name=None) -> pydot.Dot:
"""
Visualize a torch.fx.Graph with graphviz
Example:
>>> # xdoctest: +REQUIRES(module:pydot)
>>> # define module
>>> class MyModule(torch.nn.Module):
>>> def __init__(self):
>>> super().__init__()
>>> self.linear = torch.nn.Linear(4, 5)
>>> def forward(self, x):
>>> return self.linear(x).clamp(min=0.0, max=1.0)
>>> module = MyModule()
>>> # trace the module
>>> symbolic_traced = torch.fx.symbolic_trace(module)
>>> # setup output file
>>> import ubelt as ub
>>> dpath = ub.Path.appdir('torch/tests/FxGraphDrawer').ensuredir()
>>> fpath = dpath / 'linear.svg'
>>> # draw the graph
>>> g = FxGraphDrawer(symbolic_traced, "linear")
>>> g.get_dot_graph().write_svg(fpath)
"""
if submod_name is None:
return self.get_main_dot_graph()
else:
return self.get_submod_dot_graph(submod_name)
def get_main_dot_graph(self) -> pydot.Dot:
return self._dot_graphs[self._name]
def get_submod_dot_graph(self, submod_name) -> pydot.Dot:
return self._dot_graphs[f"{self._name}_{submod_name}"]
def get_all_dot_graphs(self) -> Dict[str, pydot.Dot]:
return self._dot_graphs
def _get_node_style(self, node: torch.fx.Node) -> Dict[str, str]:
template = {
"shape": self.dot_graph_shape,
"fillcolor": "#CAFFE3",
"style": '"filled,rounded"',
"fontcolor": "#000000",
}
if node.op in _COLOR_MAP:
template["fillcolor"] = _COLOR_MAP[node.op]
else:
# Use a random color for each node; based on its name so it's stable.
target_name = node._pretty_print_target(node.target)
target_hash = int(hashlib.md5(target_name.encode()).hexdigest()[:8], 16)
template["fillcolor"] = _HASH_COLOR_MAP[target_hash % len(_HASH_COLOR_MAP)]
return template
def _get_leaf_node(
self, module: torch.nn.Module, node: torch.fx.Node
) -> torch.nn.Module:
py_obj = module
assert isinstance(node.target, str)
atoms = node.target.split(".")
for atom in atoms:
if not hasattr(py_obj, atom):
raise RuntimeError(
str(py_obj) + " does not have attribute " + atom + "!"
)
py_obj = getattr(py_obj, atom)
return py_obj
def _typename(self, target: Any) -> str:
if isinstance(target, torch.nn.Module):
ret = torch.typename(target)
elif isinstance(target, str):
ret = target
else:
ret = _get_qualified_name(target)
# Escape "{" and "}" to prevent dot files like:
# https://gist.github.com/SungMinCho/1a017aab662c75d805c5954d62c5aabc
# which triggers `Error: bad label format (...)` from dot
return ret.replace("{", r"\{").replace("}", r"\}")
# shorten path to avoid drawing long boxes
# for full path = '/home/weif/pytorch/test.py'
# return short path = 'pytorch/test.py'
def _shorten_file_name(
self,
full_file_name: str,
truncate_to_last_n: int = 2,
):
splits = full_file_name.split('/')
if len(splits) >= truncate_to_last_n:
return '/'.join(splits[-truncate_to_last_n:])
return full_file_name
def _get_node_label(
self,
module: torch.fx.GraphModule,
node: torch.fx.Node,
skip_node_names_in_args: bool,
parse_stack_trace: bool,
) -> str:
def _get_str_for_args_kwargs(arg):
if isinstance(arg, tuple):
prefix, suffix = r"|args=(\l", r",\n)\l"
arg_strs_list = [_format_arg(a, max_list_len=8) for a in arg]
elif isinstance(arg, dict):
prefix, suffix = r"|kwargs={\l", r",\n}\l"
arg_strs_list = [
f"{k}: {_format_arg(v, max_list_len=8)}"
for k, v in arg.items()
]
else: # Fall back to nothing in unexpected case.
return ""
# Strip out node names if requested.
if skip_node_names_in_args:
arg_strs_list = [a for a in arg_strs_list if "%" not in a]
if len(arg_strs_list) == 0:
return ""
arg_strs = prefix + r",\n".join(arg_strs_list) + suffix
if len(arg_strs_list) == 1:
arg_strs = arg_strs.replace(r"\l", "").replace(r"\n", "")
return arg_strs.replace("{", r"\{").replace("}", r"\}")
label = "{" + f"name=%{node.name}|op_code={node.op}\n"
if node.op == "call_module":
leaf_module = self._get_leaf_node(module, node)
label += r"\n" + self._typename(leaf_module) + r"\n|"
extra = ""
if hasattr(leaf_module, "__constants__"):
extra = r"\n".join(
[f"{c}: {getattr(leaf_module, c)}" for c in leaf_module.__constants__] # type: ignore[union-attr]
)
label += extra + r"\n"
else:
label += f"|target={self._typename(node.target)}" + r"\n"
if len(node.args) > 0:
label += _get_str_for_args_kwargs(node.args)
if len(node.kwargs) > 0:
label += _get_str_for_args_kwargs(node.kwargs)
label += f"|num_users={len(node.users)}" + r"\n"
tensor_meta = node.meta.get('tensor_meta')
label += self._tensor_meta_to_label(tensor_meta)
# for original fx graph
# print buf=buf0, n_origin=6
buf_meta = node.meta.get('buf_meta', None)
if buf_meta is not None:
label += f"|buf={buf_meta.name}" + r"\n"
label += f"|n_origin={buf_meta.n_origin}" + r"\n"
# for original fx graph
# print file:lineno code
if parse_stack_trace and node.stack_trace is not None:
parsed_stack_trace = _parse_stack_trace(node.stack_trace)
fname = self._shorten_file_name(parsed_stack_trace.file)
label += f"|file={fname}:{parsed_stack_trace.lineno} {parsed_stack_trace.code}" + r"\n"
return label + "}"
def _tensor_meta_to_label(self, tm) -> str:
if tm is None:
return ""
elif isinstance(tm, TensorMetadata):
return self._stringify_tensor_meta(tm)
elif isinstance(tm, list):
result = ""
for item in tm:
result += self._tensor_meta_to_label(item)
return result
elif isinstance(tm, dict):
result = ""
for v in tm.values():
result += self._tensor_meta_to_label(v)
return result
elif isinstance(tm, tuple):
result = ""
for item in tm:
result += self._tensor_meta_to_label(item)
return result
else:
raise RuntimeError(f"Unsupported tensor meta type {type(tm)}")
def _stringify_tensor_meta(self, tm: TensorMetadata) -> str:
result = ""
if not hasattr(tm, "dtype"):
print("tm", tm)
result += "|" + "dtype" + "=" + str(tm.dtype) + r"\n"
result += "|" + "shape" + "=" + str(tuple(tm.shape)) + r"\n"
result += "|" + "requires_grad" + "=" + str(tm.requires_grad) + r"\n"
result += "|" + "stride" + "=" + str(tm.stride) + r"\n"
if tm.is_quantized:
assert tm.qparams is not None
assert "qscheme" in tm.qparams
qscheme = tm.qparams["qscheme"]
if qscheme in {
torch.per_tensor_affine,
torch.per_tensor_symmetric,
}:
result += "|" + "q_scale" + "=" + str(tm.qparams["scale"]) + r"\n"
result += "|" + "q_zero_point" + "=" + str(tm.qparams["zero_point"]) + r"\n"
elif qscheme in {
torch.per_channel_affine,
torch.per_channel_symmetric,
torch.per_channel_affine_float_qparams,
}:
result += "|" + "q_per_channel_scale" + "=" + str(tm.qparams["scale"]) + r"\n"
result += "|" + "q_per_channel_zero_point" + "=" + str(tm.qparams["zero_point"]) + r"\n"
result += "|" + "q_per_channel_axis" + "=" + str(tm.qparams["axis"]) + r"\n"
else:
raise RuntimeError(f"Unsupported qscheme: {qscheme}")
result += "|" + "qscheme" + "=" + str(tm.qparams["qscheme"]) + r"\n"
return result
def _get_tensor_label(self, t: torch.Tensor) -> str:
return str(t.dtype) + str(list(t.shape)) + r"\n"
# when parse_stack_trace=True
# print file:lineno code
def _to_dot(
self,
graph_module: torch.fx.GraphModule,
name: str,
ignore_getattr: bool,
ignore_parameters_and_buffers: bool,
skip_node_names_in_args: bool,
parse_stack_trace: bool,
) -> pydot.Dot:
"""
Actual interface to visualize a fx.Graph. Note that it takes in the GraphModule instead of the Graph.
If ignore_parameters_and_buffers is True, the parameters and buffers
created with the module will not be added as nodes and edges.
"""
# "TB" means top-to-bottom rank direction in layout
dot_graph = pydot.Dot(name, rankdir="TB")
buf_name_to_subgraph = {}
for node in graph_module.graph.nodes:
if ignore_getattr and node.op == "get_attr":
continue
style = self._get_node_style(node)
dot_node = pydot.Node(
node.name, label=self._get_node_label(graph_module, node, skip_node_names_in_args, parse_stack_trace), **style
)
current_graph = dot_graph
buf_meta = node.meta.get('buf_meta', None)
if buf_meta is not None and buf_meta.n_origin > 1:
buf_name = buf_meta.name
if buf_name not in buf_name_to_subgraph:
buf_name_to_subgraph[buf_name] = pydot.Cluster(buf_name, label=buf_name)
current_graph = buf_name_to_subgraph.get(buf_name)
current_graph.add_node(dot_node)
def get_module_params_or_buffers():
for pname, ptensor in chain(
leaf_module.named_parameters(), leaf_module.named_buffers()
):
pname1 = node.name + "." + pname
label1 = (
pname1 + "|op_code=get_" + "parameter"
if isinstance(ptensor, torch.nn.Parameter)
else "buffer" + r"\l"
)
dot_w_node = pydot.Node(
pname1,
label="{" + label1 + self._get_tensor_label(ptensor) + "}",
**_WEIGHT_TEMPLATE,
)
dot_graph.add_node(dot_w_node)
dot_graph.add_edge(pydot.Edge(pname1, node.name))
if node.op == "call_module":
leaf_module = self._get_leaf_node(graph_module, node)
if not ignore_parameters_and_buffers and not isinstance(leaf_module, torch.fx.GraphModule):
get_module_params_or_buffers()
for subgraph in buf_name_to_subgraph.values():
subgraph.set('color', 'royalblue')
subgraph.set('penwidth', '2')
dot_graph.add_subgraph(subgraph)
for node in graph_module.graph.nodes:
if ignore_getattr and node.op == "get_attr":
continue
for user in node.users:
dot_graph.add_edge(pydot.Edge(node.name, user.name))
return dot_graph
else:
if not TYPE_CHECKING:
@compatibility(is_backward_compatible=False)
class FxGraphDrawer:
def __init__(
self,
graph_module: torch.fx.GraphModule,
name: str,
ignore_getattr: bool = False,
ignore_parameters_and_buffers: bool = False,
skip_node_names_in_args: bool = True,
parse_stack_trace: bool = False,
dot_graph_shape: Optional[str] = None,
):
raise RuntimeError('FXGraphDrawer requires the pydot package to be installed. Please install '
'pydot through your favorite Python package manager.')
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