def flatten(x: torch.fx.node.Argument) -> NodeList:
"""
Stores nodes in x to a list and returns the list.
"""
r: NodeList = []
map_arg(x, r.append)
return r
def load_arg_impl(arg_or_args):
if quantized is None:
return map_arg(arg_or_args, load_x)
if isinstance(quantized, bool):
return map_arg(arg_or_args, load_quantized if quantized else load_non_quantized)
elif isinstance(quantized, (tuple, list)):
assert isinstance(arg_or_args, (tuple, list)), arg_or_args
loaded_args = []
# for now, we only support quantizing positional arguments
for i, a in enumerate(arg_or_args):
if i in quantized:
loaded_args.append(map_arg(a, load_quantized))
else:
loaded_args.append(map_arg(a, load_non_quantized))
return type(arg_or_args)(loaded_args)
def _find_quants(self, quant_ctor):
quants = {}
def visit_arg(n):
# note: we have to measure quantization information
# even for nodes where we might not use it because it is already
# quantized. This is because each match has the option to
# say NotImplemented (if for instance, it is an __add__ and the data type is not appropriate)
if n.name not in quants:
quants[n.name] = quant_ctor(self, n)
for node in self.graph.nodes:
if node.name in self.matches:
map_arg(node.args, visit_arg)
map_arg(node.kwargs, visit_arg)
return quants
def _find_quants(self, graph, matches):
quants = {}
def visit(node, qconfig):
def visit_arg(arg):
# note: we have to measure quantization information
# even for nodes where we might not use it because it is already
# quantized. This is because each match has the option to
# say NotImplemented (if for instance, it is an __add__ and the data type is not appropriate)
is_weight = False
if isinstance(node, Node) and node.op == 'call_function' and node.target in WEIGHT_INDEX_DICT:
for i, node_arg in enumerate(node.args):
if arg is node_arg and i in WEIGHT_INDEX_DICT[node.target]:
is_weight = True
if self.quant_type != QuantType.DYNAMIC or is_weight:
# overwrite previous quant config
quants[arg.name] = (DefaultQuant(self, arg), qconfig, is_weight)
return visit_arg
for node in graph.nodes:
if node.name in matches:
root_node, matched, obj, qconfig = matches[node.name]
# don't attach observer/fake_quant for CopyNode
if isinstance(obj, CopyNode):
qconfig = None
if root_node is node:
# matched[-1] is the first op in the sequence and
# matched[0] is the last op in the sequence
# inputs
map_arg(matched[-1].args, visit(matched[-1], qconfig))
map_arg(matched[-1].kwargs, visit(matched[-1], qconfig))
# output
map_arg(matched[0], visit(None, qconfig))
return quants
def replace_target_nodes_with(
fx_module: GraphModule,
old_op: str,
old_target: Target,
new_op: str,
new_target: Target,
):
"""Modifies all nodes in fx_module.graph.nodes which match the specified op code and target,
and updates them to match the new op code and target"""
new_graph = Graph()
val_map: Dict[Node, Node] = {}
for node in fx_module.graph.nodes:
if node.op == old_op and node.target == old_target:
args = map_arg(node.args, lambda n: val_map[n])
kwargs = map_arg(node.kwargs, lambda n: val_map[n])
assert isinstance(args, tuple)
assert isinstance(kwargs, dict)
val_map[node] = new_graph.create_node(new_op, new_target, args,
kwargs, node.name)
else:
val_map[node] = new_graph.node_copy(node, lambda n: val_map[n])
fx_module.graph = new_graph
def quantize(self):
self.quantized_graph = Graph()
self.delegate = DelegateBase(self.quantized_graph)
env = {}
quant_env = {}
def load_arg(n, quantized):
if not quantized:
if n.name not in env and n.name in quant_env:
env[n.name] = Proxy(quant_env[n.name]).dequantize().node
return env[n.name]
else:
if n.name not in quant_env and n.name in env:
quant_env[n.name] = self.quants[n.name].quantize(
env[n.name])
return quant_env[n.name]
def copy_recursive(node):
def load_or_emit(n):
if n.name in env or e.name in quant_env:
return load_arg(n, quantized=False)
else:
return copy_recusive(n)
r = env[node.name] = self.quantized_graph.node_copy(
node, lambda n: load_arg(n, quantized=False))
return r
for node in self.graph.nodes:
root_node, obj = self.matches.get(node.name, (None, None))
if root_node is None:
# not quantized just copy it
env[node.name] = self.quantized_graph.node_copy(
node, lambda n: load_arg(n, quantized=False))
elif root_node is node:
r = obj.quantize(
self, node, lambda a: map_arg(
a, lambda n: load_arg(n, quantized=True)))
if r is NotImplemented:
# quantizer choose to to quantize the node take the entire match, and just copy it over
env[node.name] = copy_recursive(node)
else:
quant_env[node.name] = r
self.quantized_graph.output(
load_arg(self.graph.result, quantized=False))
return GraphModule(self.root, self.quantized_graph)
def _find_quants(self, graph, matches):
"""
Takes the nodes in the input graph and pending matches, and finds and
returns the input and output nodes which need to be quantized.
Inputs:
- graph: an fx.Graph object
- matches: output of self._find_matches function
Outputs a map of
node_name -> (QuantizeHandler instance (always DefaultQuant), qconfig)
"""
quants = {}
def visit(node, qconfig):
def visit_arg(arg):
# note: we have to measure quantization information
# even for nodes where we might not use it because it is already
# quantized. This is because each match has the option to
# say NotImplemented (if for instance, it is an __add__ and the data type is not appropriate)
is_weight = False
if isinstance(
node, Node
) and node.op == 'call_function' and node.target in WEIGHT_INDEX_DICT:
for i, node_arg in enumerate(node.args):
if arg is node_arg and i in WEIGHT_INDEX_DICT[
node.target]:
is_weight = True
if qconfig is not None and \
(activation_is_statically_quantized(qconfig) or is_weight):
# overwrite previous quant config
quants[arg.name] = (DefaultQuant(self,
arg), qconfig, is_weight)
return visit_arg
for node in graph.nodes:
if node.name in matches:
root_node, matched, obj, qconfig = matches[node.name]
# don't attach observer/fake_quant for CopyNode
if isinstance(obj, CopyNode):
qconfig = None
if root_node is node:
# matched[-1] is the first op in the sequence and
# matched[0] is the last op in the sequence
# inputs
map_arg(matched[-1].args, visit(matched[-1], qconfig))
map_arg(matched[-1].kwargs, visit(matched[-1], qconfig))
# output
if isinstance(obj, StandaloneModuleQuantizeHandler):
# we don't insert observer for output of custom
# module
continue
map_arg(matched[0], visit(None, qconfig))
return quants
def load_arg(a):
return map_arg(a, lambda node: env[node.name])
def _convert(self, model, inplace=False, debug=False, is_dynamic_quant=False):
self.restore_state(model)
if not inplace:
model = copy.deepcopy(model)
self.is_dynamic_quant = is_dynamic_quant
# run weight observers before inserting quant dequant nodes
# for dynamic quantization
if self.is_dynamic_quant:
self._run_weight_observers(model)
# move to cpu since we only have quantized cpu kernels
model.eval().cpu()
self.modules = dict(model.named_modules())
matches = self._find_matches(model.graph, self.modules, self.patterns)
quants = self._find_quants(model.graph, matches)
self.quantized_graph = Graph()
env = {}
quant_env = {}
def load_non_quantized(n):
if n.name not in env:
assert n.name in quant_env, \
'trying to load float node but did not find node:' + n.name + \
' in quantized or non quantized environment, env: ' + str(env) + \
' quant_env:' + str(quant_env)
env[n.name] = Proxy(quant_env[n.name]).dequantize().node
return env[n.name]
def load_quantized(n):
if n.name not in quant_env:
assert n.name in env, \
'trying to load quantized node but did not find node:' + n.name + \
' in float environment:' + str(env)
assert n.name in quants, 'did not find quant object for node:' + n.name
quant = quants[n.name][0]
quant_env[n.name] = quant.convert(self, env[n.name])
return quant_env[n.name]
def load_x(n):
assert n.name in env or n.name in quant_env, \
'node ' + n.name + ' does not exist in either environment'
if n.name in quant_env:
return quant_env[n.name]
else:
return env[n.name]
def load_arg(quantized):
"""
Input: quantized, which can be None, list, boolean or tuple
- if quantized is a list or tuple, then arg should be a list and the args with corresponding
indexes will be quantized
- if quantized is a boolean, then all args will be quantized/not quantized
- if quantized is None, then we'll load the node as long as it exists
Output: fn which takes arg_or_args, and loads them from the corresponding
environment depending on the value of quantized.
"""
assert quantized is None or isinstance(quantized, (tuple, list, bool)), type(quantized)
def load_arg_impl(arg_or_args):
if quantized is None:
return map_arg(arg_or_args, load_x)
if isinstance(quantized, bool):
return map_arg(arg_or_args, load_quantized if quantized else load_non_quantized)
elif isinstance(quantized, (tuple, list)):
assert isinstance(arg_or_args, (tuple, list)), arg_or_args
loaded_args = []
# for now, we only support quantizing positional arguments
for i, a in enumerate(arg_or_args):
if i in quantized:
loaded_args.append(map_arg(a, load_quantized))
else:
loaded_args.append(map_arg(a, load_non_quantized))
return type(arg_or_args)(loaded_args)
return load_arg_impl
def is_quantized(node):
if isinstance(node, Node):
assert node.name in env or node.name in quant_env, 'Expecting node to be in the environment'
# there might be nodes appearing in both environemnts, but quant_env will take
# precedence
if node.name in quant_env:
return True
elif node.name in env:
return False
elif isinstance(node, list):
quantized = map(is_quantized, node)
if all(quantized):
return True
elif not any(quantized):
return False
else:
raise Exception("partially quantized inputs in list not handled yet")
for node in model.graph.nodes:
root_node, matched, obj, qconfig = matches.get(node.name, (None, None, None, None))
if root_node is node:
if qconfig is None:
result = self.quantized_graph.node_copy(node, load_non_quantized)
quantized = False
else:
result = obj.convert(self, node, load_arg)
# Need to get correct quantized/non-quantized state for the output of CopyNode
if isinstance(obj, CopyNode):
assert node.op in [
'call_module',
'call_function',
'call_method'], \
'CopyNode of type ' + node.op + ' is not handled'
quantized = is_quantized(node.args[0])
else:
quantized = True
# output of dynamic quantization is not quantized
if self.is_dynamic_quant:
quantized = False
if quantized:
quant_env[node.name] = result
else:
env[node.name] = result
continue
elif root_node is not None:
continue
# handle activation post process calls
if node.op == 'call_module':
if node.target.split('.')[-1].startswith('activation_post_process_'):
observer_module = self.modules[node.target]
prev_node = node.args[0]
if observer_module.dtype == torch.float16:
# activations are not quantized for
# fp16 dynamic quantization
# copy the activaiton_post_process node here
# since we may need it when we insert prepack
# op for weight of linear, this will be removed
# later in a separate pass
env[node.name] = self.quantized_graph.node_copy(node, load_non_quantized)
continue
if prev_node.name in quant_env:
# if previous node is already quantized, we'll just remove the activation_post_process
quant_env[node.name] = quant_env[prev_node.name]
continue
# replace activation post process with quantization ops
root_module = self.modules['']
quant_env[node.name] = quantize_node(
root_module, self.quantized_graph,
load_non_quantized(node.args[0]), observer_module)
continue
# dequantize inputs for the node that are not quantized
env[node.name] = self.quantized_graph.node_copy(node, load_non_quantized)
self.quantized_graph.output(map_arg(model.graph.result, load_non_quantized))
# remove activation post process
act_post_process_removed_graph = Graph()
env = {}
def load_arg(a):
return map_arg(a, lambda node: env[node.name])
for node in self.quantized_graph.nodes:
if node.op == 'call_module' and \
node.target.split('.')[-1].startswith('activation_post_process_'):
# remove activation post process
env[node.name] = env[node.args[0].name]
else:
env[node.name] = act_post_process_removed_graph.node_copy(node, load_arg)
act_post_process_removed_graph.output(map_arg(self.quantized_graph.result, load_arg))
to_be_removed = []
for name, _ in model.named_modules():
if name.split('.')[-1].startswith('activation_post_process_'):
to_be_removed.append(name)
for n in to_be_removed:
delattr(model, n)
_remove_qconfig(model)
model = GraphModule(model, act_post_process_removed_graph)
return model
def split_by_tags(gm: torch.fx.GraphModule,
tags: List[str]) -> torch.fx.GraphModule:
"""
Splits a GraphModule using tags on its graph nodes. We honor the order of
tags. For example, we have tags = ["a", "b", "c"], the function will create
the initial submodules in the order of "a_0", "b_1", "c_2".
To set a tag:
gm.graph.nodes[idx].tag = "mytag"
This will result in all nodes with the same tag being extracted and placed in their
own submodule. For placeholder, output and get_attr node, the tag is ignored. placeholder
and output nodes are created when needed while get_attr nodes get copied to submodules
where they are used.
Given the following module def:
class SimpleModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear1 = torch.nn.Linear(...)
self.linear2 = torch.nn.Linear(...)
self.linear3 = torch.nn.Linear(...)
def forward(self, in1, in2):
r1 = self.linear1(in1)
r2 = self.linear2(in2)
r3 = torch.cat([r1, r2])
return self.linear3(r3)
Marking the node corresponding to in1 with the tag sc.REQUEST_ONLY.lower() results in the following split:
ro_0:
def forward(self, in1):
self = self.root
linear1 = self.linear1(in1)
return linear1
main_1:
def forward(self, in2, linear1):
self = self.root
linear2 = self.linear2(in2)
cat_1 = torch.cat([linear1, linear2])
linear3 = self.linear3(cat_1)
return linear3
main_0:
def forward(self, in1, in2):
self = self.root
ro_0 = self.ro_0(in1)
main_1 = self.main_1(in2, ro_0)
return main_1
"""
def flatten(x: torch.fx.node.Argument) -> NodeList:
"""
Stores nodes in x to a list and returns the list.
"""
r: NodeList = []
map_arg(x, r.append)
return r
# Mapping from node in original module to node in created submodule.
node_remapping: Dict[torch.fx.Node, torch.fx.Node] = {}
# Mapping from node in orignal module or created submodules to
# corresponding component.
node_to_component: Dict[torch.fx.Node, Component] = {}
# Mapping from tag to the corresponding component.
tag_to_component: Dict[str, Component] = {}
# Stores all components.
all_components: List[Component] = []
# Stores nodes that will be used in main graph.
used_in_main: NodeSet = set()
# Main graph after split.
main_g = torch.fx.Graph()
# Mapping from node in original module to node in main graph after split.
main_remapping: Dict[torch.fx.Node, torch.fx.Node] = {}
# Output node of original module.
output_node: Optional[torch.fx.Node] = None
# Create a component for each tag, we don't expect to create other components afterwards.
for tag in tags:
comp = Component(torch.fx.Graph(), len(all_components), f"{tag}")
all_components.append(comp)
tag_to_component[tag] = comp
# Traverse the nodes in original graph and take care of them.
for node in gm.graph.nodes:
if node.op == "output":
if output_node is not None:
raise RuntimeError("Multiple output nodes in graph!")
output_node = node
continue
# Placeholders in the original graph get copied to main graph.
if node.op == "placeholder":
main_remapping[node] = main_g.placeholder(node.name,
type_expr=node.type)
continue
# Get_attr nodes are ignored because we are not tagging them.
# Instead, we copy them directly to the submodules use them afterwards.
if node.op == "get_attr":
continue
# Now we process callable nodes which are nodes with op of call_module,
# call_function or call_method. Every callable nodes should be tagged.
assert hasattr(node, "tag")
upstream_components = [
node_to_component[x]
for x in flatten(node.args) + flatten(node.kwargs)
if x.op not in {"placeholder", "get_attr"}
]
comp = tag_to_component[node.tag]
node_to_component[node] = comp
# Max order of upperstream components.
mx = max((c.order for c in upstream_components), default=0)
# Expect the componet for `node` has higher order then its upstream components.
assert comp.order >= mx
# Map a input of `node` to nodes in the component's graph.
def remap_func(x):
# If input is a get_attr node, copy it to current component's graph.
# Returns the get_attr node in current component's graph.
if x.op == "get_attr":
if x not in comp.getattr_maps:
comp.getattr_maps[x] = comp.graph.get_attr(
x.target, type_expr=x.type)
return comp.getattr_maps[x]
# If input is not a placeholder, it should have been put into a component
# already. If it's the current component then we return the correcspoding
# node in the component.
if x.op != "placeholder" and node_to_component[x] == comp:
return node_remapping[x]
# If input is a placeholder or it's in other components, we want to make it
# as a placeholder in current component's graph.
if x not in comp.orig_inputs:
comp.orig_inputs.append(x)
comp.input_placeholders.append(
comp.graph.placeholder(x.name, type_expr=x.type))
used_in_main.add(x)
return comp.input_placeholders[next(
i for i, y in enumerate(comp.orig_inputs) if x is y)]
n = comp.graph.node_copy(node, remap_func)
n.tag = node.tag # type: ignore[attr-defined]
node_remapping[node] = n
node_to_component[n] = comp
if output_node is None:
raise RuntimeError("Graph had no output node!")
for x in flatten(output_node.args[0]):
used_in_main.add(x)
# If a node is used in main graph then we mark it as an output in the component
# it belongs to.
for n in used_in_main:
if n.op != "placeholder":
node_to_component[n].orig_outputs.append(n)
# Now we create a graphmodule for each component.
for comp in all_components:
outs = tuple(map(node_remapping.__getitem__, comp.orig_outputs))
# Take care of the args of FX output node. If there's a single
# output then the output node args is like (output_single), else
# if there're multiple outputs then the output node args is like
# ((output_0, output_1, ...)).
comp.graph.output(outs[0] if len(outs) == 1 else outs)
# Loop through all module calls (call_module) and param feteches (get_attr)
# in this component, creating HolderModules as necessary to match the path.
# e.g. if in the original module there's a get_attr node fetches "conv.weight".
# We create a HolderModule as root -> add a HolderModule named "conv" ->
# make "weight" a attribute of "conv" HolderModule and point to conv.weight in
# the original module.
root = HolderModule({})
for n in comp.graph.nodes:
if n.op not in ("call_module", "get_attr"):
continue
target = n.target
assert isinstance(target, str)
target_name_parts = target.split(".")
curr = root
orig_gm = gm
for name in target_name_parts[:-1]:
if not hasattr(curr, name):
curr.add_module(name, HolderModule({}))
curr = getattr(curr, name)
orig_gm = getattr(orig_gm, name)
leaf_node_name = target_name_parts[-1]
leaf_node = getattr(orig_gm, leaf_node_name)
# Relies on custom __setattr__ magic.
setattr(curr, leaf_node_name, leaf_node)
comp.gm = torch.fx.GraphModule(root, comp.graph)
# Create a call_module node in main graph.
main_node = main_g.call_module(
comp.name,
args=tuple(map(main_remapping.__getitem__, comp.orig_inputs)),
kwargs=None,
)
if len(outs) == 1:
main_remapping[comp.orig_outputs[0]] = main_node
else:
for i, o in enumerate(comp.orig_outputs):
# Use Proxy to record getitem access.
main_remapping[o] = torch.fx.Proxy(
main_node)[i].node # type: ignore[index]
main_g.output(map_arg(output_node.args[0], main_remapping.__getitem__))
main_root = HolderModule({comp.name: comp.gm for comp in all_components})
return torch.fx.GraphModule(main_root, main_g)
