def test_fetch(self):
attrs_for_lowering: Dict[str, List[str]] = {
"torch.nn.modules.conv.Conv2d": [
"weight", "bias", "kernel_size", "stride", "padding",
"dilation", "groups", "padding_mode"
],
"torch.nn.modules.batchnorm.BatchNorm2d":
["weight", "bias", "running_mean", "running_var", "eps"],
}
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 3, 2)
self.bn = torch.nn.BatchNorm2d(3)
def forward(self, a):
a = self.conv(a)
a += a
return self.bn(a)
mod = TestModule()
traced = symbolic_trace(mod)
lift_lowering_attrs_to_nodes(traced)
for node in traced.graph.nodes:
if node.op == "call_module":
assert hasattr(node, "attrs_for_lowering")
para_list = attrs_for_lowering[node.attrs_for_lowering["name"]]
# node.attrs_for_lowering has an addition field of class name
assert len(para_list) + 1 == len(node.attrs_for_lowering)
for p_name in para_list:
assert p_name in node.attrs_for_lowering
def serialize_module(fx_module: GraphModule,
weights: Dict,
name_prefix="") -> Dict:
"""Recursively Serializes a graph module (fx_module) to a dictionary which is later exported to JSON.
It also adds all weights the provided weights dictionary by qualified_name.
Dictionary Schema:
MODULE
{
modules: {module_name: MODULE],
nodes: [NODE],
weights {qualified_name: WEIGHT},
}
NODE
{
shape: [],
dtype: dtype,
target: target,
op_code: op_code,
name: name,
args: [],
kwargs: {}
}
WEIGHT
{
dtype: dtype,
is_quantized: bool,
shape: [],
quantization_info: QUANTIZATION
}
QUANTIZATION
{
qscheme: qscheme,
q_scale: float,
q_zero_point: float,
q_per_channel_scales, [],
q_per_channel_zero_points: [],
q_per_channel_axis, int
}
"""
serialized_dict: Dict[str, Any] = {}
serialized_dict["modules"] = {}
serialized_dict["weights"] = {}
serialized_dict["nodes"] = []
parameters = fx_module.named_parameters()
prefix = f"{name_prefix}." if name_prefix else ""
submodules = dict(fx_module.named_modules())
for name, p in parameters:
if isinstance(p, torch.Tensor):
weight = serialize_weight(p)
serialized_dict["weights"][prefix + name] = weight
weights[prefix + name] = p
# Note: lift_lowering_attrs_to_nodes is only used to support leaf modules
# that cannot currently be symbolically traced into, e.g. batch norm.
lift_lowering_attrs_to_nodes(fx_module)
for node in fx_module.graph.nodes:
node_rep: Dict[str, Any] = {}
# Get shape/type info, currently not needed for call_module.
if node.op != "call_module" or not isinstance(submodules[node.target],
GraphModule):
shape, dtype = get_shape_and_dtype(node)
node_rep["shape"] = serialize_shape(shape)
node_rep["dtype"] = str(dtype)
# Recurse down into any submodules we are calling.
if node.op == "call_module":
if isinstance(submodules[node.target], GraphModule):
serialized_module = serialize_module(
getattr(fx_module, node.target), weights, node.target)
serialized_dict["modules"][node.target] = serialized_module
else:
node_rep["parameters"] = serialize_leaf_module(
node,
serialized_dict["weights"],
weights,
prefix + node.target,
)
if node.op == "call_function":
node_rep["target"] = _get_qualified_name(node.target)
else:
node_rep["target"] = str(node.target)
# Make sure we capture all constants.
if node.op == "get_attr":
# If we are targeting a parent constant we update the target.
if node.target.startswith("parent."):
node.name = node.name[len("parent."):]
node_rep["target"] = str(node.target[len("parent."):])
weight = serialize_weight(
weights[node.target[len("parent."):]])
serialized_dict["weights"][
node.target[len("parent."):]] = weight
else:
# Iterate through the module hierarchy to find the attr.
target = fx_module
split = node.target.split(".")
assert len(split)
while len(split):
target = getattr(target, split.pop(0))
qualname = prefix + node.target
# Check that the target is a tensor, and that we haven't added it already from a leaf module.
if isinstance(target,
torch.Tensor) and qualname not in weights:
weight = serialize_weight(target)
serialized_dict["weights"][prefix + node.target] = weight
weights[prefix + node.target] = target
node_rep["op_code"] = node.op
node_rep["name"] = node.name
if node.op == "output":
def get_output_info(arg: Node) -> Argument:
shape, dtype = get_shape_and_dtype(arg)
return {
"is_node": True,
"name": str(arg),
"shape": serialize_shape(shape),
"dtype": str(dtype),
}
node_rep["args"] = map_arg(
node.args,
get_output_info,
)
# If there're multiple outputs then node_rep["args"][0] will be a tuple.
# In this case we want to unpack the tuple.
if isinstance(node_rep["args"][0], tuple):
node_rep["args"] = node_rep["args"][0]
else:
node_rep["args"] = map_arg(
node.args, lambda arg: {
"is_node": True,
"name": str(arg)
})
node_rep["kwargs"] = map_arg(
node.kwargs, lambda arg: {
"is_node": True,
"name": str(arg)
})
serialized_dict["nodes"] += [node_rep]
return serialized_dict
def serialize_module(fx_module: GraphModule, weights: Dict, name_prefix="") -> Dict:
"""Recursively Serializes a graph module (fx_module) to a dictionary which is later exported to JSON.
It also adds all weights the provided weights dictionary by qualified_name.
Dictionary Schema:
MODULE
{
modules: {module_name: MODULE],
nodes: [NODE],
weights {qualified_name: WEIGHT},
}
NODE
{
shape: [],
stride: [],
dtype: dtype,
is_quantized: bool,
target: target,
op_code: op_code,
name: name,
args: [],
kwargs: {}
}
WEIGHT
{
dtype: dtype,
is_quantized: bool,
shape: [],
QUANTIZATION,
}
QUANTIZATION
{
qscheme: qscheme,
q_scale: float,
q_zero_point: float,
q_per_channel_scales, [],
q_per_channel_zero_points: [],
q_per_channel_axis, int
}
"""
serialized_dict: Dict[str, Any] = {}
serialized_dict["modules"] = {}
serialized_dict["weights"] = {}
serialized_dict["nodes"] = []
submodules = dict(fx_module.named_modules())
prefix = f"{name_prefix}." if name_prefix else ""
def add_weight_tensors(named_tensors):
for name, p in named_tensors:
if name.startswith("parent.") or not isinstance(p, torch.Tensor):
continue
weight_dict = serialize_weight(p, weights, prefix + name)
serialized_dict["weights"].update(weight_dict)
weights[prefix + name] = p
add_weight_tensors(fx_module.named_parameters())
add_weight_tensors(fx_module.named_buffers())
def get_node_info(node):
tensor_meta = get_tensor_meta(node)
node_rep = {
"shape": serialize_shape(tensor_meta.shape),
"dtype": str(tensor_meta.dtype),
"requires_grad": str(tensor_meta.requires_grad),
"stride": serialize_stride(tensor_meta.stride),
"is_quantized": tensor_meta.is_quantized,
}
if tensor_meta.is_quantized:
node_rep["qscheme"] = str(tensor_meta.qscheme)
if tensor_meta.qscheme in {
torch.per_tensor_affine,
torch.per_tensor_symmetric,
}:
node_rep["q_scale"] = tensor_meta.q_scale
node_rep["q_zero_point"] = tensor_meta.q_zero_point
return node_rep
# Note: lift_lowering_attrs_to_nodes is only used to support leaf modules
# that cannot currently be symbolically traced into, e.g. batch norm.
lift_lowering_attrs_to_nodes(fx_module)
for node in fx_module.graph.nodes:
node_rep: Dict[str, Any] = {}
# Get shape/type info, currently not needed for call_module node
# whose target is a GraphModule and output node.
if (
not (
node.op == "call_module"
and isinstance(submodules[node.target], GraphModule)
)
and node.op != "output"
):
node_rep.update(get_node_info(node))
# Recurse down into any submodules we are calling.
if node.op == "call_module":
if isinstance(submodules[node.target], GraphModule):
serialized_module = serialize_module(
getattr(fx_module, node.target), weights, node.target
)
serialized_dict["modules"][node.target] = serialized_module
else:
node_rep["parameters"] = serialize_leaf_module(
node,
serialized_dict["weights"],
weights,
prefix + node.target,
)
if node.op == "call_function":
node_rep["target"] = _get_qualified_name(node.target)
else:
node_rep["target"] = str(node.target)
# Make sure we capture all constants.
if node.op == "get_attr":
# If we are targeting a parent constant we update the target.
if node.target.startswith("parent."):
stripped_name = node.target[len("parent.") :]
node.name = stripped_name
node_rep["target"] = stripped_name
weight = serialize_weight(
weights[stripped_name], weights, node.target[len("parent.") :]
)
# For quantized embedding tables we need to update the shape/type,
# so we check if the users of this get_attr is a quantized EB and this is the weight for the EB.
user_targets = {
_get_qualified_name(
n.target
).replace("torch.fx.experimental.fx_acc.", "").replace("glow.fb.fx.", ""): n
for n in node.users.keys()
}
if (
"acc_ops.embedding_bag_byte_rowwise_offsets" in user_targets
and str(
user_targets[
"acc_ops.embedding_bag_byte_rowwise_offsets"
].kwargs["weight"]
)
== stripped_name
):
weight[stripped_name]["dtype"] = "acc.uint8fused"
# Same as above, but for the 4 bit version.
if (
"acc_ops.embedding_bag_4bit_rowwise_offsets" in user_targets
and str(
user_targets[
"acc_ops.embedding_bag_4bit_rowwise_offsets"
].kwargs["weight"]
)
== stripped_name
):
weight[stripped_name]["dtype"] = "acc.uint4fused"
serialized_dict["weights"].update(weight)
else:
# Find the actual target parameter/buffer from the fx_module.
submod_path, _, target_name = node.target.rpartition(".")
submod: Optional[torch.nn.Module] = (
fx_module.get_submodule(submod_path) if submod_path else fx_module
)
assert submod is not None, f"submod {submod_path} not found"
target = getattr(submod, target_name, None)
assert target is not None, f"{target_name} not an attr of {submod_path}"
qualname = prefix + node.target
# Check that the target is a tensor, and that we haven't added it already from a leaf module.
if isinstance(target, torch.Tensor) and qualname not in weights:
weight = serialize_weight(target, weights, qualname)
serialized_dict["weights"].update(weight)
weights[qualname] = target
node_rep["op_code"] = node.op
node_rep["name"] = node.name
def get_user_info(user_node: Argument) -> Any:
return {"is_node": True, "name": str(user_node)}
def get_arg_info(arg: Argument) -> Any:
if isinstance(arg, torch.fx.Node):
return {"is_node": True, "name": str(arg)}
elif isinstance(arg, (torch.dtype, torch.memory_format, torch.qscheme)):
return str(arg)
else:
return arg
def get_output_arg_info(arg: Node) -> Dict[str, Any]:
node_rep: Dict[str, Any] = get_arg_info(arg)
node_rep.update(get_node_info(arg))
return node_rep
if node.op == "output":
node_rep["args"] = map_arg(
node.args,
get_output_arg_info,
)
# If there're multiple outputs then node_rep["args"][0] will be a tuple.
# In this case we want to unpack the tuple.
if isinstance(node_rep["args"][0], tuple):
node_rep["args"] = node_rep["args"][0]
else:
node_rep["args"] = map_aggregate(node.args, get_arg_info)
node_rep["kwargs"] = map_aggregate(node.kwargs, get_arg_info)
node_rep["users"] = map_aggregate(list(node.users.keys()), get_user_info)
serialized_dict["nodes"] += [node_rep]
return serialized_dict
def serialize_module(fx_module: GraphModule, weights: Dict, name_prefix="") -> Dict:
"""Recursively Serializes a graph module (fx_module) to a dictionary which is later exported to JSON.
It also adds all weights the provided weights dictionary by qualified_name.
Dictionary Schema:
MODULE
{
modules: {module_name: MODULE],
nodes: [NODE],
weights {qualified_name: WEIGHT},
}
NODE
{
shape: [],
dtype: dtype,
is_quantized: bool,
target: target,
op_code: op_code,
name: name,
args: [],
kwargs: {}
}
WEIGHT
{
dtype: dtype,
is_quantized: bool,
shape: [],
QUANTIZATION,
}
QUANTIZATION
{
qscheme: qscheme,
q_scale: float,
q_zero_point: float,
q_per_channel_scales, [],
q_per_channel_zero_points: [],
q_per_channel_axis, int
}
"""
serialized_dict: Dict[str, Any] = {}
serialized_dict["modules"] = {}
serialized_dict["weights"] = {}
serialized_dict["nodes"] = []
submodules = dict(fx_module.named_modules())
prefix = f"{name_prefix}." if name_prefix else ""
def add_weight_tensors(named_tensors):
for name, p in named_tensors:
if name.startswith("parent.") or not isinstance(p, torch.Tensor):
continue
weight = serialize_weight(p)
serialized_dict["weights"][prefix + name] = weight
weights[prefix + name] = p
add_weight_tensors(fx_module.named_parameters())
add_weight_tensors(fx_module.named_buffers())
def get_node_info(node):
shape, dtype = get_shape_and_dtype(node)
tensor_meta = node.meta.get('tensor_meta')
if not tensor_meta:
raise RuntimeError(f'Node {node} has no tensor metadata! Ensure shape '
f'propagation has been run!')
node_rep = {
"shape": serialize_shape(shape),
"dtype": str(dtype),
"is_quantized": tensor_meta.is_quantized,
}
if tensor_meta.is_quantized:
node_rep["qscheme"] = str(tensor_meta.qscheme)
if tensor_meta.qscheme in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
node_rep["q_scale"] = tensor_meta.q_scale
node_rep["q_zero_point"] = tensor_meta.q_zero_point
return node_rep
# Note: lift_lowering_attrs_to_nodes is only used to support leaf modules
# that cannot currently be symbolically traced into, e.g. batch norm.
lift_lowering_attrs_to_nodes(fx_module)
for node in fx_module.graph.nodes:
node_rep: Dict[str, Any] = {}
# Get shape/type info, currently not needed for call_module node
# whose target is a GraphModule and output node.
if (
not (
node.op == "call_module"
and isinstance(submodules[node.target], GraphModule)
)
and node.op != "output"
):
node_rep.update(get_node_info(node))
# Recurse down into any submodules we are calling.
if node.op == "call_module":
if isinstance(submodules[node.target], GraphModule):
serialized_module = serialize_module(
getattr(fx_module, node.target), weights, node.target
)
serialized_dict["modules"][node.target] = serialized_module
else:
node_rep["parameters"] = serialize_leaf_module(
node,
serialized_dict["weights"],
weights,
prefix + node.target,
)
if node.op == "call_function":
node_rep["target"] = _get_qualified_name(node.target)
else:
node_rep["target"] = str(node.target)
# Make sure we capture all constants.
if node.op == "get_attr":
# If we are targeting a parent constant we update the target.
if node.target.startswith("parent."):
stripped_name = node.target[len("parent.") :]
node.name = stripped_name
node_rep["target"] = stripped_name
weight = serialize_weight(weights[stripped_name])
serialized_dict["weights"][stripped_name] = weight
else:
# Find the actual target parameter/buffer from the fx_module.
submod_path, _, target_name = node.target.rpartition(".")
submod: Optional[torch.nn.Module] = (
fx_module.get_submodule(submod_path) if submod_path else fx_module
)
assert submod is not None, f"submod {submod_path} not found"
target = getattr(submod, target_name, None)
assert target is not None, f"{target_name} not an attr of {submod_path}"
qualname = prefix + node.target
# Check that the target is a tensor, and that we haven't added it already from a leaf module.
if isinstance(target, torch.Tensor) and qualname not in weights:
weight = serialize_weight(target)
serialized_dict["weights"][qualname] = weight
weights[qualname] = target
node_rep["op_code"] = node.op
node_rep["name"] = node.name
def get_arg_info(arg: Argument) -> Any:
if isinstance(arg, torch.fx.Node):
return {"is_node": True, "name": str(arg)}
elif isinstance(arg, torch.dtype):
return str(arg)
else:
return arg
def get_output_arg_info(arg: Node) -> Dict[str, Any]:
node_rep: Dict[str, Any] = get_arg_info(arg)
node_rep.update(get_node_info(arg))
return node_rep
if node.op == "output":
node_rep["args"] = map_arg(
node.args,
get_output_arg_info,
)
# If there're multiple outputs then node_rep["args"][0] will be a tuple.
# In this case we want to unpack the tuple.
if isinstance(node_rep["args"][0], tuple):
node_rep["args"] = node_rep["args"][0]
else:
node_rep["args"] = map_aggregate(
node.args, get_arg_info
)
node_rep["kwargs"] = map_aggregate(
node.kwargs, get_arg_info
)
serialized_dict["nodes"] += [node_rep]
return serialized_dict
def serialize_module(fx_module: GraphModule,
weights: Dict,
name_prefix="") -> Dict:
"""Recursively Serializes a graph module (fx_module) to a dictionary which is later exported to JSON.
It also adds all weights the provided weights dictionary by qualified_name.
Dictionary Schema:
MODULE
{
modules: {module_name: MODULE],
nodes: [NODE],
weights {qualified_name: WEIGHT},
}
NODE
{
shape: [],
dtype: dtype,
target: target,
op_code: op_code,
name: name,
args: [],
kwargs: {}
}
WEIGHT
{
dtype: dtype,
is_quantized: bool,
shape: [],
quantization_info: QUANTIZATION
}
QUANTIZATION
{
qscheme: qscheme,
q_scale: float,
q_zero_point: float,
q_per_channel_scales, [],
q_per_channel_zero_points: [],
q_per_channel_axis, int
}
"""
serialized_dict: Dict[str, Any] = {}
serialized_dict["modules"] = {}
serialized_dict["weights"] = {}
serialized_dict["nodes"] = []
parameters = fx_module.named_parameters()
prefix = f"{name_prefix}." if name_prefix else ""
submodules = dict(fx_module.named_modules())
for name, p in parameters:
if isinstance(p, torch.Tensor):
weight = serialize_weight(p)
serialized_dict["weights"][prefix + name] = weight
weights[prefix + name] = p
lift_lowering_attrs_to_nodes(fx_module)
for node in fx_module.graph.nodes:
node_rep: Dict[str, Any] = {}
# Get shape/type info, currently not needed for call_module.
if node.op != "call_module" or not isinstance(submodules[node.target],
GraphModule):
shape = getattr(node, "shape", None)
if shape:
node_rep["shape"] = serialize_shape(shape)
else:
raise RuntimeError(
"Node has no shape attr, this is likely because shape propagation has not been run on this Graph."
)
dtype = getattr(node, "dtype", None)
if dtype:
node_rep["dtype"] = str(dtype)
else:
raise RuntimeError(
"Node has no dtype attr, this is likely because shape propagation has not been run on this Graph."
)
# Recurse down into any submodules we are calling.
if node.op == "call_module":
if isinstance(submodules[node.target], GraphModule):
serialized_module = serialize_module(
getattr(fx_module, node.target), weights, node.target)
serialized_dict["modules"][node.target] = serialized_module
else:
node_rep["parameters"] = serialize_leaf_module(
node,
serialized_dict["weights"],
weights,
prefix + node.target,
)
if node.op == "call_function":
node_rep["target"] = _get_qualified_name(node.target)
else:
node_rep["target"] = str(node.target)
# Make sure we capture all constants.
if node.op == "get_attr":
target = getattr(fx_module, node.target)
qualname = prefix + node.target
if isinstance(target, torch.Tensor) and qualname not in weights:
weight = serialize_weight(target)
serialized_dict["weights"][prefix + node.target] = weight
weights[prefix + node.target] = target
node_rep["op_code"] = node.op
node_rep["name"] = node.name
node_rep["args"] = map_arg(
node.args, lambda arg: {
"is_node": True,
"name": str(arg)
})
node_rep["kwargs"] = map_arg(
node.kwargs, lambda arg: {
"is_node": True,
"name": str(arg)
})
serialized_dict["nodes"] += [node_rep]
return serialized_dict
