def run_node(self, n : Node) -> Any:
try:
result = super().run_node(n)
except Exception:
traceback.print_exc()
raise RuntimeError(
f"ShapeProp error for: node={n.format_node()} with "
f"meta={n.meta}"
)
found_tensor = False
def extract_tensor_meta(obj):
if isinstance(obj, torch.Tensor):
nonlocal found_tensor
found_tensor = True
return _extract_tensor_metadata(obj)
else:
return obj
meta = map_aggregate(result, extract_tensor_meta)
if found_tensor:
n.meta['tensor_meta'] = meta
n.meta['type'] = type(result)
return result
def __torch_function__(self, func, types, args=(), kwargs={}):
namespace, func_name = func.split("::")
func = getattr(getattr(torch.ops, namespace), func_name)
outs = kwargs['val']
rets = []
proxy_args = map_aggregate(
args, lambda i: i.proxy if isinstance(i, PythonTensor) else i)
out_proxy = func(*proxy_args)
if len(outs) == 1 and isinstance(outs[0], torch.Tensor):
return [PythonTensor(outs[0], out_proxy)]
for idx, out in enumerate(outs):
if isinstance(out, torch.Tensor):
rets.append(PythonTensor(out, out_proxy[idx]))
else:
rets.append(out)
return rets
def run_node(self, n):
result = super().run_node(n)
found_tensor = False
def extract_tensor_meta(obj):
if isinstance(obj, torch.Tensor):
nonlocal found_tensor
found_tensor = True
return obj
else:
return obj
from torch.fx.node import map_aggregate
concrete_value = map_aggregate(result, extract_tensor_meta)
if found_tensor:
n.meta['concrete_value'] = concrete_value
return result
def run_node(self, n: Node) -> Any:
result = super().run_node(n)
found_tensor = False
def extract_tensor_meta(obj):
if isinstance(obj, torch.Tensor):
nonlocal found_tensor
found_tensor = True
return extract_tensor_metadata(obj)
else:
return obj
meta = map_aggregate(result, extract_tensor_meta)
if found_tensor:
n.meta['tensor_meta'] = meta
n.meta['type'] = type(result)
return result
def run_node(self, n: Node) -> Any:
args, kwargs = self.fetch_args_kwargs_from_env(n)
def get_type(arg):
if isinstance(arg, fx.Node):
return n.meta['type'] if 'type' in n.meta else None
return type(arg)
arg_types = map_aggregate(n.args, get_type)
assert (isinstance(arg_types, tuple))
arg_types = tuple([create_type_hint(i) for i in arg_types])
kwarg_types = {k: get_type(v) for k, v in kwargs.items()}
if n.op == 'call_function':
out = self.call_function(n.target, args, kwargs, arg_types,
kwarg_types)
else:
out = super().run_node(n)
self.node_map[out] = n
return out
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: [],
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 __call__(self, *args, **kwargs):
new_args = map_aggregate(args, convert_to_dispatch_proxy)
new_kwargs = map_aggregate(kwargs, convert_to_dispatch_proxy)
orig_module_call = torch.nn.Module.__call__
orig_nn_sequential_forward = torch.nn.Sequential.forward
def _patched_module_call(self, *args, **kwargs):
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f"\nstarting fqn {fqn}")
nonlocal cur_module
old_module = cur_module
cur_module = self
try:
parent_module = module_stack[-1] if len(
module_stack) else None
module_stack.append(self)
hook_type = get_module_hook_type(parent_module, cur_module)
if enable_logging:
logger.debug(
f"_patched_module_call {type(self)} " +
# f"arg_types {[type(arg) for arg in args]} " +
f"arg_dtypes {[arg.dtype if isinstance(arg, torch.Tensor) else None for arg in args]} "
+ f"hook_type {hook_type}")
if hook_type is HookType.OP_HOOKS:
# before hooks
assert parent_module is not None
assert isinstance(parent_module._auto_quant_state,
AutoQuantizationState)
qstate = parent_module._auto_quant_state
if enable_logging:
logger.debug(qstate)
qstate.validate_cur_op(cur_module)
_, args, kwargs = qstate.op_convert_before_hook(
cur_module, args, kwargs, cur_module)
# forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
output = qstate.op_convert_after_hook(
cur_module, output)
qstate.mark_cur_op_complete(cur_module)
elif hook_type is HookType.MODULE_IO_HOOKS:
cur_qstate = cur_module._auto_quant_state
if enable_logging:
logger.debug(cur_qstate)
cur_qstate.validate_is_at_first_idx()
# before hooks (TODO)
# forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
assert isinstance(cur_qstate, AutoQuantizationState)
output = cur_qstate.outputs_convert_hook(output)
cur_qstate.validate_is_at_last_seen_idx()
elif hook_type is HookType.ARG_DEQUANTS:
# disabling torch function to prevent infinite recursion on
# getset
# TODO(future PR): handle more dtypes
with torch._C.DisableTorchFunction():
new_args = []
for arg in args:
if isinstance(
arg,
torch.Tensor) and arg.is_quantized:
dequant = arg.dequantize().as_subclass(
QuantizationConvertTensorProxy
) # type: ignore[arg-type]
new_args.append(dequant)
else:
new_args.append(arg)
args = tuple(new_args)
output = orig_module_call(self, *args, **kwargs)
else:
output = orig_module_call(self, *args, **kwargs)
if enable_logging:
logger.debug(
f"_patched_module_call {type(self)} " +
# f"out {type(output)} " +
f"dtype {output.dtype if isinstance(output, torch.Tensor) else None} "
+ "end")
logger.debug(f"ending fqn {fqn}\n")
return output
finally:
module_stack.pop()
cur_module = old_module
torch.nn.Module.__call__ = _patched_module_call
torch.nn.Sequential.forward = _nn_sequential_patched_forward # type: ignore[assignment]
try:
for k, v in self.named_modules():
module_id_to_fqn[id(v)] = k
if hasattr(v, '_auto_quant_state'):
v._auto_quant_state.reset_to_new_call()
needs_io_hooks = hasattr(self, '_auto_quant_state')
# handle module input dtype conversions
# TODO(implement)
output = super().__call__(*new_args, **new_kwargs)
# handle module output dtype conversions
if needs_io_hooks:
qstate = self._auto_quant_state
assert isinstance(qstate, AutoQuantizationState)
output = qstate.outputs_convert_hook(output)
def unwrap_proxy(a):
if isinstance(a, QuantizationConvertTensorProxy):
a.__class__ = torch.Tensor # type: ignore[assignment]
return a
output = map_aggregate(output, unwrap_proxy)
return output
finally:
torch.nn.Module.__call__ = orig_module_call
torch.nn.Sequential.forward = orig_nn_sequential_forward # type: ignore[assignment]
def __call__(self, *args, **kwargs):
new_args = map_aggregate(args, convert_to_interception_proxy)
new_kwargs = map_aggregate(kwargs, convert_to_interception_proxy)
orig_module_call = torch.nn.Module.__call__
orig_nn_sequential_forward = torch.nn.Sequential.forward
def _patched_module_call(self, *args, **kwargs):
if enable_logging:
logger.debug(f"_patched_module_call: {type(self)}")
nonlocal cur_module
old_module = cur_module
cur_module = self
try:
parent_module = module_stack[-1] if len(
module_stack) else None
module_stack.append(self)
fqn = module_id_to_fqn.get(id(self), None)
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f"\nstarting fqn {fqn}")
hook_type = get_module_hook_type(parent_module, cur_module)
if hook_type is HookType.OP_HOOKS:
assert parent_module is not None
parent_qstate = parent_module._auto_quant_state
assert isinstance(parent_qstate, AutoQuantizationState)
# before hooks
if not first_call:
parent_qstate.validate_cur_op(cur_module)
args, kwargs = parent_qstate.op_prepare_before_hook(
cur_module, args, kwargs, first_call, qtensor_id,
fqn, cur_module)
# original forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
# TODO is it correct to call_cur_module twice here?
output = parent_qstate.op_prepare_after_hook(
cur_module, output, args, first_call, qtensor_id,
cur_module)
parent_qstate.mark_cur_op_complete(cur_module)
elif hook_type is HookType.MODULE_IO_HOOKS:
# TODO(future PR): add inputs io hook
cur_qstate = cur_module._auto_quant_state
cur_qstate.validate_is_at_first_idx()
# original forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
assert isinstance(cur_qstate, AutoQuantizationState)
output = cur_qstate.outputs_prepare_hook(
output, first_call, qtensor_id)
cur_qstate.validate_is_at_last_seen_idx()
elif hook_type is HookType.ARG_DEQUANTS:
output = orig_module_call(self, *args, **kwargs)
# if this fp32 was inplace, make sure to set the output dtype
# back to torch.float
if hasattr(output, '_qtensor_info'):
del output._qtensor_info
else:
output = orig_module_call(self, *args, **kwargs)
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f"\nending fqn {fqn}")
return output
finally:
module_stack.pop()
cur_module = old_module
torch.nn.Module.__call__ = _patched_module_call
torch.nn.Sequential.forward = _nn_sequential_patched_forward # type: ignore[assignment]
nonlocal first_call
try:
# Create a list before iterating because we are adding new
# named modules inside the loop.
named_modules = list(self.named_modules())
for k, v in named_modules:
# k is the global FQN, i.e. 'foo.bar.baz'
# v is the module instance
#
# we need to associate the global FQN with SeenOp
# for modules, this is the module FQN
# for functions, this is the parent module FQN
module_id_to_fqn[id(v)] = k
has_qconfig = hasattr(v,
'qconfig') and v.qconfig is not None
if has_qconfig and not is_leaf(v):
if first_call:
if v is self:
# for the top level module only, specify input
# and output dtypes
v._auto_quant_state = AutoQuantizationState(
v.qconfig, input_dtypes, output_dtypes)
pass
else:
v._auto_quant_state = AutoQuantizationState(
v.qconfig)
else:
if not isinstance(v, AutoQuantizationState):
assert hasattr(v, '_auto_quant_state')
v._auto_quant_state.reset_to_new_call()
output = super().__call__(*new_args, **new_kwargs)
return output
finally:
torch.nn.Module.__call__ = orig_module_call
torch.nn.Sequential.forward = orig_nn_sequential_forward # type: ignore[assignment]
first_call = False
def __call__(self, *args, **kwargs):
new_args = map_aggregate(args, convert_to_dispatch_proxy)
new_kwargs = map_aggregate(kwargs, convert_to_dispatch_proxy)
orig_module_call = torch.nn.Module.__call__
orig_nn_sequential_forward = torch.nn.Sequential.forward
def _patched_module_call(self, *args, **kwargs):
nonlocal cur_module
old_module = cur_module
cur_module = self
nonlocal global_disable_torch_function_override
try:
parent_module = module_stack[-1] if len(
module_stack) else None
module_stack.append(self)
hook_type = get_module_hook_type(parent_module, cur_module)
if enable_logging:
fqn_for_logging = module_id_to_fqn.get(id(self), None)
logger.debug(
f" fqn: {fqn_for_logging} " +
f"_cl_ {type(self)} " +
f"arg_dtypes {[arg.dtype if isinstance(arg, torch.Tensor) else None for arg in args]} "
+ f"hook_type {hook_type}")
if hook_type is HookType.OP_HOOKS:
# before hooks
qstate: AutoQuantizationState = \
parent_module._auto_quant_state # type: ignore[union-attr, assignment]
qstate.validate_cur_op(cur_module)
# If we are in this hook, `cur_module` is a leaf module.
# Therefore, we do not need to override any of its
# children. Disabling the overrides for performance.
old_global_disable_torch_function_override = \
global_disable_torch_function_override
global_disable_torch_function_override = True
_, args, kwargs = qstate.op_convert_before_hook(
cur_module, args, kwargs, cur_module)
# forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
output = qstate.op_convert_after_hook(
cur_module, output, global_op_idx)
# Re-enable the override.
global_disable_torch_function_override = \
old_global_disable_torch_function_override
qstate.mark_cur_op_complete(cur_module)
elif hook_type is HookType.MODULE_IO_HOOKS:
cur_qstate: AutoQuantizationState = cur_module._auto_quant_state
cur_qstate.reset_to_new_call()
# before hooks (TODO)
# forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
# For the sake of performance, we assume no overrides
# are needed for quantizing/dequantizing things
old_global_disable_torch_function_override = \
global_disable_torch_function_override
global_disable_torch_function_override = True
output = cur_qstate.outputs_convert_hook(output)
global_disable_torch_function_override = \
old_global_disable_torch_function_override
cur_qstate.validate_is_at_last_seen_idx()
elif hook_type is HookType.ARG_DEQUANTS:
# TODO(future PR): handle more dtypes
new_args = []
for arg in args:
if isinstance(arg,
torch.Tensor) and arg.is_quantized:
dequant = arg.dequantize().as_subclass(
QuantizationConvertTensorProxy
) # type: ignore[arg-type]
new_args.append(dequant)
else:
new_args.append(arg)
args = tuple(new_args)
output = orig_module_call(self, *args, **kwargs)
else:
output = orig_module_call(self, *args, **kwargs)
if enable_logging:
fqn_for_logging = module_id_to_fqn.get(id(self), None)
logger.debug(
f" fqn: {fqn_for_logging} " +
f"_cl_ {type(self)} " +
f"dtype {output.dtype if isinstance(output, torch.Tensor) else None} "
+ "end")
return output
finally:
module_stack.pop()
cur_module = old_module
torch.nn.Module.__call__ = _patched_module_call
torch.nn.Sequential.forward = _nn_sequential_patched_forward # type: ignore[assignment]
try:
global_op_idx[0] = 0
output = super().__call__(*new_args, **new_kwargs)
def unwrap_proxy(a):
if isinstance(a, QuantizationConvertTensorProxy):
a.__class__ = torch.Tensor # type: ignore[assignment]
return a
output = map_aggregate(output, unwrap_proxy)
return output
finally:
torch.nn.Module.__call__ = orig_module_call
torch.nn.Sequential.forward = orig_nn_sequential_forward # type: ignore[assignment]
def __call__(self, *args, **kwargs):
new_args = map_aggregate(args, convert_to_interception_proxy)
new_kwargs = map_aggregate(kwargs, convert_to_interception_proxy)
orig_module_call = torch.nn.Module.__call__
orig_nn_sequential_forward = torch.nn.Sequential.forward
def _patched_module_call(self, *args, **kwargs):
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f" fqn:{fqn} _cl_: {type(self)} start")
nonlocal cur_module
old_module = cur_module
cur_module = self
try:
parent_module = module_stack[-1] if len(
module_stack) else None
module_stack.append(self)
fqn = module_id_to_fqn.get(id(self), None)
hook_type = get_module_hook_type(parent_module, cur_module)
if hook_type is HookType.OP_HOOKS:
parent_qstate: AutoQuantizationState = \
parent_module._auto_quant_state # type: ignore[union-attr, assignment]
# before hooks
if not first_call:
parent_qstate.validate_cur_op(cur_module)
# If we are in this hook, `cur_module` is a leaf module.
# Therefore, we do not need to override any of its
# children. Disabling the overrides for performance.
nonlocal global_disable_torch_function_override
old_global_disable_torch_function_override = \
global_disable_torch_function_override
global_disable_torch_function_override = True
if first_call:
# mypy ignore is used instead of assert because this
# runs on every forward and assert has a performance cost
args, kwargs = parent_qstate.first_call_op_prepare_before_hook(
cur_module,
args,
kwargs,
qtensor_id,
fqn,
cur_module, # type: ignore[arg-type]
OpQuantizeabilityType.QUANTIZEABLE)
else:
# mypy ignore is used instead of assert because this
# runs on every forward and assert has a performance cost
args, kwargs = parent_qstate.op_prepare_before_hook(
cur_module, args,
kwargs) # type: ignore[arg-type]
# original forward
output = orig_module_call(self, *args, **kwargs)
# Re-enable the overrides.
global_disable_torch_function_override = \
old_global_disable_torch_function_override
# after hooks
if first_call:
output = parent_qstate.first_call_op_prepare_after_hook(
cur_module, output, args, qtensor_id,
OpQuantizeabilityType.QUANTIZEABLE)
else:
output = parent_qstate.op_prepare_after_hook(
cur_module, output, args, global_op_idx)
parent_qstate.mark_cur_op_complete(cur_module)
elif hook_type is HookType.MODULE_IO_HOOKS:
# TODO(future PR): add inputs io hook
cur_qstate = cur_module._auto_quant_state
cur_qstate.reset_to_new_call()
# original forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
if first_call:
output = cur_qstate.first_call_outputs_prepare_hook(
output, qtensor_id)
else:
output = cur_qstate.outputs_prepare_hook(output)
cur_qstate.validate_is_at_last_seen_idx()
elif hook_type is HookType.ARG_DEQUANTS:
if first_call and parent_module is not None:
parent_qstate_fc = getattr(parent_module,
'_auto_quant_state',
None)
if parent_qstate_fc:
args, kwargs = \
parent_qstate_fc.first_call_op_prepare_before_hook(
cur_module, args, kwargs, qtensor_id, fqn,
cur_module,
OpQuantizeabilityType.NOT_QUANTIZEABLE)
output = orig_module_call(self, *args, **kwargs)
# if this fp32 was inplace, make sure to set the output dtype
# back to torch.float
if hasattr(output, '_qtensor_info'):
del output._qtensor_info
if first_call and parent_module is not None:
parent_qstate_fc = getattr(parent_module,
'_auto_quant_state',
None)
if parent_qstate_fc:
output = \
parent_qstate_fc.first_call_op_prepare_after_hook(
cur_module, output, args, qtensor_id,
OpQuantizeabilityType.NOT_QUANTIZEABLE)
else:
output = orig_module_call(self, *args, **kwargs)
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f" fqn:{fqn} _cl_: {type(self)} end")
return output
finally:
module_stack.pop()
cur_module = old_module
torch.nn.Module.__call__ = _patched_module_call
torch.nn.Sequential.forward = _nn_sequential_patched_forward # type: ignore[assignment]
nonlocal first_call
try:
if first_call:
# Create a list before iterating because we are adding new
# named modules inside the loop.
named_modules = list(self.named_modules())
# Record module instances which are leaves or children of leaves
leaves = set()
for fqn, child in named_modules:
if is_leaf(child, prepare_custom_config_dict):
for _, child_child in child.named_modules():
leaves.add(child_child)
self._fqn_to_auto_quant_state_map = AutoQuantizationStateModuleDict(
)
for fqn, v in named_modules:
# fqn is the global FQN, i.e. 'foo.bar.baz'
# v is the module instance
#
# we need to associate the global FQN with SeenOp
# for modules, this is the module FQN
# for functions, this is the parent module FQN
module_id_to_fqn[id(v)] = fqn
if v in leaves:
continue
if v is self:
# for the top level module only, specify input
# and output dtypes
auto_quant_state = AutoQuantizationState(
qconfig_dict, fqn, input_dtypes, output_dtypes)
else:
auto_quant_state = AutoQuantizationState(
qconfig_dict, fqn)
# The code below registers the auto_quant_state object
# of the child in the module hierarchy of the parent,
# and adds the auto_quant_state object to the child
# with a raw __setattr__, without registering it in
# the module hierarchy of the child.
# This is solving the problem of both storing extra state
# (observers) as well as not modifying the meaning of user
# code in child modules which iterates over all module
# children.
#
# This narrows down the issue of dynamically adding
# children to only affect the top level module and not
# the children.
# On the parent, register this module in the FQN map
fqn_to_use_for_key = \
get_fqn_valid_for_module_dict_key(fqn)
self._fqn_to_auto_quant_state_map[fqn_to_use_for_key] = \
auto_quant_state
# On the child, manually set the attribute without
# going through the `torch.nn.Module.__setattr__`
# function, to prevent this object from appearing in
# the child's module hierarchy.
object.__setattr__(v, '_auto_quant_state',
auto_quant_state)
global_op_idx[0] = 0
output = super().__call__(*new_args, **new_kwargs)
if first_call:
for _, v in self.named_modules():
if hasattr(v, '_auto_quant_state'):
v._auto_quant_state.match_fusion_patterns()
v._auto_quant_state.insert_observers(v)
return output
finally:
torch.nn.Module.__call__ = orig_module_call
torch.nn.Sequential.forward = orig_nn_sequential_forward # type: ignore[assignment]
first_call = False
def __call__(self, *args, **kwargs):
new_args = map_aggregate(args, convert_to_interception_proxy)
new_kwargs = map_aggregate(kwargs, convert_to_interception_proxy)
orig_module_call = torch.nn.Module.__call__
orig_nn_sequential_forward = torch.nn.Sequential.forward
def _patched_module_call(self, *args, **kwargs):
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f" fqn:{fqn} _cl_: {type(self)} start")
nonlocal cur_module
old_module = cur_module
cur_module = self
try:
parent_module = module_stack[-1] if len(
module_stack) else None
module_stack.append(self)
fqn = module_id_to_fqn.get(id(self), None)
hook_type = get_module_hook_type(parent_module, cur_module)
if first_call and hook_type is not HookType.OP_HOOKS and \
parent_module is not None:
parent_qstate_fc = getattr(parent_module,
'_auto_quant_state', None)
if parent_qstate_fc:
parent_qstate_fc.add_seen_op_type_without_op_hooks(
type(cur_module))
if hook_type is HookType.OP_HOOKS:
parent_qstate: AutoQuantizationState = \
parent_module._auto_quant_state # type: ignore[union-attr, assignment]
# before hooks
if not first_call:
parent_qstate.validate_cur_op(cur_module)
# If we are in this hook, `cur_module` is a leaf module.
# Therefore, we do not need to override any of its
# children. Disabling the overrides for performance.
nonlocal global_disable_torch_function_override
old_global_disable_torch_function_override = \
global_disable_torch_function_override
global_disable_torch_function_override = True
# mypy ignore is used instead of assert because this
# runs on every forward and assert has a performance cost
args, kwargs = parent_qstate.op_prepare_before_hook(
cur_module, args, kwargs, first_call, qtensor_id,
fqn, cur_module) # type: ignore[arg-type]
# original forward
output = orig_module_call(self, *args, **kwargs)
# Re-enable the overrides.
global_disable_torch_function_override = \
old_global_disable_torch_function_override
# after hooks
output = parent_qstate.op_prepare_after_hook(
cur_module, output, args, first_call, qtensor_id,
global_op_idx)
parent_qstate.mark_cur_op_complete(cur_module)
elif hook_type is HookType.MODULE_IO_HOOKS:
# TODO(future PR): add inputs io hook
cur_qstate = cur_module._auto_quant_state
cur_qstate.reset_to_new_call()
# original forward
output = orig_module_call(self, *args, **kwargs)
# after hooks
output = cur_qstate.outputs_prepare_hook(
output, first_call, qtensor_id)
cur_qstate.validate_is_at_last_seen_idx()
elif hook_type is HookType.ARG_DEQUANTS:
output = orig_module_call(self, *args, **kwargs)
# if this fp32 was inplace, make sure to set the output dtype
# back to torch.float
if hasattr(output, '_qtensor_info'):
del output._qtensor_info
else:
output = orig_module_call(self, *args, **kwargs)
if enable_logging:
fqn = module_id_to_fqn.get(id(self), None)
logger.debug(f" fqn:{fqn} _cl_: {type(self)} end")
return output
finally:
module_stack.pop()
cur_module = old_module
torch.nn.Module.__call__ = _patched_module_call
torch.nn.Sequential.forward = _nn_sequential_patched_forward # type: ignore[assignment]
nonlocal first_call
try:
if first_call:
# Create a list before iterating because we are adding new
# named modules inside the loop.
named_modules = list(self.named_modules())
# Record module instances which are leaves or children of leaves
leaves = set()
for fqn, child in named_modules:
if is_leaf(child, prepare_custom_config_dict):
for _, child_child in child.named_modules():
leaves.add(child_child)
for fqn, v in named_modules:
# fqn is the global FQN, i.e. 'foo.bar.baz'
# v is the module instance
#
# we need to associate the global FQN with SeenOp
# for modules, this is the module FQN
# for functions, this is the parent module FQN
module_id_to_fqn[id(v)] = fqn
if v in leaves:
continue
if v is self:
# for the top level module only, specify input
# and output dtypes
v._auto_quant_state = AutoQuantizationState(
qconfig_dict, fqn, input_dtypes, output_dtypes)
pass
else:
v._auto_quant_state = AutoQuantizationState(
qconfig_dict, fqn)
global_op_idx[0] = 0
output = super().__call__(*new_args, **new_kwargs)
if first_call:
for _, v in self.named_modules():
if hasattr(v, '_auto_quant_state'):
v._auto_quant_state.insert_observers(v)
return output
finally:
torch.nn.Module.__call__ = orig_module_call
torch.nn.Sequential.forward = orig_nn_sequential_forward # type: ignore[assignment]
first_call = False
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 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.qparams["qscheme"])
if tensor_meta.qparams["qscheme"] in {
torch.per_tensor_affine,
torch.per_tensor_symmetric,
}:
node_rep["q_scale"] = tensor_meta.qparams["scale"]
node_rep["q_zero_point"] = tensor_meta.qparams["zero_point"]
# Add all extra lowering_info that was provided in node.meta.
lowering_info = node.meta.get("lowering_info")
if lowering_info is not None:
overlapping_keys = node_rep.keys() & lowering_info.keys()
assert (
len(overlapping_keys) == 0
), f"Overlap found between lowering_info and node_rep: {overlapping_keys}"
node_rep.update(lowering_info)
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."):
qualname = node.target[len("parent."):]
node.name = qualname
node_rep["target"] = qualname
else:
qualname = prefix + node.target
# 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}"
# 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)
_update_weight_fused_dtypes(weight, qualname, node)
serialized_dict["weights"].update(weight)
weights[qualname] = target
elif node.op == "placeholder":
ph_type = node.meta.get("ph_type", "")
assert (
ph_type == "" or ph_type == "input_ph"
or ph_type == "output_ph"
), "When present, placeholder type must be 'input_ph' or 'ouput_ph'"
if ph_type == "input_ph":
node_rep["ph_type"] = "input_ph"
elif ph_type == "output_ph":
node_rep["ph_type"] = "output_ph"
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 or
# list. In this case we want to unpack the tuple or list.
if isinstance(node_rep["args"][0], (tuple, list)):
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
