def partition_and_infer(subgraph):
def get_out_node_ids():
# Gets the final output nodes - producer nodes of graph output tensors without other outputs.
with subgraph.node_ids():
out_node_ids = set()
for out in subgraph.outputs:
if not out.outputs and not isinstance(out, Constant):
for n_inp in out.inputs:
out_node_ids.add(n_inp.id)
return out_node_ids
# Compute each output node in a separate subgraph.
out_node_ids = get_out_node_ids()
constant_values = {}
for index in out_node_ids: # Have to use index since 'node' is not in part
part = subgraph.copy()
out_node = part.nodes[index]
part.outputs = out_node.outputs
part.name = "Folding: {:}".format(
[out.name for out in part.outputs])
part.cleanup(remove_unused_graph_inputs=True)
names = [out.name for out in part.outputs]
try:
# Determining types is not trivial, and ONNX-RT does its own type inference.
sess = rt.InferenceSession(
export_onnx(part,
do_type_check=False).SerializeToString())
values = sess.run(names, {})
except Exception as err:
G_LOGGER.warning(
"Inference failed for subgraph: {:}. Note: Error was:\n{:}"
.format(part.name, err))
if partitioning == "recursive":
G_LOGGER.verbose(
"Attempting to recursively partition subgraph")
# Partition failed, peel off last node.
# We only need to remove one node, so avoid doing an expensive call to cleanup()
part.outputs = out_node.inputs
del part.nodes[part.nodes.index(out_node)]
out_node.outputs.clear()
out_node.inputs.clear()
else:
G_LOGGER.info(
"You may see better results if you set partitioning='recursive'"
)
if not error_ok:
raise err
constant_values.update(partition_and_infer(part))
else:
constant_values.update(
{name: val
for name, val in zip(names, values)})
return constant_values
def register_func(func):
if hasattr(Graph, func.__name__):
G_LOGGER.warning("Registered function: {:} is hidden by a Graph attribute or function with the same name. "
"This function will never be called!".format(func.__name__))
# Default behavior is to register functions for all opsets.
if opsets is None:
Graph.GLOBAL_FUNC_MAP[func.__name__] = func
else:
for opset in opsets:
Graph.OPSET_FUNC_MAP[opset][func.__name__] = func
return func
def add_to_tensor_map(tensor):
if not tensor.is_empty():
if tensor.name in tensor_map and not (tensor_map[tensor.name]
is tensor):
msg = "Found distinct tensors that share the same name:\n[id: {:}] {:}\n[id: {:}] {:}\n".format(
id(tensor_map[tensor.name]),
tensor_map[tensor.name],
id(tensor),
tensor,
)
msg += (
"Note: Producer node(s) of first tensor:\n{:}\nProducer node(s) of second tensor:\n{:}"
.format(
tensor_map[tensor.name].inputs,
tensor.inputs,
))
if check_duplicates:
G_LOGGER.critical(msg)
G_LOGGER.warning(msg)
tensor_map[tensor.name] = tensor
def fold_constants(self,
fold_shapes=True,
recurse_subgraphs=True,
partitioning=None,
error_ok=True):
"""
Folds constants in-place in the graph. The graph must be topologically sorted prior to
calling this function (see `toposort()`).
This function will not remove constants after folding them. In order to get rid of
these hanging nodes, you can run the `cleanup()` function.
*Note: Due to how this function is implemented, the graph must be exportable to ONNX,
and evaluable in ONNX-Runtime. Additionally, ONNX-Runtime must be installed.*
Args:
fold_shapes (bool):
Whether to fold `Shape` nodes in the graph.
This requires shapes to be inferred in the graph, and can only fold
static shapes.
Defaults to True.
recurse_subgraphs (bool):
Whether to recursively fold constants in subgraphs.
Defaults to True.
partitioning (Union[str, None]):
Whether/How to partition the graph so that errors in folding one
part of a model do not affect other parts. Available modes are:
- None: Do not partition the graph. If inference fails, no constants are folded.
- "basic": Partition the graph. If inference fails in one partition, other partitions will
remain unaffected.
- "recursive": Parition the graph recursively. If inference fails in a partition, the partition
will be further paritioned.
Defaults to None.
error_ok (bool):
Whether inference errors should be suppressed.
When this is enabled, any errors encountered during inference will be re-raised.
Defaults to True.
Returns:
self
"""
import onnxruntime as rt
from onnx_graphsurgeon.exporters.onnx_exporter import export_onnx
PARTITIONING_MODES = [None, "basic", "recursive"]
if partitioning not in PARTITIONING_MODES:
G_LOGGER.critical(
"Argument for parameter 'partitioning' must be one of: {:}".
format(PARTITIONING_MODES))
G_LOGGER.debug("Folding constants in {:}".format(self.name))
graph_clone = self.copy()
clone_tensors = graph_clone.tensors()
# We find graph constants in two passes:
# Pass 1 finds all Constant tensors in the graph, then walks over their outputs.
# Pass 2 searches for Shape nodes that have variable inputs (i.e. not marked const in pass 1)
# and turns them into Constants iff the input has a statically known shape.
def update_foldable_outputs(graph_constants):
def is_foldable(node):
def all_tensors_const(tensors):
return all([t.name in graph_constants for t in tensors])
if not all_tensors_const(node.inputs):
return False
all_subgraph_foreign_tensors_const = True
for attr in node.attrs.values():
if isinstance(attr, Graph):
foreign_tensors = attr._foreign_tensors().values()
all_subgraph_foreign_tensors_const &= all_tensors_const(
foreign_tensors)
return all_subgraph_foreign_tensors_const
# Walks along the outputs of graph_constants to see if they can also be computed statically.
# Since the graph is topologically sorted, this should find all constant nodes in the graph.
for node in graph_clone.nodes:
if is_foldable(node):
graph_constants.update(
{out.name: out
for out in node.outputs})
return graph_constants
# Pass 1: Non-shape Constant Folding
graph_constants = {
name: tensor
for name, tensor in clone_tensors.items()
if isinstance(tensor, Constant)
}
# Replaces outputs of Constant nodes with constant tensors
for tensor in clone_tensors.values():
if len(tensor.inputs) == 1:
node = tensor.inputs[0]
if node.op == "Constant":
graph_constants[tensor.name] = tensor.to_constant(
node.attrs["value"]._values
) # Using ._values avoids copying
graph_constants[tensor.name].inputs.clear()
graph_constants = update_foldable_outputs(graph_constants)
# Pass 2: Shape Folding
def get_producer(tensor, op):
"""
Get the producer of the specified tensor iff it matches op
"""
if len(tensor.inputs) != 1:
return None
node = tensor.inputs[0]
if node.op != op:
return None
return node
def get_input(node, index=0):
"""
Get the input tensor of a node iff the input tensor is not already marked a graph constant.
"""
if node is None:
return None
inp = node.inputs[index]
# If the input was already found to be a constant, it will be folded anyway.
if inp.name in graph_constants:
return None
return inp
def handle_shape(tensor):
inp = get_input(get_producer(tensor, "Shape"))
if inp is None:
return None
if inp.shape is None or misc.is_dynamic_shape(inp.shape):
return None
return np.array(inp.shape, dtype=np.int64)
def handle_shape_gather(tensor):
gather = get_producer(tensor, "Gather")
if gather is None:
return None
data = gather.inputs[0]
indices_tensor = gather.inputs[1]
inp = get_input(get_producer(data, "Shape"))
if inp is None or inp.shape is None:
return None
if not isinstance(indices_tensor, Constant):
return None
indices = indices_tensor.values
if not indices.shape: # Scalar-case
shape = inp.shape[int(indices)]
if misc.is_dynamic_dimension(shape):
return None
else:
shape = [inp.shape[index] for index in indices]
if misc.is_dynamic_shape(shape):
return None
return np.array(shape, dtype=np.int64)
# Finds the static shape of a shape node output if possible, otherwise returns None.
def lower_shape(tensor):
SHAPE_FOLD_FUNCS = [handle_shape, handle_shape_gather]
for fold_func in SHAPE_FOLD_FUNCS:
shape = fold_func(tensor)
if shape is not None:
return shape
if fold_shapes:
for tensor in clone_tensors.values():
shape_of = lower_shape(tensor)
if shape_of is not None:
G_LOGGER.ultra_verbose(
"Folding shape tensor: {:} to: {:}".format(
tensor.name, shape_of))
graph_constants[tensor.name] = tensor.to_constant(shape_of)
graph_constants[tensor.name].inputs.clear()
graph_constants = update_foldable_outputs(graph_constants)
def partition_and_infer(subgraph):
def get_out_node_ids():
# Gets the final output nodes - producer nodes of graph output tensors without other outputs.
with subgraph.node_ids():
out_node_ids = set()
for out in subgraph.outputs:
if not out.outputs and not isinstance(out, Constant):
for n_inp in out.inputs:
out_node_ids.add(n_inp.id)
return out_node_ids
# Compute each output node in a separate subgraph.
out_node_ids = get_out_node_ids()
constant_values = {}
for index in out_node_ids: # Have to use index since 'node' is not in part
part = subgraph.copy()
out_node = part.nodes[index]
part.outputs = out_node.outputs
part.name = "Folding: {:}".format(
[out.name for out in part.outputs])
part.cleanup(remove_unused_graph_inputs=True)
names = [out.name for out in part.outputs]
try:
# Determining types is not trivial, and ONNX-RT does its own type inference.
sess = rt.InferenceSession(
export_onnx(part,
do_type_check=False).SerializeToString())
values = sess.run(names, {})
except Exception as err:
G_LOGGER.warning(
"Inference failed for subgraph: {:}. Note: Error was:\n{:}"
.format(part.name, err))
if partitioning == "recursive":
G_LOGGER.verbose(
"Attempting to recursively partition subgraph")
# Partition failed, peel off last node.
# We only need to remove one node, so avoid doing an expensive call to cleanup()
part.outputs = out_node.inputs
del part.nodes[part.nodes.index(out_node)]
out_node.outputs.clear()
out_node.inputs.clear()
else:
G_LOGGER.info(
"You may see better results if you set partitioning='recursive'"
)
if not error_ok:
raise err
constant_values.update(partition_and_infer(part))
else:
constant_values.update(
{name: val
for name, val in zip(names, values)})
return constant_values
# Next, evaluate the foldable variables with ONNX-Runtime
graph_clone.outputs = [
t for t in graph_constants.values() if not isinstance(t, Constant)
]
graph_clone.cleanup(remove_unused_graph_inputs=True)
# Using ._values avoids a deep copy of the values.
constant_values = {
name: tensor._values
for name, tensor in graph_constants.items()
if isinstance(tensor, Constant)
}
if graph_clone.outputs:
if partitioning:
constant_values.update(partition_and_infer(graph_clone))
else:
names = [t.name for t in graph_clone.outputs]
try:
sess = rt.InferenceSession(
export_onnx(graph_clone,
do_type_check=False).SerializeToString())
values = sess.run(names, {})
constant_values.update(
{name: val
for name, val in zip(names, values)})
except Exception as err:
G_LOGGER.warning(
"Inference failed. You may want to try enabling partitioning to see better results. "
"Note: Error was:\n{:}".format(err))
G_LOGGER.verbose(
"Note: Graph was:\n{:}".format(graph_clone))
if not error_ok:
raise
elif not constant_values:
G_LOGGER.info(
"Could not find any nodes in this graph ({:}) that can be folded. "
"This could mean that constant folding has already been run on this graph. "
"Skipping.".format(self.name))
# Finally, replace the Variables in the original graph with constants.
if constant_values:
graph_tensors = self.tensors()
for name, values in constant_values.items():
tensor = graph_tensors[name]
if not isinstance(tensor, Constant):
tensor.to_constant(values)
tensor.inputs.clear() # Constants do not need inputs
# Folding subgraphs after the outer graph can lead to better folding.
def fold_subgraphs():
for node in self.nodes:
for attr in node.attrs.values():
if isinstance(attr, Graph):
attr.fold_constants(fold_shapes=fold_shapes,
partitioning=partitioning)
if recurse_subgraphs:
fold_subgraphs()
return self
def fold_constants(self,
fold_shapes=True,
recurse_subgraphs=True,
partitioning=None,
error_ok=True):
"""
Folds constants in-place in the graph. The graph must be topologically sorted prior to
calling this function (see `toposort()`).
This function will not remove constants after folding them. In order to get rid of
these hanging nodes, you can run the `cleanup()` function.
*Note: Due to how this function is implemented, the graph must be exportable to ONNX,
and evaluable in ONNX-Runtime. Additionally, ONNX-Runtime must be installed.*
Args:
fold_shapes (bool):
Whether to fold `Shape` nodes in the graph.
This requires shapes to be inferred in the graph, and can only fold
static shapes.
Defaults to True.
recurse_subgraphs (bool):
Whether to recursively fold constants in subgraphs.
Defaults to True.
partitioning (Union[str, None]):
Whether/How to partition the graph so that errors in folding one
part of a model do not affect other parts. Available modes are:
- None: Do not partition the graph. If inference fails, no constants are folded.
- "basic": Partition the graph. If inference fails in one partition, other partitions will
remain unaffected.
- "recursive": Parition the graph recursively. If inference fails in a partition, the partition
will be further paritioned.
Defaults to None.
error_ok (bool):
Whether inference errors should be suppressed.
When this is enabled, any errors encountered during inference will be re-raised.
Defaults to True.
Returns:
self
"""
import onnxruntime as rt
from onnx_graphsurgeon.exporters.onnx_exporter import export_onnx
PARTITIONING_MODES = [None, "basic", "recursive"]
if partitioning not in PARTITIONING_MODES:
G_LOGGER.critical(
"Argument for parameter 'partitioning' must be one of: {:}".
format(PARTITIONING_MODES))
# First perform shape tensor cast elision on the graph prior to other constant folding
# Search for Cast(s) (from int -> float) -> intermediate operator (with float constants) -> Cast(s) (back to int)
# This pattern is problematic for TensorRT since these operations may be performed on Shape Tensors, which
# are not allowed to be floating point type. Attempt to fold the pattern here
VALID_CAST_ELISION_OPS = [
"Add", "Sub", "Mul", "Div", "Max", "Min", "Equal", "Greater",
"Less", "Concat"
]
def run_cast_elision(node):
import onnx
if node.op not in VALID_CAST_ELISION_OPS:
return
# Get list of input nodes
inp_casts = [
inp_node for inp_tensor in node.inputs
for inp_node in inp_tensor.inputs
if inp_node.op == "Cast" and inp_node.attrs["to"] == 1
]
# No cast nodes found, return early
if not inp_casts:
return
# Ensure that all input cast nodes are casting from the same type
final_type = None
for inp in inp_casts:
curr_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[
inp.inputs[0].dtype]
final_type = final_type or curr_type
if final_type != curr_type:
return
# Check validity and get list of output nodes
out_casts = []
for out_tensor in node.outputs:
for out_node in out_tensor.outputs:
if out_node.op != "Cast" or out_node.attrs["to"] not in [
6, 7
]:
# Can exit early if any of the output nodes are not valid casts
return
out_casts.append(out_node)
# Check that all final cast types are the same.
curr_type = out_node.attrs["to"]
if final_type != curr_type:
return
# If all checks passed - update constant values.
for inp in node.inputs:
if isinstance(inp, Constant):
inp.values = inp.values.astype(
onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[final_type])
# "Remove" casts nodes by changing I/O node operators to Identity. Update corresponding tensor dtypes as well
def replace_with_identity(cast_node, change_dtype):
cast_node.op = "Identity"
cast_node.attrs = {}
getattr(
cast_node, change_dtype
)[0].dtype = onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[final_type]
G_LOGGER.debug("Cast node {:} elided".format(cast_node.name))
for inp in inp_casts:
replace_with_identity(inp, change_dtype="outputs")
for out in out_casts:
replace_with_identity(out, change_dtype="inputs")
# Perform shape tensor cast elision:
if fold_shapes:
G_LOGGER.debug(
"Performing shape tensor cast elision in {:}".format(
self.name))
try:
for node in self.nodes:
run_cast_elision(node)
except Exception as err:
if not error_ok:
raise err
G_LOGGER.warning("'{:}' routine failed with: {:}".format(
"Shape tensor cast elision", err))
G_LOGGER.debug("Folding constants in {:}".format(self.name))
graph_clone = self.copy()
clone_tensors = graph_clone.tensors()
# We find graph constants in two passes:
# Pass 1 finds all Constant tensors in the graph, then walks over their outputs.
# Pass 2 searches for Shape nodes that have variable inputs (i.e. not marked const in pass 1)
# and turns them into Constants iff the input has a statically known shape.
def update_foldable_outputs(graph_constants):
def is_foldable(node):
def all_tensors_const(tensors):
return all([t.name in graph_constants for t in tensors])
if not all_tensors_const(node.inputs):
return False
all_subgraph_foreign_tensors_const = True
for attr in node.attrs.values():
if isinstance(attr, Graph):
foreign_tensors = attr._foreign_tensors().values()
all_subgraph_foreign_tensors_const &= all_tensors_const(
foreign_tensors)
return all_subgraph_foreign_tensors_const
# Walks along the outputs of graph_constants to see if they can also be computed statically.
# Since the graph is topologically sorted, this should find all constant nodes in the graph.
for node in graph_clone.nodes:
if is_foldable(node):
graph_constants.update(
{out.name: out
for out in node.outputs})
return graph_constants
# Pass 1: Non-shape Constant Folding
graph_constants = {
name: tensor
for name, tensor in clone_tensors.items()
if isinstance(tensor, Constant)
}
# Replaces outputs of Constant nodes with constant tensors
for tensor in clone_tensors.values():
if len(tensor.inputs) == 1:
node = tensor.inputs[0]
if node.op == "Constant":
graph_constants[tensor.name] = tensor.to_constant(
node.attrs["value"]._values
) # Using ._values avoids copying
graph_constants[tensor.name].inputs.clear()
graph_constants = update_foldable_outputs(graph_constants)
# Pass 2: Shape Folding
def get_producer(tensor, op):
"""
Get the producer of the specified tensor iff it matches op
"""
if len(tensor.inputs) != 1:
return None
node = tensor.inputs[0]
if node.op != op:
return None
return node
def get_input(node, index=0):
"""
Get the input tensor of a node iff the input tensor is not already marked a graph constant.
"""
if node is None:
return None
inp = node.inputs[index]
# If the input was already found to be a constant, it will be folded anyway.
if inp.name in graph_constants:
return None
return inp
def get_scalar_value(tensor):
"""
Gets the scalar value of a tensor with a single item
"""
if not tensor.shape:
return tensor.values
else:
return list(tensor.values)[0]
def fold_shape(tensor):
inp = get_input(get_producer(tensor, "Shape"))
if inp is None:
return None
if inp.shape is None or misc.is_dynamic_shape(inp.shape):
return None
return np.array(inp.shape, dtype=np.int64)
def fold_shape_gather(tensor):
gather = get_producer(tensor, "Gather")
if gather is None:
return None
data = gather.inputs[0]
indices_tensor = gather.inputs[1]
inp = get_input(get_producer(data, "Shape"))
if inp is None or inp.shape is None:
return None
if not isinstance(indices_tensor, Constant):
return None
indices = indices_tensor.values
if not indices.shape: # Scalar-case
shape = inp.shape[int(indices)]
if misc.is_dynamic_dimension(shape):
return None
else:
shape = [inp.shape[index] for index in indices]
if misc.is_dynamic_shape(shape):
return None
return np.array(shape, dtype=np.int64)
def fold_shape_slice(tensor):
slice = get_producer(tensor, "Slice")
if slice is None:
return None
data = slice.inputs[0]
if len(slice.inputs) >= 3:
starts, ends = slice.inputs[1:3]
if any(not isinstance(t, Constant) for t in [starts, ends]):
return None
starts, ends = get_scalar_value(starts), get_scalar_value(ends)
elif "starts" in slice.attrs and "ends" in slice.attrs:
starts, ends = slice.attrs["starts"][0], slice.attrs["ends"][0]
else:
return None
inp = get_input(get_producer(data, "Shape"))
if inp is None or inp.shape is None:
return None
# For shape tensors, we can only slice on the 0th dimension.
if len(slice.inputs) > 3:
axes = slice.inputs[3]
if not isinstance(axes, Constant):
return None
if get_scalar_value(axes) != 0:
return None
elif "axes" in slice.attrs:
if slice.attrs["axes"][0] != 0:
return None
steps = 1
if len(slice.inputs) > 4:
steps = slice.inputs[4]
if not isinstance(steps, Constant):
return None
steps = get_scalar_value(steps)
elif "steps" in slice.attrs:
steps = slice.attrs["steps"][0]
shape = inp.shape[starts:ends:steps]
if misc.is_dynamic_shape(shape):
return None
return np.array(shape, dtype=np.int64)
if fold_shapes:
# NOTE: The order of shape folding passes is important to maximize how much we fold (phase-ordering problem).
SHAPE_FOLD_FUNCS = [
fold_shape_gather, fold_shape_slice, fold_shape
]
for shape_fold_func in SHAPE_FOLD_FUNCS:
try:
for tensor in clone_tensors.values():
shape_of = shape_fold_func(tensor)
if shape_of is not None:
G_LOGGER.ultra_verbose(
"Folding shape tensor: {:} to: {:}".format(
tensor.name, shape_of))
graph_constants[tensor.name] = tensor.to_constant(
shape_of)
graph_constants[tensor.name].inputs.clear()
except Exception as err:
if not error_ok:
raise err
G_LOGGER.warning("'{:}' routine failed with:\n{:}".format(
shape_fold_func.__name__, err))
else:
graph_constants = update_foldable_outputs(graph_constants)
def partition_and_infer(subgraph):
def get_out_node_ids():
# Gets the final output nodes - producer nodes of graph output tensors without other outputs.
with subgraph.node_ids():
out_node_ids = set()
for out in subgraph.outputs:
if not out.outputs and not isinstance(out, Constant):
for n_inp in out.inputs:
out_node_ids.add(n_inp.id)
return out_node_ids
# Compute each output node in a separate subgraph.
out_node_ids = get_out_node_ids()
constant_values = {}
for index in out_node_ids: # Have to use index since 'node' is not in part
part = subgraph.copy()
out_node = part.nodes[index]
part.outputs = out_node.outputs
part.name = "Folding: {:}".format(
[out.name for out in part.outputs])
part.cleanup(remove_unused_graph_inputs=True)
names = [out.name for out in part.outputs]
try:
# Determining types is not trivial, and ONNX-RT does its own type inference.
sess = rt.InferenceSession(
export_onnx(part,
do_type_check=False).SerializeToString())
values = sess.run(names, {})
except Exception as err:
G_LOGGER.warning(
"Inference failed for subgraph: {:}. Note: Error was:\n{:}"
.format(part.name, err))
if partitioning == "recursive":
G_LOGGER.verbose(
"Attempting to recursively partition subgraph")
# Partition failed, peel off last node.
# We only need to remove one node, so avoid doing an expensive call to cleanup()
part.outputs = out_node.inputs
del part.nodes[part.nodes.index(out_node)]
out_node.outputs.clear()
out_node.inputs.clear()
else:
G_LOGGER.info(
"You may see better results if you set partitioning='recursive'"
)
if not error_ok:
raise err
constant_values.update(partition_and_infer(part))
else:
constant_values.update(
{name: val
for name, val in zip(names, values)})
return constant_values
# Next, evaluate the foldable variables with ONNX-Runtime
# Only evaluate foldable values that have non-foldable outputs or are graph outputs.
# Otherwise, if all the outputs are foldable, then we can just evaluate the outputs directly.
def should_eval_foldable(tensor):
non_const = not isinstance(tensor, Constant)
is_graph_output = not tensor.outputs
has_non_foldable_outputs = any(out.name not in graph_constants
for out in tensor.outputs)
return non_const and (is_graph_output or has_non_foldable_outputs)
graph_clone.outputs = [
t for t in graph_constants.values() if should_eval_foldable(t)
]
G_LOGGER.debug("Folding tensors: {:}".format(graph_clone.outputs))
graph_clone.cleanup(remove_unused_graph_inputs=True)
# Using ._values avoids a deep copy of the values.
constant_values = {
name: tensor._values
for name, tensor in graph_constants.items()
if isinstance(tensor, Constant)
}
if graph_clone.outputs:
if partitioning:
constant_values.update(partition_and_infer(graph_clone))
else:
names = [t.name for t in graph_clone.outputs]
try:
sess = rt.InferenceSession(
export_onnx(graph_clone,
do_type_check=False).SerializeToString())
values = sess.run(names, {})
constant_values.update(
{name: val
for name, val in zip(names, values)})
except Exception as err:
G_LOGGER.warning(
"Inference failed. You may want to try enabling partitioning to see better results. "
"Note: Error was:\n{:}".format(err))
G_LOGGER.verbose(
"Note: Graph was:\n{:}".format(graph_clone))
if not error_ok:
raise
elif not constant_values:
G_LOGGER.info(
"Could not find any nodes in this graph ({:}) that can be folded. "
"This could mean that constant folding has already been run on this graph. "
"Skipping.".format(self.name))
# Finally, replace the Variables in the original graph with constants.
if constant_values:
graph_tensors = self.tensors()
for name, values in constant_values.items():
tensor = graph_tensors[name]
if not isinstance(tensor, Constant):
tensor.to_constant(values)
tensor.inputs.clear() # Constants do not need inputs
# Folding subgraphs after the outer graph can lead to better folding.
def fold_subgraphs():
for node in self.nodes:
for attr in node.attrs.values():
if isinstance(attr, Graph):
attr.fold_constants(fold_shapes=fold_shapes,
partitioning=partitioning)
if recurse_subgraphs:
fold_subgraphs()
return self
