Module furiosa.quantizer.frontend.onnx.transformer.utils
Expand source code
import warnings
import onnx
from onnx import numpy_helper
from onnx.helper import make_model, make_tensor_value_info, make_opsetid
from furiosa_sdk_quantizer.frontend.onnx.quantizer.utils import __PRODUCER__
from furiosa_sdk_quantizer.frontend.onnx import __DOMAIN__, __OPSET_VERSION__
def name_nodes(model):
for idx, node in enumerate(model.graph.node):
node.name = '%s_%d' % (node.op_type, idx)
return model
def eliminate_unused_initializer(model):
model = _eliminate_unused_quantization_annotation(model)
node_input_names = [node_input for node in model.graph.node for node_input in node.input]
qtensor_names = [qtensor_name.value for annot in model.graph.quantization_annotation
for qtensor_name in annot.quant_parameter_tensor_names]
unused_initializer = list()
for init in model.graph.initializer:
# Even if an init is not an input of a node, do not remove it if defined in graph.quantization_annotation.
if init.name not in node_input_names and init.name not in qtensor_names:
unused_initializer.append(init)
for unused in unused_initializer:
model.graph.initializer.remove(unused)
return model
def eliminate_unused_input(model):
node_input_names = [node_input for node in model.graph.node for node_input in node.input]
unused_input = list()
for input in model.graph.input:
if input.name not in node_input_names:
unused_input.append(input)
for unused in unused_input:
model.graph.input.remove(unused)
return model
def eliminate_unused_output(model):
node_output_names = [node_output for node in model.graph.node for node_output in node.output]
unused_output = list()
for output in model.graph.output:
if output.name not in node_output_names:
unused_output.append(output)
for unused in unused_output:
model.graph.output.remove(unused)
return model
def eliminate_unused_value_info(model):
node_output_names = [node_output for node in model.graph.node for node_output in node.output]
graph_output_names = [vi.name for vi in model.graph.output]
unused_value_info = list()
for value_info in model.graph.value_info:
if value_info.name not in node_output_names:
unused_value_info.append(value_info)
if value_info.name in graph_output_names:
unused_value_info.append(value_info)
for unused in unused_value_info:
model.graph.value_info.remove(unused)
return model
def _eliminate_unused_quantization_annotation(model):
node_input_names = [node_input for node in model.graph.node for node_input in node.input]
node_output_names = [node_output for node in model.graph.node for node_output in node.output]
unused_quant_annot = list()
for quant_annot in model.graph.quantization_annotation:
if quant_annot.tensor_name not in set(node_input_names + node_output_names):
unused_quant_annot.append(quant_annot)
for unused in unused_quant_annot:
model.graph.quantization_annotation.remove(unused)
return model
def eliminate_unused_protos(model):
funcs = [
eliminate_unused_initializer,
eliminate_unused_input,
eliminate_unused_output,
eliminate_unused_value_info,
]
for func in funcs:
model = func(model)
return model
def include_initializer_to_graph_input(model):
input_value_names = [inp.name for inp in model.graph.input]
for init in model.graph.initializer:
if init.name not in input_value_names:
dims = numpy_helper.to_array(init).shape
value_info = make_tensor_value_info(init.name, init.data_type, dims)
model.graph.input.append(value_info)
# do not append duplicated initializer to graph input
input_value_names.append(init.name)
return model
def rebuild_model(model, new_nodes, eliminate=True, renaming=True):
# remove all nodes and re-make model.graph based on newly given nodes.
model.graph.ClearField('node')
model.graph.node.extend(new_nodes)
default_opset = make_opsetid(__DOMAIN__, __OPSET_VERSION__)
model = make_model(model.graph, opset_imports=[default_opset])
# eliminate all unused protos such as initializer, input, output, and value_info.
if eliminate:
model = eliminate_unused_protos(model)
# include initializer to graph input
model = include_initializer_to_graph_input(model)
# rename node.name
if renaming:
model = name_nodes(model)
model.producer_name = __PRODUCER__
return model
def fix_batch_size_as_one(model):
"""
fix batch_size = 1 if dim_param is given.
"""
for input in model.graph.input:
try:
batch_dim = input.type.tensor_type.shape.dim[0]
except IndexError:
continue
if batch_dim.dim_param:
warnings.warn(
"Dynamic batch size is detected at input_name: {}. "
"Fix batch_size=1 for valid shape inference.".format(input.name))
input.type.tensor_type.shape.dim[0].dim_value = 1
return model
def make_conv_bias_name_unique(model):
# Renames Conv operators' biases, if necessary, to make their names
# unique so that the biases can be associated with different
# quantization scale parameters.
initializer = {init.name: init for init in model.graph.initializer}
seen = set()
for node in model.graph.node:
if node.op_type != "Conv" or len(node.input) < 3:
continue
bias = node.input[2]
if bias not in seen:
seen.add(bias)
continue
tensor = onnx.TensorProto()
tensor.CopyFrom(initializer[bias])
# HACK: This attempts to give the bias tensor a new unique name.
# Although it is unlikely, there is a possibility that the new
# name is already occupied by a tensor in the model.
tensor.name = f"{bias}_{node.output[0]}"
node.input[2] = tensor.name
model.graph.initializer.append(tensor)
return model
Functions
def eliminate_unused_initializer(model)
-
Expand source code
def eliminate_unused_initializer(model): model = _eliminate_unused_quantization_annotation(model) node_input_names = [node_input for node in model.graph.node for node_input in node.input] qtensor_names = [qtensor_name.value for annot in model.graph.quantization_annotation for qtensor_name in annot.quant_parameter_tensor_names] unused_initializer = list() for init in model.graph.initializer: # Even if an init is not an input of a node, do not remove it if defined in graph.quantization_annotation. if init.name not in node_input_names and init.name not in qtensor_names: unused_initializer.append(init) for unused in unused_initializer: model.graph.initializer.remove(unused) return model
def eliminate_unused_input(model)
-
Expand source code
def eliminate_unused_input(model): node_input_names = [node_input for node in model.graph.node for node_input in node.input] unused_input = list() for input in model.graph.input: if input.name not in node_input_names: unused_input.append(input) for unused in unused_input: model.graph.input.remove(unused) return model
def eliminate_unused_output(model)
-
Expand source code
def eliminate_unused_output(model): node_output_names = [node_output for node in model.graph.node for node_output in node.output] unused_output = list() for output in model.graph.output: if output.name not in node_output_names: unused_output.append(output) for unused in unused_output: model.graph.output.remove(unused) return model
def eliminate_unused_protos(model)
-
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def eliminate_unused_protos(model): funcs = [ eliminate_unused_initializer, eliminate_unused_input, eliminate_unused_output, eliminate_unused_value_info, ] for func in funcs: model = func(model) return model
def eliminate_unused_value_info(model)
-
Expand source code
def eliminate_unused_value_info(model): node_output_names = [node_output for node in model.graph.node for node_output in node.output] graph_output_names = [vi.name for vi in model.graph.output] unused_value_info = list() for value_info in model.graph.value_info: if value_info.name not in node_output_names: unused_value_info.append(value_info) if value_info.name in graph_output_names: unused_value_info.append(value_info) for unused in unused_value_info: model.graph.value_info.remove(unused) return model
def fix_batch_size_as_one(model)
-
fix batch_size = 1 if dim_param is given.
Expand source code
def fix_batch_size_as_one(model): """ fix batch_size = 1 if dim_param is given. """ for input in model.graph.input: try: batch_dim = input.type.tensor_type.shape.dim[0] except IndexError: continue if batch_dim.dim_param: warnings.warn( "Dynamic batch size is detected at input_name: {}. " "Fix batch_size=1 for valid shape inference.".format(input.name)) input.type.tensor_type.shape.dim[0].dim_value = 1 return model
def include_initializer_to_graph_input(model)
-
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def include_initializer_to_graph_input(model): input_value_names = [inp.name for inp in model.graph.input] for init in model.graph.initializer: if init.name not in input_value_names: dims = numpy_helper.to_array(init).shape value_info = make_tensor_value_info(init.name, init.data_type, dims) model.graph.input.append(value_info) # do not append duplicated initializer to graph input input_value_names.append(init.name) return model
def make_conv_bias_name_unique(model)
-
Expand source code
def make_conv_bias_name_unique(model): # Renames Conv operators' biases, if necessary, to make their names # unique so that the biases can be associated with different # quantization scale parameters. initializer = {init.name: init for init in model.graph.initializer} seen = set() for node in model.graph.node: if node.op_type != "Conv" or len(node.input) < 3: continue bias = node.input[2] if bias not in seen: seen.add(bias) continue tensor = onnx.TensorProto() tensor.CopyFrom(initializer[bias]) # HACK: This attempts to give the bias tensor a new unique name. # Although it is unlikely, there is a possibility that the new # name is already occupied by a tensor in the model. tensor.name = f"{bias}_{node.output[0]}" node.input[2] = tensor.name model.graph.initializer.append(tensor) return model
def name_nodes(model)
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def name_nodes(model): for idx, node in enumerate(model.graph.node): node.name = '%s_%d' % (node.op_type, idx) return model
def rebuild_model(model, new_nodes, eliminate=True, renaming=True)
-
Expand source code
def rebuild_model(model, new_nodes, eliminate=True, renaming=True): # remove all nodes and re-make model.graph based on newly given nodes. model.graph.ClearField('node') model.graph.node.extend(new_nodes) default_opset = make_opsetid(__DOMAIN__, __OPSET_VERSION__) model = make_model(model.graph, opset_imports=[default_opset]) # eliminate all unused protos such as initializer, input, output, and value_info. if eliminate: model = eliminate_unused_protos(model) # include initializer to graph input model = include_initializer_to_graph_input(model) # rename node.name if renaming: model = name_nodes(model) model.producer_name = __PRODUCER__ return model