mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-17 21:10:43 +00:00
0c1a5098dc
### Description Disable two PERF* rules in ruff to allow better readability. Rational commented inline. This change also removes the unused noqa directives because of the rule change. ### Motivation and Context Readability
350 lines
11 KiB
Python
350 lines
11 KiB
Python
### Be noted: this script is developed against the model exported from Megatron GPT2 Pretraining script.
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import sys
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import numpy as np
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import onnx
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from onnx import numpy_helper
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if len(sys.argv) < 2:
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print("Please give model path...")
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exit(1)
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input_model_name = sys.argv[1]
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output_model_name = input_model_name[:-5] + "_optimized.onnx"
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model = onnx.load(input_model_name)
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def add_name(model):
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i = 0
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for node in model.graph.node:
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node.name = "%s_%d" % (node.op_type, i)
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i += 1
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def find_input_node(model, arg):
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result = []
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for node in model.graph.node:
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for output in node.output:
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if output == arg:
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result.append(node)
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return result[0] if len(result) == 1 else None
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def find_output_node(model, arg):
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result = []
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for node in model.graph.node:
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for input in node.input:
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if input == arg:
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result.append(node)
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return result[0] if len(result) == 1 else None
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def find_initializer(model, arg):
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for initializer in model.graph.initializer:
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if initializer.name == arg:
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return initializer
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return None
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def find_input(model, arg):
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for graph_input in model.graph.input:
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if graph_input.name == arg:
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return graph_input
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return None
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def find_all_fused_nodes(model, concat_node):
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result = []
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candidate = [concat_node]
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while len(candidate) > 0:
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node = candidate[0]
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candidate.pop(0)
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result.append(node)
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if node.op_type == "Shape":
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continue
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for input in node.input:
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input_node = find_input_node(model, input)
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if input_node is not None:
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candidate.append(input_node)
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return result
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def get_node_index(model, node):
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i = 0
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while i < len(model.graph.node):
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if model.graph.node[i] == node:
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break
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i += 1
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return i if i < len(model.graph.node) else None
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def add_const(model, name, output, t_value=None, f_value=None):
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const_node = model.graph.node.add()
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const_node.op_type = "Constant"
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const_node.name = name
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const_node.output.extend([output])
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attr = const_node.attribute.add()
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attr.name = "value"
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if t_value is not None:
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attr.type = 4
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attr.t.CopyFrom(t_value)
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else:
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attr.type = 1
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attr.f = f_value
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return const_node
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def process_concat(model):
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new_nodes = {}
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delete_nodes = []
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for node in model.graph.node:
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if node.op_type != "Concat":
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continue
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skip = False
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input_nodes = []
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for input in node.input:
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concat_input_node = find_input_node(model, input)
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if concat_input_node.op_type != "Unsqueeze":
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skip = True
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input_nodes.append(concat_input_node)
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if skip is True:
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continue
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# figure out target shape
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shape = []
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for input_node in input_nodes:
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const_input = find_input_node(model, input_node.input[0])
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if const_input.op_type != "Constant":
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shape.append(0)
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else:
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attr = const_input.attribute
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assert len(attr) == 1
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assert attr[0].name == "value"
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assert attr[0].type == 4
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data = numpy_helper.to_array(attr[0].t)
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shape.append(np.asscalar(data))
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print(f"concat node: {node.name}, new_shape is: {shape}")
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# find out the nodes need to be deleted.
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fuse_nodes = find_all_fused_nodes(model, node)
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reshape_node = find_output_node(model, node.output[0])
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assert reshape_node.op_type == "Reshape"
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new_nodes[get_node_index(model, reshape_node)] = shape
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for n in fuse_nodes:
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delete_nodes.append(get_node_index(model, n))
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# insert new shape to reshape
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index = 0
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for reshape_node_index in new_nodes:
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shape_tensor = numpy_helper.from_array(np.asarray(new_nodes[reshape_node_index], dtype=np.int64))
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const_node = add_const(model, "concat_shape_node_%d" % index, "concat_shape_%d" % index, shape_tensor)
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index += 1
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reshape_node = model.graph.node[reshape_node_index]
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reshape_node.input[1] = const_node.output[0]
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# delete nodes
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delete_nodes.sort(reverse=True)
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for delete_node in delete_nodes:
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del model.graph.node[delete_node]
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def replace_input_arg(model, arg, new_arg):
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for node in model.graph.node:
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i = 0
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while i < len(node.input):
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if node.input[i] == arg:
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node.input[i] = new_arg
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i += 1
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def find_weight_index(model, name):
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index = 0
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for w in model.graph.initializer:
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if w.name == name:
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return index
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index += 1
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return None
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def find_input_index(model, name):
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index = 0
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for w in model.graph.input:
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if w.name == name:
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return index
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index += 1
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return None
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def fix_transpose(model):
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transpose = []
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for node in model.graph.node:
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if node.op_type == "Transpose":
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weight = find_initializer(model, node.input[0])
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if weight is not None:
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result = []
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for n in model.graph.node:
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for input in n.input:
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if input == weight.name:
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result.append(n)
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if len(result) > 1:
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continue
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perm = node.attribute[0]
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assert perm.name == "perm"
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perm = perm.ints
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assert len(perm) == 2 and perm[0] == 1 and perm[1] == 0
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transpose.append((get_node_index(model, node), weight))
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for t in transpose:
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node = model.graph.node[t[0]]
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weight = numpy_helper.to_array(t[1])
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assert len(weight.shape) == 2
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weight = weight.transpose(perm)
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new_weight = numpy_helper.from_array(weight, "%s_transposed" % t[1].name)
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model.graph.initializer.extend([new_weight])
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replace_input_arg(model, node.output[0], new_weight.name)
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transpose.sort(reverse=True)
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for t in transpose:
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del model.graph.node[t[0]]
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old_ws = []
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old_graph_inputs = []
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for t in transpose:
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if find_output_node(model, t[1].name) is None:
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old_ws.append(find_weight_index(model, t[1].name))
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old_graph_inputs.append(find_input_index(model, t[1].name))
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old_ws.sort(reverse=True)
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old_graph_inputs.sort(reverse=True)
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for g_i in old_graph_inputs:
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print(model.graph.input[g_i].name)
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del model.graph.input[g_i]
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print("clean up old weights")
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for w_i in old_ws:
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print(model.graph.initializer[w_i].name)
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del model.graph.initializer[w_i]
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def process_dropout(model):
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dropouts = []
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index = 0
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for node in model.graph.node:
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if node.op_type == "Dropout":
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new_dropout = model.graph.node.add()
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new_dropout.op_type = "TrainableDropout"
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new_dropout.name = "TrainableDropout_%d" % index
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# make ratio node
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ratio = np.asarray([node.attribute[0].f], dtype=np.float32)
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print(ratio.shape)
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ratio_value = numpy_helper.from_array(ratio)
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ratio_node = add_const(
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model, "dropout_node_ratio_%d" % index, "dropout_node_ratio_%d" % index, t_value=ratio_value
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)
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print(ratio_node)
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new_dropout.input.extend([node.input[0], ratio_node.output[0]])
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new_dropout.output.extend(node.output)
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dropouts.append(get_node_index(model, node))
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index += 1
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dropouts.sort(reverse=True)
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for d in dropouts:
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del model.graph.node[d]
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def remove_input_ids_check_subgraph(model):
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aten_node = None
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for node in model.graph.node:
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if node.op_type == "ATen":
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aten_node = node
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for i in node.input:
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input_node = find_input_node(model, i)
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if input_node and input_node.op_type == "ATen":
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assert node.op_type == "Gather"
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node.input[1] = "input_ids"
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break
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removed_nodes = []
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removed_nodes.append(find_input_node(model, aten_node.input[2]))
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removed_nodes.append(find_input_node(model, aten_node.input[3]))
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cast_node = find_input_node(model, aten_node.input[1])
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cast_node2 = find_input_node(model, cast_node.input[0])
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or_node = find_input_node(model, cast_node2.input[0])
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removed_nodes.extend(get_nodes_to_remove(or_node.input[0]))
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removed_nodes.extend(get_nodes_to_remove(or_node.input[1]))
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removed_nodes.extend([cast_node, cast_node2, or_node, aten_node])
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remove_node_index = []
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for n in removed_nodes:
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remove_node_index.append(get_node_index(model, n))
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remove_node_index = list(set(remove_node_index))
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remove_node_index.sort(reverse=True)
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for d in remove_node_index:
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print("Removing useless node ", model.graph.node[d].name)
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del model.graph.node[d]
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def get_nodes_to_remove(input_id):
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cast_node3 = find_input_node(model, input_id)
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not_node3 = find_input_node(model, cast_node3.input[0])
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if not_node3.op_type == "Not":
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less_node = find_input_node(model, not_node3.input[0])
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else:
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assert not_node3.op_type == "Less"
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less_node = not_node3
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for less_input in less_node.input:
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less_input_node = find_input_node(model, less_input)
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if less_input_node and less_input_node.op_type == "Constant":
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const_node = less_input_node
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break
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return [cast_node3, not_node3, less_node, const_node]
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def fix_split(model):
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# having split attribute, Split op shape inferencing bring 0, so we remove them.
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for node in model.graph.node:
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if node.op_type == "Split":
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index = 0
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need_remove = False
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for attr in node.attribute:
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if attr.name == "split":
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need_remove = True
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break
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index += 1
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if need_remove:
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print("Removing attribute split for ", node.name)
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del node.attribute[index]
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def align_attention_mask_dim(model):
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for model_input in model.graph.input:
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if model_input.name == "attention_mask":
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model_input.type.tensor_type.shape.dim[0].dim_param = "batch"
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# add name to nodes
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add_name(model)
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# replace garther&concat to reshape
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process_concat(model)
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# constant fold transpose
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fix_transpose(model)
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# replace dropout with trainable dropout
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process_dropout(model)
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remove_input_ids_check_subgraph(model)
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fix_split(model)
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align_attention_mask_dim(model)
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# set opset version to 10
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model.opset_import[0].version = 10
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f = open(output_model_name, "wb") # noqa: SIM115
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f.write(model.SerializeToString())
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f.close()
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