External Data and Big Models#
protobuf does not support files bigfer than 2 Gb and that limit is usually exceeded for language models such as Llama. onnx overcomes that limit by saving the weights outside the model. The main file only keeps the filename the weights the model are stored in.
Save a big model#
Let’s assume the model is in memory. It needs to be saved
with the weights outside the onnx file. Here is a short example
on how to do it. It relies on function
save_model.
<<<
import os
import pprint
import numpy as np
import onnx
import onnx.helper as oh
import onnx.numpy_helper as onp
from onnx_extended.tools import save_model
def _get_model():
X = oh.make_tensor_value_info("X", onnx.TensorProto.FLOAT, [None, None])
Z = oh.make_tensor_value_info("Z", onnx.TensorProto.INT64, [None, None])
graph = oh.make_graph(
[
oh.make_node("Mul", ["X", "X"], ["X2"]),
oh.make_node("Add", ["X2", "Y"], ["z1"]),
oh.make_node("Mul", ["z1", "W"], ["z2"]),
oh.make_node("Cast", ["z2"], ["Z"], to=onnx.TensorProto.INT64),
],
"add",
[X],
[Z],
[
onp.from_array(np.arange(16).reshape((-1, 4)).astype(np.float32), name="Y"),
onp.from_array(
(np.arange(16).reshape((-1, 4)) + 100).astype(np.float32), name="W"
),
],
)
onnx_model = oh.make_model(
graph, opset_imports=[oh.make_opsetid("", 18)], ir_version=8
)
return onnx_model
model = _get_model()
# size_threshold: every constant tensor whose size is above this
# threshold is taken out externally.
save_model(model, "an_onnx_model.onnx", external=True, size_threshold=15)
pprint.pprint([n for n in os.listdir() if "an_onnx" in n])
print("--------------------------------------")
print(model)
>>>
['an_onnx_model.onnx', 'an_onnx_model.onnx.data']
--------------------------------------
ir_version: 8
opset_import {
domain: ""
version: 18
}
graph {
node {
input: "X"
input: "X"
output: "X2"
op_type: "Mul"
}
node {
input: "X2"
input: "Y"
output: "z1"
op_type: "Add"
}
node {
input: "z1"
input: "W"
output: "z2"
op_type: "Mul"
}
node {
input: "z2"
output: "Z"
op_type: "Cast"
attribute {
name: "to"
type: INT
i: 7
}
}
name: "add"
initializer {
dims: 4
dims: 4
data_type: 1
name: "Y"
external_data {
key: "location"
value: "an_onnx_model.onnx.data"
}
external_data {
key: "offset"
value: "0"
}
external_data {
key: "length"
value: "64"
}
data_location: EXTERNAL
}
initializer {
dims: 4
dims: 4
data_type: 1
name: "W"
external_data {
key: "location"
value: "an_onnx_model.onnx.data"
}
external_data {
key: "offset"
value: "64"
}
external_data {
key: "length"
value: "64"
}
data_location: EXTERNAL
}
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
}
}
}
}
}
output {
name: "Z"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
dim {
}
}
}
}
}
}
The data is stored externally just close to the models
and it has to be that way. At the ned of the example, the model
is printed on a the standard output. We can see it was modified
and now it does not contain the weights anymore but only their location.
It is possible to restore the weights and put them back in the onnx structure.
Function load_external does it.
It needs an extra parameter to indicate the location of the weights.
from onnx_extended.tools import load_external
load_external(model, ".")
Load a big model#
When loading the model back, two options are possible.
The first is load everything including the external data.
load_external
can either load the weights (external=True) or loads the
structure of the model and leaves the weights on the disk
(external=False).
<<<
from onnx_extended.tools import load_model
model = load_model("an_onnx_model.onnx", external=False)
print(model)
>>>
ir_version: 8
opset_import {
domain: ""
version: 18
}
graph {
node {
input: "X"
input: "X"
output: "X2"
op_type: "Mul"
}
node {
input: "X2"
input: "Y"
output: "z1"
op_type: "Add"
}
node {
input: "z1"
input: "W"
output: "z2"
op_type: "Mul"
}
node {
input: "z2"
output: "Z"
op_type: "Cast"
attribute {
name: "to"
type: INT
i: 7
}
}
name: "add"
initializer {
dims: 4
dims: 4
data_type: 1
name: "Y"
external_data {
key: "location"
value: "an_onnx_model.onnx.data"
}
external_data {
key: "offset"
value: "0"
}
external_data {
key: "length"
value: "64"
}
data_location: EXTERNAL
}
initializer {
dims: 4
dims: 4
data_type: 1
name: "W"
external_data {
key: "location"
value: "an_onnx_model.onnx.data"
}
external_data {
key: "offset"
value: "64"
}
external_data {
key: "length"
value: "64"
}
data_location: EXTERNAL
}
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
}
}
}
}
}
output {
name: "Z"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
dim {
}
}
}
}
}
}
Example with Llama#
The Llama model is big. An onnx version can be retrieved from this github repository microsoft/Llama-2-Onnx. As it takes time to play with the whole, it can be interested to extract the first layers.
import os
import onnx
from onnx_extended.tools import load_model, save_model, load_external
from onnx_extended.tools.onnx_nodes import select_model_inputs_outputs
llama = (
"Llama-2-Onnx/7B_FT_float16/ONNX/LlamaV2_7B_FT_float16.onnx"
)
# load model without loading the weights
onx = load_model(llama, external=False)
# extract a piece of it from the inputs to a some intermediate output
outputs = ["/transformer/block_list.1/attention/Gather_output_0"]
new_onx = select_model_inputs_outputs(onx, outputs)
# load external data on the subpart: the weights are still on disk
load_external(new_onx, os.path.dirname(llama))
# save model without any external data
name = "models/llama_16_block_list_1.onnx"
save_model(new_onx, name, external=False)
The name of all intermediate results can be obtained with the following command line. It runs shape inference and stores the results in a dataframe.
python -m onnx_extended display \
--external=0 -s types_shapes.xlsx \
-m ./Llama-2-Onnx/7B_FT_float16/ONNX/LlamaV2_7B_FT_float16.onnx