oneflow.nn.Graph

Base class for running neural networks in Static Graph Mode.

Currently, there are two main ways to run models in deep learning frameworks, namely dynamic graphs and static graphs , which are also conventionally referred to as Eager Mode and Static Graph Mode in OneFlow.

Both approaches have their advantages and disadvantages, and OneFlow provides support for both approaches, with Eager mode being the default.

Generally speaking, dynamic graphs are easier to use and static graphs have more performance advantages. oneflow.nn.Graph module is provided by OneFlow to allow users to build static graphs and train models with Eager-like programming conventions.

Eager Mode

OneFlow runs in Eager mode by default.

OneFlow’s nn.Graph is programmed in a style very similar to Eager Mode, so it is possible to make small changes and get large performance gains.

The following script shows the process of building a neural network in eager mode using the interface under oneflow.nn :

import oneflow as flow
import oneflow.nn as nn

class ModuleMyLinear(nn.Module):
    def __init__(self, in_features, out_features):
        super().__init__()
        self.weight = nn.Parameter(flow.randn(in_features, out_features))
        self.bias = nn.Parameter(flow.randn(out_features))

    def forward(self, input):
        return flow.matmul(input, self.weight) + self.bias

linear_model = ModuleMyLinear(4, 3)

Eager nn.Module can be reused by nn.Graph. The above script for eager mode can be changed to static Graph mode by adding just a few lines of code, which consists of the following steps:

• Define your customized graph as a subclass of nn.Graph

• At the beginning of __init__. Call super().__init__() to let OneFlow do the necessary initialization of the Graph

• Reuse the nn.Module object in Eager mode in __init__ (self.model = model)

• Describe the computation in the build method

• Instantiate your graph then call it.

class GraphMyLinear(nn.Graph):
    def __init__(self):
        super().__init__()
        self.model = linear_model

    def build(self, input):
        return self.model(input)

graph_mylinear = GraphMyLinear()
input = flow.randn(1, 4)
out = graph_mylinear(input)
print(out)

tensor([[-0.3298, -3.7907,  0.1661]], dtype=oneflow.float32)

Static Graph Mode

Constructing it

__init__	Initializes internal Graph states.
build	The build() method must be overridden to define neural network computaion logic.
add_optimizer	Add an optimizer, an learning rate scheduler to the graph.
set_grad_scaler	Set the GradScaler for gradient and loss scaling.

Execute Graph

__call__

Call nn.Graph subclass instance to run your customized graph.

Graph Config option

enable_amp	If set to true, then graph will use mixed precision mode, it means use both float16 and float32 during model training.
enable_zero	Enable ZeRO redundancy optimizer.
allow_fuse_model_update_ops	If set to true, try to fuse cast + scale + l1_l2_regularize_gradient + model_update to one op to improve performance.
allow_fuse_add_to_output	If set to true, try to fuse a binary element-wise add operetor to one of the predecessors to improve performance.
allow_fuse_cast_scale	If set to true, try to fuse cast and scalar_mul_by_tensor to improve performance.
set_gradient_accumulation_steps	Set num of steps to accumulate gradient.
enable_cudnn_conv_heuristic_search_algo	Whether enable cudnn conv operatioin to use heuristic search algorithm.

Block Config option

set_stage	Set stage id and placement of nn.Module/ModuleBlock in pipeline parallelism.
activation_checkpointing	Set/Get whether do activation checkpointing in this nn.Module.

Save & Load Model

state_dict	Returns a dictionary containing a whole state of the graph.
load_state_dict	Copies module’s states and other graph states from state_dict into this graph.

Debug

name	Name auto-generated for this graph.
debug	Open or close debug mode of the graph.
__repr__	For printing the graph structure.