ACL Graph

ACL Graph#

Why do we need ACL Graph?#

In LLM inference, each token requires nearly a thousand operator executions. When host launching operators are slower than device, it will cause host bound. In severe cases, the device will be idle for more than half of the time. To solve this problem, we use graph in LLM inference.

eager mode:

host:   |  launch op1  |  launch op2  |  launch op3  |  launch op4  |  launch op5  |

device:                | run op1 |free| run op2 |free| run op3 |free| run op4 |free| run op5 |

        | <-----                           total time                                 -----> |

graph mode:

host:   |  launch graph  |

device:                  | run op1 | run op2 | run op3 | run op4 | run op5 |

        | <-----                    total time                      -----> |

How to use ACL Graph?#

ACL Graph is enabled by default in V1 Engine, just need to check that enforce_eager is not set to True. More details see: Graph Mode Guide

How it works?#

In short, graph mode works in two steps: capture and replay. When the engine starts, we capture all of the ops in the model forward and save it as a graph. When a request comes in, we just replay the graph on the device and wait for the result.

But in reality, graph mode is not that simple.

Padding and Bucketing#

Due to the fact that a graph can only replay the ops captured before, without doing tiling and checking graph input, we need to ensure the consistency of the graph input. However, we know that the model input’s shape depends on the request scheduled by the Scheduler, so we can’t ensure consistency.

Obviously, we can solve this problem by capturing the biggest shape and padding all of the model inputs to it. But this will bring a lot of redundant computing and make performance worse. So we can capture multiple graphs with different shapes, and pad the model input to the nearest graph, which will greatly reduce redundant computing. But when max_num_batched_tokens is very large, the number of graphs that need to be captured will also become very large. We know that when the input tensor’s shape is large, the computing time will be very long, and graph mode is not necessary in this case. So all of the things we need to do are:

  1. Set a threshold;

  2. When num_scheduled_tokens is bigger than the threshold, use eager_mode;

  3. Capture multiple graphs within a range below the threshold;

|    graph1    |
|           graph2           |
|                    graph3                    |
|                              graph4                              |    # the threshold

| input1 | pad |    # use graph1
|           input2           |  # don't need pad
|                      input3                      |      pad      |    # use graph4
|                                    input4                                    |    # use eager mode

Piecewise and Full graph#

Due to the increasing complexity of the attention layer in current LLMs, we can’t ensure all types of attention can run in graph. In MLA, prefill_tokens and decode_tokens have different calculation methods, so when a batch has both prefills and decodes in MLA, graph mode is difficult to handle this situation.

vLLM solves this problem with piecewise graph mode. We use eager mode to launch attention’s ops, and use graph to deal with others. But this also brings some problems: The cost of launching ops has become large again. Although much smaller than eager mode, it will also lead to host bound when the CPU is poor or num_tokens is small.

Altogether, we need to support both piecewise and full graph mode.

  1. When attention can run in graph, we tend to choose full graph mode to achieve optimal performance;

  2. When full graph does not work, use piecewise graph as a substitute;

  3. When piecewise graph’s performance is not good and full graph mode is blocked, separate prefills and decodes, and use full graph mode in decode_only situations. Because when a batch includes prefill requests, usually num_tokens will be quite big and not cause host bound.

Currently, due to stream resource constraint, we can only support a few buckets in piecewise graph mode now, which will cause redundant computing and may lead to performance degradation compared with eager mode.

How is it implemented?#

vLLM has already implemented most of the modules in graph mode. You can see more details at: CUDA Graphs

When in graph mode, vLLM will call current_platform.get_static_graph_wrapper_cls to get the current device’s graph model wrapper, so what we need to do is implement the graph mode wrapper on Ascend: ACLGraphWrapper.

vLLM has added support_torch_compile decorator to all models. This decorator will replace the __init__ and forward interface of the model class. When forward is called, the code inside the ACLGraphWrapper will be executed, and it will do capture or replay as mentioned above.

When using piecewise graph, we just need to follow the above-mentioned process. But when in full graph, due to the complexity of the attention, sometimes we need to update attention op’s params before execution. So we implement update_attn_params and update_mla_attn_params functions for full graph mode. During forward, memory will be reused between different ops, so we can’t update attention op’s params before forward. In ACL Graph, we use torch.npu.graph_task_update_begin and torch.npu.graph_task_update_end to do it, and use torch.npu.ExternalEvent to ensure order between param updates and op executions.

DFX#

Stream resource constraint#

Currently, we can only capture 1800 graphs at most, due to the limitation of ACL graph that a graph requires at least a separate stream. This number is bounded by the number of streams, which is 2048; we save 248 streams as a buffer. Besides, there are many variables that can affect the number of buckets:

  • Piecewise graph divides the model into num_hidden_layers + 1 sub modules, based on the attention layer. Every sub module is a single graph which needs to cost a stream, so the number of buckets in piecewise graph mode is very tight compared with full graph mode.

  • The number of streams required for a graph is related to the number of comm domains. Each comm domain will increase one stream consumed by a graph.

  • When multi-stream is explicitly called in a sub module, it will consume an additional stream.

There are some other rules about ACL Graph and stream. Currently, we use func update_aclgraph_sizes to calculate the maximum number of buckets and update graph_batch_sizes to ensure stream resource is sufficient.

We will expand the stream resource limitation in the future.

Limitations#

  1. FULL and FULL_AND_PIECEWISE are not supported now;

  2. When use ACL Graph and MTP and num_speculative_tokens > 1, as vLLM don’t support this case in v0.11.0, we need to set cudagraph_capture_sizes explicitly.

  3. use_inductor is not supported now;

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