From 1c647325a84ba5c4b60994c33410fb47cc3bd863 Mon Sep 17 00:00:00 2001 From: "ziang.song" Date: Wed, 31 Jul 2024 08:34:27 +0800 Subject: [PATCH] added readme and path --- docs/en/guide/pp.mdx | 126 ++++++++++++++++++++++++++++++++++++++- docs/public/guide/pp.png | Bin 0 -> 84478 bytes 2 files changed, 124 insertions(+), 2 deletions(-) create mode 100644 docs/public/guide/pp.png diff --git a/docs/en/guide/pp.mdx b/docs/en/guide/pp.mdx index 055d7d8..f2ecdb3 100644 --- a/docs/en/guide/pp.mdx +++ b/docs/en/guide/pp.mdx @@ -1,3 +1,125 @@ -# veScale Pipeline Parallel +# veScale Pipeline Parallel (PP) -# Coming Soon \ No newline at end of file +## TLDR + +PP + +## What is PP? + +`Pipeline Parallel` (`PP`) partitions layers of a model across multiple devices to form a pipelined execution of the training. +`PP` takes as input a list of microbatches of data per iteration and performs pipelined training execution (forward, backward, and optimizer update) on each microbatch, while overlaps communication with computation on each device. + +## Why veScale PP? + +Existing `PP` systems suffer multiple drawbacks as below, which prevent productization within a company: + +- _Complex API_: assuming that model developers are also systems experts in `PP` + +- _Hacking model code_: requiring manually rewrite the model code to run `PP` + +- _Lacking single device abstraction_: requiring manually rewrite the training script to be `PP` device-specific + +- _Lacking options of pipeline construction_: relying on a single option of graph tracing, or perfect graph tracing, or solely manual construction of the pipeline. + +- _Lacking customizability of pipeline schedule_: deeply coupling the entire runtime (e.g., compute, communication) with a specific `PP` schedule (e.g., `1F1B`) + +- _Lacking diverse model support_: supporting only sequential model architecture without branching, or supporting only pipeline stages having single input or single output without multiple input/output. + +## What is veScale PP? + +`veScale PP` offers a new `PP` framework that is both _**Easy-to-Use**_ and _**Easy-to-Customize**_, thus it is used internally in our production. +Especially, `veScale PP` provides: + +- _Easy API_: hiding the complexity of `PP` systems and runtimes from model developers + +- _Zero model code change_: keeping the original torch model code as it is for transparent pipelined models + +- _Single device abstraction_: keeping the single device training script as it is for transparent pipelined training on multiple devices + +- _Multiple options of pipeline construction_: user can flexibly choose modes: + + - `GRAPH_EAGER` mode automatically traces and parses the model into a graph, splits the graph into pipeline stages, and constructs each stage for pipeline execution + + - graph tracer can also be choices or users + + - `MANUAL_EAGER` mode manually constructs each pipeline stage for pipeline execution, without graph tracing, parsing, and splitting. + +- _Customizable pipeline schedule_: empowering users to define their custom pipeline schedules, beyond our built-in schedule as below: + + - `1F1B` + + - `Interleaved 1F1B` + + - `Zero Bubble` + +- _Support diverse models_: support comprehensive model archictures for non-sequential models, multiple-input-multiple-output stages, and etc. + +## Why is veScale PP a better option than its counterparts? + +- Compared with Megatron-LM's PP, `veScale PP` offers not only a better __Ease-of-Use__ experience in all aspects (easy API, zero model code, single device abstraction, options of pipeline construction) but also a plus of __Customizability__ allowing users to conveniently customize new pipeline schedules. + +- Compared with DeepSpeed, `veScale PP` requires no modification of model code. It further supports multi-stage scheduling for non-sequential multimodal architecture and multi-input settings instead of being constrained by `nn.Sequential`'s syntax. + +- Compared with the pre-release torchtitan, `veScale PP` provides: i) single device abstraction of training script, ii) wider options of graph tracer support, iii) wider model architecture support, and iv) guarantees bitwise accuracy alignment between `PP` and single device code. + +## How does veScale PP work? + +Spinning up a `PP` job typically requires three steps: i) trace and parse model graph, ii) construct pipeline stage, and iii) execute pipeline schedule. Each step is handled by `PipeParser`, `PipeModule`, and `PipeEngine`. Upon receiving the model definition, `PipeParser` (`GRAPH_EAGER` mode) breaks down the model code to the intermediate representation of low-level modules and operators up to the granularity of your choice. Under `MANUAL_EAGER` mode, users only need to assign stage modules and their communication relationships. `PipeModule` collects parameters and operators, and optimizer states belonging to the same stage, and resolves communication topology among devices. `PipeEngine` will schedule steps to execute training according to pipeline schedules. + +## How to use veScale PP? + +- Example of using `GRAPH_EAGER` mode: + + ```python + # zero model code change + class EightMLP(nn.Module): + def __init__(self, ...): + self.mlp1 = MLP(...) + ... + self.mlp8 = MLP(...) + def forward(...): + ... + + # An EightMLP is composed of 8 submodules called MLP + model = EightMLP() + # or model = deferred_init(EightMLP) + + from vescale.plan import PipelineParallelPlan, PipelineScheduleType, PipelineSplitMethodType, ModeType + from vescale.pipe import construct_pipeline_stage + from vescale.engine import PipeEngine + from vescale.dtensor.device_mesh import DeviceMesh + + # create 3-dim DeviceMesh + device_mesh = DeviceMesh("cuda", [[[0]], [[1]], [[2]], [[3]]], mesh_dim_names=("PP", "DP", "TP")) + + # prepare plan for pipeline parallelism + pipe_plan = PipelineParallelPlan( + mode=ModeType.GRAPH_EAGER, + split_method=PipelineSplitMethodType.MANUAL, + num_stages=4, + virtual_chunks=2, + smallest_unsplittable_units=[f"mlp{i + 1}" for i in range(8)], # maintain hierarchy of each MLP module + split_points=["mlp2", "mlp4", "mlp6", "mlp8"], # managed pipeline split points by fully qualified names + overlap_p2p_comm=True, # speedup option + schedule_type=PipelineScheduleType.INTERLEAVED_1F1B, + ) + + # parse model graph, split graph, and construct pipeline stage + pipe_stage = construct_pipeline_stage(model, pipe_plan, device_mesh) + + # prepare pipeline schedule and execution engine + engine = PipeEngine(pipe_stage, device_mesh, pipe_plan) + + # train PP model as if on single device + for minibatch_data in dataloader: + minibatch_loss, microbatch_outputs = engine(minibatch_data) + minibatch_loss.backward() + ... + + ``` + +- Example of using `MANUAL_EAGER` mode: Coming Soon. + +- APIs can be found in `/vescale/pipe/pipe_stage.py` and `/vescale/pipe/pipe.py` + +- More examples can be found in `/test/parallel/pipeline/api/test_simple_api.py` \ No newline at end of file diff --git a/docs/public/guide/pp.png b/docs/public/guide/pp.png new file mode 100644 index 0000000000000000000000000000000000000000..edabe7fb8a670f81cb3ecb2a199311e4bb34dec5 GIT binary patch literal 84478 zcmeEv2OyPg|G!m2W*j1WBpe)jWp8C?9h+kwnT z1DctY25la_>YFXJ-fZA5XJzfbY$D?;J;9T0iuTXwW#Yq=;5+ zYjanC7ZA)Oz{S5iEkwIK%-YflIWmNAZ^Cva$dmVG;$mfD?%=+A{nw_0>xf)UX9owM zzkNj8J+S{3!Q)aeID#RO>!_`8NgIC7mQPMm&Be*&f~1nBvKDx6aDbWqq7J{UoAWox z`O61(w3Za)($;~pJ4@nnSX9ru@z9Zax z+=ARdr+^mNlSEh89)thc)%beeKc^^?_<$(E0G;6K42U4--Y{SY|K9BP#E1(5+zH}l z+`ls=gnN(JR{I!|i*K*dWKR$*zjcnd{Lk-_80M-prDkLEFhVZfLSuP1_TPpb@mRxt^R@) z1qJya0=!=*i?|KIDEwcuA_TI>P)JTha@{^o6hJT*;{JZ%L}cUNhZFbia-T-oljpyP 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