Comments (4)
July 18 - July 24
Feature Implementation
- Pipeline (hao, zhuohan)
- Implement nccl-based send and recv
- Implement automatic slicing algorithm
- SPMD (lianmin)
- Benchmark deepspeed
- Implement Zero Redundancy Optimizer
- Memory Optimization (yonghao)
- Integrate rematerializaiton and swapping into the GPT benchmark
Performance Goal
- Match 2d parallelism in Megatron-LM
- Match 3d parallelism in Megatron-LM
July 25 - July 31
Feature Implementation
- Pipeline (hao, zhuohan)
- Implement nccl-based send and recv
- Implement automatic slicing algorithm
- Implement advanced scheduling algorithms
- SPMD (lianmin)
- Let the auto-sharding pass generate reduce-scatter instead of all-reduce
- Memory Optimization (yonghao)
- Integrate rematerializaiton and swapping into the GPT benchmark
Performance Goal
- Match 2d parallelism in Megatron-LM
- Match 3d parallelism in Megatron-LM
- Match ZeRO optimizer (stage 2) in deepspeed
Aug. 1 - Aug. 15
Feature Implementation
-
Test on new models
- Mixture of Expert
- wide-resnet
- Conformer
- DLRM
- TransGAN
-
Auto-tuner
- Write a simple auto-tuner to perform search
Performance Goal
- Outperform the data-parallel only on new models
Aug. 15 - Aug. 21
Feature Implementation
- Paper writing
Performance Goal
- Match strong manual baseline on homogeneous models
- Outperform all baselines on heterogeneous models
- Perfect weak scaling
from alpa.
Evaluation Plan
On 5 models, show
-
- Our unified parallelism vs. intra-operator parallelism only vs. inter-operator parallelism only vs. data-parallel only
-
- Our algorithm vs. manual approaches
- Megatron-LM
- Deepspeed
- PyTorch data-parallel / Horovod
- Add a memory-saving only baseline.
-
- Our algorithm vs. other simple heuristics
-
- Weak scaling test
from alpa.
More developments needed for paper submission and MVP
- (Zhuohan) Integrate stage discovery #67 into the current Parax;
- (Zhuohan) Integrate layer grouping DP with stage discovery #67
- (Hao, Yonghao) Finish and Integrate mesh slicing with the above;
- (Lianmin) Gradient accumulation support, see #72;
- (Hao) Make Pipeline-only parallelism performance matching that of Megatron.
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Close this because this is outdated. Will open a new one
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Related Issues (20)
- Will alpa support jax 0.4.x and cuda 12.x?
- cupy package mismatches with CUDA version in the docs HOT 2
- Unable to use pipeline parallelism with multi-node meshes HOT 1
- PLS, a paper related question I want to ask HOT 1
- Question abuot licence / usage HOT 1
- Problem in building Alpa-modified Jaxlib. HOT 5
- IndexError: `InlinedVector::at(size_type) const` failed bounds check
- Check failed: operand_dim < ins->operand(0)->shape().rank() (2 vs. 2)Does not support this kind of Gather. HOT 2
- How to build debug-version Alpa-modified jaxlib HOT 3
- when i check installation by running python3 -m alpa.test_install,AssertionError happend HOT 6
- Unsupported parallel mode in shard-only auto perf test: load_solution
- How to use Alpa to serve BERT models
- Error about python3 -m alpa.test_install
- A question about file /alpa/benchmark/gen_serving_database.py
- Any solution to support llama2 finetune?
- Why did you choose ray instead of using torch distributed? HOT 2
- Ray spill out of disk error when using alpa to auto-parallelize llama HOT 2
- [Bug] Segment fault when using alpa to parallelize llama with jax 0.4.6 environment HOT 2
- How to profile Alpa models and get the trace HOT 1
- Check Installation failled HOT 1
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