cirquit
PhD student with a focus on machine learning, distributed systems and functional programming.
Alexander Isenko
September '23
Profiling Memory Utilization in PyTorch FSDP
1
Who Am I?
5th year PhD student (expected graduation in May '24)
Interests: Distributed DL, Performance Optimization
Alexander Isenko, Ruben Mayer, Jeffrey Jedele, Hans-Arno Jacobsen
ACM SIGMOD International Conference of Data 2022
Alexander Isenko, Ruben Mayer, Hans-Arno Jacobsen
VLDB 2024
2
What Is Fully-Sharded Data Parallel Training?
Distributed Data Parallel (DDP)
Fully Sharded Data Parallel (FSDP) [1]
Full Model
Forward
Backward
All-Reduce
opt.step()
Backward
GPU #1
Shard
All-Gather
Forward
All-Gather
For all layers
For all layers
ReduceSctr.
opt.step()
GPU #2
Full Model
Forward
Backward
All-Reduce
opt.step()
Backward
Shard
All-Gather
Forward
All-Gather
ReduceSctr.
opt.step()
GPU #1
GPU #2
Grads
Weights
Weights
Grads
3
Memory Utilization In Deep Learning Training
Increased memory size enables higher minibatch sizes
Leads to better parallelism due to bigger tasks and better Tensor Core utilization
Enables to "fit" the minibatch better in waves [2]
4
Memory Bandwidth in Deep Learning Training
Memory Bandwidth is essential for DL
Many DL operations are memory bound due to their algorithmic intensity [3,4]
5
Let's profile FSDP!
Varying Minibatch Sizes
Model: T5-3B
Fully Sharded FSDP configuration (ZeRO-3)
Seq2Seq Grammar Correction Task
Sequence Length: 512
ZeRO-1
ZeRO-2
ZeRO-3
2xA100 SXM4 80G
PyTorch Nightly (15.09.2023)
CUDA 12.1
Activation Recomputation (30% memory savings)
50 steps
[5]
6
Throughput vs. Memory Utilization
MB: 69 slow
MB: 8,16,32 increase memory consumption over time!
MB: 64, 69 fluctuates around max. memory consumption
7
CUDA Caching Allocator (CCA) [6,7]
Segment
Marked Free Block
Block
Rounding Buffer
Free Block
Free Segment
Memory Fragmentation = Allocated Segment Size / Total Segment Size [8]
8
Memory Fragmentation
Fragmentation is responsible for higher memory utilization
cudaAlloc retries
limit throughput
9
Reason For Additional Allocations
Post by Jane Xu (Meta Software Engineer) on Sep. 5th [9]
10
Solutions
Reimplementation of how FSDP manages synchronization and memory management
Memory Management Plugin by ColossialAI (Gemini System)
CCA Tuning Parameters [10]
Chunk-based Memory Manager
Dynamic Memory Manager
backend
(currently) only supports PyTorch 1.13
(currently) only works with CUDA 11.7
max_split_size_mb
roundup_power2_divisions
roundup_bypass_threshold_mb
expandable_segments (nightly since April '23)
Goal: Stable training with deterministic memory consumption
garbage_collection_threshold
11
CCA: Expandable Segments
Minibatch Size: 8
default
default
exp-seg.
exp-seg.
12
CCA: Expandable Segments
MB: 64
MB: 69
default
default
exp-seg.
exp-seg.
default
default
exp-seg.
exp-seg.
MB: 73 possible (backup slides)
13
CCA: Expandable Segments
1. Seems help with fragmentation when below maximum memory utilization.
3. Not officially documented, unknown plans for this feature.
2. Enables higher minibatch training, but takes an even higher penalty in throughput.
14
Gemini Memory Management System
Varying Minibatch Sizes
Model: GPT2-XL (1.6B)
Casual Language Modeling (random data)
Sequence Length: 1024
2xA100 SXM4 80G
Activation Recomputation
50 steps
Fully Sharded FSDP configuration (ZeRO-3) vs. CAI_Gemini (ZeRO-3 DDP + Gemini)
use_orig_params=False [11]
15
FSDP PyTorch 1.13 vs FSDP Nightly
FSDP PyTorch 1.13
FSDP PyTorch Nightly
23% speedup
constant memory consumption
16
FSDP Nightly vs Gemini ZeRO-3 PyTorch 1.13
Gemini PyTorch 1.13
FSDP PyTorch Nightly
17
FSDP vs. Gemini Insights
1. Gemini supports higher minibatch sizes (30% bigger for GPT2-XL).
2. Training throughput with Gemini is, at best, 3% faster.
3. Fragmentation is not fully "solved" with Gemini as it still showed up MB=16.
4. GPT2-XL is not prone to fragmentation compared to the T5 model.
5. When comparing PyTorch 1.13 only, Gemini is faster by 23% and enables 50% bigger minibatches.
18
Conclusion
FSDP still needs to be rewritten, the hot-fix with CCA parametrization is not a long-term solution
Memory management with Gemini seems very promising, but it is tightly coupled with ColossialAI
No fragmentation with FSDP + GPT2-XL: Reason unknown
Test with LLaMa-2
Test with Foundation Model Stack
Many runs with both Gemini (GPT2) and FSDP (T5) worked for 4-25 steps before crashing!
The current trend of leaving 10-20% of HBM free is preferable to maximize throughput and training stability
19
Future Work
After cleaning up the codebase [12], we plan to publish a blogpost with the results
Add the insights to the PyTorch forum discussion initiated by Jane Xu
Future collaborations are planned, please contact me if anything emerges!
5th year PhD student (expected graduation in May '24)
Interests: Distributed DL, Performance Optimization
Backup slides: Experimental Setup, FSDP 1.13 vs Gemini 1.13, Intermediate Timings, Expandable Segments MB:73, Model FLOPs Calculations
20
Backup Slides: Experimental Setup
21
Backup Slides: CCA Expandable Segments
FSDP-9 = MB:69
FSDP-10 = MB:73
Less fluctuation in memory consumption, but a higher cudaAlloc Retries count
Fragmentation is worse!
MB:64 has ~ 3.9k tk/s, so performance is still worse
default
default
exp-seg.
exp-seg.
22
Backup Slides: FSDP 1.13 vs Gemini 1.13
MB:48 Gemini is ~ 23% faster, ~ 50% less mem. consumption
FSDP PyTorch 1.13
Gemini PyTorch 1.13
23
Backup Slides: FSDP Nightly vs Gemini 1.13
FSDP PyTorch Nightly
Gemini PyTorch
1.13
MB:8
MB:16
MB:32
MB:48
MB:64
MB:8
MB:16
MB:32
MB:48
MB:64
MB:88
MB:98
24
Backup Slides: Performance In DL Training
nvidia-smi
Device Utilization
GPU utilization is deceptive! [1]
Samples per second
May not be comparable between models
NLP uses different sequence lengths
CV uses different sizes images
No additional insights when changing hardware except "faster" or "slower"
Only useful when model and hardware is fixed
We need a metric that incorporates the model and hardware information!
FLOPs and Memory Bandwidth are not easily measured without intrusive profiling
25
Backup Slides: Performance In DL Training
[2] PaLM: Scaling Language Modeling with Pathways https://arxiv.org/pdf/2204.02311.pdf
"How well is the hardware used?"
"How well is the model suited to leverage the available hardware?"
[4] Andrey Karpathy FLOPs Calculation: https://github.com/karpathy/nanoGPT/blob/master/transformer_sizing.ipynb
[3] The FLOPs Calculus of Language Model Training https://medium.com/@dzmitrybahdanau/the-flops-calculus-of-language-model-training-3b19c1f025e4
26
Backup Slides: Performance In DL Training
MFU: 9% - why are we so bad?
Current SotA [2]
[2] PaLM: Scaling Language Modeling with Pathways https://arxiv.org/pdf/2204.02311.pdf
2015_Logo_TUM_RGB Created with Sketch. Alexander Isenko September '23 Profiling Memory Utilization in PyTorch FSDP
Profling Memory Utilization in PyTorch FSDP
By cirquit
Profling Memory Utilization in PyTorch FSDP
Profling Memory Utilization in PyTorch FSDP
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