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| # Copyright 2024 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, |
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| compute_environment: LOCAL_MACHINE | |
| debug: false | |
| distributed_type: FSDP | |
| downcast_bf16: 'no' | |
| enable_cpu_affinity: false | |
| fsdp_config: | |
| fsdp_activation_checkpointing: false | |
| fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP | |
| fsdp_backward_prefetch: BACKWARD_PRE | |
| fsdp_cpu_ram_efficient_loading: true |
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| import time | |
| from transformers import AutoTokenizer, LlamaForCausalLM | |
| from accelerate.utils import set_seed | |
| set_seed(42) | |
| file_size = 132 # 70B | |
| # file_size = 30 # 8B | |
| start_time = time.time() |
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| name: Deploy to GitHub Pages | |
| on: | |
| push: | |
| branches: [ "main", "master" ] | |
| workflow_dispatch: | |
| jobs: | |
| deploy: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - uses: actions/checkout@v3 |
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| [P2P (Peer-to-Peer) GPU Bandwidth Latency Test] | |
| Device: 0, NVIDIA GeForce RTX 4090, pciBusID: 1, pciDeviceID: 0, pciDomainID:0 | |
| Device: 1, NVIDIA GeForce RTX 4090, pciBusID: 2, pciDeviceID: 0, pciDomainID:0 | |
| Device=0 CANNOT Access Peer Device=1 | |
| Device=1 CANNOT Access Peer Device=0 | |
| ***NOTE: In case a device doesn't have P2P access to other one, it falls back to normal memcopy procedure. | |
| So you can see lesser Bandwidth (GB/s) and unstable Latency (us) in those cases. | |
| P2P Connectivity Matrix |
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| import builtins | |
| import fcntl | |
| import os | |
| import socket | |
| import torch | |
| import torch.distributed as dist | |
| print("STARTED") | |
| def print(*args, **kwargs): |
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| import torch | |
| from transformers import AutoModel, AutoConfig, AutoModelForSequenceClassification | |
| def get_model_memory(model: torch.nn.Module): | |
| """ | |
| Returns the memory usage of the given model | |
| """ | |
| total_memory = 0 | |
| for param in model.parameters(): | |
| total_memory += param.numel() * param.element_size() |
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