Antigravity Agent
181 lines
6.3 KiB
Markdown
181 lines
6.3 KiB
Markdown
---
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id: wiki-2026-0508-parallel-computing-in-ai
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title: Parallel Computing in AI
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [AI parallelism, distributed AI training, AI parallel computing]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [parallelism, ai, distributed, gpu, training]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Parallel Computing in AI
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## 매 한 줄
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> **"매 AI 의 parallel 은 단순 distributed 가 아니라 4축 (Data, Tensor, Pipeline, Expert) × 2 모드 (training/inference) 의 조합 problem"**. 매 2026 Llama 3 405B 학습 = 16384 GPU, GPT-5 추론 = 매 expert sharding + speculative decoding. 매 parallel strategy 의 mismatch = 매 ROI 폭락.
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## 매 핵심
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### 매 4 축
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1. **Data Parallel (DP)**: 매 batch 분할, 매 weight 동기.
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2. **Tensor Parallel (TP)**: 매 single layer 의 weight matrix 분할.
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3. **Pipeline Parallel (PP)**: 매 layer stack 분할.
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4. **Expert Parallel (EP)**: 매 MoE 의 expert 분할.
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### 매 + 보조 axis
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- **Sequence Parallel (SP)**: 매 long context 의 token 차원 분할 (Ring Attention, Ulysses).
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- **Context Parallel (CP)**: 매 attention 의 KV partial.
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- **ZeRO sharding (DP variant)**: 매 optimizer/grad/param 분할.
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### 매 training vs inference
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| 측면 | Training | Inference |
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|---|---|---|
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| 주축 | DP + ZeRO | TP + EP |
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| Batch | 큰 micro-batch | 매 1 user → 매 small |
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| Memory | activation 중심 | KV cache 중심 |
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| Comm | all-reduce (grad) | all-reduce (TP), all-to-all (MoE) |
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| 도구 | DeepSpeed, Megatron, FSDP | vLLM, TensorRT-LLM, SGLang |
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### 매 hardware constraint
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- **NVLink** (intra-node, 900GB/s): 매 TP 친화.
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- **InfiniBand / NVLink Switch** (inter-node, 400Gbps NDR): 매 PP/DP.
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- **HBM3e** (192GB on B200): 매 더 큰 model fit + 매 sharding 절약.
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## 💻 패턴
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### FSDP2 (PyTorch 2.6+, 2026)
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```python
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import torch
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from torch.distributed.fsdp import fully_shard, MixedPrecisionPolicy
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model = build_transformer()
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mp = MixedPrecisionPolicy(param_dtype=torch.bfloat16, reduce_dtype=torch.float32)
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for layer in model.layers:
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fully_shard(layer, mp_policy=mp)
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fully_shard(model, mp_policy=mp)
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opt = torch.optim.AdamW(model.parameters(), lr=3e-4, fused=True)
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for batch in loader:
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loss = model(batch).loss
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loss.backward()
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opt.step(); opt.zero_grad()
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```
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### TP with DTensor / device_mesh
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```python
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from torch.distributed.tensor import DTensor, Shard, Replicate
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from torch.distributed.device_mesh import init_device_mesh
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mesh = init_device_mesh("cuda", (DP, TP), mesh_dim_names=("dp","tp"))
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# Column-parallel linear (매 weight col-sharded)
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w = nn.Parameter(torch.empty(out, in_))
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w_dt = DTensor.from_local(w, mesh["tp"], [Shard(0)])
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# 매 forward: x @ w_dt.T → 매 partial output, all-reduce 자동
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```
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### MoE expert parallel (DeepSpeed-MoE / Megatron)
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```python
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class MoELayer(nn.Module):
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def __init__(self, experts, top_k=2):
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super().__init__()
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self.experts = nn.ModuleList(experts) # 매 sharded across EP group
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self.gate = nn.Linear(d, len(experts))
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self.k = top_k
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def forward(self, x):
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logits = self.gate(x)
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topk = logits.topk(self.k, dim=-1)
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# 매 all-to-all dispatch tokens to expert-owning rank
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dispatched = all_to_all_dispatch(x, topk.indices, ep_group)
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out = local_experts(dispatched)
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return all_to_all_combine(out, topk.indices, topk.values, ep_group)
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```
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### vLLM tensor-parallel inference
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(
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model="meta-llama/Llama-3.3-70B-Instruct",
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tensor_parallel_size=4, # 매 4 GPU TP
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pipeline_parallel_size=1,
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gpu_memory_utilization=0.92,
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enable_chunked_prefill=True,
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enable_prefix_caching=True,
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)
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out = llm.generate(["매 안녕"], SamplingParams(temperature=0.7, max_tokens=128))
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```
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### Ring Attention (sequence parallel for 1M context)
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```python
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def ring_attention(q, k, v, sp_group):
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# 매 each rank holds 1/N of sequence
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out = torch.zeros_like(q); lse = torch.full(...)
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k_blk, v_blk = k, v
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for step in range(world_size(sp_group)):
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partial, l = flash_attn_partial(q, k_blk, v_blk)
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out, lse = log_sum_exp_combine(out, lse, partial, l)
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k_blk, v_blk = ring_send_recv(k_blk, v_blk, sp_group)
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return out
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```
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### 3D parallel mesh (Megatron-Core 2026)
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```python
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mesh = init_device_mesh(
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"cuda", (DP_replica, DP_shard, PP, TP),
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mesh_dim_names=("dp_r","dp_s","pp","tp"),
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)
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# Llama 3 405B: dp_r=16, dp_s=8, pp=16, tp=8 → 16384 GPUs
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| ≤ 8B model, 1 node | DP + ZeRO-2 |
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| 70B, multi-node | FSDP2 (DP shard) + TP intra-node |
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| 405B+ | TP × PP × DP (3D) |
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| MoE (Mixtral, GPT-5 style) | + EP (all-to-all) |
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| 1M+ context | + SP (Ring/Ulysses) |
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| Inference 1 user | TP only, no DP |
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| Inference batch | TP + continuous batching (vLLM) |
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| MoE inference | EP + speculative decoding |
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**기본값**: 매 training = FSDP2 + TP, 매 inference = vLLM TP.
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## 🔗 Graph
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- 부모: [[Distributed Computing]] · [[High Performance Computing]]
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- 변형: [[Data Parallelism]] · [[Tensor Parallelism]] · [[Pipeline Parallelism]] · [[Expert Parallelism]]
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- 응용: [[LLM Training]] · [[vLLM]] · [[DeepSpeed]] · [[Megatron-LM]]
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- Adjacent: [[ZeRO Optimizer]] · [[FSDP]] · [[Ring Attention]] · [[Mixture of Experts]]
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## 🤖 LLM 활용
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**언제**: 매 model 또는 batch 가 single-GPU mem 초과. 매 throughput / latency SLO 의 multi-GPU 필요.
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**언제 X**: 매 single GPU fits + 매 batch latency 만족 — 매 multi-GPU overhead 가 손해.
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## ❌ 안티패턴
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- **TP across nodes**: 매 IB 의 NVLink 대비 5-10x 느림 → 매 stall.
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- **PP without DP**: 매 batch 의 micro-batch 한계 → 매 throughput cap.
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- **MoE EP without all-to-all opt**: 매 NCCL all-to-all 의 성능 ↓ → 매 GroupedGEMM kernel 필요.
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- **Sequence parallel without flash-attn**: 매 attention recompute 폭증.
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- **Mixed precision without loss scaling** (FP16): 매 underflow → 매 loss NaN. → BF16 권장.
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## 🧪 검증 / 중복
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- Verified (Megatron-LM paper, FSDP2 docs 2026, vLLM docs, DeepSpeed-MoE paper).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 4-axis parallelism + training/inference split |
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