At a glance: what you need to run Context Parallel
At a Glance: Time: ~2–3 hours to first working run · Prereqs: Multi-GPU distributed PyTorch familiarity, NCCL-backed process group, torchrun · Hardware: ≥4 NVIDIA GPUs with NVLink or InfiniBand (H100s recommended for production scale) · Cost: 4-GPU smoke test runs on any DGX-class node; 1M-token training requires a multi-node H100 cluster
PyTorch's Context Parallel feature, published in the PyTorch 2.12.0+cu130 unstable tutorial family, shards a long input sequence across distributed ranks so each GPU handles only a contiguous context slice during attention. The mechanism is torch.distributed.tensor.experimental.context_parallel() — a context manager that, per the PyTorch docs, "allows users to create a Python context where the SDPA function (torch.nn.functional.scaled_dot_product_attention) will be automatically replaced with Ring Attention."
Three hard prerequisites apply before you write a line of training code:
- Multi-GPU execution — the docs reference launch is
torchrun --standalone --nnodes=1 --nproc-per-node=4 cp_sdpa_example.py. Context Parallel has no single-device mode. - NCCL-backed process group — the tutorial checks
dist.is_nccl_available()before proceeding. Other backends are not supported in this path. - Unstable API — the tutorial carries the unstable label, meaning API names and behavior can shift across PyTorch releases. Pin your build and re-verify after upgrades.
Prerequisites and environment setup
Context Parallel requires NCCL — not optionally, but structurally. The distributed attention kernel depends on point-to-point KV exchange collectives that only NCCL exposes in the PyTorch distributed stack. The tutorial hard-checks availability at runtime, so a CPU or Gloo backend will fail before the first forward pass.
Before installing, confirm your environment satisfies:
torch.distributedinitialized withbackend="nccl"torchrunavailable for rank coordination- CUDA toolkit compatible with the
cu130build tag (CUDA 13.0)
$ torchrun --standalone --nnodes=1 --nproc-per-node=4 cp_sdpa_example.py
The --standalone flag runs the rendezvous server in-process, eliminating the need for an external coordinator on a single node. For multi-node runs, replace --standalone with --rdzv-backend=c10d --rdzv-endpoint=<master>:29500.
# Minimum environment settings for NCCL-backed Context Parallel
NCCL_DEBUG: INFO # surface NCCL topology and ring errors early
NCCL_IB_DISABLE: 0 # keep InfiniBand enabled on multi-node setups
TORCH_NCCL_BLOCKING_WAIT: 1 # convert hangs to explicit timeouts instead of silent stalls
MASTER_ADDR: localhost
MASTER_PORT: 29500
Hardware, driver, and topology checks
Practical scale testing demands both fast GPUs and a fast fabric. The Ring Attention algorithm, which underlies Context Parallel, is communication-heavy by design: it rotates KV shards around a logical ring of devices on every attention layer. On NVLink-connected A100s or H100s this is tolerable; on PCIe-only servers it becomes the dominant cost at sequence lengths beyond ~32K tokens.
The Ring Attention paper (arXiv:2310.01889) quantifies the memory pressure: "batch size of 1, processing 100 million tokens ... requires over 1000 GB of memory for a modest model with a hidden size of 1024." That number illustrates why sequence sharding exists — and why NVLink or InfiniBand is not optional at meaningful context lengths.
Run these checks before launching a distributed job:
# Verify GPU count, driver, and CUDA
$ nvidia-smi --query-gpu=name,driver_version,memory.total --format=csv
$ python -c "import torch; print(torch.version.cuda, torch.cuda.device_count())"
# Check NCCL availability and version
$ python -c "import torch.distributed as dist; print(dist.is_nccl_available(), torch.cuda.nccl.version())"
# Probe interconnect topology (NVLink lanes or IB ports)
$ nvidia-smi topo -m
NVIDIA H100 nodes with NVLink 4.0 are a strong fit for this workload, especially when the ring exchange must stay inside a single high-bandwidth fabric. On InfiniBand HDR200 clusters, verify NCCL_IB_HCA points to the active HCA device to avoid falling back to Ethernet.
Install the matching PyTorch build
The tutorial is versioned against PyTorch 2.12.0+cu130. Install the matching wheel to avoid API drift from the torch.distributed.tensor.experimental namespace, which the unstable label implies can change between minor releases.
# Install PyTorch 2.12.0 with CUDA 13.0 (cu130) from the nightly/test index
$ pip install torch==2.12.0+cu130 torchvision torchaudio \
--index-url https://download.pytorch.org/whl/cu130
# Pin these versions in your requirements file
torch: "2.12.0+cu130"
cuda: "13.0"
nccl: ">=2.20" # check with torch.cuda.nccl.version()
Watch Out: The
torch.distributed.tensor.experimentalnamespace is explicitly unstable. If you upgrade to a later nightly, re-verify thatcontext_parallelis importable and that its call signature hasn't changed before running any long-context job.
How Context Parallel shards the sequence across ranks
Context Parallel solves one specific problem: standard SDPA requires the full query, key, and value tensors to reside on a single device. Context Parallel eliminates that constraint by assigning each rank a contiguous slice of the sequence and communicating KV blocks across ranks via a ring topology.
Per the PyTorch docs: "With torch.distributed.tensor.experimental.context_parallel(), users can easily shard the Tensor input and parallelize the execution of the SDPA function."
flowchart LR
subgraph Input Sequence ["Input sequence L = 4096 tokens (cp_degree = 4)"]
S0["Slice 0\ntokens 0–1023\nRank 0"]
S1["Slice 1\ntokens 1024–2047\nRank 1"]
S2["Slice 2\ntokens 2048–3071\nRank 2"]
S3["Slice 3\ntokens 3072–4095\nRank 3"]
end
S0 --> R0["Rank 0\nQ₀, K₀, V₀"]
S1 --> R1["Rank 1\nQ₁, K₁, V₁"]
S2 --> R2["Rank 2\nQ₂, K₂, V₂"]
S3 --> R3["Rank 3\nQ₃, K₃, V₃"]
R0 -- "KV ring\nexchange\n(NCCL P2P)" --> R1
R1 -- "KV ring\nexchange" --> R2
R2 -- "KV ring\nexchange" --> R3
R3 -- "KV ring\nexchange" --> R0
R0 --> O0["Partial attn\noutput, Rank 0"]
R1 --> O1["Partial attn\noutput, Rank 1"]
R2 --> O2["Partial attn\noutput, Rank 2"]
R3 --> O3["Partial attn\noutput, Rank 3"]
KV shards rotate through the ring until every rank has computed attention against all KV content, accumulating partial softmax-normalized scores at each step.
What each rank owns during attention
At any given ring step, rank $r$ holds: - Its local query slice for the current step - The current KV shard (K_j, V_j) (received from rank (r-1 \mod N), forwarded to rank (r+1 \mod N) after the local compute step) - An accumulator for the running attention output and the log-sum-exp needed for numerically stable softmax across shards
The PyTorch docs describe this precisely: "Ring Attention shuffles the KV shards and calculates the partial attention scores, repeats until all KV shards have been used on each device." After $N$ ring steps, each rank holds the correct output slice for its query tokens, and no all-reduce over activations is required — only the ring P2P collectives that move KV blocks.
NCCL's isend/irecv primitives handle the KV transfer asynchronously, which means communication can overlap with the local SDPA computation if the implementation schedules them correctly (the original Ring Attention paper was designed around this overlap property).
Why ring attention is the enabling primitive
Ring Attention, introduced in "Ring Attention with Blockwise Transformers for Near-Infinite Context", builds on Blockwise Transformers to distribute attention across devices without materializing the full KV tensor on any single GPU. The paper describes it as a method that "uses blockwise computation of self-attention and feedforward to distribute long sequences across multiple devices while fully overlapping the communication of key-value blocks."
The claimed scale is substantial: the paper reports the ability to "enable the training of sequences that exceed 100 million in length without making approximations to attention" — up to 512× longer than attention baselines at the time. These are research results from the original paper, not a PyTorch product guarantee, but they establish the theoretical ceiling that motivated PyTorch's adoption of the primitive.
sequenceDiagram
participant R0 as Rank 0
participant R1 as Rank 1
participant R2 as Rank 2
participant R3 as Rank 3
Note over R0,R3: Step 0 — each rank computes attn(Qᵣ, Kᵣ, Vᵣ)
R0->>R1: send K₀V₀
R1->>R2: send K₁V₁
R2->>R3: send K₂V₂
R3->>R0: send K₃V₃
Note over R0,R3: Step 1 — each rank computes attn(Qᵣ, K_{r-1}, V_{r-1}), accumulates
R0->>R1: send K₃V₃
R1->>R2: send K₀V₀
R2->>R3: send K₁V₁
R3->>R0: send K₂V₂
Note over R0,R3: Steps 2…N-1 — continue rotation; accumulate partial scores
Note over R0,R3: Final — each rank holds correct output slice Oᵣ
The critical property is that partial attention accumulation uses the online softmax formulation, so partial scores computed on different KV shards combine correctly into the final normalized output without a separate reduction pass.
Step 1: Define the model and sequence layout
The model must route its attention computation through torch.nn.functional.scaled_dot_product_attention (SDPA) for the context_parallel() context manager to intercept it. Any model using a custom CUDA attention kernel that bypasses the SDPA dispatch path will not automatically benefit from Context Parallel without modification.
This makes the step a model-compatibility check rather than a model-selection endorsement: verify the attention path in the model you already plan to train, then confirm that max_position_embeddings or the equivalent positional limit covers the target sequence length before initialization.
import torch
from transformers import LlamaConfig, LlamaModel
config = LlamaConfig()
model = LlamaModel(config).to(torch.bfloat16).cuda()
Choose a partitionable long-context architecture
A transformer is compatible with Context Parallel if it satisfies three structural conditions:
- SDPA dispatch path — attention must call
torch.nn.functional.scaled_dot_product_attention, not a fused kernel that bypasses PyTorch dispatch - Causal or full attention — causal masks work natively; sliding-window attention requires verifying that the window does not exceed the per-rank slice size
- Sequence-first or batch-first tensor layout — Context Parallel shards on the sequence dimension, so the model must accept
(batch, seq, heads, dim)or(seq, batch, ...)without internal reshapes that reconstitute the full sequence
# Verify the attention module dispatches through SDPA before wrapping
import torch.nn.functional as F
def check_sdpa_dispatch(model):
# A simple probe: replace F.scaled_dot_product_attention temporarily
# and confirm the model calls it during a forward pass.
call_log = []
original = F.scaled_dot_product_attention
def probe(*args, **kwargs):
call_log.append(True)
return original(*args, **kwargs)
F.scaled_dot_product_attention = probe
dummy_input = torch.randint(0, 1000, (1, 64)).cuda()
with torch.no_grad():
model(dummy_input)
F.scaled_dot_product_attention = original
assert len(call_log) > 0, "Model does not route through SDPA — Context Parallel will not apply"
print(f"SDPA called {len(call_log)} times per forward pass")
check_sdpa_dispatch(model)
When composing with Tensor Parallelism (TP) or Pipeline Parallelism (PP), apply Context Parallel as the innermost parallelism — CP shards the sequence dimension, TP shards the head/model dimension, and PP shards layers. TorchTitan codifies this 4D parallelism composition for Llama training, and the same planning can be extended in adjacent stacks such as DeepSpeed-Ulysses-Attention when sequence parallelism is the governing constraint.
Step 2: Create the distributed process group
The Context Parallel API requires a ProcessGroup object — it does not automatically use the default process group, so you must create and pass a dedicated group. This is the most commonly missing piece that fragments the docs experience: the tutorial shows the API but the group construction lives in surrounding boilerplate.
# Launch 4 ranks on a single node
$ torchrun --standalone --nnodes=1 --nproc-per-node=4 train_cp.py
import os
import torch
import torch.distributed as dist
def init_distributed():
dist.init_process_group(backend="nccl") # NCCL required; Gloo will fail
rank = dist.get_rank()
world_size = dist.get_world_size()
local_rank = int(os.environ["LOCAL_RANK"])
# Bind each rank to its GPU before any tensor allocation
torch.cuda.set_device(local_rank)
# Create a dedicated process group for Context Parallel.
# For a pure CP run, all ranks form one CP group.
# In a 4D-parallel setup, you would create sub-groups per CP dimension.
cp_ranks = list(range(world_size))
cp_group = dist.new_group(ranks=cp_ranks, backend="nccl")
return rank, world_size, cp_group
Set rank-local devices and world size
Each rank must bind to exactly one GPU before allocating any tensors or initializing the model. Binding after tensor allocation causes NCCL to operate on tensors whose storage lives on the wrong device, producing either incorrect results or a silent hang at the first collective.
def setup_rank_device():
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
# Bind to local GPU — LOCAL_RANK is the per-node index
torch.cuda.set_device(local_rank)
device = torch.device(f"cuda:{local_rank}")
assert world_size == dist.get_world_size(), (
f"World size mismatch: env says {world_size}, "
f"dist says {dist.get_world_size()}. This will deadlock."
)
return rank, local_rank, world_size, device
Watch Out: On multi-node runs,
RANKis the global rank andLOCAL_RANKis the per-node rank. Always bindtorch.cuda.set_devicetoLOCAL_RANK, notRANK. UsingRANKon a 2-node 4-GPU-per-node setup will map ranks 4–7 to non-existent GPU indices on node 1.
Step 3: Wire Context Parallel into the training loop
The context_parallel() context manager wraps the forward pass of the attention module. It intercepts the SDPA call and substitutes the Ring Attention implementation for that scope. The model itself does not change — only the dispatch behavior of F.scaled_dot_product_attention within that Python context.
import torch
import torch.distributed as dist
from torch.distributed.tensor.experimental import context_parallel
def train_step(model, batch, cp_group, optimizer):
# batch["input_ids"] shape: (batch_size, full_seq_len)
# Context Parallel shards seq_len across cp_group ranks internally
input_ids = batch["input_ids"].cuda()
labels = batch["labels"].cuda()
optimizer.zero_grad()
# The context_parallel() context manager intercepts F.scaled_dot_product_attention
# inside this scope and replaces it with Ring Attention across cp_group ranks.
with context_parallel(cp_group):
outputs = model(input_ids=input_ids, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
return loss.item()
Wrap attention with the context-parallel path
The context manager applies at the Python scope level, not at the module level. Every F.scaled_dot_product_attention call within the with context_parallel(cp_group): block is intercepted. This means the wrapper applies naturally across all attention layers of a multi-layer transformer in one context scope — you do not wrap each layer individually.
# Minimal illustration of the API surface — single attention call
from torch.distributed.tensor.experimental import context_parallel
import torch.nn.functional as F
def forward_with_cp(query, key, value, cp_group):
# query/key/value are full-length tensors on entry;
# context_parallel shards them internally along the sequence dimension
with context_parallel(cp_group):
output = F.scaled_dot_product_attention(
query, key, value,
attn_mask=None,
dropout_p=0.0,
is_causal=True, # causal mask works natively with CP
)
return output
Handle KV exchange and synchronization
The ring KV exchange is managed internally by PyTorch's Ring Attention implementation — you do not write explicit dist.send/dist.recv calls. The synchronization boundary is implicit: the context_parallel() context manager ensures all NCCL P2P operations complete before returning the attention output.
# If you need to inspect the communication pattern, enable NCCL debug logging
# before launching — this produces ring-topology diagnostics without code changes.
# Set in your environment before calling torchrun:
# NCCL_DEBUG=INFO NCCL_DEBUG_SUBSYS=ALL
# After the training step, verify no NCCL operations are pending
def check_nccl_sync(cp_group):
# A lightweight barrier confirms all ranks exited the CP context cleanly
dist.barrier(group=cp_group)
Watch Out: Do not mix
context_parallel()blocks with manualtorch.distributed.all_reducecalls on activations within the same group in the same forward pass. The Ring Attention implementation assumes exclusive use of the CP group's communication bandwidth during the attention step. Competing collectives will cause incorrect KV routing or a deadlock.
Step 4: Verify correctness before scaling up
Run a parity check on a short sequence (≤2048 tokens fits on a single GPU) before any long-context run. The reference is a single-device forward pass with no parallelism; the distributed run should produce outputs within floating-point tolerance for BF16.
$ torchrun --standalone --nnodes=1 --nproc-per-node=4 train_cp.py --verify-parity
Check output parity on a short sequence
import torch
import torch.distributed as dist
from torch.distributed.tensor.experimental import context_parallel
import torch.nn.functional as F
def parity_check(rank, cp_group, seq_len=512, d_model=64, n_heads=4):
torch.manual_seed(42) # same seed on all ranks for reproducible input
head_dim = d_model // n_heads
# Full tensors on every rank for reference comparison
q = torch.randn(1, n_heads, seq_len, head_dim, device="cuda", dtype=torch.float32)
k = torch.randn(1, n_heads, seq_len, head_dim, device="cuda", dtype=torch.float32)
v = torch.randn(1, n_heads, seq_len, head_dim, device="cuda", dtype=torch.float32)
# Single-device reference (only rank 0 computes; others receive)
ref_out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
# Distributed Context Parallel output
with context_parallel(cp_group):
cp_out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
# Each rank holds its slice of cp_out; gather to rank 0 for comparison
gathered = [torch.zeros_like(cp_out) for _ in range(dist.get_world_size(cp_group))]
dist.all_gather(gathered, cp_out, group=cp_group)
cp_full = torch.cat(gathered, dim=2) # reassemble along seq dim
if rank == 0:
max_diff = (ref_out - cp_full).abs().max().item()
print(f"Parity check passed. Max absolute diff: {max_diff:.2e}")
Watch memory and communication counters
Memory reduction is the primary signal that sequence sharding is working. Each rank should hold roughly $1/N$ of the full KV activation memory. Use PyTorch's memory stats and NCCL's built-in counters to confirm this before committing to a long run.
| Metric | 1 GPU (baseline) | 4 GPUs CP | 8 GPUs CP | Notes |
|---|---|---|---|---|
| KV activation memory per rank | 100% | ~25% | ~12.5% | Scales as 1/cp_degree |
| NCCL KV transfer per layer | 0 | 2 × KV shard | 2 × KV shard | Send + recv per ring step |
| Ring steps per attention layer | 0 | N−1 = 3 | N−1 = 7 | Increases with cp_degree |
Pro Tip: Run
torch.cuda.memory_summary(device="cuda")after the first training step to confirm per-rank peak memory dropped proportionally to cp_degree. If peak memory did not decrease, the model's attention path is not routing through SDPA and the context manager is not applying.
Common pitfalls when training long-context models
The Ring Attention paper's communication model assumes KV blocks transfer latency is fully overlapped by local compute. At real sequence lengths this holds on NVLink and InfiniBand; it breaks down when network-adjacent PCIe or Ethernet introduces high-latency collectives. The failure modes split between hard crashes (NCCL errors, OOM) and silent correctness failures (wrong outputs with no error raised).
Process-group misconfiguration and deadlocks
Distributed jobs hang at the first NCCL collective when the process group is misconfigured. Common causes: wrong backend, rank count not matching --nproc-per-node, or a group created after one rank has already exited a barrier.
Watch Out: If your job hangs indefinitely without an error message, the most likely cause is a collective operation that one or more ranks never reach. Set
TORCH_NCCL_BLOCKING_WAIT=1to convert the hang into a timeout error with a traceback.
# Diagnostic flags to set before launching a hung job
$ NCCL_DEBUG=INFO \
NCCL_DEBUG_SUBSYS=INIT,GRAPH \
TORCH_NCCL_BLOCKING_WAIT=1 \
TORCH_DISTRIBUTED_DEBUG=DETAIL \
torchrun --standalone --nnodes=1 --nproc-per-node=4 train_cp.py
NCCL_DEBUG=INFO prints the ring topology detected at init time — confirm that all ranks appear in the ring before the first training step. TORCH_DISTRIBUTED_DEBUG=DETAIL logs every collective call with its expected participants, which isolates the first operation where ranks diverge.
When context parallel is the wrong fit
Context Parallel is the right primitive when the bottleneck is sequence length exceeding per-device memory. It is the wrong primitive in three scenarios:
Pro Tip: Choose the parallelism strategy that matches your bottleneck. CP solves sequence-length memory; TP solves model-width compute; PP solves model-depth memory; FSDP2 solves parameter/optimizer-state memory. Mismatching strategy to bottleneck adds communication overhead without solving the actual constraint.
# Decision sketch — not a runtime check, but a configuration guide
# Use Context Parallel when:
# seq_len > per_device_kv_memory_budget / (2 * n_heads * head_dim * sizeof(dtype))
# AND you have fast interconnect (NVLink or IB)
# Use Tensor Parallelism (TP) when:
# model is wide (large hidden_size) and fits in layers but not in width per device
# AND per-layer AllReduce cost is acceptable on your fabric
# Use Pipeline Parallelism (PP) when:
# model depth (layer count) is the primary memory constraint
# AND you can tolerate pipeline bubble overhead (typically 1/pp_degree efficiency loss)
# Use FSDP2 when:
# parameter + optimizer state memory is the constraint (not activation memory)
# AND sequences are short enough that standard attention is feasible per device
In practice, Llama 3-8B with sequence lengths under 32K fits on a single H100-80GB with FSDP2 sharding across 8 ranks — adding CP at short context only introduces ring communication overhead with no memory benefit. Reserve CP for sequence lengths where per-rank KV memory drops below the threshold your batch size requires.
Production considerations for 1M-token training runs
Moving from a 4-GPU smoke test to 1M-token training is not primarily a software change — it is a cluster and cost planning problem. A related Blockwise RingAttention training paper reports "gradually increase context size from 4K to 1M tokens" as their curriculum strategy, which is the correct engineering approach: validate at shorter lengths and grow context progressively rather than targeting 1M from epoch 1.
A separate inference study (Context Parallelism for Scalable Million-Token Inference) reports "near-linear scaling for long-context prefill latency with up to 128 H100 GPUs across 16 nodes." This result is from inference prefill, not end-to-end training, and it is adjacent evidence for the ring communication primitive rather than proof of training throughput.
Production Note: TorchTitan's 4D parallelism recipe composes CP with TP, PP, and FSDP2 for Llama training. If you are building a production training stack rather than a research prototype, start from TorchTitan's configuration rather than assembling the four parallelism dimensions from scratch.
Cost, wall-clock time, and cluster planning
| Context length | Min. CP degree | Min. GPUs (H100-80GB) | KV mem per rank | Estimated ring comm overhead |
|---|---|---|---|---|
| 32K tokens | 1 (no CP) | 1 | ~4 GB | None |
| 128K tokens | 4 | 4 | ~4 GB | Low (NVLink) / Medium (IB) |
| 512K tokens | 16 | 16 | ~4 GB | Medium (NVLink) / High (IB) |
| 1M tokens | 32–64 | 32–64 | ~4–8 GB | High — IB HDR200+ required |
Memory figures are order-of-magnitude estimates based on the Ring Attention paper's memory model (>1000 GB for hidden size 1024 at 100M tokens) scaled to realistic hidden sizes; your actual numbers depend on model width, dtype, and batch size. The "min. CP degree" assumes you want to keep KV activations under ~8 GB per rank for a model with hidden size ~4096 and 32 heads.
Questions readers ask before adopting Context Parallel
Context Parallel occupies a specific niche in the PyTorch distributed stack. The PyTorch docs position it alongside TP, PP, and FSDP2 as a complementary primitive rather than a replacement. The key questions before adoption are about fit, not just feasibility.
Is Context Parallel faster than standard data parallel training?
No. Context Parallel adds ring KV communication that data parallel training does not have, so it is not a general speedup at standard sequence lengths. Its benefit is that it makes sequences fit when the full KV tensor would otherwise exceed device memory.
Pro Tip: Use CP only when per-device KV activation memory is the binding constraint. If your model already fits at the target sequence length with FSDP2 or standard DDP, keep the simpler parallelism stack and avoid the extra ring traffic.
Can PyTorch Context Parallel train 1M-token sequences?
Yes, with sufficient hardware and a training curriculum that increases context length in stages. The Ring Attention paper reports training sequences that exceed 100 million tokens without approximations, and the Blockwise RingAttention training work describes a progression from 4K to 1M tokens. PyTorch's context_parallel() uses the same ring-based attention primitive, so the same hardware and curriculum constraints apply.
Pro Tip: Treat 1M tokens as an upper bound to engineer toward, not a switch to flip. In practice, long-context runs need 32–64 H100s with InfiniBand, progressive context-length ramping, gradient checkpointing, and loss/NaN monitoring from the first step.
Sources and References
- Introduction to Context Parallel — PyTorch 2.12.0+cu130 (unstable) — Primary source for the
context_parallel()API, launch command, and NCCL requirements - Ring Attention with Blockwise Transformers for Near-Infinite Context — arXiv:2310.01889 — Original Ring Attention paper; source for memory estimates and sequence-length claims
- World Model on Million-Length Video And Language With Blockwise RingAttention — arXiv:2402.08268 — Practical 1M-token training curriculum reference
- Context Parallelism for Scalable Million-Token Inference — arXiv:2411.01783 — Near-linear scaling result on 128 H100 GPUs (inference prefill)
- TorchTitan — PyTorch 4D Parallelism Training Reference — Production-grade composition of CP, TP, PP, and FSDP2 for Llama training
- DeepSpeed-Ulysses-Attention — Adjacent sequence-parallel attention stack for comparison with CP in broader training-system planning
Keywords: PyTorch 2.12.0+cu130, PyTorch Distributed, torch.distributed, NCCL, Ring Attention, TorchTitan, Llama 3-8B, FSDP2, Tensor Parallelism, Pipeline Parallelism, NVIDIA H100, InfiniBand, DeepSpeed-Ulysses-Attention, Blockwise Transformers
