step_train/src/utils/dist_utils.py

"""Manual data-parallel helpers for 8-GPU training (torchrun).

Why manual all-reduce instead of DistributedDataParallel:
  The training loops here are non-standard for DDP —
    * FlexQwen3 has a custom forward signature (no attention_mask kwarg) and the
      per-user cartridge KV-prefix is passed via `self.cache`, NOT through the
      module args DDP intercepts.
    * One optimizer step spans MANY forward+backward passes (cross-user grad
      accumulation; DAPO recomputes logprobs per trajectory). DDP's autograd hooks
      assume one backward per forward and would all-reduce on every micro-backward,
      or require `no_sync()` gymnastics that interact badly with `cache.clear()`.
  So each rank holds a FULL model on its own GPU, trains on a disjoint data shard,
  and we all-reduce the (tiny — LoRA-only) gradients ONCE right before each step.
  This reproduces DDP's gradient math exactly while sidestepping every fragility.

Collective-safety rule (deadlock avoidance): every rank MUST call each collective
the same number of times. Callers guarantee this by (a) truncating per-rank shards
to a common length, and (b) gating step/skip decisions on globally-reduced scalars,
never on per-rank-local counts.

When WORLD_SIZE<=1 (plain `python ...`) every function is a no-op and behaviour is
identical to the original single-GPU code.
"""

import os
from typing import Iterable

import torch

try:
    import torch.distributed as dist
    _DIST_IMPORTABLE = True
except Exception:  # pragma: no cover
    dist = None
    _DIST_IMPORTABLE = False


def _env_world_size() -> int:
    try:
        return int(os.environ.get("WORLD_SIZE", "1"))
    except ValueError:
        return 1


def setup_distributed():
    """Init the process group from torchrun env vars.

    Returns a dict: {rank, local_rank, world_size, is_distributed}. Safe to call
    when not launched under torchrun (returns the single-process context).
    """
    world_size = _env_world_size()
    if not _DIST_IMPORTABLE or world_size <= 1:
        return {"rank": 0, "local_rank": 0, "world_size": 1, "is_distributed": False}

    rank = int(os.environ.get("RANK", "0"))
    local_rank = int(os.environ.get("LOCAL_RANK", str(rank)))
    backend = "nccl" if torch.cuda.is_available() else "gloo"
    if not dist.is_initialized():
        # 🔴 Collective timeout sized for rank skew, NOT for OOM. DAPO's phase-1 rollout has
        # NO collective — each rank independently runs per_rank_anchors×K rollouts whose
        # sequence lengths (+ flex_attention compile cost) vary, so the fastest rank can
        # reach the phase-2 all_reduce ~10min before the slowest. NCCL's default 600s
        # watchdog would abort on that skew. We set NCCL_TIMEOUT_MIN=20 (step ~14min,
        # measured skew ~10min → 20min covers it with margin). It does NOT need to cover
        # single-rank OOM hangs anymore — those are handled by the OOM-tolerant symmetric
        # skip (all_reduce_flag), so no rank is ever left waiting on a crashed peer. A
        # tighter 20min also means a genuine hang is detected in ~20min, not an hour.
        from datetime import timedelta
        timeout_min = int(os.environ.get("NCCL_TIMEOUT_MIN", "20"))
        dist.init_process_group(backend=backend, timeout=timedelta(minutes=timeout_min))
    if torch.cuda.is_available():
        torch.cuda.set_device(local_rank)
    return {
        "rank": rank,
        "local_rank": local_rank,
        "world_size": world_size,
        "is_distributed": True,
    }


def is_initialized() -> bool:
    return bool(_DIST_IMPORTABLE and dist.is_available() and dist.is_initialized())


def get_rank() -> int:
    return dist.get_rank() if is_initialized() else 0


def get_world_size() -> int:
    return dist.get_world_size() if is_initialized() else 1


def is_main_process() -> bool:
    return get_rank() == 0


def barrier():
    if is_initialized():
        dist.barrier()


def cleanup_distributed():
    if is_initialized():
        dist.destroy_process_group()


def _reduce_device() -> str:
    return "cuda" if torch.cuda.is_available() else "cpu"


def all_reduce_value(value, op: str = "sum"):
    """All-reduce a python scalar across ranks. Returns the reduced python float.

    op in {"sum","mean","min","max"}. No-op (returns value) when single-process.
    """
    if not is_initialized():
        return value
    t = torch.tensor([float(value)], dtype=torch.float64, device=_reduce_device())
    op_map = {
        "sum": dist.ReduceOp.SUM,
        "mean": dist.ReduceOp.SUM,
        "min": dist.ReduceOp.MIN,
        "max": dist.ReduceOp.MAX,
    }
    dist.all_reduce(t, op=op_map[op])
    if op == "mean":
        t /= get_world_size()
    return t.item()


def all_reduce_flag(local_flag: bool) -> bool:
    """Global logical-OR of a boolean across ranks: returns True iff ANY rank passed True.

    Implemented as MAX over {0.0, 1.0}. Used for OOM synchronization — if any rank hit a
    CUDA OOM this step, ALL ranks learn it and skip the optimizer step together, keeping
    the per-step collective count identical on every rank (no NCCL desync). No-op
    (returns local_flag) when single-process.
    """
    return bool(all_reduce_value(1.0 if local_flag else 0.0, op="max"))


def all_reduce_floats(values, op: str = "sum"):
    """All-reduce a LIST of python floats in ONE collective. Returns a python list.

    Used to reduce many per-MS sum/count scalars at once (e.g. reward_sum/reward_cnt
    for SM/PM/VM/NM) without issuing one collective per key. The list length and order
    MUST be identical across ranks (callers build it from a fixed key order). No-op
    (returns list(values)) when single-process.
    """
    vals = [float(v) for v in values]
    if not is_initialized() or not vals:
        return vals
    t = torch.tensor(vals, dtype=torch.float64, device=_reduce_device())
    op_map = {"sum": dist.ReduceOp.SUM, "mean": dist.ReduceOp.SUM,
              "min": dist.ReduceOp.MIN, "max": dist.ReduceOp.MAX}
    dist.all_reduce(t, op=op_map[op])
    if op == "mean":
        t /= get_world_size()
    return t.tolist()


def _grad_chunk_numel() -> int:
    """Maximum number of gradient elements per coalesced all-reduce chunk.

    LoRA runs stay as one collective because their total grad size is far below this.
    Full-FT Qwen2.5 has ~7.6B trainable elements; one flattened all-reduce would allocate
    a huge contiguous buffer and enqueue a single 7.6B-element NCCL op. Chunking keeps the
    collective order deterministic while reducing peak temporary memory and making NCCL
    progress easier to diagnose. Set MANUAL_DP_GRAD_CHUNK_NUMEL=0 to restore one buffer.
    """
    try:
        return int(os.environ.get("MANUAL_DP_GRAD_CHUNK_NUMEL", "250000000"))
    except ValueError:
        return 250000000


def _iter_param_chunks(plist):
    max_numel = _grad_chunk_numel()
    if max_numel <= 0:
        yield plist
        return
    chunk = []
    n = 0
    for p in plist:
        p_numel = p.grad.numel()
        if chunk and n + p_numel > max_numel:
            yield chunk
            chunk = []
            n = 0
        chunk.append(p)
        n += p_numel
    if chunk:
        yield chunk


def all_reduce_grads(params: Iterable[torch.nn.Parameter], op: str = "sum"):
    """In-place all-reduce of `.grad` over ranks (SUM by default), coalesced in a
    deterministic set of chunks.

    🔴 Call AFTER local backward/accumulation and BEFORE grad-clip + optimizer.step,
    so the clip operates on the synced gradient and every rank steps with identical
    grads (weights stay bit-identical across ranks).

    🔴 Coalesced chunks (not one per param): we flatten consecutive grads into contiguous
    buffers, all_reduce each buffer, then copy back. Two reasons:
      (1) Correctness/robustness: the chunks are derived solely from the fixed param list
          and MANUAL_DP_GRAD_CHUNK_NUMEL, so every rank calls the same collectives in the
          same order. Per-param all_reduce would expose NCCL to param-list drift.
      (2) Memory: LoRA still uses one small buffer, while full-FT avoids a single
          7.6B-element temporary buffer / NCCL op.
    `params` MUST be the SAME fixed list (same order, same length) on every rank — the
    caller guarantees this via a name-sorted cached list. grad=None → zero-filled so the
    buffer layout is identical across ranks even when a rank produced no gradient.
    """
    if not is_initialized():
        return
    plist = list(params)
    for p in plist:
        if p.grad is None:
            p.grad = torch.zeros_like(p)
    for chunk in _iter_param_chunks(plist):
        grads = [p.grad for p in chunk]
        flat = torch._utils._flatten_dense_tensors(grads)
        dist.all_reduce(flat, op=dist.ReduceOp.SUM)
        if op == "mean":
            flat /= get_world_size()
        for p, synced in zip(chunk, torch._utils._unflatten_dense_tensors(flat, grads)):
            p.grad.copy_(synced)


def build_zero_optimizer(params, lr: float, weight_decay: float = 0.0):
    """Build an AdamW optimizer, ZeRO-1-sharded across ranks when distributed.

    For FULL fine-tuning the AdamW optimizer state of a 7B model (~90GB fp32 m/v) cannot
    fit on one 80GB GPU. ZeroRedundancyOptimizer (torch built-in ZeRO stage 1) shards the
    optimizer STATE across ranks: each rank owns AdamW state for ~1/world_size of the
    params, runs step() only on its shard, then all_gathers the updated params so every
    rank ends with identical weights.

    Compatibility with the existing manual all-reduce scheme: grads are still produced on
    EVERY param on EVERY rank and synced by all_reduce_grads(op="sum") BEFORE step(). ZeRO
    does NOT touch gradient computation/sync — it only partitions the optimizer UPDATE. So
    clip_grad_norm_ over the full param list (identical synced grads on every rank) stays
    correct and the gradient math is unchanged. (LoRA mode keeps plain AdamW; this helper
    is only used by the full-FT branch.)

    Single-process (world_size<=1) → plain AdamW (no ZeRO machinery, identical behaviour).
    """
    plist = list(params)
    if not is_initialized() or get_world_size() <= 1:
        return torch.optim.AdamW(plist, lr=lr, weight_decay=weight_decay)
    from torch.distributed.optim import ZeroRedundancyOptimizer
    return ZeroRedundancyOptimizer(
        plist,
        optimizer_class=torch.optim.AdamW,
        lr=lr,
        weight_decay=weight_decay,
    )


def zero_optimizer_full_state_dict(optimizer):
    """Return the FULL (unsharded) optimizer state_dict on rank 0, None elsewhere.

    ZeroRedundancyOptimizer shards optimizer state across ranks. consolidate_state_dict(to=0)
    is a COLLECTIVE (every rank must call it) that gathers all shards onto rank 0. After it,
    ONLY rank 0 may call state_dict() — non-zero ranks raise "Optimizer state has not been
    consolidated on this rank". So we consolidate on all ranks but return the dict on rank 0
    only (callers save on rank 0 anyway). Plain AdamW (no consolidate) → state_dict() direct.
    """
    if hasattr(optimizer, "consolidate_state_dict"):
        optimizer.consolidate_state_dict(to=0)  # collective — all ranks must call
        return optimizer.state_dict() if get_rank() == 0 else None
    return optimizer.state_dict()