ViperEkura
45cc048fe9
- shard the Muon Newton-Schulz orthogonalization over the FSDP mesh instead of partial local slices - import HF checkpoints faithfully: per-head RoPE permutation for q/k projections and qk-norm, qwen3, shared experts, and qk-norm before RoPE (changes numerics for existing use_qk_norm checkpoints) - make preprocessing and resume self-contained: backfill realigned bucket keys by semantics (masks ones, rest zeros) and snapshot tokenizer files into every checkpoint - keep RL consistent: sync the offline GRPO old_model each optimizer step and validate online strategies through a public one-off-rollout hook that leaves the replay cache untouched - fix streaming serving: withhold partial tool-call prefixes with a stream-end flush, stream tool-call arguments from the raw source span, and terminate SSE frames with a blank line - fix sampling semantics: capture logprobs before top-k/top-p mutate logits in place and detect greedy pipelines polymorphically instead of isinstance bookkeeping
460 lines
15 KiB
Python
460 lines
15 KiB
Python
"""Unified training executor — parallel strategy + gradient accumulation."""
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import contextlib
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import logging
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import os
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from contextlib import contextmanager
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from typing import Any, Callable, Dict, Optional, Tuple
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.distributed.fsdp import (
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FSDPModule,
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fully_shard,
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)
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from torch.distributed.tensor import DTensor
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LRScheduler
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from astrai.factory import BaseFactory
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from astrai.parallel.setup import get_rank, get_world_size
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logger = logging.getLogger(__name__)
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_COMPILE_PREFIX = "_orig_mod."
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def strip_compile_prefix(
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state_dict: Dict[str, torch.Tensor],
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) -> Dict[str, torch.Tensor]:
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"""Drop the ``_orig_mod.`` key prefix ``torch.compile`` adds.
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``OptimizedModule.state_dict()`` prefixes every key, so checkpoints or
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reference-model copies taken from a compiled model fail to load into a
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plain module (strict) or silently load nothing (non-strict). Stripping
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here, at the single source every consumer reads from, keeps saved keys
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canonical regardless of compile mode.
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"""
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if any(key.startswith(_COMPILE_PREFIX) for key in state_dict):
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state_dict = {
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key.removeprefix(_COMPILE_PREFIX): value
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for key, value in state_dict.items()
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}
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return state_dict
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def broadcast_state_dict(
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state_dict: Optional[Dict[str, torch.Tensor]],
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src: int = 0,
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) -> Optional[Dict[str, torch.Tensor]]:
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"""Broadcast a state_dict from *src* rank to all ranks.
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Tensors stay on their original device (GPU) for the broadcast.
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All ranks must call this collectively.
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On non-distributed runs, returns *state_dict* unchanged.
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"""
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if not dist.is_initialized() or dist.get_world_size() == 1:
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return state_dict
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rank = dist.get_rank()
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# Broadcast metadata (keys, shapes, dtypes, device kind) so non-src
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# ranks can allocate matching empty tensors on their own device.
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if rank == src:
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first = next(iter(state_dict.values()), None)
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on_cuda = first is not None and first.is_cuda
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metadata = [
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(k, tuple(v.shape), v.dtype, on_cuda) for k, v in state_dict.items()
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]
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else:
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metadata = None
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metadata_list = [metadata]
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dist.broadcast_object_list(metadata_list, src=src)
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metadata = metadata_list[0]
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# Non-src ranks allocate empty tensors with the broadcasted metadata.
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# The allocation must sit on *this* rank's local device: process groups
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# are pinned per-rank via ``device_id=``, so NCCL rejects a tensor
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# allocated on the src rank's device (and the cross-GPU allocation
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# would be wrong here anyway).
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if rank != src:
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device = (
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torch.device("cuda", torch.cuda.current_device())
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if any(m[3] for m in metadata)
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else torch.device("cpu")
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)
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state_dict = {
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k: torch.empty(s, dtype=d, device=device) for k, s, d, _ in metadata
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}
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# Broadcast each tensor in-place.
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for tensor in state_dict.values():
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dist.broadcast(tensor, src=src)
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return state_dict
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def create_ref_model(
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model_fn: Callable[[], nn.Module],
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executor: Optional["BaseExecutor"] = None,
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model: Optional[nn.Module] = None,
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state_dict: Optional[Dict[str, torch.Tensor]] = None,
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device: Optional[str] = None,
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) -> Optional[nn.Module]:
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"""Create a frozen reference model from executor or state dict.
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In distributed mode (FSDP), ``unwrap_model`` returns ``None`` on
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non-rank-0. The state_dict is broadcast from rank-0 to all ranks
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so every rank gets a complete copy.
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"""
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if state_dict is None and executor is not None and model is not None:
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state_dict = executor.unwrap_model(model)
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# FSDP's unwrap_model returns None on non-rank-0. Broadcast from
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# rank-0 so every rank receives a complete state_dict.
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if executor is not None and executor.use_distributed:
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state_dict = broadcast_state_dict(state_dict)
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if state_dict is None:
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return None
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state_dict = strip_compile_prefix(state_dict)
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ref_model = model_fn()
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ref_model.load_state_dict(state_dict)
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ref_model.requires_grad_(False)
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ref_model.eval()
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if device is not None:
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ref_model = ref_model.to(device=device)
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return ref_model
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class GradientState:
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def __init__(self, grad_accum_steps: int = 1):
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self.num_steps = max(grad_accum_steps, 1)
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self._step: int = 0
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self._sync_gradients: bool = True
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@property
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def sync_gradients(self) -> bool:
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return self._sync_gradients
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def _do_sync(self):
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self._step += 1
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self._sync_gradients = self._step % self.num_steps == 0
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class AccumOptimizer:
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def __init__(self, optimizer: Optimizer, gradient_state: GradientState):
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self.optimizer = optimizer
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self.gradient_state = gradient_state
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def step(self, closure=None):
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if self.gradient_state.sync_gradients:
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self.optimizer.step(closure)
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def zero_grad(self):
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if self.gradient_state.sync_gradients:
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self.optimizer.zero_grad()
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@property
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def param_groups(self):
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return self.optimizer.param_groups
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def state_dict(self):
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return self.optimizer.state_dict()
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def load_state_dict(self, d):
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self.optimizer.load_state_dict(d)
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class AccumScheduler:
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def __init__(self, scheduler: LRScheduler, gradient_state: GradientState):
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self.scheduler = scheduler
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self.gradient_state = gradient_state
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def step(self):
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if self.gradient_state.sync_gradients:
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self.scheduler.step()
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def state_dict(self):
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return self.scheduler.state_dict()
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def load_state_dict(self, d):
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self.scheduler.load_state_dict(d)
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def get_last_lr(self):
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return self.scheduler.get_last_lr()
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class BaseExecutor:
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def __init__(self, grad_accum_steps: int = 1):
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self.gradient_state = GradientState(grad_accum_steps)
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def prepare(
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self,
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model_fn: Callable[[], nn.Module],
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optimizer_fn: Optional[Callable[[nn.Module], Optimizer]] = None,
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scheduler_fn: Optional[Callable[[Optimizer], LRScheduler]] = None,
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before_wrap: Optional[Callable[[nn.Module], nn.Module]] = None,
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after_wrap: Optional[Callable[[nn.Module], nn.Module]] = None,
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) -> Tuple[nn.Module, Optional[Optimizer], Optional[LRScheduler]]:
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model = model_fn()
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if before_wrap is not None:
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model = before_wrap(model)
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model = self._prepare_model(model)
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if after_wrap is not None:
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model = after_wrap(model)
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optimizer = None
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scheduler = None
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if optimizer_fn is not None:
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optimizer = optimizer_fn(model)
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if scheduler_fn is not None:
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scheduler = scheduler_fn(optimizer)
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optimizer = AccumOptimizer(optimizer, self.gradient_state)
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if scheduler is not None:
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scheduler = AccumScheduler(scheduler, self.gradient_state)
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return model, optimizer, scheduler
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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return model
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def _no_sync(self, model: nn.Module):
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return contextlib.nullcontext()
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@contextmanager
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def accumulate(self, model: nn.Module):
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self.gradient_state._do_sync()
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if not self.gradient_state.sync_gradients:
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with self._no_sync(model):
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yield
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else:
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yield
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def backward(self, loss: torch.Tensor):
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loss.backward()
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def unwrap_model(self, model: nn.Module):
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return strip_compile_prefix(model.state_dict())
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@contextmanager
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def checkpoint_context(self, model: nn.Module):
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if self.use_distributed:
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dist.barrier()
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state_dict = self._gather_state_dict(model)
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yield state_dict
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if self.use_distributed:
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dist.barrier()
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def _gather_state_dict(self, model: nn.Module):
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state_dict = self.unwrap_model(model)
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if self.use_distributed and get_rank() != 0:
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return None
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return state_dict
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@property
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def use_distributed(self) -> bool:
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return get_world_size() > 1
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@property
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def sync_gradients(self) -> bool:
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return self.gradient_state.sync_gradients
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@property
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def grad_accum_steps(self) -> int:
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return self.gradient_state.num_steps
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def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
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total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
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if isinstance(total_norm, torch.Tensor):
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return total_norm.item()
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return total_norm
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class ExecutorFactory(BaseFactory[BaseExecutor]):
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pass
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@ExecutorFactory.register("none")
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class NoneExecutor(BaseExecutor):
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pass
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@ExecutorFactory.register("ddp")
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class DDPExecutor(BaseExecutor):
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def __init__(
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self,
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grad_accum_steps: int = 1,
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dim: int = 0,
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broadcast_buffers: bool = True,
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init_sync: bool = True,
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process_group=None,
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bucket_cap_mb: int = 25,
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find_unused_parameters: bool = False,
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check_reduction: bool = False,
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gradient_as_bucket_view: bool = False,
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static_graph: bool = False,
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delay_all_reduce_named_params=None,
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param_to_hook_all_reduce=None,
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mixed_precision=None,
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device_mesh=None,
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):
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super().__init__(grad_accum_steps=grad_accum_steps)
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self._ddp_kwargs = dict(
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dim=dim,
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broadcast_buffers=broadcast_buffers,
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init_sync=init_sync,
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process_group=process_group,
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bucket_cap_mb=bucket_cap_mb,
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find_unused_parameters=find_unused_parameters,
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check_reduction=check_reduction,
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gradient_as_bucket_view=gradient_as_bucket_view,
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static_graph=static_graph,
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delay_all_reduce_named_params=delay_all_reduce_named_params,
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param_to_hook_all_reduce=param_to_hook_all_reduce,
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mixed_precision=mixed_precision,
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device_mesh=device_mesh,
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)
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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if not self.use_distributed:
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logger.warning("DDP backend selected but world_size=1, model not wrapped")
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return model
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local_rank = int(os.environ.get("LOCAL_RANK", get_rank()))
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model = DDP(
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model,
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device_ids=[local_rank],
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output_device=local_rank,
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**self._ddp_kwargs,
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)
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logger.info("Model wrapped with DDP (world_size=%d)", get_world_size())
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return model
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def _no_sync(self, model: nn.Module):
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if isinstance(model, DDP):
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return model.no_sync()
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return contextlib.nullcontext()
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def unwrap_model(self, model: nn.Module):
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if isinstance(model, DDP):
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return strip_compile_prefix(model.module.state_dict())
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return strip_compile_prefix(model.state_dict())
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@ExecutorFactory.register("fsdp")
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class FSDPExecutor(BaseExecutor):
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"""FSDP executor using `torch.distributed.fsdp.fully_shard` (per-module API).
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Wraps each child module individually via ``fully_shard``.
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Skips the root model because ``ABC + Generic[T]`` in the MRO makes
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``fully_shard``'s dynamic ``__class__`` assignment fail at the CPython level.
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Original ``Parameter`` objects are preserved (as DTensors) — no
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``FlatParameter``, no ``use_orig_params=True`` hack.
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"""
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def __init__(
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self,
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grad_accum_steps: int = 1,
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mesh: Optional[Any] = None,
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mp_policy: Optional[Any] = None,
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reshard_after_forward: bool = False,
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):
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super().__init__(grad_accum_steps=grad_accum_steps)
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self._mesh = mesh
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self._mp_policy = mp_policy
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self._reshard_after_forward = reshard_after_forward
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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if not self.use_distributed:
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logger.warning("FSDP backend selected but world_size=1, model not wrapped")
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return model
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kwargs = dict(
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mesh=self._mesh,
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mp_policy=self._mp_policy,
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reshard_after_forward=self._reshard_after_forward,
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)
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kwargs = {k: v for k, v in kwargs.items() if v is not None}
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for child in model.children():
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if isinstance(child, nn.ModuleList):
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for sub in child:
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fully_shard(sub, **kwargs)
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else:
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fully_shard(child, **kwargs)
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logger.info(
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"FSDP wrapping applied to %d direct children (root skipped for ABC compat)",
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len(list(model.children())),
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)
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return model
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@contextmanager
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def _no_sync(self, model: nn.Module):
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fsdp_modules = [m for m in model.modules() if isinstance(m, FSDPModule)]
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if fsdp_modules:
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for m in fsdp_modules:
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m.set_requires_gradient_sync(False, recurse=True)
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try:
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yield
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finally:
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for m in fsdp_modules:
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m.set_requires_gradient_sync(True, recurse=True)
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else:
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yield
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def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
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if not self.use_distributed:
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return super().clip_grad_norm(model, max_norm)
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# FSDP params are DTensors (sharded across ranks), and
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# get_total_norm reduces them to a globally-reduced replicated
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# DTensor — no extra all-reduce is needed. Manually summing the
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# local slice again would inflate the norm by sqrt(world_size)
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# and over-clip every step.
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total_norm = torch.nn.utils.get_total_norm(
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[p.grad for p in model.parameters() if p.grad is not None],
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)
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if isinstance(total_norm, DTensor):
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total_norm = total_norm.full_tensor()
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clip_coef = max_norm / (total_norm + 1e-6)
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clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
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for p in model.parameters():
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if p.grad is not None:
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p.grad.mul_(clip_coef_clamped)
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return total_norm.item()
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def unwrap_model(self, model: nn.Module):
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if not self.use_distributed:
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return model.state_dict()
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# unshard() and full_tensor() are collective ops — all ranks must
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# participate. Non-rank-0 ranks still call them but discard results.
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for module in model.modules():
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if isinstance(module, FSDPModule):
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module.unshard()
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state_dict = model.state_dict()
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result = {}
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for k, v in state_dict.items():
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k = k.removeprefix(_COMPILE_PREFIX)
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if isinstance(v, DTensor):
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full = v.full_tensor()
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if get_rank() == 0:
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result[k] = full
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elif get_rank() == 0:
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result[k] = v
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for module in model.modules():
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if isinstance(module, FSDPModule):
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module.reshard()
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if get_rank() != 0:
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return None
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return result
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