diff --git a/README.md b/README.md index e82238f9..ec9bf30a 100644 --- a/README.md +++ b/README.md @@ -260,8 +260,8 @@ areno train \ ``` The tuner uses dummy-loaded model weights and synthetic token rows, respects -the configured sequence limits and tensor-parallel size, and enables -`--drop-rollout-state` for the tuned run. See `docs/cli/training.rst` for the +the configured sequence limits and tensor-parallel size, and uses the default +dropped rollout state for the tuned run. See `docs/cli/training.rst` for the full tuning rules. For Agentic RL, add `--agent-fn` to supply an agent function. The agent calls the local OpenAI-compatible endpoint, including `tools` and `tool_choice` when needed, and returns explicit `AgentTrajectoryTurn` objects. AReno converts those turns into trainable assistant outputs and masks tool results by default: diff --git a/areno/api/config.py b/areno/api/config.py index cfd5b27b..8aaa4654 100644 --- a/areno/api/config.py +++ b/areno/api/config.py @@ -68,7 +68,7 @@ class MlxConfig: prefill_step_size: int = 2048 max_kv_size: int | None = None decode_progress_interval_s: float = 10.0 - keep_rollout_state: bool = True + keep_rollout_state: bool = False logits_chunk_size: int = 4096 compile_train_step: bool = True gradient_checkpointing: bool = True diff --git a/areno/api/trainer_config.py b/areno/api/trainer_config.py index 7db26af3..00dd9bd2 100644 --- a/areno/api/trainer_config.py +++ b/areno/api/trainer_config.py @@ -69,7 +69,12 @@ class TrainerConfig: multimodal_projector_lr_decay_steps: int | None = None multimodal_projector_lr_decay_style: str | None = None activation_checkpointing: bool = True - keep_rollout_state: bool = True + fp8_checkpoint_activations: bool | None = None + fp8_checkpoint_group_size: int = 128 + fp8_checkpoint_stochastic: bool = False + fp8_checkpoint_warmup_steps: int = 0 + fp8_checkpoint_fallback_layers: tuple[int, ...] = () + keep_rollout_state: bool = False optimizer_state_offload: str | bool = "none" optimizer_state_offload_dir: str | None = None optimizer_state_offload_batch_size: int = 1 @@ -89,12 +94,25 @@ def __post_init__(self) -> None: self.backend = default_backend_type().value.lower() else: self.backend = self.backend.lower() + if self.fp8_checkpoint_activations is None: + self.fp8_checkpoint_activations = self.backend == "cuda" and self.activation_checkpointing + self.fp8_checkpoint_fallback_layers = tuple(self.fp8_checkpoint_fallback_layers) if self.backend not in {"cuda", "mlx"}: raise ValueError("backend must be one of: cuda, mlx") if self.adam_4bit and self.adam_8bit: raise ValueError("adam_4bit and adam_8bit are mutually exclusive") if self.adam_4bit and self.backend != "cuda": raise ValueError("adam_4bit is only supported by the CUDA backend") + if self.fp8_checkpoint_activations and self.backend != "cuda": + raise ValueError("fp8_checkpoint_activations is only supported by the CUDA backend") + if self.fp8_checkpoint_activations and not self.activation_checkpointing: + raise ValueError("fp8_checkpoint_activations requires activation_checkpointing") + if self.fp8_checkpoint_group_size not in {0, 128, 256}: + raise ValueError("fp8_checkpoint_group_size must be one of: 0, 128, 256") + if self.fp8_checkpoint_warmup_steps < 0: + raise ValueError("fp8_checkpoint_warmup_steps must be non-negative") + if any(layer < 0 for layer in self.fp8_checkpoint_fallback_layers): + raise ValueError("fp8_checkpoint_fallback_layers must contain non-negative indices") if self.attn_backend not in {"flash", "native"}: raise ValueError("attn_backend must be one of: flash, native") if self.model_hub not in {"hf", "modelscope"}: @@ -216,6 +234,11 @@ def cuda_config(self): optimizer=self.optimizer_config(), runtime={ "activation_checkpointing": self.activation_checkpointing, + "fp8_checkpoint_activations": self.fp8_checkpoint_activations, + "fp8_checkpoint_group_size": self.fp8_checkpoint_group_size, + "fp8_checkpoint_stochastic": self.fp8_checkpoint_stochastic, + "fp8_checkpoint_warmup_steps": self.fp8_checkpoint_warmup_steps, + "fp8_checkpoint_fallback_layers": self.fp8_checkpoint_fallback_layers, "keep_rollout_state": self.keep_rollout_state, "optimizer_state_offload": self.optimizer_state_offload, "optimizer_state_offload_dir": self.optimizer_state_offload_dir, @@ -266,6 +289,11 @@ def cuda_config(self): optimizer=self.optimizer_config(), runtime={ "activation_checkpointing": self.activation_checkpointing, + "fp8_checkpoint_activations": self.fp8_checkpoint_activations, + "fp8_checkpoint_group_size": self.fp8_checkpoint_group_size, + "fp8_checkpoint_stochastic": self.fp8_checkpoint_stochastic, + "fp8_checkpoint_warmup_steps": self.fp8_checkpoint_warmup_steps, + "fp8_checkpoint_fallback_layers": self.fp8_checkpoint_fallback_layers, "keep_rollout_state": self.keep_rollout_state, "optimizer_state_offload": self.optimizer_state_offload, "optimizer_state_offload_dir": self.optimizer_state_offload_dir, diff --git a/areno/cli/train.py b/areno/cli/train.py index 1de993a3..d09d42be 100644 --- a/areno/cli/train.py +++ b/areno/cli/train.py @@ -132,6 +132,7 @@ def flash_attention_unsupported_model_reason(model_config): "score_micro_bs", "gradient_accumulation_steps", "activation_checkpointing", + "fp8_checkpoint_activations", "lora_rank", "lora_alpha", "lora_dropout", @@ -240,6 +241,7 @@ def _trainer_config_from_options(**options) -> TrainerConfig: args.rollout_devices = getattr(args, "rollout_devices", None) args.policy_sync_bucket_mb = getattr(args, "policy_sync_bucket_mb", 64) args.adam_4bit = getattr(args, "adam_4bit", False) + args.fp8_checkpoint_activations = getattr(args, "fp8_checkpoint_activations", None) args.optimizer_state_offload = getattr(args, "optimizer_state_offload", "none") args.optimizer_state_offload_dir = getattr(args, "optimizer_state_offload_dir", None) args.optimizer_state_offload_batch_size = getattr(args, "optimizer_state_offload_batch_size", 1) @@ -525,6 +527,7 @@ def _format_training_config_summary( [ ("max_steps", _format_optional(config.max_steps)), ("sequence_parallel", _sequence_parallel_for_summary(config, model_config)), + ("fp8_ckpt_activations", _format_bool(config.fp8_checkpoint_activations)), ("mini_bs", str(config.mini_bs)), ("score_micro_bs", str(config.score_micro_bs)), ("gradient_accumulation_steps", _format_optional(config.gradient_accumulation_steps, default="auto")), @@ -848,6 +851,7 @@ def _trainer_config_from_args(args) -> TrainerConfig: args.rollout_devices = getattr(args, "rollout_devices", None) args.policy_sync_bucket_mb = getattr(args, "policy_sync_bucket_mb", 64) args.adam_4bit = getattr(args, "adam_4bit", False) + args.fp8_checkpoint_activations = getattr(args, "fp8_checkpoint_activations", None) args.unfreeze_multimodal_tower = getattr(args, "unfreeze_multimodal_tower", False) args.unfreeze_multimodal_projector = getattr(args, "unfreeze_multimodal_projector", False) args.multimodal_tower_lr = getattr(args, "multimodal_tower_lr", None) @@ -907,6 +911,7 @@ def _trainer_config_from_args(args) -> TrainerConfig: multimodal_projector_lr_decay_steps=args.multimodal_projector_lr_decay_steps, multimodal_projector_lr_decay_style=args.multimodal_projector_lr_decay_style, activation_checkpointing=args.activation_checkpointing, + fp8_checkpoint_activations=args.fp8_checkpoint_activations, keep_rollout_state=not args.drop_rollout_state, optimizer_state_offload=args.optimizer_state_offload, optimizer_state_offload_dir=args.optimizer_state_offload_dir, @@ -967,6 +972,7 @@ def _trainer_config_from_args(args) -> TrainerConfig: multimodal_projector_lr_decay_steps=args.multimodal_projector_lr_decay_steps, multimodal_projector_lr_decay_style=args.multimodal_projector_lr_decay_style, activation_checkpointing=args.activation_checkpointing, + fp8_checkpoint_activations=args.fp8_checkpoint_activations, keep_rollout_state=not args.drop_rollout_state, optimizer_state_offload=args.optimizer_state_offload, optimizer_state_offload_dir=args.optimizer_state_offload_dir, @@ -1035,6 +1041,7 @@ def _trainer_config_from_args(args) -> TrainerConfig: multimodal_projector_lr_decay_steps=args.multimodal_projector_lr_decay_steps, multimodal_projector_lr_decay_style=args.multimodal_projector_lr_decay_style, activation_checkpointing=args.activation_checkpointing, + fp8_checkpoint_activations=args.fp8_checkpoint_activations, keep_rollout_state=not args.drop_rollout_state, optimizer_state_offload=args.optimizer_state_offload, optimizer_state_offload_dir=args.optimizer_state_offload_dir, @@ -1104,6 +1111,7 @@ def _trainer_config_from_args(args) -> TrainerConfig: multimodal_projector_lr_decay_steps=args.multimodal_projector_lr_decay_steps, multimodal_projector_lr_decay_style=args.multimodal_projector_lr_decay_style, activation_checkpointing=args.activation_checkpointing, + fp8_checkpoint_activations=args.fp8_checkpoint_activations, keep_rollout_state=not args.drop_rollout_state, optimizer_state_offload=args.optimizer_state_offload, optimizer_state_offload_dir=args.optimizer_state_offload_dir, @@ -1232,6 +1240,7 @@ def section(title: str, names: list[str]) -> dict: "attn_backend", "eager_decode", "activation_checkpointing", + "fp8_checkpoint_activations", "keep_rollout_state", "optimizer_state_offload", "optimizer_state_offload_dir", @@ -1753,8 +1762,15 @@ def _dataset_builder_for_suffix(suffix: str) -> str: help="Enable decoder-layer activation recompute during training.", ) @click.option( - "--drop-rollout-state", - is_flag=True, + "--fp8-ckpt-activations/--no-fp8-ckpt-activations", + "fp8_checkpoint_activations", + default=None, + help="Store activation-checkpoint boundary tensors in FP8 E4M3 (enabled by default on CUDA).", +) +@click.option( + "--drop-rollout-state/--keep-rollout-state", + default=True, + show_default=True, help="Release completed rollout KV/cache state after each step.", ) @click.option( diff --git a/areno/dashboard/dist/assets/index-B4DqcGQ-.js b/areno/dashboard/dist/assets/index-CTi7Ypoy.js similarity index 84% rename from areno/dashboard/dist/assets/index-B4DqcGQ-.js rename to areno/dashboard/dist/assets/index-CTi7Ypoy.js index e0cdf8c2..998d7259 100644 --- a/areno/dashboard/dist/assets/index-B4DqcGQ-.js +++ b/areno/dashboard/dist/assets/index-CTi7Ypoy.js @@ -14,7 +14,7 @@ * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. - */var np;function Yx(){if(np)return Ae;np=1;var 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document<"u"?document.getElementById("root"):null;ug&&Jx.createRoot(ug).render(m.jsx(Kk,{})); +`:"",x.output||((J=x.check)==null?void 0:J.detail)||"No details returned."]}),m.jsx("button",{className:"primaryButton fullButton",onClick:()=>b(null),children:"Done"})]})]})}function IA(n,i){const u=(n==="train"?[["algo",i.algo],["ckpt",i.ckpt],["dataset-path",i.dataset_path],["dataset-loader-fn",i.dataset_loader_fn],["reward-fn-path",i.reward_fn_path],["world-size",i.world_size],["tp-size",i.tp_size],["batch-size",i.batch_size],["mini-bs",i.mini_bs],["n-samples",i.n_samples],["max-new-tokens",i.max_new_tokens]]:[["model-path",i.model_path],["host",i.host],["port",i.port],["world-size",i.world_size],["tp-size",i.tp_size],["max-running-prompts",i.max_running_prompts],["default-max-tokens",i.default_max_tokens]]).filter(([,o])=>o!==""&&o!==void 0&&o!==null).map(([o,c])=>` --${o} ${hf(c)}`);return[`areno ${n} 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document<"u"?document.getElementById("root"):null;ug&&Jx.createRoot(ug).render(m.jsx(Kk,{})); diff --git a/areno/dashboard/dist/index.html b/areno/dashboard/dist/index.html index 9bab6daf..aa033356 100644 --- a/areno/dashboard/dist/index.html +++ b/areno/dashboard/dist/index.html @@ -4,7 +4,7 @@ AReno Dashboard - + diff --git a/areno/dashboard/server.py b/areno/dashboard/server.py index 9fd9c183..74b180fb 100644 --- a/areno/dashboard/server.py +++ b/areno/dashboard/server.py @@ -713,7 +713,6 @@ def build_train_command(config: dict[str, Any]) -> list[str]: "--adam-4bit": config.get("adam_4bit"), "--unfreeze-mm-tower": config.get("unfreeze_multimodal_tower"), "--unfreeze-mm-projector": config.get("unfreeze_multimodal_projector"), - "--drop-rollout-state": config.get("drop_rollout_state"), "--eager-decode": config.get("eager_decode"), "--disable-thinking": config.get("disable_thinking"), "--train-tool-results": config.get("train_tool_results"), @@ -723,6 +722,14 @@ def build_train_command(config: dict[str, Any]) -> list[str]: command.append( "--activation-checkpointing" if bool_like(activation_checkpointing) else "--no-activation-checkpointing" ) + fp8_checkpoint_activations = config.get("fp8_checkpoint_activations") + if fp8_checkpoint_activations not in (None, ""): + command.append( + "--fp8-ckpt-activations" if bool_like(fp8_checkpoint_activations) else "--no-fp8-ckpt-activations" + ) + drop_rollout_state = config.get("drop_rollout_state") + if drop_rollout_state not in (None, ""): + command.append("--drop-rollout-state" if bool_like(drop_rollout_state) else "--keep-rollout-state") use_kl_loss = config.get("use_kl_loss") if use_kl_loss not in (None, ""): command.append("--use-kl-loss" if bool_like(use_kl_loss) else "--no-use-kl-loss") diff --git a/areno/engine/config.py b/areno/engine/config.py index fbc6e0f6..4db76626 100644 --- a/areno/engine/config.py +++ b/areno/engine/config.py @@ -64,7 +64,12 @@ class RuntimeConfig: attn_backend: Literal["flash", "native"] = "flash" compile_model: bool = True activation_checkpointing: bool = True - keep_rollout_state: bool = True + fp8_checkpoint_activations: bool | None = None + fp8_checkpoint_group_size: int = 128 + fp8_checkpoint_stochastic: bool = False + fp8_checkpoint_warmup_steps: int = 0 + fp8_checkpoint_fallback_layers: tuple[int, ...] = () + keep_rollout_state: bool = False optimizer_state_offload: Literal["none", "cpu", "disk"] | bool = "none" optimizer_state_offload_dir: str | None = None optimizer_state_offload_batch_size: int = 1 @@ -75,8 +80,19 @@ class RuntimeConfig: ) def __post_init__(self) -> None: + if self.fp8_checkpoint_activations is None: + self.fp8_checkpoint_activations = self.activation_checkpointing + self.fp8_checkpoint_fallback_layers = tuple(self.fp8_checkpoint_fallback_layers) if self.attn_backend not in {"flash", "native"}: raise ValueError("runtime.attn_backend must be one of: flash, native") + if self.fp8_checkpoint_activations and not self.activation_checkpointing: + raise ValueError("runtime.fp8_checkpoint_activations requires activation_checkpointing") + if self.fp8_checkpoint_group_size not in {0, 128, 256}: + raise ValueError("runtime.fp8_checkpoint_group_size must be one of: 0, 128, 256") + if self.fp8_checkpoint_warmup_steps < 0: + raise ValueError("runtime.fp8_checkpoint_warmup_steps must be non-negative") + if any(layer < 0 for layer in self.fp8_checkpoint_fallback_layers): + raise ValueError("runtime.fp8_checkpoint_fallback_layers must contain non-negative indices") if isinstance(self.optimizer_state_offload, bool): self.optimizer_state_offload = "cpu" if self.optimizer_state_offload else "none" if self.optimizer_state_offload not in {"none", "cpu", "disk"}: diff --git a/areno/engine/runtime/fp8_checkpoint.py b/areno/engine/runtime/fp8_checkpoint.py new file mode 100644 index 00000000..237d70bd --- /dev/null +++ b/areno/engine/runtime/fp8_checkpoint.py @@ -0,0 +1,228 @@ +"""FP8 storage for tensors saved at activation-checkpoint boundaries. + +The model forward still consumes BF16 tensors. Only the copy retained by +autograd for the later checkpoint recomputation is quantized, so this module +does not change the original forward graph. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from threading import Lock +from typing import Any + +import torch + +_FP8_DTYPE = torch.float8_e4m3fn +_FP8_MAX = torch.finfo(_FP8_DTYPE).max + + +@dataclass(slots=True) +class FP8CheckpointTensor: + """Quantized tensor payload returned from a saved-tensor pack hook.""" + + values: torch.Tensor + scales: torch.Tensor + shape: torch.Size + dtype: torch.dtype + group_size: int + + +@dataclass(slots=True) +class _MemoryStats: + boundaries: int = 0 + fallback_boundaries: int = 0 + warmup_boundaries: int = 0 + original_bytes: int = 0 + stored_bytes: int = 0 + + +_STATS = _MemoryStats() +_RANGE_AMAX: dict[int, torch.Tensor] = {} +_ROUNDING_GENERATORS: dict[str, torch.Generator] = {} +_LOCK = Lock() + + +def reset_fp8_checkpoint_stats() -> None: + """Reset process-local counters collected for the next train microbatch.""" + + with _LOCK: + # Reset in place. Compiled saved-tensor pack paths can retain the + # original stats object captured during their first trace; rebinding + # ``_STATS`` would make those paths update a stale object while the + # metrics reader observes a new, permanently-zero object. + _STATS.boundaries = 0 + _STATS.fallback_boundaries = 0 + _STATS.warmup_boundaries = 0 + _STATS.original_bytes = 0 + _STATS.stored_bytes = 0 + + +def fp8_checkpoint_metrics() -> dict[str, float]: + """Return checkpoint-boundary compression counters as training metrics.""" + + with _LOCK: + stats = _MemoryStats( + boundaries=_STATS.boundaries, + fallback_boundaries=_STATS.fallback_boundaries, + warmup_boundaries=_STATS.warmup_boundaries, + original_bytes=_STATS.original_bytes, + stored_bytes=_STATS.stored_bytes, + ) + reduction = 0.0 + if stats.original_bytes: + reduction = 1.0 - stats.stored_bytes / stats.original_bytes + return { + "fp8_ckpt_boundaries": float(stats.boundaries), + "fp8_ckpt_fallback_boundaries": float(stats.fallback_boundaries), + "fp8_ckpt_warmup_boundaries": float(stats.warmup_boundaries), + "fp8_ckpt_original_bytes": float(stats.original_bytes), + "fp8_ckpt_stored_bytes": float(stats.stored_bytes), + "fp8_ckpt_storage_reduction": float(reduction), + } + + +def _tensor_bytes(tensor: torch.Tensor) -> int: + return tensor.numel() * tensor.element_size() + + +def _record_compressed(original: torch.Tensor, packed: FP8CheckpointTensor) -> None: + with _LOCK: + _STATS.boundaries += 1 + _STATS.original_bytes += _tensor_bytes(original) + _STATS.stored_bytes += _tensor_bytes(packed.values) + _tensor_bytes(packed.scales) + + +def _record_fallback(tensor: torch.Tensor, *, warmup: bool = False) -> None: + with _LOCK: + _STATS.fallback_boundaries += 1 + _STATS.warmup_boundaries += int(warmup) + size = _tensor_bytes(tensor) + _STATS.original_bytes += size + _STATS.stored_bytes += size + + +def _stochastic_round(values: torch.Tensor) -> torch.Tensor: + """Add unbiased E4M3-bin noise without advancing the caller's RNG.""" + + key = str(values.device) + with _LOCK: + generator = _ROUNDING_GENERATORS.get(key) + if generator is None: + generator = torch.Generator(device=values.device) + generator.manual_seed(0xA8E0 + (values.device.index or 0)) + _ROUNDING_GENERATORS[key] = generator + random = torch.rand(values.shape, dtype=values.dtype, device=values.device, generator=generator) + magnitude = values.abs() + normal_step = torch.pow(2.0, torch.floor(torch.log2(magnitude.clamp_min(2.0**-9))) - 3.0) + step = normal_step.clamp_min(2.0**-9) + noise = (random - 0.5) * step + return torch.where(magnitude == 0, values, values + noise) + + +def pack_fp8_checkpoint_tensor( + tensor: torch.Tensor, + *, + group_size: int = 128, + stochastic: bool = False, +) -> FP8CheckpointTensor: + """Quantize a BF16 checkpoint tensor with E4M3 group-wise scales. + + ``group_size=0`` selects one scale per token. A non-divisible hidden + dimension falls back to per-token scaling rather than retaining padding. + """ + + if tensor.dtype != torch.bfloat16: + raise TypeError("FP8 checkpoint compression requires a BF16 tensor") + if tensor.ndim < 2 or tensor.numel() == 0: + raise ValueError("FP8 checkpoint compression requires a non-empty tensor with ndim >= 2") + if group_size not in {0, 128, 256}: + raise ValueError("FP8 checkpoint group_size must be one of: 0, 128, 256") + + hidden_size = tensor.shape[-1] + actual_group_size = hidden_size if group_size == 0 or hidden_size % group_size else group_size + rows = tensor.reshape(-1, hidden_size).float() + groups = rows.reshape(rows.shape[0], -1, actual_group_size) + amax = groups.abs().amax(dim=-1, keepdim=True) + scales = torch.where(amax == 0, torch.ones_like(amax), amax / _FP8_MAX) + normalized = (groups / scales).clamp(-_FP8_MAX, _FP8_MAX) + if stochastic: + normalized = _stochastic_round(normalized).clamp(-_FP8_MAX, _FP8_MAX) + values = normalized.to(_FP8_DTYPE) + return FP8CheckpointTensor( + values=values, + scales=scales, + shape=tensor.shape, + dtype=tensor.dtype, + group_size=actual_group_size, + ) + + +def unpack_fp8_checkpoint_tensor(packed: FP8CheckpointTensor) -> torch.Tensor: + """Restore a checkpoint payload to its original shape and dtype.""" + + restored = packed.values.float() * packed.scales + return restored.reshape(packed.shape).to(packed.dtype) + + +def _layer_owner(layer_fn: Any) -> Any: + if isinstance(layer_fn, torch.nn.Module): + return layer_fn + return getattr(layer_fn, "__self__", layer_fn) + + +def checkpoint_layer_index(layer_fn: Any) -> int | None: + """Resolve a stable decoder/routed-expert index from a checkpoint callable.""" + + owner = _layer_owner(layer_fn) + for candidate in (owner, getattr(owner, "self_attn", None), getattr(owner, "attention", None)): + if candidate is None: + continue + for name in ("layer_idx", "routing_layer_slot"): + value = getattr(candidate, name, None) + if isinstance(value, int): + return value + return None + + +def checkpoint_saved_tensor_hooks( + layer_fn: Any, + *, + group_size: int, + stochastic: bool, + warmup: bool, + fallback_layers: tuple[int, ...], +): + """Build hooks that compress only the input saved by a checkpoint call.""" + + layer_index = checkpoint_layer_index(layer_fn) + force_fallback = layer_index is not None and layer_index in fallback_layers + + def pack(tensor: torch.Tensor): + if tensor.dtype != torch.bfloat16 or tensor.ndim < 2 or tensor.numel() == 0: + return tensor + if warmup: + if layer_index is not None: + value = tensor.detach().abs().amax() + with _LOCK: + previous = _RANGE_AMAX.get(layer_index) + _RANGE_AMAX[layer_index] = value if previous is None else torch.maximum(previous, value) + _record_fallback(tensor, warmup=True) + return tensor + if force_fallback: + _record_fallback(tensor) + return tensor + try: + packed = pack_fp8_checkpoint_tensor(tensor, group_size=group_size, stochastic=stochastic) + except (RuntimeError, TypeError, ValueError): + _record_fallback(tensor) + return tensor + _record_compressed(tensor, packed) + return packed + + def unpack(value): + if isinstance(value, FP8CheckpointTensor): + return unpack_fp8_checkpoint_tensor(value) + return value + + return torch.autograd.graph.saved_tensors_hooks(pack, unpack) diff --git a/areno/engine/runtime/metadata.py b/areno/engine/runtime/metadata.py index cdf60164..12a33f64 100644 --- a/areno/engine/runtime/metadata.py +++ b/areno/engine/runtime/metadata.py @@ -24,6 +24,12 @@ class TrainMeta: packed: bool = False sequence_parallel: bool = False activation_checkpointing: bool = False + fp8_checkpoint_activations: bool = True + fp8_checkpoint_group_size: int = 128 + fp8_checkpoint_stochastic: bool = False + fp8_checkpoint_warmup_steps: int = 0 + fp8_checkpoint_fallback_layers: tuple[int, ...] = () + global_step: int = 0 num_padding_tokens: int = 0 routing_replay: torch.Tensor | None = None diff --git a/areno/engine/runtime/recompute.py b/areno/engine/runtime/recompute.py index 1915d803..c1b77e3d 100644 --- a/areno/engine/runtime/recompute.py +++ b/areno/engine/runtime/recompute.py @@ -9,6 +9,7 @@ from torch.utils.checkpoint import checkpoint from areno.engine.parallel.collectives import sequence_parallel_region +from areno.engine.runtime.fp8_checkpoint import checkpoint_saved_tensor_hooks from areno.engine.runtime.metadata import InferMeta, TrainMeta @@ -39,6 +40,7 @@ def checkpoint_layer( *args: Any, train_meta: TrainMeta | None = None, infer_meta: InferMeta | None = None, + compress_boundary: bool = True, ) -> Any: """Checkpoint one decoder layer, recomputing its activations in backward.""" @@ -52,12 +54,25 @@ def recompute(states: torch.Tensor) -> Any: with sequence_parallel_region(bool(train_meta.sequence_parallel)): return layer_fn(states, *args) - return checkpoint( - recompute, - hidden_states, - use_reentrant=False, - preserve_rng_state=True, + def run_checkpoint() -> Any: + return checkpoint( + recompute, + hidden_states, + use_reentrant=False, + preserve_rng_state=True, + ) + + if not compress_boundary or not getattr(train_meta, "fp8_checkpoint_activations", False): + return run_checkpoint() + hooks = checkpoint_saved_tensor_hooks( + layer_fn, + group_size=train_meta.fp8_checkpoint_group_size, + stochastic=train_meta.fp8_checkpoint_stochastic, + warmup=train_meta.global_step < train_meta.fp8_checkpoint_warmup_steps, + fallback_layers=train_meta.fp8_checkpoint_fallback_layers, ) + with hooks: + return run_checkpoint() @_disable_dynamo_frame @@ -89,5 +104,6 @@ def checkpoint_routed_moe_layer( topk_weight, train_meta=train_meta, infer_meta=infer_meta, + compress_boundary=False, ) return attended + expert_output diff --git a/areno/engine/runtime/train_step.py b/areno/engine/runtime/train_step.py index 03cd8714..ff6a9575 100644 --- a/areno/engine/runtime/train_step.py +++ b/areno/engine/runtime/train_step.py @@ -64,6 +64,17 @@ def _train_meta(data_pack: dict[str, Any], tokens: torch.Tensor, *, sequence_par packed=True, sequence_parallel=sequence_parallel, activation_checkpointing=bool(data_pack.get("_activation_checkpointing_enabled", False)), + fp8_checkpoint_activations=bool( + data_pack.get( + "_fp8_checkpoint_activations_enabled", + data_pack.get("_activation_checkpointing_enabled", False), + ) + ), + fp8_checkpoint_group_size=int(data_pack.get("_fp8_checkpoint_group_size", 128)), + fp8_checkpoint_stochastic=bool(data_pack.get("_fp8_checkpoint_stochastic", False)), + fp8_checkpoint_warmup_steps=int(data_pack.get("_fp8_checkpoint_warmup_steps", 0)), + fp8_checkpoint_fallback_layers=tuple(data_pack.get("_fp8_checkpoint_fallback_layers", ())), + global_step=int(data_pack.get("_global_step", 0)), num_padding_tokens=int(data_pack.get("packed_singleton_padding", 0)), routing_replay=data_pack.get("packed_routing_replay"), ) diff --git a/areno/engine/training.py b/areno/engine/training.py index e0dcf585..7672f05f 100644 --- a/areno/engine/training.py +++ b/areno/engine/training.py @@ -11,6 +11,7 @@ from areno.engine.modeling import param_grad, unwrap_model from areno.engine.parallel.context import get_tp_context from areno.engine.protocol import TrainPayload +from areno.engine.runtime.fp8_checkpoint import fp8_checkpoint_metrics, reset_fp8_checkpoint_stats from areno.engine.runtime.logprobs import ( packed_next_token_logprobs, packed_next_token_logprobs_from_hidden, @@ -114,7 +115,16 @@ def _train_step( torch.cuda.synchronize(worker.device) torch.cuda.reset_peak_memory_stats(worker.device) data_pack = to_device(data_pack, worker.device) - data_pack["_activation_checkpointing_enabled"] = worker.config.runtime.activation_checkpointing + runtime = worker.config.runtime + data_pack["_activation_checkpointing_enabled"] = runtime.activation_checkpointing + data_pack["_fp8_checkpoint_activations_enabled"] = runtime.fp8_checkpoint_activations + data_pack["_fp8_checkpoint_group_size"] = runtime.fp8_checkpoint_group_size + data_pack["_fp8_checkpoint_stochastic"] = runtime.fp8_checkpoint_stochastic + data_pack["_fp8_checkpoint_warmup_steps"] = runtime.fp8_checkpoint_warmup_steps + data_pack["_fp8_checkpoint_fallback_layers"] = runtime.fp8_checkpoint_fallback_layers + data_pack["_global_step"] = worker._global_step + if runtime.fp8_checkpoint_activations: + reset_fp8_checkpoint_stats() tokens = data_pack["input_ids"].long() position_ids = data_pack.get("position_ids") train_meta = _train_meta( @@ -213,6 +223,7 @@ def _train_step( {"lr": current_lr}, multimodal_lrs, {"sequence_parallel": float(model_kwargs["train_meta"].sequence_parallel)}, + fp8_checkpoint_metrics() if runtime.fp8_checkpoint_activations else None, {"grad_norm": grad_norm} if grad_norm is not None else None, multimodal_grad_metrics, {"clipped_grad_norm": clipped_grad_norm} if clipped_grad_norm is not None else None, diff --git a/areno/models/bailing_v3/model.py b/areno/models/bailing_v3/model.py index a6d0bcd8..baacdbde 100644 --- a/areno/models/bailing_v3/model.py +++ b/areno/models/bailing_v3/model.py @@ -91,6 +91,7 @@ ) from areno.engine.parallel.context import get_tp_context from areno.engine.runtime.metadata import InferMeta, TrainMeta +from areno.engine.runtime.recompute import checkpoint_layer from areno.engine.runtime.routing_replay import resolve_sigmoid_routes from areno.models._shared.dynamo_wrappers import ( _areno_depthwise_causal_conv1d_silu_decode_no_compile, @@ -1520,6 +1521,18 @@ def __init__(self, config: ModelConfig, layer_idx: int): else BailingDenseMLP(config, config.intermediate_size) ) + def _attention_block( + self, + hidden_states: torch.Tensor, + position_ids: torch.Tensor, + train_meta: TrainMeta | None, + infer_meta: InferMeta | None, + ) -> torch.Tensor: + return self.attention(self.input_layernorm(hidden_states), position_ids, train_meta, infer_meta) + + def _dense_mlp_block(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.mlp(self.post_attention_layernorm(hidden_states)) + def forward( self, hidden_states: torch.Tensor, @@ -1527,17 +1540,30 @@ def forward( train_meta: TrainMeta | None, infer_meta: InferMeta | None, ) -> torch.Tensor: - # Standard pre-norm residual: norm -> sublayer -> add. + # Checkpoint attention for every Ling/Bailing V3 layer. Sparse MoE + # routing remains outside recomputation so its load counters are not + # accumulated twice and rollout-replayed routes stay authoritative. residual = hidden_states - hidden_states = residual + self.attention( - self.input_layernorm(hidden_states), position_ids, train_meta, infer_meta + hidden_states = residual + checkpoint_layer( + self._attention_block, + hidden_states, + position_ids, + train_meta, + infer_meta, + train_meta=train_meta, + infer_meta=infer_meta, ) residual = hidden_states - mlp_input = self.post_attention_layernorm(hidden_states) if isinstance(self.mlp, BailingSparseMoeBlock): + mlp_input = self.post_attention_layernorm(hidden_states) num_padding_tokens = train_meta.num_padding_tokens if train_meta is not None else 0 return residual + self.mlp(mlp_input, num_padding_tokens) - return residual + self.mlp(mlp_input) + return residual + checkpoint_layer( + self._dense_mlp_block, + hidden_states, + train_meta=train_meta, + infer_meta=infer_meta, + ) class BailingMoeV3ForCausalLM(nn.Module): @@ -1695,7 +1721,7 @@ def reset_recurrent_cache_slots(self, slots: torch.Tensor) -> None: slots = slots.to(device=next(self.parameters()).device, dtype=torch.long) for layer in self.layers: attn = layer.attention - if isinstance(attn, (BailingLinearAttention, BailingKDAAttention)) and attn.state_cache.numel() > 0: + if isinstance(attn, BailingLinearAttention | BailingKDAAttention) and attn.state_cache.numel() > 0: attn.state_cache.index_fill_(0, slots, 0) if isinstance(attn, BailingKDAAttention) and attn.conv_cache.numel() > 0: attn.conv_cache.index_fill_(0, slots, 0) diff --git a/areno/models/gemma4/model.py b/areno/models/gemma4/model.py index 2ef5eb88..3dc40018 100644 --- a/areno/models/gemma4/model.py +++ b/areno/models/gemma4/model.py @@ -1602,6 +1602,7 @@ def _gemma4_moe_feedforward_no_compile( dense_input, train_meta=train_meta, infer_meta=infer_meta, + compress_boundary=False, ) router_logits = layer.router(residual) topk_idx, topk_weight = layer.moe.route(router_logits) @@ -1624,6 +1625,7 @@ def _gemma4_moe_feedforward_no_compile( topk_weight, train_meta=train_meta, infer_meta=infer_meta, + compress_boundary=False, ) if moe_sequence_parallel: moe_hidden = scatter_to_sequence_parallel_region(moe_hidden) diff --git a/dashboard/src/main.jsx b/dashboard/src/main.jsx index fb2ddcdf..7493a0dc 100644 --- a/dashboard/src/main.jsx +++ b/dashboard/src/main.jsx @@ -258,7 +258,8 @@ const defaultTrainConfig = { tp_size: 1, attn_backend: "flash", activation_checkpointing: true, - drop_rollout_state: false, + fp8_checkpoint_activations: true, + drop_rollout_state: true, eager_decode: false, disable_thinking: false, batch_size: 8, @@ -1789,13 +1790,18 @@ function inferPlanRunTool(plan) { function commandForPlan(tool, parameters = {}) { const mode = tool === "start_serve" ? "serve" : "train"; const args = []; - const negativeFlags = new Set(["activation_checkpointing", "use_kl_loss"]); + const negativeFlags = new Map([ + ["activation_checkpointing", "--no-activation-checkpointing"], + ["fp8_checkpoint_activations", "--no-fp8-ckpt-activations"], + ["drop_rollout_state", "--keep-rollout-state"], + ["use_kl_loss", "--no-use-kl-loss"], + ]); for (const [key, value] of Object.entries(parameters)) { if (key === "extra_args" || value === "" || value === null || value === undefined) continue; const flag = `--${key.replaceAll("_", "-")}`; if (isPlanBoolean(value)) { if (planBoolValue(value)) args.push(flag); - else if (negativeFlags.has(key)) args.push(`--no-${key.replaceAll("_", "-")}`); + else if (negativeFlags.has(key)) args.push(negativeFlags.get(key)); continue; } args.push(`${flag} ${shellQuote(value)}`); @@ -2878,6 +2884,7 @@ function trainLauncherSections(algo) { field("tp_size", "TP", true), selectField("attn_backend", "Attention", ["flash", "native"], true), checkField("activation_checkpointing", "Activation ckpt"), + checkField("fp8_checkpoint_activations", "FP8 ckpt activations"), checkField("drop_rollout_state", "Drop rollout state"), checkField("eager_decode", "Eager decode"), checkField("disable_thinking", "Disable thinking"), diff --git a/docs/cli/training.rst b/docs/cli/training.rst index 5034c984..55c6cbd1 100644 --- a/docs/cli/training.rst +++ b/docs/cli/training.rst @@ -194,10 +194,11 @@ different visible GPUs for rollout: ``--eager-decode`` Disable decode CUDA graph and run rollout decode eagerly. CUDA only. -``--drop-rollout-state`` +``--drop-rollout-state / --keep-rollout-state`` Drop completed rollout KV/cache state after each step to save device - memory. By default, Areno keeps reusable rollout state between steps for - lower setup overhead. Supported by both CUDA and MLX. + memory. Dropping is enabled by default. Use ``--keep-rollout-state`` to + retain reusable state between steps for lower setup overhead. Supported by + both CUDA and MLX. ``--attn-backend [flash|native]`` Attention backend. Default: ``flash``. Use ``native`` to run without @@ -284,8 +285,8 @@ Search is deliberately conservative: skipped and that concurrency is used directly for training-parameter tuning; * training uses the rollout-selected concurrency to derive a batch size, then tries larger to smaller ``--mini-bs`` values; -* ``--drop-rollout-state`` is enabled for the tuned run so rollout memory does - not remain resident during the training probe or optimizer step. +* the default dropped rollout state is used for the tuned run so rollout + memory does not remain resident during the training probe or optimizer step. ``--mem-frac FLOAT`` Target maximum GPU memory fraction for tuning. Default: ``0.9``. Lower this @@ -339,6 +340,13 @@ in its description; flags for other algorithms are ignored. Enable decoder-layer activation recompute during training. Default: enabled. +``--fp8-ckpt-activations / --no-fp8-ckpt-activations`` + Store the BF16 tensors retained at decoder activation-checkpoint boundaries + as group-wise FP8 E4M3 and restore them before backward recomputation. The + original forward remains BF16. Enabled by default on CUDA when activation + checkpointing is enabled. Use ``--no-fp8-ckpt-activations`` to retain BF16 + boundary tensors. CUDA only. + ``--optimizer-state-offload [none|cpu|disk]`` Select CUDA optimizer-state residency. ``none`` keeps state on the training device. ``cpu`` moves state to host memory between train calls. ``disk`` diff --git a/docs/concepts/backend-topology.rst b/docs/concepts/backend-topology.rst index 4d3b7f78..f6118ad3 100644 --- a/docs/concepts/backend-topology.rst +++ b/docs/concepts/backend-topology.rst @@ -77,8 +77,9 @@ DPO and PPO. MLX runs with ``world-size=1`` and ``tp-size=1`` on unified memory. CUDA device lists, independent rollout partitions, NCCL policy synchronization, -and CUDA graph capture do not apply. ``--drop-rollout-state`` controls whether -completed rollout cache state is retained across session boundaries. +and CUDA graph capture do not apply. Completed rollout cache state is dropped +at session boundaries by default; ``--keep-rollout-state`` retains it across +boundaries. Shared behavior --------------- diff --git a/docs/concepts/multimodal-inputs.rst b/docs/concepts/multimodal-inputs.rst index 24b62eab..b72b23ea 100644 --- a/docs/concepts/multimodal-inputs.rst +++ b/docs/concepts/multimodal-inputs.rst @@ -156,5 +156,6 @@ Troubleshooting * Set ``ARENO_LOG_COMPLETIONS=1`` during agentic smoke tests to verify output and tool-call formatting before launching a long run. * On Apple Silicon, reduce ``--mini-bs`` and ``--max-running-prompts`` first; - use ``--drop-rollout-state`` when retained KV/cache state competes with - training activations. See :doc:`../getting-started/mlx`. + completed rollout KV/cache state is dropped by default so it does not + compete with training activations. Use ``--keep-rollout-state`` only when + retaining reusable state is preferable. See :doc:`../getting-started/mlx`. diff --git a/docs/getting-started/cuda.rst b/docs/getting-started/cuda.rst index 963ed981..6b2cd47e 100644 --- a/docs/getting-started/cuda.rst +++ b/docs/getting-started/cuda.rst @@ -92,14 +92,22 @@ The main CUDA controls are: ``--max-running-prompts N`` Caps active rollout sequences and their KV-cache demand. -``--drop-rollout-state`` - Releases completed rollout state at the session boundary rather than - retaining reusable cache and graph state for the next rollout. +``--drop-rollout-state / --keep-rollout-state`` + Completed rollout state is released at the session boundary by default. + Use ``--keep-rollout-state`` to retain reusable cache and graph state for + the next rollout. ``--activation-checkpointing`` Recomputes supported decoder activations during backward. It is enabled by default. +``--fp8-ckpt-activations / --no-fp8-ckpt-activations`` + Stores activation-checkpoint boundary tensors in FP8 E4M3 by default while + keeping the original forward in BF16. Use the negative flag to retain BF16 + boundary storage. Ling/Bailing V3 checkpoints attention in every decoder + layer and dense MLP blocks; sparse routing and routed experts remain outside + recomputation so routing load counters are updated exactly once. + ``--optimizer-state-offload cpu`` Moves optimizer state to host memory between train calls. diff --git a/docs/getting-started/mlx.rst b/docs/getting-started/mlx.rst index 2bd699f7..a0f8e094 100644 --- a/docs/getting-started/mlx.rst +++ b/docs/getting-started/mlx.rst @@ -87,9 +87,10 @@ options have the largest effect on MLX unified-memory use: Caps active rollout sequences. Reduce it when KV cache or multimodal prefill features dominate memory. -``--drop-rollout-state`` - Releases completed rollout KV/cache state at the rollout-session boundary - instead of retaining it for the next rollout. +``--drop-rollout-state / --keep-rollout-state`` + Completed rollout KV/cache state is released at the rollout-session + boundary by default. Use ``--keep-rollout-state`` to retain it for the next + rollout. ``--adam-8bit`` Stores Adam moment state in the MLX backend's 8-bit representation. This diff --git a/docs/models/supported.rst b/docs/models/supported.rst index d5dbfa29..d6fc83d7 100644 --- a/docs/models/supported.rst +++ b/docs/models/supported.rst @@ -27,6 +27,9 @@ family is available on both backends. ``qwen3_5_moe`` layouts, with Areno MoE kernels. * - Bailing MoE Linear v2 - Local model adapter for Bailing MoE Linear v2 checkpoints. + * - Ling / Bailing MoE V3 + - ``bailing_hybrid`` checkpoints with softmax/KDA attention, sparse MoE, + activation checkpointing, and FP8 checkpoint-boundary storage. * - Gemma4 - Gemma4 text and conditional-generation checkpoints. Native Gemma4 and Gemma4 Unified processors support image, audio, and video inputs for diff --git a/tests/test_config_data_cpu.py b/tests/test_config_data_cpu.py index 81ab4fd5..6c242af7 100644 --- a/tests/test_config_data_cpu.py +++ b/tests/test_config_data_cpu.py @@ -643,15 +643,97 @@ def test_rollout_config_respects_explicit_max_running_prompts(self): self.assertEqual(cfg.resolved_max_running_prompts(), 64) - def test_trainer_config_keeps_rollout_state_by_default(self): - """Runtime defaults should favor rollout speed unless explicitly disabled.""" + def test_trainer_config_drops_rollout_state_by_default(self): + """Runtime defaults should favor lower memory unless explicitly retained.""" cfg = TrainerConfig(algo="sft", ckpt="unused", dataset_path="unused") - self.assertTrue(cfg.keep_rollout_state) + self.assertFalse(cfg.keep_rollout_state) self.assertEqual(cfg.optimizer_state_offload_batch_size, 1) - self.assertTrue(cfg.cuda_config().runtime["keep_rollout_state"]) + self.assertFalse(cfg.cuda_config().runtime["keep_rollout_state"]) self.assertEqual(cfg.cuda_config().runtime["optimizer_state_offload_batch_size"], 1) - self.assertTrue(cfg.mlx_config().keep_rollout_state) + self.assertFalse(cfg.mlx_config().keep_rollout_state) + + def test_fp8_checkpoint_activations_default_to_cuda_checkpointing(self): + cuda = TrainerConfig(algo="sft", backend="cuda", ckpt="unused", dataset_path="unused") + cuda_without_checkpointing = TrainerConfig( + algo="sft", + backend="cuda", + ckpt="unused", + dataset_path="unused", + activation_checkpointing=False, + ) + mlx = TrainerConfig(algo="sft", backend="mlx", ckpt="unused", dataset_path="unused") + + self.assertTrue(cuda.fp8_checkpoint_activations) + self.assertFalse(cuda_without_checkpointing.fp8_checkpoint_activations) + self.assertFalse(mlx.fp8_checkpoint_activations) + self.assertTrue(RuntimeConfig().fp8_checkpoint_activations) + + def test_fp8_checkpoint_activation_config_reaches_cuda_runtime(self): + """The SDK knob should preserve advanced compression controls.""" + cfg = TrainerConfig( + algo="sft", + backend="cuda", + ckpt="unused", + dataset_path="unused", + fp8_checkpoint_activations=True, + fp8_checkpoint_group_size=256, + fp8_checkpoint_stochastic=True, + fp8_checkpoint_warmup_steps=2, + fp8_checkpoint_fallback_layers=(0, 7), + ) + + runtime = cfg.cuda_config().runtime + self.assertTrue(runtime["fp8_checkpoint_activations"]) + self.assertEqual(runtime["fp8_checkpoint_group_size"], 256) + self.assertTrue(runtime["fp8_checkpoint_stochastic"]) + self.assertEqual(runtime["fp8_checkpoint_warmup_steps"], 2) + self.assertEqual(runtime["fp8_checkpoint_fallback_layers"], (0, 7)) + + def test_fp8_checkpoint_activation_config_rejects_invalid_combinations(self): + with self.assertRaisesRegex(ValueError, "requires activation_checkpointing"): + TrainerConfig( + algo="sft", + backend="cuda", + ckpt="unused", + dataset_path="unused", + activation_checkpointing=False, + fp8_checkpoint_activations=True, + ) + with self.assertRaisesRegex(ValueError, "only supported by the CUDA backend"): + TrainerConfig( + algo="sft", + backend="mlx", + ckpt="unused", + dataset_path="unused", + fp8_checkpoint_activations=True, + ) + with self.assertRaisesRegex(ValueError, "group_size"): + RuntimeConfig(fp8_checkpoint_group_size=64) + + def test_fp8_checkpoint_activation_settings_reach_train_metadata(self): + tokens = torch.ones((1, 4), dtype=torch.long) + meta = train_step_runtime._train_meta( + { + "train_cu_seqlens": torch.tensor([0, 4], dtype=torch.int32), + "_activation_checkpointing_enabled": True, + "_fp8_checkpoint_activations_enabled": True, + "_fp8_checkpoint_group_size": 256, + "_fp8_checkpoint_stochastic": True, + "_fp8_checkpoint_warmup_steps": 3, + "_fp8_checkpoint_fallback_layers": (1, 5), + "_global_step": 2, + }, + tokens, + sequence_parallel=False, + ) + + self.assertTrue(meta.fp8_checkpoint_activations) + self.assertEqual(meta.fp8_checkpoint_group_size, 256) + self.assertTrue(meta.fp8_checkpoint_stochastic) + self.assertEqual(meta.fp8_checkpoint_warmup_steps, 3) + self.assertEqual(meta.fp8_checkpoint_fallback_layers, (1, 5)) + self.assertEqual(meta.global_step, 2) def test_train_cli_drop_rollout_state_inverts_runtime_flag(self): """The public CLI exposes the memory-saving inverse of keep_rollout_state.""" @@ -662,13 +744,16 @@ def test_train_cli_drop_rollout_state_inverts_runtime_flag(self): self.assertFalse(cfg.keep_rollout_state) self.assertFalse(cfg.mlx_config().keep_rollout_state) + kept = train_cli._trainer_config_from_args(_train_args(algo="sft", drop_rollout_state=False)) + self.assertTrue(kept.keep_rollout_state) + def test_train_cli_optimizer_state_offload_reaches_cuda_runtime(self): """SFT can offload optimizer state without changing rollout-state retention.""" args = _train_args(algo="sft", optimizer_state_offload="cpu") cfg = train_cli._trainer_config_from_args(args) - self.assertTrue(cfg.keep_rollout_state) + self.assertFalse(cfg.keep_rollout_state) self.assertEqual(cfg.optimizer_state_offload, "cpu") self.assertEqual(cfg.cuda_config().runtime["optimizer_state_offload"], "cpu") @@ -1061,7 +1146,7 @@ def _train_args(**overrides): weight_decay=1e-2, grad_clip_norm=1.0, activation_checkpointing=True, - drop_rollout_state=False, + drop_rollout_state=True, optimizer_state_offload="none", optimizer_state_offload_dir=None, optimizer_state_offload_batch_size=1, diff --git a/tests/test_dashboard_metrics_path_cpu.py b/tests/test_dashboard_metrics_path_cpu.py index c355b6cf..aba8c915 100644 --- a/tests/test_dashboard_metrics_path_cpu.py +++ b/tests/test_dashboard_metrics_path_cpu.py @@ -8,6 +8,7 @@ DashboardState, Job, agent_language_instruction, + build_train_command, repair_action_for_check, sample_media_references, start_runtime_repair, @@ -91,6 +92,16 @@ def test_agent_language_instruction_follows_dashboard_language(): assert "English" in agent_language_instruction({"language": "en"}) +def test_train_command_emits_memory_saving_boolean_choices(): + defaults = build_train_command({"fp8_checkpoint_activations": True, "drop_rollout_state": True}) + disabled = build_train_command({"fp8_checkpoint_activations": False, "drop_rollout_state": False}) + + assert "--fp8-ckpt-activations" in defaults + assert "--drop-rollout-state" in defaults + assert "--no-fp8-ckpt-activations" in disabled + assert "--keep-rollout-state" in disabled + + @pytest.mark.parametrize( ("name", "package", "command_tail"), [ diff --git a/tests/test_moe_sequence_parallel_cpu.py b/tests/test_moe_sequence_parallel_cpu.py index 4c27fcb5..ba479179 100644 --- a/tests/test_moe_sequence_parallel_cpu.py +++ b/tests/test_moe_sequence_parallel_cpu.py @@ -133,6 +133,92 @@ def shared_experts(states): torch.testing.assert_close(output.float(), torch.full_like(output.float(), 5)) +def test_bailing_v3_decoder_checkpoints_attention_and_dense_mlp(monkeypatch): + pytest.importorskip("triton") + import areno.models.bailing_v3.model as bailing_v3 + + checkpointed = [] + + def checkpoint(function, states, *args, train_meta=None, infer_meta=None, **kwargs): + del train_meta, infer_meta, kwargs + checkpointed.append(function.__name__) + return function(states, *args) + + monkeypatch.setattr(bailing_v3, "checkpoint_layer", checkpoint) + + class DenseMlp: + pass + + def attention_block(states, position_ids, train_meta, infer_meta): + del position_ids, train_meta, infer_meta + return states * 2 + + def dense_mlp_block(states): + return states * 3 + + layer = SimpleNamespace( + _attention_block=attention_block, + _dense_mlp_block=dense_mlp_block, + mlp=DenseMlp(), + ) + hidden = torch.ones((1, 2, 2)) + + output = bailing_v3.BailingDecoderLayer.forward( + layer, + hidden, + torch.arange(2).unsqueeze(0), + SimpleNamespace(num_padding_tokens=0), + None, + ) + + assert checkpointed == ["attention_block", "dense_mlp_block"] + torch.testing.assert_close(output, torch.full_like(output, 12)) + + +def test_bailing_v3_decoder_keeps_sparse_router_outside_recompute(monkeypatch): + pytest.importorskip("triton") + import areno.models.bailing_v3.model as bailing_v3 + + checkpointed = [] + sparse_calls = [] + + def checkpoint(function, states, *args, train_meta=None, infer_meta=None, **kwargs): + del train_meta, infer_meta, kwargs + checkpointed.append(function.__name__) + return function(states, *args) + + class SparseMlp: + def __call__(self, states, num_padding_tokens): + sparse_calls.append(num_padding_tokens) + return states * 3 + + monkeypatch.setattr(bailing_v3, "checkpoint_layer", checkpoint) + monkeypatch.setattr(bailing_v3, "BailingSparseMoeBlock", SparseMlp) + + def attention_block(states, position_ids, train_meta, infer_meta): + del position_ids, train_meta, infer_meta + return states * 2 + + layer = SimpleNamespace( + _attention_block=attention_block, + post_attention_layernorm=lambda states: states, + mlp=SparseMlp(), + ) + hidden = torch.ones((1, 2, 2)) + + output = bailing_v3.BailingDecoderLayer.forward( + layer, + hidden, + torch.arange(2).unsqueeze(0), + SimpleNamespace(num_padding_tokens=1), + None, + ) + + assert checkpointed == ["attention_block"] + assert sparse_calls == [1] + torch.testing.assert_close(output, torch.full_like(output, 12)) + + def test_bailing_router_load_ignores_alignment_tokens(monkeypatch): pytest.importorskip("triton") import areno.models.bailing.model as bailing diff --git a/tests/test_recompute_cpu.py b/tests/test_recompute_cpu.py index cbaae558..6611150d 100644 --- a/tests/test_recompute_cpu.py +++ b/tests/test_recompute_cpu.py @@ -5,6 +5,13 @@ import torch from areno.engine.parallel.collectives import is_sequence_parallel_active +from areno.engine.runtime import fp8_checkpoint as fp8_checkpoint_module +from areno.engine.runtime.fp8_checkpoint import ( + fp8_checkpoint_metrics, + pack_fp8_checkpoint_tensor, + reset_fp8_checkpoint_stats, + unpack_fp8_checkpoint_tensor, +) from areno.engine.runtime.metadata import InferMeta, TrainMeta from areno.engine.runtime.recompute import checkpoint_layer, checkpoint_routed_moe_layer, should_checkpoint_layer @@ -12,6 +19,16 @@ class RecomputeTest(unittest.TestCase): """Activation checkpoint tests use tiny CPU tensors and no model weights.""" + def test_fp8_checkpoint_stats_reset_preserves_compiled_reference(self): + captured_stats = fp8_checkpoint_module._STATS + + reset_fp8_checkpoint_stats() + + self.assertIs(fp8_checkpoint_module._STATS, captured_stats) + captured_stats.boundaries += 1 + self.assertEqual(fp8_checkpoint_metrics()["fp8_ckpt_boundaries"], 1.0) + reset_fp8_checkpoint_stats() + def test_should_checkpoint_layer_requires_training_forward(self): """Recompute is enabled only for grad-enabled training forwards.""" train_meta = TrainMeta(activation_checkpointing=True) @@ -41,6 +58,105 @@ def layer_fn(x, bias): self.assertTrue(torch.equal(x.grad, torch.tensor([2.0, 4.0, 6.0]))) self.assertGreaterEqual(calls["count"], 1) + def test_fp8_boundary_storage_reduces_bytes_and_restores_bf16(self): + """Group-128 E4M3 storage should be far smaller than its BF16 source.""" + states = torch.randn((2, 4, 256), dtype=torch.bfloat16) + + packed = pack_fp8_checkpoint_tensor(states, group_size=128) + restored = unpack_fp8_checkpoint_tensor(packed) + + self.assertEqual(packed.values.dtype, torch.float8_e4m3fn) + self.assertEqual(restored.dtype, torch.bfloat16) + self.assertEqual(restored.shape, states.shape) + stored_bytes = packed.values.numel() * packed.values.element_size() + stored_bytes += packed.scales.numel() * packed.scales.element_size() + original_bytes = states.numel() * states.element_size() + self.assertLessEqual(stored_bytes, original_bytes * 0.55) + self.assertTrue(torch.allclose(restored.float(), states.float(), rtol=0.13, atol=0.13)) + + def test_fp8_checkpoint_keeps_forward_exact_and_gradient_close(self): + """Only backward recomputation observes restored FP8 boundary values.""" + source = torch.randn((2, 3, 256), dtype=torch.bfloat16) + exact_states = source.clone().requires_grad_(True) + fp8_states = source.clone().requires_grad_(True) + + def layer_fn(states): + return torch.sin(states.float()).square().sum() + + exact = checkpoint_layer( + layer_fn, + exact_states, + train_meta=TrainMeta(activation_checkpointing=True), + ) + reset_fp8_checkpoint_stats() + compressed = checkpoint_layer( + layer_fn, + fp8_states, + train_meta=TrainMeta(activation_checkpointing=True, fp8_checkpoint_activations=True), + ) + self.assertTrue(torch.equal(exact, compressed)) + exact.backward() + compressed.backward() + + cosine = torch.nn.functional.cosine_similarity( + exact_states.grad.float().flatten(), fp8_states.grad.float().flatten(), dim=0 + ) + self.assertGreaterEqual(float(cosine), 0.99) + metrics = fp8_checkpoint_metrics() + self.assertEqual(metrics["fp8_ckpt_boundaries"], 1.0) + self.assertGreaterEqual(metrics["fp8_ckpt_storage_reduction"], 0.45) + + def test_fp8_checkpoint_warmup_fallback_and_opt_out(self): + """Warm-up, layer fallback, and sensitive boundaries retain BF16.""" + + class Layer(torch.nn.Module): + layer_idx = 2 + + def forward(self, states): + return states.float().square().sum() + + for meta, compress_boundary in ( + ( + TrainMeta( + activation_checkpointing=True, + fp8_checkpoint_activations=True, + fp8_checkpoint_warmup_steps=1, + global_step=0, + ), + True, + ), + ( + TrainMeta( + activation_checkpointing=True, + fp8_checkpoint_activations=True, + fp8_checkpoint_fallback_layers=(2,), + ), + True, + ), + (TrainMeta(activation_checkpointing=True, fp8_checkpoint_activations=True), False), + ): + reset_fp8_checkpoint_stats() + states = torch.randn((1, 2, 256), dtype=torch.bfloat16, requires_grad=True) + output = checkpoint_layer( + Layer(), + states, + train_meta=meta, + compress_boundary=compress_boundary, + ) + output.backward() + metrics = fp8_checkpoint_metrics() + self.assertEqual(metrics["fp8_ckpt_boundaries"], 0.0) + self.assertEqual(metrics["fp8_ckpt_storage_reduction"], 0.0) + + def test_stochastic_fp8_pack_preserves_caller_rng(self): + """Boundary rounding must not perturb dropout RNG in the exact forward.""" + states = torch.randn((2, 2, 256), dtype=torch.bfloat16) + torch.manual_seed(1234) + rng_before = torch.random.get_rng_state() + pack_fp8_checkpoint_tensor(states, stochastic=True) + rng_after = torch.random.get_rng_state() + self.assertTrue(torch.equal(rng_before, rng_after)) + def test_checkpoint_layer_bypasses_checkpoint_for_infer_meta(self): """Inference metadata must bypass checkpointing even when train_meta opts in.""" calls = {"count": 0} diff --git a/tests/test_train_cli_config_cpu.py b/tests/test_train_cli_config_cpu.py index ffd57030..5c88e45f 100644 --- a/tests/test_train_cli_config_cpu.py +++ b/tests/test_train_cli_config_cpu.py @@ -924,6 +924,38 @@ def test_train_help_places_epochs_under_basic_not_checkpointing(): assert "--epochs" not in output[checkpoint:] +def test_train_help_exposes_fp8_checkpoint_activation_flag(): + output = _help_output() + + assert "--fp8-ckpt-activations" in output + assert "--no-fp8-ckpt-activations" in output + assert "--keep-rollout-state" in output + + +def test_train_fp8_checkpoint_activation_flag_reaches_runtime(): + cfg = _trainer_config_from_options(**_options(algo="sft", backend="cuda", fp8_checkpoint_activations=True)) + + assert cfg.fp8_checkpoint_activations is True + assert cfg.cuda_config().runtime["fp8_checkpoint_activations"] is True + + +def test_train_memory_saving_defaults_can_be_disabled(): + defaults = _trainer_config_from_options(**_options(algo="sft", backend="cuda")) + disabled = _trainer_config_from_options( + **_options( + algo="sft", + backend="cuda", + fp8_checkpoint_activations=False, + drop_rollout_state=False, + ) + ) + + assert defaults.fp8_checkpoint_activations is True + assert defaults.keep_rollout_state is False + assert disabled.fp8_checkpoint_activations is False + assert disabled.keep_rollout_state is True + + def test_train_help_remains_complete_and_groups_every_declared_option(): ctx = train_cli.click.Context(train_cli.train_command) declared = { @@ -1005,7 +1037,7 @@ def _options(**overrides): weight_decay=1e-2, grad_clip_norm=1.0, activation_checkpointing=True, - drop_rollout_state=False, + drop_rollout_state=True, eager_decode=False, attn_backend="flash", disable_thinking=False,