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refactor: deduplicate low-risk code paths

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ViperEkura
2026-08-19 16:17:40 +08:00
parent f252af495c
commit 398e8a3ea3
12 changed files with 64 additions and 114 deletions
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astrai/config/train_config.py
+12 -12
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@@ -11,10 +11,10 @@ from torch.utils.data import Dataset
from astrai.config.base import BaseConfig
from astrai.model.components.lora import LoRAConfig
_TRAIN_TYPES = frozenset({"seq", "sft", "dpo", "grpo", "online_grpo", "online_dpo"})
_PARALLEL_MODES = frozenset({"none", "ddp", "fsdp"})
_BACKENDS = frozenset({"nccl", "gloo"})
_START_METHODS = frozenset({"spawn", "fork", "forkserver"})
TRAIN_TYPES = frozenset({"seq", "sft", "dpo", "grpo", "online_grpo", "online_dpo"})
PARALLEL_MODES = frozenset({"none", "ddp", "fsdp"})
BACKENDS = frozenset({"nccl", "gloo"})
START_METHODS = frozenset({"spawn", "fork", "forkserver"})
_COMPILE_MODES = frozenset({"default", "reduce-overhead", "max-autotune"})
@@ -129,31 +129,31 @@ class TrainConfig(BaseConfig):
@field_validator("strategy")
def _validate_strategy(cls, v: str) -> str:
if v not in _TRAIN_TYPES:
if v not in TRAIN_TYPES:
raise ValueError(
f"strategy must be one of {sorted(_TRAIN_TYPES)}, got {v!r}"
f"strategy must be one of {sorted(TRAIN_TYPES)}, got {v!r}"
)
return v
@field_validator("parallel_mode")
def _validate_parallel_mode(cls, v: str) -> str:
if v not in _PARALLEL_MODES:
if v not in PARALLEL_MODES:
raise ValueError(
f"parallel_mode must be one of {sorted(_PARALLEL_MODES)}, got {v!r}"
f"parallel_mode must be one of {sorted(PARALLEL_MODES)}, got {v!r}"
)
return v
@field_validator("backend")
def _validate_backend(cls, v: str) -> str:
if v not in _BACKENDS:
raise ValueError(f"backend must be one of {sorted(_BACKENDS)}, got {v!r}")
if v not in BACKENDS:
raise ValueError(f"backend must be one of {sorted(BACKENDS)}, got {v!r}")
return v
@field_validator("start_method")
def _validate_start_method(cls, v: str) -> str:
if v not in _START_METHODS:
if v not in START_METHODS:
raise ValueError(
f"start_method must be one of {sorted(_START_METHODS)}, got {v!r}"
f"start_method must be one of {sorted(START_METHODS)}, got {v!r}"
)
return v
astrai/dataset/dataset.py
+4 -4
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@@ -383,10 +383,10 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
transform = _build_jsonl_transform(load_path, tokenizer_path)
if transform is None:
raise FileNotFoundError(
f"JSONL dataset config not found. Expected "
f"dataset_config.json alongside *.jsonl files, pass "
f"tokenizer_path= for the built-in messages config, or "
f"use processor= for lazy on-the-fly tokenisation."
"JSONL dataset config not found. Expected "
"dataset_config.json alongside *.jsonl files, pass "
"tokenizer_path= for the built-in messages config, or "
"use processor= for lazy on-the-fly tokenisation."
)
store.load(load_path, transform=transform, **kwargs)
else:
astrai/dataset/storage.py
+1 -1
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@@ -217,7 +217,7 @@ class Store(ABC):
"""
if self._window_size <= 0:
raise RuntimeError("sample_window() requires window_size > 0 (stream mode)")
if self._window_size <= 0 or self._length <= self._window_size:
if self._length <= self._window_size:
raise IndexError(
f"Data too short for window: token_count={self._length}, "
f"window_size={self._window_size}"
astrai/inference/cache/strategy.py
Vendored
-2
View File
@@ -16,8 +16,6 @@ from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Callable, Dict, List, Optional, OrderedDict
import torch
from astrai.inference.cache.buffer import ReqToTokenPool
# ---- data contract: per-task slot state ----
astrai/model/components/mlp.py
+1 -7
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@@ -29,12 +29,6 @@ class FFNOutput(TypedDict):
router_stats: Optional[RouterStats]
class RoutedOutput(TypedDict):
hidden_states: Tensor
aux_loss: Optional[Tensor]
router_stats: Optional[RouterStats]
@FFNFactory.register("mlp")
class MLP(nn.Module):
def __init__(self, dim: int, dim_ffn: int, down_init_std: float = 0.02):
@@ -122,7 +116,7 @@ class DeepSeekMoE(nn.Module):
/ self.n_shared_experts
)
def _routed_forward(self, x: Tensor, include_aux_loss: bool) -> RoutedOutput:
def _routed_forward(self, x: Tensor, include_aux_loss: bool) -> FFNOutput:
N, D = x.shape
K = self.n_activated_experts
E = self.n_routed_experts
astrai/parallel/setup.py
+7 -11
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@@ -247,18 +247,15 @@ class LocalStrategy(LaunchStrategy):
ctx.join()
def _detect_launcher() -> str:
"""Detect the distributed launcher from environment.
Returns one of: "torchelastic", "torchrun", "external", "local".
"""
def _is_external_launcher() -> bool:
"""Whether an external launcher (torchrun/elastic/manual env) started us."""
if dist.is_torchelastic_launched():
return "torchelastic"
return True
if "LOCAL_WORLD_SIZE" in os.environ:
return "torchrun"
return True
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
return "external"
return "local"
return True
return False
def spawn_parallel_fn(
@@ -273,8 +270,7 @@ def spawn_parallel_fn(
):
if master_port is None:
master_port = find_free_port()
launcher = _detect_launcher()
if launcher in ("torchelastic", "torchrun", "external"):
if _is_external_launcher():
strategy = TorchrunStrategy(
world_size, backend, master_addr, master_port, device_type, start_method
)
astrai/serialization/checkpoint.py
+12 -20
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@@ -5,7 +5,7 @@ import json
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, Optional, Union
from typing import Any, Callable, Dict, Optional, Union
import safetensors.torch as st
import torch
@@ -22,39 +22,31 @@ def save_safetensors(state_dict: dict, path: Union[str, Path]):
st.save_file(state_dict, str(path))
def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
def _broadcast_load(loader: Callable[[], dict], broadcast: bool) -> dict:
"""Load on rank 0 and broadcast the object to all ranks."""
if not broadcast or not dist.is_initialized():
return st.load_file(str(path))
return loader()
rank = get_rank()
if rank == 0:
state_dict = st.load_file(str(path))
data = loader()
else:
state_dict = {}
tmp = [state_dict]
data = {}
tmp = [data]
dist.broadcast_object_list(tmp, src=0)
return tmp[0]
def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
return _broadcast_load(lambda: st.load_file(str(path)), broadcast)
def save_json(data: dict, path: Union[str, Path]):
with open(str(path), "w") as f:
json.dump(data, f, indent=2)
def load_json(path: Union[str, Path], broadcast: bool = False) -> dict:
if not broadcast or not dist.is_initialized():
with open(str(path), "r") as f:
return json.load(f)
rank = get_rank()
if rank == 0:
with open(str(path), "r") as f:
data = json.load(f)
else:
data = {}
tmp = [data]
dist.broadcast_object_list(tmp, src=0)
return tmp[0]
return _broadcast_load(lambda: json.loads(Path(path).read_text()), broadcast)
def save_torch(obj: Any, path: Union[str, Path]):
astrai/trainer/metric_util.py
+2 -18
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@@ -94,21 +94,5 @@ def ctx_get_grad_snr(ctx):
return tracker.snr
def ctx_get_moe_aux_loss(ctx):
return ctx.strategy._moe_metrics.get("aux_loss")
def ctx_get_router_entropy(ctx):
return ctx.strategy._moe_metrics.get("router_entropy")
def ctx_get_dead_expert_fraction(ctx):
return ctx.strategy._moe_metrics.get("dead_expert_fraction")
def ctx_get_load_imbalance_mean(ctx):
return ctx.strategy._moe_metrics.get("load_imbalance_mean")
def ctx_get_load_imbalance_max(ctx):
return ctx.strategy._moe_metrics.get("load_imbalance_max")
def ctx_get_moe_metric(ctx, key):
return ctx.strategy._moe_metrics.get(key)
astrai/trainer/strategy.py
+2 -15
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@@ -1,6 +1,6 @@
"""Training strategy implementations with factory pattern."""
from abc import ABC, abstractmethod
from abc import ABC
from typing import Callable, Dict, List, Optional, TypedDict, Union
import torch
@@ -187,7 +187,6 @@ class BaseStrategy(ABC):
self.extra_kwargs = kwargs
self._rollout_runner = None
@abstractmethod
def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
"""Compute loss for the given batch.
@@ -197,7 +196,7 @@ class BaseStrategy(ABC):
Returns:
Computed loss tensor
"""
raise NotImplementedError
return self.compute_loss_output(batch)["loss"]
def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
return self._normalize_output(self.compute_loss(batch))
@@ -328,9 +327,6 @@ class SEQStrategy(BaseStrategy):
super().__init__(model, device, **kwargs)
self.label_smoothing = label_smoothing
def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
return self.compute_loss_output(batch)["loss"]
def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
batch = move_to_device(batch, self.device)
input_ids, target_ids = batch["input_ids"], batch["target_ids"]
@@ -369,9 +365,6 @@ class SFTStrategy(BaseStrategy):
super().__init__(model, device, **kwargs)
self.label_smoothing = label_smoothing
def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
return self.compute_loss_output(batch)["loss"]
def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
batch = move_to_device(batch, self.device)
input_ids, target_ids, position_ids, loss_mask = (
@@ -426,9 +419,6 @@ class DPOStrategy(BaseStrategy):
self.beta = beta
self.reduction = reduction
def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
return self.compute_loss_output(batch)["loss"]
def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
batch = move_to_device(batch, self.device)
chosen_ids, rejected_ids = batch["chosen"], batch["rejected"]
@@ -553,9 +543,6 @@ class GRPOStrategy(BaseStrategy):
if state_dict is not None:
self.old_model.load_state_dict(state_dict)
def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
return self.compute_loss_output(batch)["loss"]
def compute_loss_output(self, batch: Dict[str, Tensor]) -> LossOutput:
batch = move_to_device(batch, self.device)
prompts = batch["prompts"]
astrai/trainer/train_callback.py
+11 -10
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@@ -3,6 +3,7 @@ import logging
import os
import sys
import time
from functools import partial
from pathlib import Path
from typing import IO, Callable, List, Optional, Protocol, runtime_checkable
@@ -17,15 +18,11 @@ from astrai.parallel import only_on_rank
from astrai.parallel.setup import get_current_device
from astrai.serialization import Checkpoint
from astrai.trainer.metric_util import (
ctx_get_dead_expert_fraction,
ctx_get_grad_norm,
ctx_get_grad_snr,
ctx_get_load_imbalance_max,
ctx_get_load_imbalance_mean,
ctx_get_loss,
ctx_get_lr,
ctx_get_moe_aux_loss,
ctx_get_router_entropy,
ctx_get_moe_metric,
ctx_get_val_loss,
)
from astrai.trainer.train_context import TrainContext
@@ -262,11 +259,15 @@ class MetricCallback(TrainCallback):
"val_loss": ctx_get_val_loss,
"grad_norm": ctx_get_grad_norm,
"grad_snr": ctx_get_grad_snr,
"moe_aux_loss": ctx_get_moe_aux_loss,
"router_entropy": ctx_get_router_entropy,
"dead_expert_fraction": ctx_get_dead_expert_fraction,
"load_imbalance_mean": ctx_get_load_imbalance_mean,
"load_imbalance_max": ctx_get_load_imbalance_max,
"moe_aux_loss": partial(ctx_get_moe_metric, key="aux_loss"),
"router_entropy": partial(ctx_get_moe_metric, key="router_entropy"),
"dead_expert_fraction": partial(
ctx_get_moe_metric, key="dead_expert_fraction"
),
"load_imbalance_mean": partial(
ctx_get_moe_metric, key="load_imbalance_mean"
),
"load_imbalance_max": partial(ctx_get_moe_metric, key="load_imbalance_max"),
}
def _metrics(self, context: TrainContext, names):
astrai/trainer/train_context.py
-1
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@@ -204,7 +204,6 @@ class TrainContextBuilder:
def _create_dataloaders(
self, context: TrainContext, train_dataset, val_dataset
) -> None:
cfg = self.config
sampler_offset = context.consumed_samples // context.world_size
if self._resume and sampler_offset > 0:
samples_per_replica = (