From 9c86e0f7459b4d66db69f169db2995efca1565de Mon Sep 17 00:00:00 2001 From: Wentai Zhang Date: Wed, 8 Apr 2026 11:23:42 +0800 Subject: [PATCH 1/7] fix: harden padded distributed eval across training engines (#1109) Keep no-drop evaluation stable when batches do not divide evenly across DP workers. Pad eval inputs with zero-weight dummy items and make engine loss reduction tolerate local empty shards so distributed and pipeline-parallel paths stay synchronized. Key changes: - pad evaluate_* dispatches to preserve DP and RW pairing invariants - skip zero-weight local loss work in FSDP, Megatron, and Archon - add eval dispatch regression tests and refresh CLI reference docs --- areal/engine/core/train_engine.py | 4 +- areal/engine/fsdp_engine.py | 8 +- areal/engine/megatron_engine.py | 8 +- areal/experimental/engine/archon_engine.py | 8 +- areal/experimental/engine/archon_runner.py | 4 +- areal/infra/controller/train_controller.py | 131 +++++-- areal/trainer/rw/rw_engine.py | 57 ++- areal/trainer/sft/lm_engine.py | 1 + areal/utils/data.py | 33 ++ tests/test_eval_dispatch.py | 434 +++++++++++++++++++++ 10 files changed, 640 insertions(+), 48 deletions(-) create mode 100644 tests/test_eval_dispatch.py diff --git a/areal/engine/core/train_engine.py b/areal/engine/core/train_engine.py index ef1bb3b917..eecd1e9ee6 100644 --- a/areal/engine/core/train_engine.py +++ b/areal/engine/core/train_engine.py @@ -56,8 +56,10 @@ def compute_total_loss_weight( .clone() .to(dtype=torch.float32) ) - assert total_weight != 0, "Total loss weight must be non-zero" dist.all_reduce(total_weight, group=dp_group) + assert total_weight > 0, ( + "Global total loss weight must be positive after all_reduce" + ) return total_weight diff --git a/areal/engine/fsdp_engine.py b/areal/engine/fsdp_engine.py index 166e49893f..15c7e66308 100644 --- a/areal/engine/fsdp_engine.py +++ b/areal/engine/fsdp_engine.py @@ -1658,6 +1658,10 @@ def _compute_logprobs_and_loss( loss_multiplier: float = 1.0, ) -> torch.Tensor: """Compute logprobs/entropy and return scaled loss.""" + local_weight = loss_weight_fn(ctx.mb_input) + if local_weight == 0: + return logits.mean() * 0.0 + if self.config.is_critic and self.enable_tree_training: raise NotImplementedError( "Tree training with critic model is not supported yet." @@ -1669,7 +1673,7 @@ def _compute_logprobs_and_loss( if ctx.trie_node is None or not ctx.trie_node.all_sequence_ids: # Return zero loss that maintains gradient connection to logits # This ensures backward() works correctly for FSDP synchronization - return logits.sum() * 0.0 + return logits.mean() * 0.0 # For tree training, use gather_packed_tree_vocab_stats to properly # unpack vocab stats from tree structure back to per-sequence format. @@ -1712,7 +1716,7 @@ def _compute_logprobs_and_loss( values = values[: -ctx.pad_length] loss = loss_fn(values, ctx.mb_input) - loss_scale = loss_weight_fn(ctx.mb_input) / total_loss_weight * loss_multiplier + loss_scale = local_weight / total_loss_weight * loss_multiplier return loss * loss_scale def _compute_forward_result( diff --git a/areal/engine/megatron_engine.py b/areal/engine/megatron_engine.py index aabe64b44d..eb486d8530 100644 --- a/areal/engine/megatron_engine.py +++ b/areal/engine/megatron_engine.py @@ -1646,6 +1646,10 @@ def _compute_logprobs_and_loss( total_loss_weight: torch.Tensor, loss_multiplier: float = 1.0, ) -> torch.Tensor: + local_weight = loss_weight_fn(inputs) + if local_weight == 0: + return output.mean() * 0.0 + if self.config.is_critic and self.enable_tree_training: raise NotImplementedError( "Tree training with critic model is not supported yet." @@ -1658,7 +1662,7 @@ def _compute_logprobs_and_loss( if trie_node is None or not trie_node.all_sequence_ids: # Return zero loss that maintains gradient connection to output # This ensures backward() works correctly for distributed synchronization - return output.sum() * 0.0 + return output.mean() * 0.0 # For tree training, use gather_packed_tree_vocab_stats to properly # unpack vocab stats from tree structure back to per-sequence format. @@ -1699,7 +1703,7 @@ def _compute_logprobs_and_loss( values = output.squeeze(-1) loss = loss_fn(values, inputs) - loss_scale = loss_weight_fn(inputs) / total_loss_weight * loss_multiplier + loss_scale = local_weight / total_loss_weight * loss_multiplier return loss * loss_scale def _compute_forward_result( diff --git a/areal/experimental/engine/archon_engine.py b/areal/experimental/engine/archon_engine.py index 3b0242ac4a..5a02eca139 100644 --- a/areal/experimental/engine/archon_engine.py +++ b/areal/experimental/engine/archon_engine.py @@ -1154,10 +1154,14 @@ def _compute_logprobs_and_loss( loss_multiplier: float = 1.0, ) -> torch.Tensor: """Compute logprobs/entropy and return scaled loss.""" + local_weight = loss_weight_fn(ctx.mb_input) + if local_weight == 0: + return logits.mean() * 0.0 + if not self.config.is_critic: result = self._gather_actor_train_outputs(logits, ctx) if result is None: - return logits.sum() * 0.0 + return logits.mean() * 0.0 logprobs, entropy, vocab_min_logits, vocab_max_logits = result loss = loss_fn( logprobs, @@ -1170,7 +1174,7 @@ def _compute_logprobs_and_loss( values = self._gather_critic_output(logits, ctx) loss = loss_fn(values, ctx.mb_input) - loss_scale = loss_weight_fn(ctx.mb_input) / total_loss_weight * loss_multiplier + loss_scale = local_weight / total_loss_weight * loss_multiplier return loss * loss_scale def _compute_forward_result( diff --git a/areal/experimental/engine/archon_runner.py b/areal/experimental/engine/archon_runner.py index f50ce0a264..517d6a61f5 100644 --- a/areal/experimental/engine/archon_runner.py +++ b/areal/experimental/engine/archon_runner.py @@ -273,13 +273,13 @@ def pp_loss_fn(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: pred = pred.squeeze(0) loss = process_output_fn(pred, ctx.to_dict()) if loss is None: - return pred.sum() * 0.0 + return pred.mean() * 0.0 return loss else: # Non-last stage: dummy loss that keeps all elements in computation graph # so autograd can compute complete pred.grad for upstream stage def pp_loss_fn(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - return pred.sum() * 0.0 + return pred.mean() * 0.0 return pp_loss_fn diff --git a/areal/infra/controller/train_controller.py b/areal/infra/controller/train_controller.py index d351664662..59bef0a85b 100644 --- a/areal/infra/controller/train_controller.py +++ b/areal/infra/controller/train_controller.py @@ -21,6 +21,7 @@ from areal.infra.rpc.rtensor import RTensor from areal.infra.utils.concurrent import run_async_task from areal.utils import logging, stats_tracker +from areal.utils.data import make_dummy_eval_item from areal.utils.network import find_free_ports from areal.utils.seqpack import balanced_greedy_partition @@ -55,34 +56,99 @@ def _is_tensor_like(obj: Any) -> bool: ) +def _item_weight(d: dict[str, Any]) -> int: + attn_mask = d.get("attention_mask") + if isinstance(attn_mask, torch.Tensor): + return int(attn_mask.sum().item()) + if isinstance(attn_mask, RTensor): + return attn_mask.data.numel() + # Fallback: first tensor's numel + for v in d.values(): + if isinstance(v, RTensor): + return v.data.numel() + if isinstance(v, torch.Tensor) and v.ndim >= 2: + return v.numel() + return 1 + + def _dispatch_tensors( item_list: list[dict[str, Any]], dp_size: int, + group_size: int = 1, ) -> tuple[list[list[dict[str, Any]]], list[list[int]]]: - """Partition trajectories across DP groups by balanced token count.""" - token_weights: list[int] = [] - for d in item_list: - attn_mask = d.get("attention_mask") - if isinstance(attn_mask, torch.Tensor): - token_weights.append(int(attn_mask.sum().item())) - elif isinstance(attn_mask, RTensor): - token_weights.append(attn_mask.data.numel()) - else: - # Fallback: first tensor's numel - w = 1 - for v in d.values(): - if isinstance(v, RTensor): - w = v.data.numel() - break - if isinstance(v, torch.Tensor) and v.ndim >= 2: - w = v.numel() - break - token_weights.append(w) - group_indices = balanced_greedy_partition(token_weights, K=dp_size) - splits = [[item_list[i] for i in idxs] for idxs in group_indices] + """Partition trajectories across DP groups by balanced token count. + + Args: + group_size: number of consecutive items that form an atomic dispatch + unit (e.g. 2 for chosen/rejected RW pairs). Groups are never + split across DP ranks. ``group_size=1`` degenerates to per-item + partitioning. + """ + n = len(item_list) + if n % group_size != 0: + raise ValueError( + f"item count ({n}) must be divisible by group_size ({group_size})" + ) + + token_weights = [_item_weight(d) for d in item_list] + n_groups = n // group_size + + group_weights = [ + sum(token_weights[g * group_size + k] for k in range(group_size)) + for g in range(n_groups) + ] + + gpart = balanced_greedy_partition(group_weights, K=dp_size) + + group_indices: list[list[int]] = [] + splits: list[list[dict[str, Any]]] = [] + for gidxs in gpart: + item_idxs: list[int] = [] + items: list[dict[str, Any]] = [] + for g in gidxs: + for k in range(group_size): + idx = g * group_size + k + item_idxs.append(idx) + items.append(item_list[idx]) + group_indices.append(item_idxs) + splits.append(items) + + assert all(len(s) % group_size == 0 for s in splits), ( + f"Post-dispatch invariant violated: shard sizes " + f"{[len(s) for s in splits]} not all divisible by group_size={group_size}" + ) return splits, group_indices +def _pad_eval_batch( + args: tuple[Any, ...], dp_size: int, group_size: int = 1 +) -> tuple[Any, ...]: + """Pad the first tensor-like arg to a multiple of ``dp_size * group_size``. + + Called before dispatch for explicit evaluation controller paths so that + ``balanced_greedy_partition`` always receives a divisible input. + Dummy items have zero attention/loss masks and contribute nothing + to metrics or loss. + """ + result = list(args) + pad_target = dp_size * group_size + for i, arg in enumerate(result): + if isinstance(arg, list) and arg and _is_tensor_like(arg): + n = len(arg) + pad_count = (-n) % pad_target + if pad_count > 0: + padded = list(arg) + template = arg[0] + padded.extend(make_dummy_eval_item(template) for _ in range(pad_count)) + result[i] = padded + logger.info( + f"Eval dispatch: padded {pad_count} dummy items " + f"(total {len(padded)}) for dp_size={dp_size}" + ) + break # only pad the first tensor-like arg + return tuple(result) + + def _merge_tensors( results: list[Any], group_indices: list[list[int]] ) -> list[Any] | None: @@ -388,6 +454,20 @@ async def _async_custom_function_call(self, method: str, *args, **kwargs): results = await self._call_workers(method, dp_args, dp_kwargs) return self._collect_results(results, group_indices) + def _pad_eval_dispatch_args( + self, + args: tuple[Any, ...], + kwargs: dict[str, Any], + *, + group_size: int, + ) -> tuple[tuple[Any, ...], dict[str, Any]]: + """Pad eval batches for explicit algorithm-level evaluation dispatch.""" + kwargs = dict(kwargs) + args = _pad_eval_batch( + args, self.parallel_strategy.dp_size, group_size=group_size + ) + return args, kwargs + def _prepare_dispatch( self, *args, **kwargs ) -> tuple[list[list[Any]], dict[str, list[Any]], list[list[int]] | None]: @@ -396,12 +476,13 @@ def _prepare_dispatch( Returns (dp_split_args, dp_split_kwargs, group_indices). group_indices is non-None only for tensor dispatches. """ + group_size = kwargs.pop("group_size", 1) if _is_tensor_like(args) or _is_tensor_like(kwargs): - return self._partition_inputs(*args, **kwargs) + return self._partition_inputs(group_size, *args, **kwargs) return self._replicate_inputs(*args, **kwargs) def _partition_inputs( - self, *args, **kwargs + self, group_size: int, /, *args, **kwargs ) -> tuple[list[list[Any]], dict[str, list[Any]], list[list[int]]]: """Partition tensor args across DP groups; replicate others.""" dp_size = self.parallel_strategy.dp_size @@ -411,7 +492,9 @@ def _split(item: Any) -> list[Any]: nonlocal group_indices if _is_tensor_like(item): if group_indices is None: - splits, group_indices = _dispatch_tensors(item, dp_size) + splits, group_indices = _dispatch_tensors( + item, dp_size, group_size=group_size + ) return splits return [[item[i] for i in idxs] for idxs in group_indices] return [item] * dp_size diff --git a/areal/trainer/rw/rw_engine.py b/areal/trainer/rw/rw_engine.py index 781f5a344f..5331732082 100644 --- a/areal/trainer/rw/rw_engine.py +++ b/areal/trainer/rw/rw_engine.py @@ -4,7 +4,6 @@ from areal.api import TrainEngine from areal.infra import TrainController -from areal.infra.platforms import current_platform from areal.utils import logging, stats_tracker from areal.utils.data import batched_call from areal.utils.perf_tracer import trace_perf @@ -12,6 +11,27 @@ logger = logging.getLogger("RWEngine") +def _rw_valid_pairs(x: dict[str, Any]) -> torch.Tensor: + seqlens = x["cu_seqlens"][1:] - x["cu_seqlens"][:-1] + return seqlens.view(-1, 2).ne(0).all(dim=1) + + +def _rw_loss_weight(x: dict[str, Any]) -> torch.Tensor: + return _rw_valid_pairs(x).count_nonzero().float() + + +def _log_empty_rw_stats(device: torch.device) -> None: + n_pairs = torch.zeros(1, dtype=torch.bool, device=device) + stats_tracker.denominator(n_pairs=n_pairs) + stats_tracker.stat( + correct_ratio=torch.zeros(1, dtype=torch.float32, device=device), + pos_score=torch.zeros(1, dtype=torch.float32, device=device), + neg_score=torch.zeros(1, dtype=torch.float32, device=device), + loss=torch.zeros(1, dtype=torch.float32, device=device), + denominator="n_pairs", + ) + + class RWEngine: def __init__(self, engine: TrainEngine): self.engine = engine @@ -23,15 +43,13 @@ def train_rw(self, data: list[dict[str, Any]]) -> None: def _train_rw(self, data: dict[str, Any]) -> None: """Train on a batch (reward model).""" + if _rw_loss_weight(data) == 0: + _log_empty_rw_stats(data["cu_seqlens"].device) self.engine.train() stats = self.engine.train_batch( input_=data, loss_fn=compute_rw_loss, - loss_weight_fn=lambda x: torch.tensor( - x["cu_seqlens"].shape[0] - 1, - dtype=torch.float, - device=current_platform.current_device(), - ), + loss_weight_fn=_rw_loss_weight, ) stats_tracker.scalar(**stats) @@ -41,15 +59,13 @@ def evaluate_rw(self, data: list[dict[str, Any]]) -> None: batched_call(self._evaluate_rw, data, unpack=False) def _evaluate_rw(self, data: dict[str, Any]) -> None: + if _rw_loss_weight(data) == 0: + _log_empty_rw_stats(data["cu_seqlens"].device) self.engine.eval() self.engine.eval_batch( input_=data, loss_fn=compute_rw_loss, - loss_weight_fn=lambda x: torch.tensor( - x["cu_seqlens"].shape[0] - 1, - dtype=torch.float, - device=current_platform.current_device(), - ), + loss_weight_fn=_rw_loss_weight, ) @@ -58,16 +74,27 @@ def train_rw(self, *args, **kwargs): self._custom_function_call("train_rw", *args, **kwargs) def evaluate_rw(self, *args, **kwargs): - self._custom_function_call("evaluate_rw", *args, **kwargs) + # rw_modeling_collate_fn produces 2 sequences (chosen + rejected) per + # example; group_size=2 keeps each pair on the same DP rank. + args, kwargs = self._pad_eval_dispatch_args(args, kwargs, group_size=2) + self._custom_function_call("evaluate_rw", *args, group_size=2, **kwargs) def compute_rw_loss(scores: torch.Tensor, input_: dict[str, Any]) -> torch.Tensor: + device = scores.device cu_seqlens = input_["cu_seqlens"] seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).cpu() - n_pairs = (cu_seqlens.shape[0] - 1) // 2 + valid_pairs = _rw_valid_pairs(input_) + if not valid_pairs.any(): + _log_empty_rw_stats(device) + return torch.zeros((), dtype=torch.float32, device=device) + + valid_pair_mask = valid_pairs.to(device=device) + terminal_indices = seqlens.cumsum(0).to(device=device) - 1 assert scores.shape[0] == seqlens.sum(), (scores.shape, seqlens.sum()) - scores = scores[seqlens.cumsum(0) - 1].view(-1, 2).float() + scores = scores[terminal_indices].view(-1, 2)[valid_pair_mask].float() + loss = -(torch.nn.functional.logsigmoid(scores[:, 0] - scores[:, 1])) logging_loss = loss.detach() loss = loss.mean() @@ -75,7 +102,7 @@ def compute_rw_loss(scores: torch.Tensor, input_: dict[str, Any]) -> torch.Tenso # Logging. with torch.no_grad(): stats_tracker.denominator( - n_pairs=torch.ones(n_pairs, dtype=torch.bool, device=scores.device), + n_pairs=torch.ones(scores.shape[0], dtype=torch.bool, device=device), ) stats_tracker.stat( correct_ratio=(scores[:, 0] > scores[:, 1]).detach().float(), diff --git a/areal/trainer/sft/lm_engine.py b/areal/trainer/sft/lm_engine.py index 48e3c23792..10883c4a0c 100644 --- a/areal/trainer/sft/lm_engine.py +++ b/areal/trainer/sft/lm_engine.py @@ -46,6 +46,7 @@ def train_lm(self, *args, **kwargs): self._custom_function_call("train_lm", *args, **kwargs) def evaluate_lm(self, *args, **kwargs): + args, kwargs = self._pad_eval_dispatch_args(args, kwargs, group_size=1) self._custom_function_call("evaluate_lm", *args, **kwargs) diff --git a/areal/utils/data.py b/areal/utils/data.py index 834ea23e97..2979f0bd7c 100644 --- a/areal/utils/data.py +++ b/areal/utils/data.py @@ -1639,3 +1639,36 @@ def _compute_approx_kl( if apply_clamp: log_ratio = log_ratio.clamp(min=-10, max=10) return log_ratio + + +def make_dummy_eval_item(template: dict[str, Any]) -> dict[str, Any]: + """Create a zero-contribution dummy item matching *template*'s schema. + + Every tensor field is replaced with a minimal all-zeros tensor that + preserves dtype and device. ``attention_mask`` and ``loss_mask`` are + set to zero so that downstream loss/metric code treats the item as + contributing nothing. + """ + + def _zero_tensor_like(tensor: torch.Tensor) -> torch.Tensor: + return torch.zeros((1, 1), dtype=tensor.dtype, device=tensor.device) + + dummy: dict[str, Any] = {} + for key, value in template.items(): + if key in {"attention_mask", "loss_mask"}: + if isinstance(value, torch.Tensor): + dummy[key] = _zero_tensor_like(value) + else: + dummy[key] = torch.zeros((1, 1), dtype=torch.bool) + continue + + if key.startswith("multi_modal_input"): + dummy[key] = [{}] + continue + + if isinstance(value, torch.Tensor): + dummy[key] = _zero_tensor_like(value) + else: + dummy[key] = copy.deepcopy(value) + + return dummy diff --git a/tests/test_eval_dispatch.py b/tests/test_eval_dispatch.py new file mode 100644 index 0000000000..ec96af3659 --- /dev/null +++ b/tests/test_eval_dispatch.py @@ -0,0 +1,434 @@ +from types import MethodType, SimpleNamespace +from typing import Any, cast +from unittest.mock import patch + +import pytest +import torch +import torch.distributed as dist +import torch.nn.functional as F + +from areal.api.cli_args import MicroBatchSpec +from areal.engine.core.train_engine import compute_total_loss_weight +from areal.infra.controller.train_controller import ( + _dispatch_tensors, + _pad_eval_batch, +) +from areal.trainer.rw.rw_engine import ( + RWController, + RWEngine, + _rw_loss_weight, + compute_rw_loss, +) +from areal.trainer.sft.lm_engine import LMController +from areal.utils.data import ( + MicroBatchList, + concat_padded_tensors, + make_dummy_eval_item, + split_padded_tensor_dict_into_mb_list, +) +from areal.utils.stats_tracker import DistributedStatsTracker + + +def _make_item(idx: int, seqlen: int = 2) -> dict[str, object]: + return { + "input_ids": torch.full((1, seqlen), idx + 1, dtype=torch.long), + "attention_mask": torch.ones((1, seqlen), dtype=torch.bool), + "loss_mask": torch.ones((1, seqlen), dtype=torch.bool), + "meta": {"id": idx}, + } + + +def _flatten_splits(splits: list[list[dict[str, object]]]) -> list[dict[str, object]]: + return [item for group in splits for item in group] + + +def _count_dummies(items: list[dict[str, object]]) -> int: + return sum( + int(cast(torch.Tensor, item["attention_mask"]).sum().item() == 0) + for item in items + ) + + +def _make_rw_pair( + pair_idx: int, chosen_len: int = 3, rejected_len: int = 2 +) -> tuple[dict[str, object], dict[str, object]]: + chosen: dict[str, object] = { + "input_ids": torch.full((1, chosen_len), pair_idx * 2 + 1, dtype=torch.long), + "attention_mask": torch.ones((1, chosen_len), dtype=torch.bool), + "meta": {"pair": pair_idx, "role": "chosen"}, + } + rejected: dict[str, object] = { + "input_ids": torch.full((1, rejected_len), pair_idx * 2 + 2, dtype=torch.long), + "attention_mask": torch.ones((1, rejected_len), dtype=torch.bool), + "meta": {"pair": pair_idx, "role": "rejected"}, + } + return chosen, rejected + + +def _build_rw_batch(n_pairs: int, chosen_len: int = 3, rejected_len: int = 2): + items: list[dict[str, object]] = [] + for p in range(n_pairs): + c, r = _make_rw_pair(p, chosen_len, rejected_len) + items.extend([c, r]) + return items + + +def _make_rw_input(seqlens: list[int]) -> dict[str, torch.Tensor]: + cu_seqlens = [0] + for seqlen in seqlens: + cu_seqlens.append(cu_seqlens[-1] + seqlen) + return {"cu_seqlens": torch.tensor(cu_seqlens, dtype=torch.int32)} + + +class TestEvalBatchPadding: + def test_pad_eval_batch_no_padding_when_divisible(self): + items = [_make_item(i) for i in range(8)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 8 + assert _count_dummies(padded) == 0 + + def test_pad_eval_batch_pads_when_not_divisible(self): + items = [_make_item(i) for i in range(7)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 8 + assert _count_dummies(padded) == 1 + + def test_pad_eval_batch_pads_when_n_less_than_dp(self): + items = [_make_item(i) for i in range(2)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 4 + assert _count_dummies(padded) == 2 + + def test_dispatch_tensors_raises_when_not_divisible(self): + items = [_make_item(i) for i in range(7)] + with pytest.raises(ValueError, match="divisible"): + _dispatch_tensors(items, dp_size=4) + + def test_pad_then_dispatch_end_to_end(self): + items = [_make_item(i) for i in range(7)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + splits, _ = _dispatch_tensors(padded, dp_size=4) + assert all(len(group) == 2 for group in splits) + assert _count_dummies(_flatten_splits(splits)) == 1 + + def test_make_dummy_eval_item_schema(self): + template: dict[str, object] = { + "input_ids": torch.tensor([[2, 3, 4]], dtype=torch.long), + "attention_mask": torch.tensor([[True, True, False]], dtype=torch.bool), + "loss_mask": torch.tensor([[1, 1, 0]], dtype=torch.int32), + "multi_modal_input": [{"image": torch.tensor([1.0])}], + "meta": {"tag": ["x"]}, + } + + dummy = make_dummy_eval_item(template) + assert set(dummy.keys()) == set(template.keys()) + assert dummy["multi_modal_input"] == [{}] + torch.testing.assert_close( + dummy["attention_mask"], + torch.zeros((1, 1), dtype=torch.bool), + rtol=0.0, + atol=0.0, + ) + torch.testing.assert_close( + dummy["loss_mask"], + torch.zeros((1, 1), dtype=cast(torch.Tensor, template["loss_mask"]).dtype), + rtol=0.0, + atol=0.0, + ) + torch.testing.assert_close( + dummy["input_ids"], + torch.zeros((1, 1), dtype=cast(torch.Tensor, template["input_ids"]).dtype), + rtol=0.0, + atol=0.0, + ) + cast(dict[str, list[str]], template["meta"])["tag"].append("y") + assert dummy["meta"] == {"tag": ["x"]} + + def test_pad_eval_batch_keeps_multimodal_payload_aligned(self): + items: list[dict[str, object]] = [] + for _ in range(3): + items.append( + { + "input_ids": torch.tensor([[11, 12, 13]], dtype=torch.long), + "attention_mask": torch.tensor( + [[True, True, True]], dtype=torch.bool + ), + "loss_mask": torch.tensor([[1, 1, 1]], dtype=torch.int32), + "multi_modal_input": [ + { + "pixel_values": torch.ones((1, 2, 2), dtype=torch.float32), + "image_grid_thw": torch.tensor( + [[1, 1, 1]], dtype=torch.int32 + ), + } + ], + } + ) + + (padded,) = _pad_eval_batch((items,), dp_size=4) + batched = concat_padded_tensors(padded) + mb_list = split_padded_tensor_dict_into_mb_list(batched, MicroBatchSpec()) + + assert len(padded) == 4 + assert len(batched["multi_modal_input"]) == batched["attention_mask"].shape[0] + assert padded[-1]["multi_modal_input"] == [{}] + for mb in mb_list.mbs: + assert len(mb["multi_modal_input"]) == mb["attention_mask"].shape[0] + + def test_pad_eval_batch_group_size_odd_dp(self): + items = [_make_item(i) for i in range(2)] + (padded,) = _pad_eval_batch((items,), dp_size=3, group_size=2) + assert len(padded) == 6 + assert len(padded) % 2 == 0 + assert len(padded) % 3 == 0 + assert _count_dummies(padded) == 4 + + def test_pad_eval_batch_group_size_even_dp(self): + items = [_make_item(i) for i in range(6)] + (padded,) = _pad_eval_batch((items,), dp_size=4, group_size=2) + assert len(padded) == 8 + assert len(padded) % 2 == 0 + assert len(padded) % 4 == 0 + assert _count_dummies(padded) == 2 + + def test_pad_eval_batch_group_size_no_pad_needed(self): + items = [_make_item(i) for i in range(6)] + (padded,) = _pad_eval_batch((items,), dp_size=3, group_size=2) + assert len(padded) == 6 + assert _count_dummies(padded) == 0 + + def test_pad_eval_batch_default_group_size_unchanged(self): + items = [_make_item(i) for i in range(7)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 8 + assert _count_dummies(padded) == 1 + + def test_compute_total_loss_weight_allows_local_zero( + self, monkeypatch: pytest.MonkeyPatch + ): + mb_list = MicroBatchList( + data={}, + mb_spec=MicroBatchSpec(), + mbs=[{"attention_mask": torch.zeros((1, 1), dtype=torch.bool)}], + group_lens=[1], + ) + + def _mock_all_reduce( + tensor: torch.Tensor, group: dist.ProcessGroup | None = None + ): + del group + tensor.add_(3.0) + + monkeypatch.setattr(dist, "all_reduce", _mock_all_reduce) + + total_weight = compute_total_loss_weight( + mb_list=mb_list, + loss_weight_fn=lambda _mb: torch.tensor(0.0), + dp_group=cast(dist.ProcessGroup, object()), + ) + + torch.testing.assert_close(total_weight, torch.tensor(3.0), rtol=0.0, atol=0.0) + + +class TestRWDispatchGrouping: + def test_dispatch_group_size_preserves_rw_pairs(self): + dp_size = 4 + items = _build_rw_batch(n_pairs=8) + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=2) + assert len(padded) == 16 + splits, _ = _dispatch_tensors(padded, dp_size=dp_size, group_size=2) + + for shard_items in splits: + assert len(shard_items) % 2 == 0, ( + f"Shard received odd item count {len(shard_items)}" + ) + for j in range(0, len(shard_items), 2): + c_meta = shard_items[j].get("meta", {}) + r_meta = shard_items[j + 1].get("meta", {}) + if c_meta.get("pair") is not None: + assert c_meta["pair"] == r_meta["pair"], ( + f"Pair split across items: {c_meta} vs {r_meta}" + ) + assert c_meta["role"] == "chosen" + assert r_meta["role"] == "rejected" + + def test_dispatch_group_size_rw_5_examples_dp4(self): + dp_size = 4 + items = _build_rw_batch(n_pairs=5) + assert len(items) == 10 + + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=2) + assert len(padded) == 16 + assert len(padded) % (dp_size * 2) == 0 + + splits, _ = _dispatch_tensors(padded, dp_size=dp_size, group_size=2) + assert len(splits) == dp_size + for shard_items in splits: + assert len(shard_items) % 2 == 0, ( + f"Shard received odd item count {len(shard_items)}, view(-1, 2) would fail" + ) + + def test_dispatch_group_size_not_divisible_raises(self): + items = _build_rw_batch(n_pairs=3) + items.append(items[0]) + with pytest.raises(ValueError, match="divisible by group_size"): + _dispatch_tensors(items, dp_size=2, group_size=2) + + def test_dispatch_group_size_1_unchanged(self): + items = [_make_item(i, seqlen=i + 1) for i in range(8)] + _, indices_default = _dispatch_tensors(items, dp_size=4) + _, indices_gs1 = _dispatch_tensors(items, dp_size=4, group_size=1) + assert indices_default == indices_gs1 + + @pytest.mark.parametrize( + "dp_size, group_size, n_items", + [ + (3, 2, 12), + (4, 2, 16), + (6, 2, 24), + (4, 3, 24), + (3, 2, 6), + (4, 2, 8), + ], + ) + def test_pad_and_dispatch_group_matrix( + self, dp_size: int, group_size: int, n_items: int + ): + items = [_make_item(i, seqlen=i % 5 + 1) for i in range(n_items)] + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=group_size) + assert len(padded) % (dp_size * group_size) == 0 + + splits, _ = _dispatch_tensors(padded, dp_size=dp_size, group_size=group_size) + assert len(splits) == dp_size + for shard in splits: + assert len(shard) % group_size == 0 + + @pytest.mark.parametrize( + "dp_size, group_size, n_raw", + [ + (3, 2, 5), + (4, 2, 7), + (4, 3, 10), + (6, 2, 3), + ], + ) + def test_pad_aligns_non_divisible_input( + self, dp_size: int, group_size: int, n_raw: int + ): + items = [_make_item(i) for i in range(n_raw)] + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=group_size) + target = dp_size * group_size + assert len(padded) % target == 0 + assert len(padded) >= n_raw + + +class TestRWDummyPairSemantics: + def test_rw_loss_weight_counts_only_valid_pairs(self): + input_data = _make_rw_input([5, 4, 0, 0]) + + loss_weight = _rw_loss_weight(input_data) + + torch.testing.assert_close(loss_weight, torch.tensor(1.0), rtol=0.0, atol=0.0) + + def test_compute_rw_loss_ignores_dummy_pairs_in_loss_and_metrics(self): + tracker = DistributedStatsTracker() + input_data = _make_rw_input([5, 4, 0, 0]) + scores = torch.tensor([0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 1.0]) + expected_loss = -F.logsigmoid(torch.tensor(2.0)) + + with patch("areal.trainer.rw.rw_engine.stats_tracker", tracker): + loss = compute_rw_loss(scores, input_data) + + stats = tracker.export(reset=True) + + torch.testing.assert_close(loss, expected_loss, rtol=1e-5, atol=1e-6) + assert stats["n_pairs"] == 1.0 + assert stats["correct_ratio/avg"] == 1.0 + assert stats["pos_score/avg"] == 3.0 + assert stats["neg_score/avg"] == 1.0 + torch.testing.assert_close( + torch.tensor(stats["loss/avg"]), + expected_loss, + rtol=1e-5, + atol=1e-6, + ) + + def test_compute_rw_loss_returns_zero_for_all_dummy_pairs(self): + tracker = DistributedStatsTracker() + input_data = _make_rw_input([0, 0]) + + with patch("areal.trainer.rw.rw_engine.stats_tracker", tracker): + loss = compute_rw_loss(torch.tensor([], dtype=torch.float32), input_data) + + stats = tracker.export(reset=True) + + torch.testing.assert_close(loss, torch.tensor(0.0), rtol=0.0, atol=0.0) + assert stats["n_pairs"] == 0.0 + assert "loss/avg" not in stats + + +class TestExplicitEvalDispatchControllers: + def test_lm_controller_evaluate_lm_explicitly_pads_batch(self): + controller = LMController.__new__(LMController) + cast(Any, controller).train_alloc = SimpleNamespace( + parallel=SimpleNamespace(dp_size=4) + ) + captured: dict[str, Any] = {} + + def _capture_call(self, method: str, *args, **kwargs): + captured["method"] = method + captured["args"] = args + captured["kwargs"] = kwargs + return None + + controller._custom_function_call = MethodType(_capture_call, controller) + + items = [_make_item(i) for i in range(7)] + controller.evaluate_lm(items) + + assert captured["method"] == "evaluate_lm" + padded_items = cast(list[dict[str, object]], captured["args"][0]) + assert len(padded_items) == 8 + assert captured["kwargs"] == {} + + def test_rw_controller_evaluate_rw_explicitly_pads_pairs(self): + controller = RWController.__new__(RWController) + cast(Any, controller).train_alloc = SimpleNamespace( + parallel=SimpleNamespace(dp_size=4) + ) + captured: dict[str, Any] = {} + + def _capture_call(self, method: str, *args, **kwargs): + captured["method"] = method + captured["args"] = args + captured["kwargs"] = kwargs + return None + + controller._custom_function_call = MethodType(_capture_call, controller) + + items = _build_rw_batch(n_pairs=5) + controller.evaluate_rw(items) + + assert captured["method"] == "evaluate_rw" + assert captured["kwargs"]["group_size"] == 2 + padded_items = cast(list[dict[str, object]], captured["args"][0]) + assert len(padded_items) == 16 + + def test_evaluate_rw_logs_zero_pair_denominator_for_all_dummy_local_batch(self): + tracker = DistributedStatsTracker() + + class _DummyEngine: + def eval(self) -> None: + return None + + def eval_batch(self, **kwargs) -> None: + del kwargs + return None + + with patch("areal.trainer.rw.rw_engine.stats_tracker", tracker): + RWEngine(_DummyEngine())._evaluate_rw(_make_rw_input([0, 0])) + + stats = tracker.export(reset=True) + + assert stats["n_pairs"] == 0.0 + assert "loss/avg" not in stats From 88e761b1193c11b8cfe8d1d01ca18901b163b213 Mon Sep 17 00:00:00 2001 From: Wei Fu <36355462+garrett4wade@users.noreply.github.com> Date: Wed, 8 Apr 2026 11:26:00 +0800 Subject: [PATCH 2/7] feat(ci): separate vllm and sglang pyproject.toml (#1141) * feat(ci): separate vllm and sglang pyproject.toml * fix(ci): support vllm pyproject in docker and install tests * fix(ci): correct Dockerfile RUN chaining for pyproject swap * fix(ci): avoid read-only pyproject bind mount writes * fix(ci): validate docker variant and sync vllm lockfile docs Fail fast on invalid Docker VARIANT values to prevent silently building the wrong backend image, and align vLLM setup instructions with CI by copying uv.vllm.lock when swapping pyproject variants. Key changes: - validate VARIANT via case statement in Dockerfile - update README/docs/agent guidance with uv.vllm.lock copy step - regenerate CLI reference docs via pre-commit hook --- .github/workflows/install-test.yml | 19 +- .pre-commit-config.yaml | 12 + AGENTS.md | 2 +- CLAUDE.md | 1 + Dockerfile | 10 +- README.md | 5 +- areal/tools/check_pyproject_consistency.py | 412 ++ docs/en/cli_reference.md | 8 +- docs/en/tutorial/installation.md | 54 +- docs/zh/cli_reference.md | 8 +- docs/zh/tutorial/installation.md | 51 +- pyproject.toml | 80 +- pyproject.vllm.toml | 328 + uv.lock | 4289 ++++-------- uv.vllm.lock | 7228 ++++++++++++++++++++ 15 files changed, 9398 insertions(+), 3109 deletions(-) create mode 100644 areal/tools/check_pyproject_consistency.py create mode 100644 pyproject.vllm.toml create mode 100644 uv.vllm.lock diff --git a/.github/workflows/install-test.yml b/.github/workflows/install-test.yml index 99c4e6cec5..b27a53de40 100644 --- a/.github/workflows/install-test.yml +++ b/.github/workflows/install-test.yml @@ -5,14 +5,18 @@ on: branches: [main] paths: - 'pyproject.toml' + - 'pyproject.vllm.toml' - 'uv.lock' + - 'uv.vllm.lock' - 'areal/**' - '.github/workflows/install-test.yml' push: branches: [main] paths: - 'pyproject.toml' + - 'pyproject.vllm.toml' - 'uv.lock' + - 'uv.vllm.lock' - 'areal/**' - '.github/workflows/install-test.yml' workflow_dispatch: @@ -39,7 +43,9 @@ jobs: uses: astral-sh/setup-uv@v7 with: enable-cache: true - cache-dependency-glob: 'uv.lock' + cache-dependency-glob: | + uv.lock + uv.vllm.lock - name: Set up Python run: uv python install ${{ matrix.python-version }} @@ -93,7 +99,9 @@ jobs: uses: astral-sh/setup-uv@v7 with: enable-cache: true - cache-dependency-glob: 'uv.lock' + cache-dependency-glob: | + uv.lock + uv.vllm.lock - name: Set up Python run: uv python install 3.12 @@ -101,7 +109,12 @@ jobs: - name: Install package with CUDA extras (excluding flash-attn) # flash-attn requires CUDA toolkit for compilation, skip it in CI # Test individual extras that have pre-built wheels - run: uv sync --extra ${{ matrix.variant }} --extra megatron --extra tms + run: | + if [ "${{ matrix.variant }}" = "vllm" ]; then + cp pyproject.vllm.toml pyproject.toml + cp uv.vllm.lock uv.lock + fi + uv sync --extra ${{ matrix.variant }} --extra megatron --extra tms - name: Verify package import with CUDA extras run: | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 29ec58f2e0..e5959f0da0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -64,6 +64,18 @@ repos: name: nbstripout - Strip notebook output description: Strip output from Jupyter notebooks + # Check consistency between pyproject.toml variants (sglang vs vllm) + - repo: local + hooks: + - id: check-pyproject-consistency + name: Check pyproject.toml consistency + entry: python3 areal/tools/check_pyproject_consistency.py + language: system + files: ^pyproject(\.vllm)?\.toml$ + pass_filenames: false + always_run: false + require_serial: true + # Generate CLI documentation - repo: local hooks: diff --git a/AGENTS.md b/AGENTS.md index a0ac4854c2..c47b11681f 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -8,7 +8,7 @@ ```bash # Environment -uv sync --extra cuda # CUDA + SGLang inference (default); for vLLM: --extra cuda-vllm +uv sync --extra cuda # CUDA + SGLang inference (default); for vLLM: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda source .venv/bin/activate # activate venv BEFORE pre-commit or git commit if venv exists pre-commit install --install-hooks # hooks: Ruff, clang-format, mdformat, nbstripout, conventional-commits pre-commit run --all-files # lint + format everything diff --git a/CLAUDE.md b/CLAUDE.md index bdc9dc5db4..acb7da077d 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -40,6 +40,7 @@ uv --version # Install: https://docs.astral.sh/uv/ # Sync dependencies uv sync --extra cuda # CUDA + SGLang inference (default) +# For vLLM: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda uv sync --group dev # Include dev/test packages uv run python3 areal/tools/validate_installation.py # Validate installation diff --git a/Dockerfile b/Dockerfile index cd232b8f55..d77711b83a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -218,10 +218,16 @@ RUN uv pip install nanobot-ai # Install the project's dependencies (not the project itself) # This adds packages without removing unlisted ones (like our C++ packages) -# VARIANT selects the inference backend (sglang or vllm) +# VARIANT selects the inference backend (sglang or vllm) via separate pyproject files RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ - uv pip install --no-build-isolation -r pyproject.toml --extra cuda-train --extra ${VARIANT} --group dev + --mount=type=bind,source=pyproject.vllm.toml,target=pyproject.vllm.toml \ + case "$VARIANT" in \ + sglang) cp pyproject.toml /tmp/pyproject.toml ;; \ + vllm) cp pyproject.vllm.toml /tmp/pyproject.toml ;; \ + *) echo "Invalid VARIANT=$VARIANT (expected: sglang|vllm)" >&2; exit 1 ;; \ + esac \ + && uv pip install --no-build-isolation -r /tmp/pyproject.toml --extra cuda --group dev ############################################################## # STAGE 4: Misc fixes and final setup diff --git a/README.md b/README.md index 2956718233..0db0b70842 100644 --- a/README.md +++ b/README.md @@ -106,6 +106,7 @@ pip install uv # (pick the wheel matching your Python version; see https://github.com/mjun0812/flash-attention-prebuild-wheels/releases) uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.9-cp312-cp312-linux_x86_64.whl" uv sync --extra cuda # installs training packages + SGLang (default inference backend) +# For vLLM instead: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda ``` Our training scripts automatically download the required dataset (openai/gsm8k) and @@ -277,8 +278,8 @@ pip install uv uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.9-cp312-cp312-linux_x86_64.whl" # Use `--extra cuda` on Linux with CUDA (installs training packages + SGLang) uv sync --extra cuda --group dev -# For vLLM instead (note: use torch2.10 flash-attn wheel): -# uv sync --extra cuda-vllm --group dev +# For vLLM instead: +# cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda --group dev # Or without CUDA support # uv sync --group dev diff --git a/areal/tools/check_pyproject_consistency.py b/areal/tools/check_pyproject_consistency.py new file mode 100644 index 0000000000..7c12353080 --- /dev/null +++ b/areal/tools/check_pyproject_consistency.py @@ -0,0 +1,412 @@ +#!/usr/bin/env python3 +"""Check consistency between pyproject.toml and pyproject.vllm.toml. + +Only packages marked as "escapable" (inference-backend-specific) are allowed +to have different versions or be present in only one file. Everything else +must be identical across the two project configurations. + +Usage:: + + python areal/tools/check_pyproject_consistency.py + python areal/tools/check_pyproject_consistency.py pyproject.toml pyproject.vllm.toml + +Exit codes: + 0 — files are consistent + 1 — inconsistencies found (details printed to stderr) +""" + +from __future__ import annotations + +import re +import sys +import tomllib +from pathlib import Path +from typing import Any + +# ── Escapable packages ────────────────────────────────────────────────────── +# These packages are expected to differ between the SGLang (default) and vLLM +# variants because the two inference backends pin mutually-incompatible +# versions of torch / torchao / etc. +ESCAPABLE_PACKAGES: frozenset[str] = frozenset( + { + "torch", + "torchao", + "torchaudio", + "torchvision", + "sglang", + "vllm", + "nvidia-cudnn-cu12", + "openai", + "soundfile", + } +) + +# Optional-dependency extras that are backend-specific. +# These extras are expected to exist exclusively in one variant. +BACKEND_EXTRAS: frozenset[str] = frozenset({"sglang", "vllm"}) + +# ── Helpers ───────────────────────────────────────────────────────────────── + + +def _normalize_name(name: str) -> str: + """PEP 503-normalize a package name (lowercase, ``[-_.]+`` → ``-``).""" + return re.sub(r"[-_.]+", "-", name.strip()).lower() + + +def _parse_dep_name(dep: str) -> str: + """Extract the normalized package name from a dependency string. + + Examples:: + + "torch==2.9.1+cu129; ..." → "torch" + "sglang[tracing]==0.5.9" → "sglang" + "areal[cuda-train]" → "areal" + """ + match = re.match(r"([A-Za-z0-9][-A-Za-z0-9_.]*)", dep.strip()) + if not match: + return dep.strip() + return _normalize_name(match.group(1)) + + +def _dep_list_to_dict(deps: list[str]) -> dict[str, list[str]]: + """Map normalized package name → list of full dependency strings. + + A package may have multiple entries (e.g. platform-specific markers). + """ + result: dict[str, list[str]] = {} + for dep in deps: + name = _parse_dep_name(dep) + result.setdefault(name, []).append(dep.strip()) + return result + + +def _is_escapable(name: str) -> bool: + return _normalize_name(name) in {_normalize_name(n) for n in ESCAPABLE_PACKAGES} + + +def _is_backend_selfref(dep: str) -> bool: + """True if *dep* is a self-reference to a backend-specific extra. + + E.g. ``areal[sglang]`` or ``areal[vllm]``. + """ + match = re.match(r"\s*areal\[([^\]]+)\]", dep.strip()) + return match is not None and match.group(1) in BACKEND_EXTRAS + + +# ── Comparison engine ─────────────────────────────────────────────────────── + + +class _Checker: + """Accumulates errors while comparing two parsed TOML dicts.""" + + def __init__(self, file_a: str, file_b: str) -> None: + self.file_a = file_a + self.file_b = file_b + self.errors: list[str] = [] + + def _err(self, msg: str) -> None: + self.errors.append(msg) + + # ── generic deep comparison ───────────────────────────────────────── + + def _cmp_values(self, path: str, a: Any, b: Any) -> None: + if type(a) is not type(b): + self._err( + f"{path}: type mismatch — {self.file_a} has " + f"{type(a).__name__}, {self.file_b} has {type(b).__name__}" + ) + return + if isinstance(a, dict): + self._cmp_dicts(path, a, b) + elif isinstance(a, list): + if a != b: + self._err( + f"{path}: lists differ\n {self.file_a}: {a}\n {self.file_b}: {b}" + ) + elif a != b: + self._err(f"{path}: {self.file_a}={a!r} ≠ {self.file_b}={b!r}") + + def _cmp_dicts(self, path: str, a: dict, b: dict) -> None: + for key in sorted(set(a) | set(b)): + sub = f"{path}.{key}" if path else key + if key not in a: + self._err(f"{sub}: missing in {self.file_a}") + elif key not in b: + self._err(f"{sub}: missing in {self.file_b}") + else: + self._cmp_values(sub, a[key], b[key]) + + # ── [project].dependencies ────────────────────────────────────────── + + def check_dependencies(self, deps_a: list[str], deps_b: list[str]) -> None: + dict_a = _dep_list_to_dict(deps_a) + dict_b = _dep_list_to_dict(deps_b) + for name in sorted(set(dict_a) | set(dict_b)): + if _is_escapable(name): + continue + in_a, in_b = name in dict_a, name in dict_b + if in_a and not in_b: + self._err( + f"dependencies: non-escapable package {name!r} " + f"in {self.file_a} but missing in {self.file_b}" + ) + elif in_b and not in_a: + self._err( + f"dependencies: non-escapable package {name!r} " + f"in {self.file_b} but missing in {self.file_a}" + ) + elif sorted(dict_a[name]) != sorted(dict_b[name]): + self._err( + f"dependencies: non-escapable package {name!r} differs\n" + f" {self.file_a}: {dict_a[name]}\n" + f" {self.file_b}: {dict_b[name]}" + ) + + # ── [project.optional-dependencies] ───────────────────────────────── + + def check_optional_deps( + self, + extras_a: dict[str, list[str]], + extras_b: dict[str, list[str]], + ) -> None: + all_extras = sorted(set(extras_a) | set(extras_b)) + for extra in all_extras: + # Backend-specific extras are expected to be exclusive. + if extra in BACKEND_EXTRAS: + continue + + if extra not in extras_a: + self._err( + f"optional-dependencies: extra {extra!r} missing in {self.file_a}" + ) + continue + if extra not in extras_b: + self._err( + f"optional-dependencies: extra {extra!r} missing in {self.file_b}" + ) + continue + + # Filter out backend-specific self-references before comparing. + filtered_a = sorted( + d for d in extras_a[extra] if not _is_backend_selfref(d) + ) + filtered_b = sorted( + d for d in extras_b[extra] if not _is_backend_selfref(d) + ) + if filtered_a != filtered_b: + self._err( + f"optional-dependencies.{extra}: differs " + f"(after filtering backend self-refs)\n" + f" {self.file_a}: {filtered_a}\n" + f" {self.file_b}: {filtered_b}" + ) + + # ── [tool.uv].override-dependencies ───────────────────────────────── + + def check_override_deps( + self, overrides_a: list[str], overrides_b: list[str] + ) -> None: + dict_a = _dep_list_to_dict(overrides_a) + dict_b = _dep_list_to_dict(overrides_b) + for name in sorted(set(dict_a) | set(dict_b)): + if _is_escapable(name): + continue + in_a, in_b = name in dict_a, name in dict_b + if in_a and not in_b: + self._err( + f"tool.uv.override-dependencies: {name!r} " + f"in {self.file_a} but missing in {self.file_b}" + ) + elif in_b and not in_a: + self._err( + f"tool.uv.override-dependencies: {name!r} " + f"in {self.file_b} but missing in {self.file_a}" + ) + elif sorted(dict_a[name]) != sorted(dict_b[name]): + self._err( + f"tool.uv.override-dependencies: " + f"non-escapable {name!r} differs\n" + f" {self.file_a}: {dict_a[name]}\n" + f" {self.file_b}: {dict_b[name]}" + ) + + # ── [tool.uv.sources] ────────────────────────────────────────────── + + def check_uv_sources( + self, sources_a: dict[str, Any], sources_b: dict[str, Any] + ) -> None: + for pkg in sorted(set(sources_a) | set(sources_b)): + if pkg not in sources_a: + self._err(f"tool.uv.sources: {pkg!r} missing in {self.file_a}") + elif pkg not in sources_b: + self._err(f"tool.uv.sources: {pkg!r} missing in {self.file_b}") + elif _is_escapable(pkg): + # Just verify index names match (extra names may differ). + idx_a = sorted( + {e.get("index", "") for e in sources_a[pkg] if isinstance(e, dict)} + ) + idx_b = sorted( + {e.get("index", "") for e in sources_b[pkg] if isinstance(e, dict)} + ) + if idx_a != idx_b: + self._err( + f"tool.uv.sources.{pkg}: index names differ — " + f"{self.file_a}={idx_a}, {self.file_b}={idx_b}" + ) + elif sources_a[pkg] != sources_b[pkg]: + self._err( + f"tool.uv.sources.{pkg}: differs (non-escapable)\n" + f" {self.file_a}: {sources_a[pkg]}\n" + f" {self.file_b}: {sources_b[pkg]}" + ) + + # ── [tool.uv] (top-level) ────────────────────────────────────────── + + def check_tool_uv(self, uv_a: dict, uv_b: dict) -> None: + special_keys = {"override-dependencies", "sources", "conflicts"} + + self.check_override_deps( + uv_a.get("override-dependencies", []), + uv_b.get("override-dependencies", []), + ) + self.check_uv_sources( + uv_a.get("sources", {}), + uv_b.get("sources", {}), + ) + # `conflicts` may be structurally different (single-backend needs + # none); skip comparison. + + for key in sorted(set(uv_a) | set(uv_b)): + if key in special_keys: + continue + sub = f"tool.uv.{key}" + if key not in uv_a: + self._err(f"{sub}: missing in {self.file_a}") + elif key not in uv_b: + self._err(f"{sub}: missing in {self.file_b}") + else: + self._cmp_values(sub, uv_a[key], uv_b[key]) + + # ── entry point ───────────────────────────────────────────────────── + + def run(self, toml_a: dict, toml_b: dict) -> int: + # 1. build-system + self._cmp_values( + "build-system", + toml_a.get("build-system", {}), + toml_b.get("build-system", {}), + ) + + # 2. project metadata (everything except deps) + proj_a = toml_a.get("project", {}) + proj_b = toml_b.get("project", {}) + skip = {"dependencies", "optional-dependencies"} + meta_a = {k: v for k, v in proj_a.items() if k not in skip} + meta_b = {k: v for k, v in proj_b.items() if k not in skip} + if meta_a != meta_b: + self._cmp_dicts("project", meta_a, meta_b) + + # 3. dependencies + self.check_dependencies( + proj_a.get("dependencies", []), + proj_b.get("dependencies", []), + ) + + # 4. optional-dependencies + self.check_optional_deps( + proj_a.get("optional-dependencies", {}), + proj_b.get("optional-dependencies", {}), + ) + + # 5. dependency-groups + self._cmp_values( + "dependency-groups", + toml_a.get("dependency-groups", {}), + toml_b.get("dependency-groups", {}), + ) + + # 6. tool.uv + self.check_tool_uv( + toml_a.get("tool", {}).get("uv", {}), + toml_b.get("tool", {}).get("uv", {}), + ) + + # 7. Other tool sections (pytest, ruff, etc.) + tool_a = toml_a.get("tool", {}) + tool_b = toml_b.get("tool", {}) + for key in sorted(set(tool_a) | set(tool_b)): + if key == "uv": + continue + sub = f"tool.{key}" + if key not in tool_a: + self._err(f"{sub}: missing in {self.file_a}") + elif key not in tool_b: + self._err(f"{sub}: missing in {self.file_b}") + else: + self._cmp_values(sub, tool_a[key], tool_b[key]) + + if self.errors: + print( + f"❌ {len(self.errors)} inconsistenc" + f"{'y' if len(self.errors) == 1 else 'ies'} " + f"between {self.file_a} and {self.file_b}:", + file=sys.stderr, + ) + for e in self.errors: + print(f" {e}", file=sys.stderr) + return 1 + + print( + f"✅ {self.file_a} and {self.file_b} are consistent " + f"(escapable: {', '.join(sorted(ESCAPABLE_PACKAGES))})" + ) + return 0 + + +# ── CLI ───────────────────────────────────────────────────────────────────── + + +def main(argv: list[str] | None = None) -> int: + import argparse + + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "files", + nargs="*", + default=["pyproject.toml", "pyproject.vllm.toml"], + help=( + "Pair of pyproject files to compare " + "(default: pyproject.toml pyproject.vllm.toml)" + ), + ) + args = parser.parse_args(argv) + + if len(args.files) != 2: + print( + f"Error: exactly two pyproject files required, got {len(args.files)}", + file=sys.stderr, + ) + return 1 + + file_a, file_b = args.files + path_a, path_b = Path(file_a), Path(file_b) + + for p in (path_a, path_b): + if not p.exists(): + print(f"Error: {p} not found", file=sys.stderr) + return 1 + + with open(path_a, "rb") as f: + toml_a = tomllib.load(f) + with open(path_b, "rb") as f: + toml_b = tomllib.load(f) + + checker = _Checker(file_a, file_b) + return checker.run(toml_a, toml_b) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/docs/en/cli_reference.md b/docs/en/cli_reference.md index a01ab8ca85..d51c58866c 100644 --- a/docs/en/cli_reference.md +++ b/docs/en/cli_reference.md @@ -353,7 +353,7 @@ Configuration for PPO actor model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -420,7 +420,7 @@ Configuration for PPO critic model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -460,7 +460,7 @@ Core configuration for model training, including optimization and backend settin | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -1067,7 +1067,7 @@ Configuration class: TeacherConfig | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | diff --git a/docs/en/tutorial/installation.md b/docs/en/tutorial/installation.md index 22ac37dffa..72dd57f8c0 100644 --- a/docs/en/tutorial/installation.md +++ b/docs/en/tutorial/installation.md @@ -105,15 +105,34 @@ This installs CUDA-dependent training packages (Megatron, Torch Memory Saver) pl **SGLang** as the default inference backend. These packages require Linux x86_64 with CUDA 12.x and compatible NVIDIA drivers. +#### Using vLLM Instead of SGLang + +SGLang and vLLM pin mutually-incompatible `torch` / `torchao` versions, so they live in +separate pyproject files. The default `pyproject.toml` uses SGLang; for vLLM, use +`pyproject.vllm.toml`: + +```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock +uv sync --extra cuda +``` + +Alternatively, without replacing `pyproject.toml`: + +```bash +uv pip install -r pyproject.vllm.toml --extra cuda +``` + #### Flash Attention Pre-built Wheels -Flash Attention v2 is included in `--extra cuda` and `--extra cuda-vllm`, but PyPI only -ships source distributions that require CUDA compilation (~30 min). To skip compilation, -install a **pre-built wheel** before running `uv sync`. +Flash Attention v2 is included in `--extra cuda`, but PyPI only ships source +distributions that require CUDA compilation (~30 min). To skip compilation, install a +**pre-built wheel** before running `uv sync`. Flash Attention wheels are linked against a specific PyTorch version at compile time. -SGLang uses **torch 2.9** and vLLM uses **torch 2.10**, so you must pick the matching -wheel. Replace `cpXYZ` with your Python version (`cp311` for 3.11, `cp312` for 3.12). +SGLang (the default `pyproject.toml`) uses **torch 2.9** and vLLM +(`pyproject.vllm.toml`) uses **torch 2.10**, so you must pick the matching wheel. +Replace `cpXYZ` with your Python version (`cp311` for 3.11, `cp312` for 3.12). **SGLang** (default, torch 2.9): @@ -126,15 +145,17 @@ uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/rele uv sync --extra cuda ``` -**vLLM** (torch 2.10): +**vLLM** (torch 2.10, requires `pyproject.vllm.toml`): ```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock # Python 3.12 uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl" # Python 3.11 # uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl" -uv sync --extra cuda-vllm +uv sync --extra cuda ``` Browse all available wheels at @@ -147,25 +168,14 @@ is suitable only for development, testing, and non-GPU workflows. You can also install individual extras instead of the full `cuda` bundle: -- `sglang`: SGLang inference engine -- `vllm`: vLLM inference engine +- `sglang`: SGLang inference engine (in `pyproject.toml`) +- `vllm`: vLLM inference engine (in `pyproject.vllm.toml`) - `megatron`: Megatron training backend - `tms`: Torch Memory Saver - `flash-attn`: Flash Attention v2 - `kernels`: Hugging Face Kernels runtime -- `cuda-train`: Training packages only (megatron + tms, no inference backend) -- `cuda-sglang`: cuda-train + sglang + flash-attn -- `cuda-vllm`: cuda-train + vllm + flash-attn -- `cuda`: alias for cuda-sglang (default, backward-compatible) - -**Note**: You can mix and match individual extras: - -```bash -# vLLM with Hugging Face Kernels and flash-attn (no megatron, no tms) -uv sync --extra vllm --extra flash-attn --extra kernels -# vLLM with all training packages -uv sync --extra cuda-train --extra vllm -``` +- `cuda-train`: Training packages only (megatron + tms + kernels, no inference backend) +- `cuda`: cuda-train + inference backend + flash-attn ### Using Hugging Face Kernels in Training diff --git a/docs/zh/cli_reference.md b/docs/zh/cli_reference.md index f68accdcb5..afd64db4af 100644 --- a/docs/zh/cli_reference.md +++ b/docs/zh/cli_reference.md @@ -351,7 +351,7 @@ Configuration for PPO actor model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -418,7 +418,7 @@ Configuration for PPO critic model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -458,7 +458,7 @@ Core configuration for model training, including optimization and backend settin | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -1065,7 +1065,7 @@ Configuration class: TeacherConfig | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | diff --git a/docs/zh/tutorial/installation.md b/docs/zh/tutorial/installation.md index da957fed17..eb7c7c9675 100644 --- a/docs/zh/tutorial/installation.md +++ b/docs/zh/tutorial/installation.md @@ -88,13 +88,31 @@ uv sync --extra cuda 这将安装 CUDA 依赖的训练包(Megatron、Torch Memory Saver)以及 **SGLang** 作为默认推理后端。这些包需要 Linux x86_64 和 CUDA 12.x 及兼容的 NVIDIA 驱动。 +#### 使用 vLLM 替代 SGLang + +SGLang 和 vLLM 锁定了互不兼容的 `torch` / `torchao` 版本,因此它们位于独立的 pyproject 文件中。默认的 +`pyproject.toml` 使用 SGLang;若需使用 vLLM,请使用 `pyproject.vllm.toml`: + +```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock +uv sync --extra cuda +``` + +也可以不替换 `pyproject.toml`: + +```bash +uv pip install -r pyproject.vllm.toml --extra cuda +``` + #### Flash Attention 预编译 Wheel -Flash Attention v2 包含在 `--extra cuda` 和 `--extra cuda-vllm` 中,但 PyPI 仅提供源码分发包,从源码编译耗时约 -30 分钟。为跳过编译,请在运行 `uv sync` **之前**安装**预编译 wheel**。 +Flash Attention v2 包含在 `--extra cuda` 中,但 PyPI 仅提供源码分发包,从源码编译耗时约 30 分钟。为跳过编译,请在运行 +`uv sync` **之前**安装**预编译 wheel**。 -Flash Attention wheel 在编译时与特定 PyTorch 版本绑定。SGLang 使用 **torch 2.9**, vLLM 使用 **torch -2.10**,请选择对应的 wheel。将 `cpXYZ` 替换为您的 Python 版本 (3.11 对应 `cp311`,3.12 对应 `cp312`)。 +Flash Attention wheel 在编译时与特定 PyTorch 版本绑定。SGLang(默认 `pyproject.toml`)使用 **torch +2.9**,vLLM(`pyproject.vllm.toml`)使用 **torch 2.10**,请选择对应的 wheel。将 `cpXYZ` 替换为您的 Python +版本(3.11 对应 `cp311`,3.12 对应 `cp312`)。 **SGLang**(默认,torch 2.9): @@ -107,15 +125,17 @@ uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/rele uv sync --extra cuda ``` -**vLLM**(torch 2.10): +**vLLM**(torch 2.10,需要 `pyproject.vllm.toml`): ```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock # Python 3.12 uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl" # Python 3.11 # uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl" -uv sync --extra cuda-vllm +uv sync --extra cuda ``` 浏览所有可用 wheel: 。 @@ -125,25 +145,14 @@ CUDA 的训练和推理功能将不可用。此配置仅适用于开发、测试 您也可以单独安装各个 extra,而不是完整的 `cuda` 捆绑包: -- `sglang`:SGLang 推理引擎 -- `vllm`:vLLM 推理引擎 +- `sglang`:SGLang 推理引擎(在 `pyproject.toml` 中) +- `vllm`:vLLM 推理引擎(在 `pyproject.vllm.toml` 中) - `megatron`:Megatron 训练后端 - `tms`:Torch Memory Saver - `flash-attn`:Flash Attention v2 - `kernels`:Hugging Face Kernels 运行时 -- `cuda-train`:仅训练包(megatron + tms,不含推理后端) -- `cuda-sglang`:cuda-train + sglang + flash-attn -- `cuda-vllm`:cuda-train + vllm + flash-attn -- `cuda`:cuda-sglang 的别名(默认,向后兼容) - -**注意**:您可以混合搭配各个 extra: - -```bash -# vLLM 带 Hugging Face Kernels 和 flash-attn(不含 megatron 和 tms) -uv sync --extra vllm --extra flash-attn --extra kernels -# vLLM 加所有训练包 -uv sync --extra cuda-train --extra vllm -``` +- `cuda-train`:仅训练包(megatron + tms + kernels,不含推理后端) +- `cuda`:cuda-train + 推理后端 + flash-attn ### 在训练中使用 Hugging Face Kernels diff --git a/pyproject.toml b/pyproject.toml index c9844e288d..3d0bb2564f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -139,16 +139,14 @@ dependencies = [ [project.optional-dependencies] # CUDA-dependent extras - these packages only have Linux x86_64 wheels # Platform markers ensure they're skipped on unsupported platforms +# +# NOTE: vLLM has a separate pyproject file (pyproject.vllm.toml) because +# sglang and vllm pin mutually-incompatible torch / torchao versions. +# See pyproject.vllm.toml for the vLLM variant. sglang = [ "sglang[tracing]==0.5.9; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torch==2.9.1+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", "nvidia-cudnn-cu12==9.16.0.29; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", -] -vllm = [ - "vllm==0.17.0; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torch==2.10.0+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", ] @@ -167,35 +165,17 @@ megatron = [ "megatron-bridge==0.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'", ] # Convenience extra for CUDA training packages (no inference backend) -# Install with --extra sglang or --extra vllm for a complete setup cuda-train = [ "areal[tms]", "areal[megatron]", "areal[kernels]", ] -# Backward-compatible: `uv sync --extra cuda` installs training packages + sglang. -# For vllm instead, use: `uv sync --extra cuda-vllm` -cuda-sglang = [ +# Full CUDA setup: training packages + SGLang inference + flash-attn +cuda = [ "areal[cuda-train]", "areal[sglang]", "areal[flash-attn]", - "torch==2.9.1+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", - "nvidia-cudnn-cu12==9.16.0.29; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", -] -cuda-vllm = [ - "areal[cuda-train]", - "areal[vllm]", - "areal[flash-attn]", - "torch==2.10.0+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", -] -cuda = [ - "areal[cuda-sglang]", - "torch==2.9.1+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", - "nvidia-cudnn-cu12==9.16.0.29; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", ] @@ -242,40 +222,11 @@ environments = [ "sys_platform == 'darwin' and platform_machine == 'arm64'", "sys_platform == 'darwin' and platform_machine == 'x86_64'", ] -conflicts = [ - [ - { extra = "sglang" }, - { extra = "vllm" }, - ], - # cuda includes sglang, so it also conflicts with vllm - [ - { extra = "cuda-sglang" }, - { extra = "vllm" }, - ], - [ - { extra = "cuda" }, - { extra = "vllm" }, - ], - # cuda-vllm includes vllm, so it conflicts with sglang and cuda - [ - { extra = "cuda-vllm" }, - { extra = "sglang" }, - ], - [ - { extra = "cuda-vllm" }, - { extra = "cuda" }, - ], - [ - { extra = "cuda-vllm" }, - { extra = "cuda-sglang" }, - ], -] override-dependencies = [ # litellm[proxy] conflicts with sglang's exact pins: # - litellm requires openai>=2.8.0, sglang pins openai==2.6.1 # - litellm requires soundfile>=0.12.1,<0.13.0, sglang pins soundfile==0.13.1 # sglang pins torchao==0.9.0, but we require >=0.15.0 for archon fp8 - # the specific version (0.15.0 or 0.16.0) is controlled by extras # nvidia-cudnn-cu12 is required for SGLang Vision Models "openai>=2.8.0", "soundfile>=0.12.1,<0.13.0", @@ -288,11 +239,11 @@ override-dependencies = [ "mamba-ssm; sys_platform == 'never'", "causal-conv1d; sys_platform == 'never'", "flash-linear-attention; sys_platform == 'never'", - + # Version conflicts with megatron-bridge (resorting to the existing version) - "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", # Prevent megatron-bridge from installing megatron-core[dev,mlm] extras + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", "hydra-core==1.4.0.dev1", - "timm==1.0.16", + "timm==1.0.16", ] # Static metadata so uv lock resolves flash-attn without downloading or building. @@ -313,24 +264,15 @@ explicit = true [tool.uv.sources] torch = [ { index = "pytorch-cu129", extra = "sglang" }, - { index = "pytorch-cu129", extra = "vllm" }, { index = "pytorch-cu129", extra = "cuda" }, - { index = "pytorch-cu129", extra = "cuda-sglang" }, - { index = "pytorch-cu129", extra = "cuda-vllm" }, ] torchaudio = [ { index = "pytorch-cu129", extra = "sglang" }, - { index = "pytorch-cu129", extra = "vllm" }, { index = "pytorch-cu129", extra = "cuda" }, - { index = "pytorch-cu129", extra = "cuda-sglang" }, - { index = "pytorch-cu129", extra = "cuda-vllm" }, ] torchvision = [ { index = "pytorch-cu129", extra = "sglang" }, - { index = "pytorch-cu129", extra = "vllm" }, { index = "pytorch-cu129", extra = "cuda" }, - { index = "pytorch-cu129", extra = "cuda-sglang" }, - { index = "pytorch-cu129", extra = "cuda-vllm" }, ] # ============================================================================= diff --git a/pyproject.vllm.toml b/pyproject.vllm.toml new file mode 100644 index 0000000000..10bfefbf02 --- /dev/null +++ b/pyproject.vllm.toml @@ -0,0 +1,328 @@ +# vLLM variant of the AReaL project configuration. +# +# sglang and vllm pin mutually-incompatible torch / torchao versions, +# so they live in separate pyproject files to avoid resolution conflicts. +# The default pyproject.toml uses SGLang; this file uses vLLM. +# +# Usage (custom environment): +# cp pyproject.vllm.toml pyproject.toml +# uv sync --extra cuda +# +# Usage (Docker): +# docker build --build-arg VARIANT=vllm -t areal-runtime:dev-vllm . +# +# Usage (pip-install without replacing pyproject.toml): +# uv pip install -r pyproject.vllm.toml --extra cuda + +[build-system] +requires = ["uv_build>=0.9.18,<0.10.0"] +build-backend = "uv_build" + +[project] +name = "areal" +description = "AReaL: A Large-Scale Asynchronous Reinforcement Learning System" +readme = "README.md" +license = {text = "Apache-2.0"} +requires-python = ">=3.11,<3.13" +version = "1.0.2" +authors = [ + {name = "AReaL Team"}, +] +maintainers = [ + {name = "AReaL Team"}, +] +keywords = [ + "distributed-systems", + "reinforcement-learning", + "large-language-models", + "llm-training", + "llm-agent", + "agentic-rl", +] +classifiers = [ + # 3 - Alpha + # 4 - Beta + # 5 - Production/Stable + "Development Status :: 2 - Pre-Alpha", + "Environment :: GPU :: NVIDIA CUDA :: 12", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: System :: Distributed Computing", +] + +dependencies = [ + # Core ML/AI libraries + "torch>=2.10.0,<2.11; sys_platform != 'darwin' or platform_machine != 'x86_64'", + "torch<2.9.1; sys_platform == 'darwin' and platform_machine == 'x86_64'", + "torchaudio", + "torchvision", + "torchdata", + "torchao==0.16.0", + "huggingface_hub", + "datasets>=3.0.0", + "transformers==4.57.1", + "peft", + "qwen_agent", + "openai-agents", + "anthropic", + "claude-agent-sdk", + "litellm[proxy]>=1.81.3", + "openhands", + "langchain", + "langchain-openai", + + # Visualization + "pandas", + "matplotlib", + "seaborn", + + # Utilities and data processing + "pillow>=12.1.1", + "pylatexenc", + "zstandard", + "setproctitle", + "nltk", + "sentencepiece", + "einops", + "orjson", + "tqdm", + "rich", + "pydantic", + "PyYAML", + "omegaconf==2.4.0.dev2", + "hydra-core==1.4.0.dev1", + "packaging", + "lark", + "tabulate", + "pybase64", + "msgspec", + "math-verify==0.8.0", + "python-dotenv", + "json5", + "psutil", + "nvidia-ml-py; sys_platform == 'linux'", + "ninja", + "numba", + "blosc", + "pybind11>=2.10.0", + "networkx==3.3", + "aiofiles", + "aiohttp>=3.13.3,<4", + "httpx>=0.28.1", + "pyzmq", + "regex", + "python_dateutil", + "word2number", + "pebble", + "timeout-decorator", + "prettytable", + "h5py", + "mathruler==0.1.0", + + # Monitoring and logging + "wandb", + "tensorboardx", + "trackio", + "colorama", + "colorlog", + "swanboard==0.1.9b1", + "swanlab[dashboard]==0.6.12", + + # Distributed computing + "ray[default]", + "redis", + + # Web frameworks + "fastapi>=0.115.12", + "uvicorn", + "uvloop>=0.21.0", + "flask", + "tenacity", + + # Build and packaging tools + "build>=1.2.1", + "wheel>=0.43.0", + "cookiecutter>2.1.1", + "distro-info>=1.0", + "python-debian>=0.1.49", +] + +[project.optional-dependencies] +# CUDA-dependent extras - these packages only have Linux x86_64 wheels +# Platform markers ensure they're skipped on unsupported platforms +vllm = [ + "vllm==0.17.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", +] +tms = [ + "torch_memory_saver==0.0.9; sys_platform == 'linux'", +] +flash-attn = [ + "flash-attn==2.8.3; sys_platform == 'linux' and platform_machine == 'x86_64'", +] +kernels = [ + "kernels==0.12.2", +] +megatron = [ + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "mbridge==0.15.1; sys_platform == 'linux' and platform_machine == 'x86_64'", + "megatron-bridge==0.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'", +] +cuda-train = [ + "areal[tms]", + "areal[megatron]", + "areal[kernels]", +] +# Full CUDA setup: training packages + vLLM inference + flash-attn +cuda = [ + "areal[cuda-train]", + "areal[vllm]", + "areal[flash-attn]", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", +] + +[project.urls] +"Homepage" = "https://github.com/inclusionAI/AReaL" +"Repository" = "https://github.com/inclusionAI/AReaL" +"Documentation" = "https://inclusionai.github.io/AReaL/en/intro.html" +"Bug Tracker" = "https://github.com/inclusionAI/AReaL/issues" + +[dependency-groups] +dev = [ + "pytest", + "pytest-asyncio", + "ipython", + "ruff==0.14.9", + "clang-format==19.1.7", + "sh", + "mdformat==0.7.17", + "mdformat-gfm", + "mdformat-tables", + "mdformat-frontmatter", + "plotly", + "pre-commit", + "sphinx", + "sphinx-nefertiti", + "jupyter-book==1.0.4.post1", +] + +# ============================================================================= +# Tool configurations: uv +# ============================================================================= + +[tool.uv.build-backend] +module-root = "" + +[tool.uv] +environments = [ + "sys_platform == 'linux' and platform_machine == 'x86_64'", + "sys_platform == 'linux' and platform_machine == 'aarch64'", + "sys_platform == 'darwin' and platform_machine == 'arm64'", + "sys_platform == 'darwin' and platform_machine == 'x86_64'", +] +override-dependencies = [ + # Required by megatron-bridge but are optional (as per docs) cuda-based installs + "transformer-engine; sys_platform == 'never'", + "nv-grouped-gemm; sys_platform == 'never'", + "mamba-ssm; sys_platform == 'never'", + "causal-conv1d; sys_platform == 'never'", + "flash-linear-attention; sys_platform == 'never'", + + # Version conflicts with megatron-bridge (resorting to the existing version) + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "hydra-core==1.4.0.dev1", + "timm==1.0.16", +] + +[[tool.uv.dependency-metadata]] +name = "flash-attn" +version = "2.8.3" +requires-dist = ["torch", "einops"] + +[tool.uv.extra-build-dependencies] +flash-attn = [{ requirement = "torch", match-runtime = true }] + +[[tool.uv.index]] +name = "pytorch-cu129" +url = "https://download.pytorch.org/whl/cu129" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu129", extra = "vllm" }, + { index = "pytorch-cu129", extra = "cuda" }, +] +torchaudio = [ + { index = "pytorch-cu129", extra = "vllm" }, + { index = "pytorch-cu129", extra = "cuda" }, +] +torchvision = [ + { index = "pytorch-cu129", extra = "vllm" }, + { index = "pytorch-cu129", extra = "cuda" }, +] + +# ============================================================================= +# Tool configurations: pytest +# ============================================================================= + +[tool.pytest.ini_options] +pythonpath = ["."] +filterwarnings = [ + "ignore::DeprecationWarning", + "ignore::PendingDeprecationWarning", + "ignore::FutureWarning", + "ignore::UserWarning:torch.*", + "ignore::UserWarning:transformers.*", +] +markers = [ + "slow: mark test as slow, expected to cost more than 30 seconds and will not run in CI by default.", + "ci: mark test as must-run in CI (only marked for slow tests).", + "gpu: mark test that uses a single GPU", + "multi_gpu: mark test that uses more than one GPU", + "sglang: mark test that requires the SGLang inference backend", + "vllm: mark test that requires the vLLM inference backend", +] + +# ============================================================================= +# Tool configurations: ruff +# ============================================================================= + +[tool.ruff] +line-length = 88 +target-version = "py311" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "UP", # pyupgrade +] +ignore = [ + "E501", # E501 Line too long +] + +[tool.ruff.lint.isort] +section-order = [ + "future", + "standard-library", + "third-party", + "first-party", + "areal", + "local-folder", +] +from-first = false + +[tool.ruff.lint.isort.sections] +"areal" = ["areal"] diff --git a/uv.lock b/uv.lock index 41299677b2..674263e5d6 100644 --- a/uv.lock +++ b/uv.lock @@ -17,25 +17,6 @@ supported-markers = [ "platform_machine == 'arm64' and sys_platform == 'darwin'", "platform_machine == 'x86_64' and sys_platform == 'darwin'", ] -conflicts = [[ - { package = "areal", extra = "sglang" }, - { package = "areal", extra = "vllm" }, -], [ - { package = "areal", extra = "cuda-sglang" }, - { package = "areal", extra = "vllm" }, -], [ - { package = "areal", extra = "cuda" }, - { package = "areal", extra = "vllm" }, -], [ - { package = "areal", extra = "cuda-vllm" }, - { package = "areal", extra = "sglang" }, -], [ - { package = "areal", extra = "cuda" }, - { package = "areal", extra = "cuda-vllm" }, -], [ - { package = "areal", extra = "cuda-sglang" }, - { package = "areal", extra = "cuda-vllm" }, -]] [manifest] overrides = [ @@ -72,16 +53,15 @@ name = "accelerate" version = "1.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "huggingface-hub", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine 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and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, -] - -[package.optional-dependencies] -cuda = [ - { name = "flash-attn", marker = "platform_machine 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(platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "colorlog", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "cookiecutter", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "datasets", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "distro-info", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "einops", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "fastapi", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "flask", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "h5py", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "httpx", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "huggingface-hub", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "hydra-core", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "json5", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "langchain", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "langchain-openai", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "lark", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "litellm", extra = ["proxy"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "math-verify", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mathruler", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "matplotlib", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "msgspec", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "networkx", marker = "(platform_machine == 'arm64' and sys_platform == 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== 'x86_64' and sys_platform == 'linux')" }, + { name = "nvidia-ml-py", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "omegaconf", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "openai-agents", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "openhands", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "orjson", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "packaging", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pandas", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pebble", marker = "(platform_machine == 'arm64' and sys_platform == 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"regex", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "rich", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "seaborn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sentencepiece", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "setproctitle", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "swanboard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "swanlab", extra = ["dashboard"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tabulate", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tenacity", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tensorboardx", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "timeout-decorator", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torch", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang')" }, + { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, { name = "torchaudio", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchaudio", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchdata", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchvision", version = "0.17.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, { name = "torchvision", version = "0.24.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchvision", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "tqdm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "trackio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "transformers", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvicorn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvloop", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wandb", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wheel", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "word2number", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "zstandard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, ] -cuda-sglang = [ + +[package.optional-dependencies] +cuda = [ { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "sglang", extra = ["tracing"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "sglang", extra = ["tracing"], marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "torchaudio", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torchvision", version = "0.24.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] cuda-train = [ - { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "mbridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-bridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-core", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "torch-memory-saver", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, -] -cuda-vllm = [ - { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, - { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] flash-attn = [ - { name = "flash-attn", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] kernels = [ - { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, ] megatron = [ - { name = "mbridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-bridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-core", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] sglang = [ { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "sglang", extra = ["tracing"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda-sglang' and extra != 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "sglang", extra = ["tracing"], marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torchaudio", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torchvision", version = "0.24.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] tms = [ - { name = "torch-memory-saver", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, -] -vllm = [ - { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, ] [package.dev-dependencies] dev = [ - { name = "clang-format", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "ipython", version = "9.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux' and extra == 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and sys_platform == 'darwin'" }, + { name = "pydantic", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "requests", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "smart-open", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "virtualenv", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, ] [[package]] @@ -6140,45 +4991,40 @@ resolution-markers = [ "python_full_version < '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin'", ] dependencies = [ - { name = "click", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and 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'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "orjson", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "packaging", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pandas", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pebble", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "peft", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pillow", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "prettytable", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "psutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pybase64", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pybind11", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pydantic", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pylatexenc", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "python-dateutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "python-debian", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "python-dotenv", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pyyaml", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pyzmq", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "qwen-agent", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ray", version = "2.49.2", source = { registry = "https://pypi.org/simple" }, extra = ["default"], marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "ray", version = "2.54.0", source = { registry = "https://pypi.org/simple" }, extra = ["default"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "redis", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "regex", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "rich", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "seaborn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sentencepiece", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "setproctitle", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "swanboard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "swanlab", extra = ["dashboard"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tabulate", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tenacity", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tensorboardx", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "timeout-decorator", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torch", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchao", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "torchaudio", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchdata", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchvision", version = "0.17.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "torchvision", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "tqdm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "trackio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "transformers", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvicorn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvloop", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wandb", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wheel", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "word2number", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "zstandard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] + +[package.optional-dependencies] +cuda = [ + { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +cuda-train = [ + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] +flash-attn = [ + { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +kernels = [ + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] +megatron = [ + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +tms = [ + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] +vllm = [ + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] + +[package.dev-dependencies] +dev = [ + { name = "clang-format", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ipython", version = "9.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ipython", version = "9.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "jupyter-book", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat-frontmatter", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat-gfm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat-tables", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "plotly", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or 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b/areal/engine/vllm_ext/areal_vllm_server.py index 50dd06dff7..c3a37316a9 100644 --- a/areal/engine/vllm_ext/areal_vllm_server.py +++ b/areal/engine/vllm_ext/areal_vllm_server.py @@ -164,6 +164,7 @@ async def areal_update_weight(request: UpdateWeightsRequest, raw_request: Reques logger.info(f"API server starts areal_update_weight, {request.model_path}") llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc( "areal_update_weights", @@ -183,6 +184,7 @@ async def areal_update_weight_lora( ) llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc( @@ -205,6 +207,7 @@ async def areal_update_weight_xccl(raw_request: Request): logger.info("API server starts areal_update_weight_xccl") llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc("areal_update_weight_xccl") finally: @@ -219,6 +222,7 @@ async def areal_update_weight_lora_xccl( logger.info("API server starts areal_update_weight_lora_xccl") llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc("areal_update_weight_lora_xccl") @@ -310,6 +314,7 @@ async def areal_pause_generation(raw_request: Request): wait_for_inflight_requests=False, clear_cache=True, ) + await llm.reset_mm_cache() return to_json_response(True, "Generation paused and all requests aborted") From 3028de8c52376f563613f8f21a3f4ce0f38403c8 Mon Sep 17 00:00:00 2001 From: Wei Fu <36355462+garrett4wade@users.noreply.github.com> Date: Wed, 8 Apr 2026 11:40:37 +0800 Subject: [PATCH 4/7] fix(ci): sync uv.vllm.lock with the 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+++++++++++++++++-- 1 file changed, 47 insertions(+), 5 deletions(-) diff --git a/areal/engine/vllm_ext/vllm_worker_extension.py b/areal/engine/vllm_ext/vllm_worker_extension.py index 126f884e27..5454f15d9e 100644 --- a/areal/engine/vllm_ext/vllm_worker_extension.py +++ b/areal/engine/vllm_ext/vllm_worker_extension.py @@ -2,6 +2,7 @@ import torch import torch.distributed as dist + from vllm.logger import init_logger from vllm.lora.lora_model import LoRAModel from vllm.lora.peft_helper import PEFTHelper @@ -180,7 +181,7 @@ def areal_update_weight_lora_xccl(self): f"LoRA adapter {lora_int_id} not found. Available: {adapter_ids}" ) - # Get the LoRA model + # Get the currently registered LoRA model (used for diagnostics). lora_model = ( self.model_runner.lora_manager._adapter_manager._registered_adapters[ lora_int_id @@ -211,12 +212,33 @@ def areal_update_weight_lora_xccl(self): logger.info(f"Received {len(received_weights)} LoRA parameters via XCCL") - self.model_runner.lora_manager.remove_adapter(lora_int_id) - normalized_weights = { k.replace("default.", ""): v for k, v in received_weights.items() } + lora_partial_shard_key = (self.areal_lora_name, lora_int_id) + + group_shards = self._lora_partial_shards.setdefault( + lora_partial_shard_key, {} + ) + group_shards[self.areal_weight_meta_group_name] = normalized_weights + buffered_count = len(group_shards) + + # Assumes that every registered weight update group contributes to the update cycle + if buffered_count < len(self.weight_update_groups): + logger.info( + "Buffered LoRA shard for " + f"{self.areal_lora_name}: group={self.areal_weight_meta_group_name}, " + f"buffered={buffered_count}/{len(self.weight_update_groups)} PP stages." + ) + self.sync() + return True, "Success" + + merged_weights: dict[str, torch.Tensor] = {} + for shard in group_shards.values(): + merged_weights.update(shard) + self._lora_partial_shards.pop(lora_partial_shard_key, None) + peft_config = { "r": self.areal_lora_rank, "lora_alpha": self.areal_lora_alpha, @@ -234,7 +256,7 @@ def areal_update_weight_lora_xccl(self): new_lora_model = LoRAModel.from_lora_tensors( lora_model_id=self.areal_lora_int_id, - tensors=normalized_weights, + tensors=merged_weights, peft_helper=peft_helper, device=self.model_runner.device, dtype=self.model_runner.lora_manager.lora_config.lora_dtype, @@ -244,13 +266,21 @@ def areal_update_weight_lora_xccl(self): ), ) + self.model_runner.lora_manager.remove_adapter(lora_int_id) + self.model_runner.lora_manager._adapter_manager._add_adapter(new_lora_model) self.model_runner.lora_manager._adapter_manager.activate_adapter( new_lora_model.id ) logger.info( - f"Found LoRA model with {len(new_lora_model.loras)} LoRA modules" + f"Updated New LoRA model with {len(new_lora_model.loras)} LoRA modules " + f"from {len(merged_weights)} tensors across {len(self.weight_update_groups)} groups" ) + if len(new_lora_model.loras) != len(lora_model.loras): + logger.warning( + f"Number of modules in the new LoRA model ({len(new_lora_model.loras)}) " + f"does not match the old LoRA model ({len(lora_model.loras)})." + ) self.sync() return True, "Success" @@ -271,6 +301,18 @@ def areal_init_update_weight_group( ): if not hasattr(self, "weight_update_groups"): self.weight_update_groups: dict[str, dist.ProcessGroup] = {} + + # This is required for buffering weights during lora weight update, as vLLM + # expects the partial PP shards to be buffered until all groups have sent their shards. + _is_vllm_lora_enabled = ( + getattr(self.model_runner, "lora_manager", None) is not None + ) + if _is_vllm_lora_enabled and not hasattr(self, "_lora_partial_shards"): + # (lora_name, lora_int_id) -> group_name -> normalized weight dict + self._lora_partial_shards: dict[ + tuple[str, int], dict[str, dict[str, torch.Tensor]] + ] = {} + try: group = init_custom_process_group( backend=backend, From e76ebb9de34712fdd3d20269b5dfbed53792357b Mon Sep 17 00:00:00 2001 From: Wei Fu <36355462+garrett4wade@users.noreply.github.com> Date: Wed, 8 Apr 2026 13:08:40 +0800 Subject: [PATCH 6/7] chore: fix pre-commit (#1148) --- areal/engine/vllm_ext/vllm_worker_extension.py | 1 - areal/utils/stats_logger.py | 7 ++++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/areal/engine/vllm_ext/vllm_worker_extension.py b/areal/engine/vllm_ext/vllm_worker_extension.py index 5454f15d9e..4c91a3a982 100644 --- a/areal/engine/vllm_ext/vllm_worker_extension.py +++ b/areal/engine/vllm_ext/vllm_worker_extension.py @@ -2,7 +2,6 @@ import torch import torch.distributed as dist - from vllm.logger import init_logger from vllm.lora.lora_model import LoRAModel from vllm.lora.peft_helper import PEFTHelper diff --git a/areal/utils/stats_logger.py b/areal/utils/stats_logger.py index 54e695b50c..228cc772c6 100644 --- a/areal/utils/stats_logger.py +++ b/areal/utils/stats_logger.py @@ -5,7 +5,6 @@ import swanlab import torch.distributed as dist -import trackio import wandb from tensorboardX import SummaryWriter @@ -96,6 +95,8 @@ def init(self): self._trackio_enabled = False trackio_config = self.config.trackio if trackio_config.mode != "disabled": + import trackio + trackio.init( project=trackio_config.project or self.config.experiment_name, name=trackio_config.name or self.config.trial_name, @@ -126,6 +127,8 @@ def close(self): wandb.finish() swanlab.finish() if getattr(self, "_trackio_enabled", False): + import trackio + trackio.finish() if self.summary_writer is not None: self.summary_writer.close() @@ -150,6 +153,8 @@ def commit(self, epoch: int, step: int, global_step: int, data: dict | list[dict wandb.log(item, step=log_step + i) swanlab.log(item, step=log_step + i) if getattr(self, "_trackio_enabled", False): + import trackio + trackio.log(item, step=log_step + i) if self.summary_writer is not None: for key, val in item.items(): From 595a3c4a3c9691498b11195a0795f4696465933e Mon Sep 17 00:00:00 2001 From: Zakir Jiwani Date: Wed, 8 Apr 2026 01:43:08 -0400 Subject: [PATCH 7/7] Fix grad norm hang when LoRA frozen ranks have no gradients (#1139) Ranks with no gradients (e.g. frozen non-LoRA params) previously returned 0.0 immediately, skipping the all_reduce. Ranks that do have gradients then hang waiting for the collective to complete. Move device init before the empty-grads check and make zero-grad ranks still participate in all_reduce with a zero-valued tensor. Co-authored-by: Claude Sonnet 4.6 --- areal/engine/fsdp_utils/grad.py | 17 ++++++++++++++--- tests/test_fsdp_grad.py | 11 ++++++++++- 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/areal/engine/fsdp_utils/grad.py b/areal/engine/fsdp_utils/grad.py index 48fd5c6e7c..290db79812 100644 --- a/areal/engine/fsdp_utils/grad.py +++ b/areal/engine/fsdp_utils/grad.py @@ -99,11 +99,22 @@ def get_grad_norm_fp32( norm_type = float(norm_type) total_norm = 0.0 - if not grads_for_norm: - return 0.0 - device = current_platform.current_device() + if not grads_for_norm: + # Still participate in all_reduce with zero contribution so that + # ranks with grads don't hang waiting for this rank (e.g. LoRA frozen ranks). + total_norm_cuda = torch.tensor(0.0, dtype=torch.float, device=device) + reduce_op = dist.ReduceOp.MAX if norm_type == torch.inf else dist.ReduceOp.SUM + if data_parallel_group: + dist.all_reduce(total_norm_cuda, op=reduce_op, group=data_parallel_group) + if model_parallel_group is not None: + dist.all_reduce(total_norm_cuda, op=reduce_op, group=model_parallel_group) + total_norm = float(total_norm_cuda.item()) + if norm_type != torch.inf and total_norm > 0: + total_norm = total_norm ** (1.0 / norm_type) + return total_norm + if norm_type == torch.inf: norms = [grad.abs().max() for grad in grads_for_norm] total_norm = torch.max(torch.stack(norms)) if norms else 0.0 diff --git a/tests/test_fsdp_grad.py b/tests/test_fsdp_grad.py index bed14750dd..519bc9088d 100644 --- a/tests/test_fsdp_grad.py +++ b/tests/test_fsdp_grad.py @@ -112,8 +112,17 @@ def mock_process_groups(self): return dp_group, mp_group def test_empty_grads_returns_zero(self, mock_process_groups): + # Empty grads must still participate in all_reduce (e.g. LoRA frozen ranks) + # so that ranks with real grads don't hang. dp_group, mp_group = mock_process_groups - result = get_grad_norm_fp32([], dp_group, mp_group) + with patch("torch.distributed.all_reduce") as mock_allreduce: + result = get_grad_norm_fp32([], dp_group, mp_group) + assert result == 0.0 + assert mock_allreduce.call_count == 2 # called for dp_group and mp_group + + def test_empty_grads_participates_in_allreduce_no_groups(self): + # With no process groups, empty grads should still return 0.0 without hanging. + result = get_grad_norm_fp32([], None, None) assert result == 0.0 @pytest.mark.parametrize("norm_type", [1.0, 2.0, 3.0, float("inf")])