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ef88d4e52b
* feat(train): parallel training engine with FSDP2, HSDP, and DCP checkpoints Replace the FSDP1 training path with a config-owned parallel-training engine: - Topology and runtime configs (--parallelism.*, --accelerator.*): dp_replicate x dp_shard degrees select single-process, DDP (unchanged default), FSDP2, or HSDP; mixed precision, first-class gradient accumulation, and FSDP/DDP tuning knobs are mirrored as plain dataclasses that build the accelerate objects at runtime, so every run is reproducible from its train_config.json alone. Accelerate env vars are guarded against configuring the engine behind the config system's back. - Declarative policy surface: policies declare FSDP2 wrap units (_fsdp_wrap_modules) and non-forward entry points (_fsdp_forward_methods); a shared engine resolves them around accelerator.prepare(). Context-parallel fields are reserved and validated to 1. - Checkpoints: selectable --checkpoint_format (safetensors | dcp | safetensors_dcp); the sharded optimizer channel is always DCP; two-phase resume (step+RNG before prepare, DCP model/optimizer after) reshards across GPU-topology changes; lerobot-convert-dcp merges DCP shards into a distributable model.safetensors offline. - Publishing: PreTrainedPolicy.push_model_to_hub is replaced by the free publish_trained_model (model + processors + card + train config, all-ranks gather with main-rank writes); PreTrainedPolicy._save_pretrained gathers state dicts internally, removing the state_dict= threading from save_pretrained. - lerobot_train is restructured around the engine: optimizer built before the single prepare() call, deferred weight load on DCP resumes, collective save_checkpoint with no call-site rank branches, dp-world-size-based sample accounting. Breaking changes: FSDP checkpoints from lerobot <= 0.6.x are not resumable (weights stay loadable via from_pretrained; pin lerobot==0.6.x to finish old runs); the `accelerate launch --config_file` yaml flow is superseded by the config flags; training autocast is owned exclusively by --accelerator.mixed_precision (policy.dtype only casts parameters). Also fixes: reward-model hub publishing crash (TypeError on extra kwargs). Verified by ~200 new CPU tests (config round-trips, checkpoint round-trips per format, two-phase resume, publisher contracts, converter equivalence, accelerate canaries), a 5-test 4-GPU suite (FSDP2 save/resume bit-exactness, HSDP/DDP loss parity, changed-topology resume, all-ranks save_pretrained, grad-accum equivalence), and end-to-end ACT (1/4/8 GPUs) + FastWAM 6B (FSDP2 + HSDP) training runs.
119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import draccus
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import pytest
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from lerobot.configs.parallelism import ContextParallelConfig, ParallelismConfig
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class TestResolve:
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def test_single_process_defaults(self):
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cfg = ParallelismConfig()
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cfg.resolve(1)
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assert (cfg.dp_replicate, cfg.dp_shard) == (1, 1)
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assert not cfg.is_sharded and not cfg.is_replicated_only
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assert cfg.dp_world_size == 1
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def test_untouched_config_fills_ddp(self):
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"""Plain `torchrun --nproc-per-node=8` with a default config resolves to DDP."""
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cfg = ParallelismConfig()
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cfg.resolve(8)
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assert cfg.dp_replicate == 8
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assert cfg.is_replicated_only and not cfg.is_sharded
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assert cfg.dp_world_size == 8
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def test_full_shard_sentinel(self):
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cfg = ParallelismConfig(dp_shard=-1)
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assert cfg.is_sharded # sharded even before resolve: -1 is an explicit opt-in
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cfg.resolve(8)
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assert cfg.dp_shard == 8 and cfg.dp_replicate == 1
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def test_hsdp_sentinel_infers_shard(self):
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cfg = ParallelismConfig(dp_replicate=2, dp_shard=-1)
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cfg.resolve(8)
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assert (cfg.dp_replicate, cfg.dp_shard) == (2, 4)
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assert cfg.dp_world_size == 8
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def test_explicit_hsdp(self):
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cfg = ParallelismConfig(dp_replicate=2, dp_shard=4)
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cfg.resolve(8)
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assert cfg.is_sharded and not cfg.is_replicated_only
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def test_product_mismatch_lists_all_degrees(self):
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cfg = ParallelismConfig(dp_replicate=2, dp_shard=2)
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with pytest.raises(ValueError, match=r"dp_replicate=2 \* dp_shard=2.*WORLD_SIZE=8"):
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cfg.resolve(8)
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def test_explicit_replicate_must_match_world(self):
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cfg = ParallelismConfig(dp_replicate=4)
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with pytest.raises(ValueError, match="WORLD_SIZE=8"):
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cfg.resolve(8)
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def test_sentinel_indivisible_world(self):
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cfg = ParallelismConfig(dp_replicate=3, dp_shard=-1)
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with pytest.raises(ValueError, match="not divisible"):
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cfg.resolve(8)
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def test_cp_fails_fast(self):
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cfg = ParallelismConfig(dp_shard=-1, context_parallel=ContextParallelConfig(ulysses_degree=2))
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with pytest.raises(ValueError, match="not implemented"):
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cfg.resolve(8)
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class TestFieldValidation:
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@pytest.mark.parametrize("kwargs", [{"dp_replicate": 0}, {"dp_shard": 0}, {"dp_shard": -2}])
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def test_bad_dp_degrees(self, kwargs):
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with pytest.raises(ValueError):
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ParallelismConfig(**kwargs)
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def test_cfg_parallel_capped_at_two(self):
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ParallelismConfig(cfg_parallel=2) # reserved but representable
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with pytest.raises(ValueError, match="cfg_parallel"):
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ParallelismConfig(cfg_parallel=3)
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@pytest.mark.parametrize("kwargs", [{"ring_degree": 0}, {"ulysses_degree": -1}])
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def test_bad_cp_degrees(self, kwargs):
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with pytest.raises(ValueError):
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ContextParallelConfig(**kwargs)
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def test_dp_world_size_undefined_before_resolve(self):
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with pytest.raises(RuntimeError, match="resolve"):
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_ = ParallelismConfig(dp_shard=-1).dp_world_size
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class TestDraccusRoundTrip:
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@pytest.mark.parametrize(
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"cfg",
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[
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ParallelismConfig(),
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ParallelismConfig(dp_replicate=2, dp_shard=4, cfg_parallel=2),
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ParallelismConfig(
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dp_shard=-1,
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context_parallel=ContextParallelConfig(ring_degree=2, ulysses_degree=4),
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),
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],
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)
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def test_encode_json_decode_identity(self, cfg):
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payload = json.loads(json.dumps(draccus.encode(cfg)))
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assert draccus.decode(ParallelismConfig, payload) == cfg
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def test_pre_existing_config_without_fields_gets_defaults(self):
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"""Checkpoints written before this feature parse with default topology."""
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assert draccus.decode(ParallelismConfig, {}) == ParallelismConfig()
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