| """Rank-8 policy using explicitly assumed official RoboTwin preprocessing.""" |
| import hashlib |
| import json |
| from pathlib import Path |
|
|
| ADAPTER_SHA = '51563f4e588174361ef8ad61db63a23da4ec33c0b115095375bd9323025cf5f0' |
| CONFIG_SHA = 'f0c1f8f2e9add85ac9bf573feba91ce52fa4bc24fb4927f175a8b7520e877449' |
| REVISION = '1d40e50988de2da6a07877e81a0941da3317733b' |
| MODEL = 'lora_r8_step10000' |
|
|
|
|
| def sha(path): |
| h = hashlib.sha256() |
| with Path(path).open('rb') as f: |
| for data in iter(lambda: f.read(8 * 1024 * 1024), b''): |
| h.update(data) |
| return h.hexdigest() |
|
|
|
|
| def load_lora_policy(root, output): |
| import torch |
| import yaml |
| from peft import LoraConfig |
| from safetensors import safe_open |
| from safetensors.torch import load_file |
| from src.phase4_policy import prepare_base_runtime, streaming_server_class |
| from src.lora_eval_loading import expand_fused_experts, inject_and_load_adapter |
|
|
| root, output = Path(root), Path(output) |
| adapter = root / 'outputs/lora_r8_smoke_preparation_20260929/adapter' |
| if sha(adapter/'adapter_model.safetensors') != ADAPTER_SHA or sha(adapter/'adapter_config.json') != CONFIG_SHA: |
| raise ValueError('Frozen adapter checksum mismatch') |
| upstream = root/'third_party/lingbot-vla-v2' |
| norm = upstream/'assets/norm_stats/robotwin.json' |
| base = root/'checkpoints/base/lingbot-vla-v2-6b' |
| |
| manifest = json.loads((root/'manifests/lingbot-vla-v2-6b.json').read_text()) |
| if manifest['revision'] != '11c703bf6a5c1f45b3b69168482da11fdbba53d7': |
| raise ValueError('Wrong base revision') |
| index = json.loads((base/'model.safetensors.index.json').read_text()) |
| siblings = {x['rfilename']: x for x in manifest['siblings']} |
| for name in sorted(set(index['weight_map'].values())): |
| if sha(base/name) != siblings[name]['lfs']['sha256']: |
| raise ValueError('Base shard checksum mismatch: ' + name) |
| checkpoint, provenance = prepare_base_runtime(base, |
| root/'checkpoints/dependencies/Qwen3-VL-4B-Instruct', norm, |
| output/'runtime', upstream=upstream) |
| yaml_path = output/'runtime/lingbotvla_cli.yaml' |
| cfg = yaml.safe_load(yaml_path.read_text()) |
| cfg['model']['moe_implementation'] = 'eager' |
| yaml_path.write_text(yaml.safe_dump(cfg, sort_keys=False)) |
|
|
| class LoraServer(streaming_server_class(upstream)): |
| def load_model_weights(self, path_to_pi_model, strict=True): |
| if not strict: |
| raise ValueError('Strict base loading required') |
| expected = self.vla.state_dict() |
| tensors = {} |
| for name in sorted(set(index['weight_map'].values())): |
| with safe_open(base/name, framework='pt', device='cpu') as shard: |
| raw = {} |
| for key in shard.keys(): |
| if index['weight_map'].get(key) != name: |
| raise ValueError('Wrong shard assignment: ' + key) |
| t = shard.get_tensor(key) |
| raw[key] = t.float() if t.is_floating_point() else t |
| for key, t in expand_fused_experts(raw).items(): |
| if key in tensors or key not in expected or t.shape != expected[key].shape: |
| raise ValueError('Converted base key/shape mismatch: ' + key) |
| tensors[key] = t |
| print('Loaded and expanded base shard', name, flush=True) |
| if set(tensors) != set(expected): |
| raise ValueError('Converted base inventory does not match eager policy') |
| self.vla.load_state_dict(tensors, strict=True, assign=True) |
| if any(t.is_meta for t in list(self.vla.parameters()) + list(self.vla.buffers())): |
| raise ValueError('Meta tensors remain after loading') |
| self.vla = inject_and_load_adapter(self.vla, |
| LoraConfig.from_pretrained(str(adapter)), |
| load_file(str(adapter/'adapter_model.safetensors'), device='cpu')) |
| self.loaded_parameter_count = sum(p.numel() for p in self.vla.parameters()) |
| self.loaded_tensor_count = len(self.vla.state_dict()) |
| print('Strict eager base + complete LoRA loaded', self.loaded_parameter_count, flush=True) |
|
|
| policy = LoraServer(path_to_pi_model=str(checkpoint), robot_norm_path=str(norm), |
| use_bf16=False, use_fp32=True, use_compile=False, use_length=50, chunk_ret=True) |
| policy.reset('robotwin') |
| metadata = {'checkpoint_source_kind': 'lora_adapter', 'adapter_revision': REVISION, |
| 'adapter_sha256': ADAPTER_SHA, 'adapter_config_sha256': CONFIG_SHA, |
| 'base_revision': manifest['revision'], 'base_shards_verified': True, |
| 'adapter_tensors_verified': 8494, 'lora_rank': 8, 'lora_alpha': 8, |
| 'moe_implementation': 'eager', 'training_configuration_verified': False, |
| 'configuration_assumption': 'User-authorized official RoboTwin model/preprocessing/normalization; eager expert layout for LoRA', |
| 'norm_sha256': sha(norm), 'runtime_yaml_sha256': sha(yaml_path)} |
| (output/'checkpoint_verified.json').write_text(json.dumps(metadata, indent=2)+'\n') |
| return policy, norm, metadata |
|
|
