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feat: add async HuggingFace export support #1681

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144 changes: 144 additions & 0 deletions tests/model/test_qwen3_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -347,6 +347,149 @@ def test_save_hf(self, device, dispatcher, ep_size):

self.assertListEqual(safetensor_keys, model_index_keys)
dist.barrier()

@parametrize.parametrize(
"device,dispatcher,ep_size",
[
("cuda", None, 1),
("cuda", "all2all", 4),
("cuda", "all2all", 8),
],
)
def test_async_save_hf(self, device, dispatcher, ep_size):
self.create_pg(device)

with tempfile.TemporaryDirectory() as tmpdir:
syncdir = [tmpdir]
if self.world_size > 1:
dist.broadcast_object_list(syncdir, src=0)
tmpdir = Path(syncdir[0])
saved_hf_path = tmpdir / "hf-1"
origin_hf_path = Path(QWEN3_MOE_PATH)
origin_index_path = origin_hf_path / "model.safetensors.index.json"
saved_index_path = saved_hf_path / "model.safetensors.index.json"

with torch.device("meta"):
cfg = get_model_config_from_hf(QWEN3_MOE_PATH)
cfg.compile_cfg = False
cfg.dispatcher = dispatcher
cfg.ep_size = ep_size
qwen_model = cfg.build().to(torch.bfloat16)

fsdp_config = FSDPConfig(
ep_size=ep_size,
cpu_offload=False,
)
qwen_model.fully_shard(fsdp_config=fsdp_config)
qwen_model.from_hf(QWEN3_MOE_PATH)

tokenizer = AutoTokenizer.from_pretrained(QWEN3_MOE_PATH, trust_remote_code=True)

qwen_model.async_save_hf(hf_dir=saved_hf_path)
qwen_model.wait_async_hf()

if dist.get_rank() == 0:
tokenizer.save_pretrained(str(saved_hf_path))

dist.barrier()

self.assertTrue(saved_hf_path.exists())
self.assertTrue(saved_index_path.exists())

dist.barrier()

if dist.get_rank() == 0:
with open(origin_index_path, "r") as f:
origin_index = json.load(f)
with open(saved_index_path, "r") as f:
saved_index = json.load(f)
with open(origin_hf_path / "config.json", "r") as f:
origin_config = json.load(f)
with open(saved_hf_path / "config.json", "r") as f:
saved_config = json.load(f)

self.assertTrue(check_dict_equal(origin_config, saved_config))
self.assertListEqual(
sorted(origin_index["weight_map"].keys()),
sorted(saved_index["weight_map"].keys()),
)

cache_fh = {}
for key in origin_index["weight_map"].keys():
origin_safetensor_name = origin_index["weight_map"][key]
saved_safetensor_name = saved_index["weight_map"][key]

if origin_safetensor_name not in cache_fh:
cache_fh[origin_safetensor_name] = safe_open(
str(origin_hf_path / origin_safetensor_name), framework="pt"
)
if saved_safetensor_name not in cache_fh:
cache_fh[saved_safetensor_name] = safe_open(
str(saved_hf_path / saved_safetensor_name), framework="pt"
)

origin_tensor = cache_fh[origin_safetensor_name].get_tensor(key)
saved_tensor = cache_fh[saved_safetensor_name].get_tensor(key)
self.assertTrue(torch.equal(origin_tensor, saved_tensor), f"tensor {key} is not equal")

safetensor_keys = []
for safetensor_path in saved_hf_path.glob("*.safetensors"):
fh = cache_fh[safetensor_path.name]
safetensor_keys.extend(fh.keys())
safetensor_keys.sort()
model_index_keys = list(saved_index["weight_map"].keys())
model_index_keys.sort()
self.assertListEqual(safetensor_keys, model_index_keys)

dist.barrier()

del qwen_model
torch.cuda.empty_cache()

input_ids = tokenizer("吃葡萄不吐葡萄皮", return_tensors="pt").input_ids.to("cuda")
labels = input_ids.clone()

hf_origin_model = AutoModelForCausalLM.from_pretrained(
origin_hf_path,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="cuda",
)
patch_hf_rms_norm(hf_origin_model)
hf_origin_model.eval()
with torch.no_grad():
origin_output = hf_origin_model(input_ids=input_ids, labels=labels)
origin_loss = origin_output.loss.detach().cpu()
origin_logits = origin_output.logits.detach().cpu()

del hf_origin_model
del origin_output
torch.cuda.empty_cache()

hf_saved_model = AutoModelForCausalLM.from_pretrained(
saved_hf_path,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="cuda",
)
patch_hf_rms_norm(hf_saved_model)
hf_saved_model.eval()
with torch.no_grad():
saved_output = hf_saved_model(input_ids=input_ids, labels=labels)
saved_loss = saved_output.loss.detach().cpu()
saved_logits = saved_output.logits.detach().cpu()

self.assertTrue(
torch.allclose(origin_loss, saved_loss, rtol=1e-2, atol=1e-2),
f"origin_loss={origin_loss.item()}, saved_loss={saved_loss.item()}",
)
self.assertTrue(torch.equal(origin_logits.argmax(dim=-1), saved_logits.argmax(dim=-1)))

del hf_saved_model
del saved_output
torch.cuda.empty_cache()

dist.barrier()

def test_fope_auto_config_with_remote_code(self):
self.create_pg('cuda')
Expand Down Expand Up @@ -462,6 +605,7 @@ def world_size(self) -> int:
def create_model_from_hf(load_from: Path, dispatcher: str, ep_size: int):
with torch.device("meta"):
cfg : Qwen3MoEConfig = get_model_config_from_hf(load_from)
cfg.compile_cfg = False
cfg.dispatcher = dispatcher
cfg.ep_size = ep_size
qwen_model = cfg.build()
Expand Down
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