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polish code with new pre-commit rule (#2923)

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This commit is contained in:
Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
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72
fastdeploy/rl/dynamic_weight_manager.py
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@@ -13,6 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
import os
import time
from multiprocessing.shared_memory import SharedMemory
@@ -46,15 +47,14 @@ class DynamicWeightManager:
logger.info(
f"✅ DynamicLoad model built successfully by {self.load_config.load_strategy}, "
f" rank={self.rank}, ranks={self.nranks}")
f" rank={self.rank}, ranks={self.nranks}"
)
@paddle.no_grad()
def _capture_model_state(self):
"""Capture and store initial model parameters state."""
for name, param in self.model.state_dict().items():
logger.debug(
f"Model param: {name}, shape={param.shape}, dtype={param.dtype}"
)
logger.debug(f"Model param: {name}, shape={param.shape}, dtype={param.dtype}")
self.state_dict[name] = param
def update_parameters(self, pid: int = 0) -> None:
@@ -73,11 +73,9 @@ class DynamicWeightManager:
if handler := strategy_handlers.get(self.load_config.load_strategy):
handler()
else:
raise ValueError(
f"Unsupported strategy: {self.load_config.load_strategy}")
raise ValueError(f"Unsupported strategy: {self.load_config.load_strategy}")
logger.info(
f"Update parameters in {time.perf_counter()-start_time:.2f}s")
logger.info(f"Update parameters in {time.perf_counter()-start_time:.2f}s")
self._finalize_update(pid)
@@ -85,7 +83,8 @@ class DynamicWeightManager:
"""Update using IPC snapshot strategy for elastic recovery."""
model_path = os.path.join(
self.parallel_config.model_name_or_path,
f"model_state.tp0{self.meta_src_id}.pdparams")
f"model_state.tp0{self.meta_src_id}.pdparams",
)
try:
ipc_state_dict = paddle.load(model_path)
@@ -94,16 +93,14 @@ class DynamicWeightManager:
ipc_state_dict = paddle.load(fallback_path)
self._update_model_from_state(ipc_state_dict, "snapshot")
logger.info(
f"IPC snapshot update parameters completed from {model_path}")
logger.info(f"IPC snapshot update parameters completed from {model_path}")
def _update_ipc(self):
"""Update using standard IPC strategy (requires Training Worker)."""
ipc_meta = paddle.load(self.ipc_path)
state_dict = self._convert_ipc_meta_to_tensor(ipc_meta)
self._update_model_from_state(state_dict, "raw")
logger.info(
f"IPC update parameters completed from file: {self.ipc_path}")
logger.info(f"IPC update parameters completed from file: {self.ipc_path}")
def clear_parameters(self, pid: int = 0) -> None:
"""Clear all model parameters and free memory."""
@@ -118,8 +115,7 @@ class DynamicWeightManager:
paddle.distributed.shutdown_process_group()
self._update_shared_status(pid, -2)
def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor],
src_type: str):
def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor], src_type: str):
"""Update model parameters from given state dictionary."""
if len(state_dict) == 0:
raise ValueError(f"No parameter found in state dict {state_dict}")
@@ -133,19 +129,14 @@ class DynamicWeightManager:
self._validate_parameter_match(name, new_param, target_param)
new_param._share_buffer_to(target_param)
update_count += 1
logger.info(
f"🆗 Updated {update_count}/{len(state_dict)} parameters from {src_type} source"
)
logger.info(f"🆗 Updated {update_count}/{len(state_dict)} parameters from {src_type} source")
def _validate_parameter_match(self, name: str, src: paddle.Tensor,
dst: paddle.Tensor):
def _validate_parameter_match(self, name: str, src: paddle.Tensor, dst: paddle.Tensor):
"""验证参数一致性"""
if src.dtype != dst.dtype:
raise TypeError(
f"Type mismatch for {name}: {src.dtype} vs {dst.dtype}")
raise TypeError(f"Type mismatch for {name}: {src.dtype} vs {dst.dtype}")
if src.shape != dst.shape:
raise ValueError(
f"Shape mismatch for {name}: {src.shape} vs {dst.shape}")
raise ValueError(f"Shape mismatch for {name}: {src.shape} vs {dst.shape}")
def _finalize_update(self, pid: int):
"""Finalize update process with verification."""
@@ -163,7 +154,7 @@ class DynamicWeightManager:
def _verify_parameters(self, operation: str):
"""Verify parameters are in expected state after operation."""
expected_initialized = (operation == "update")
expected_initialized = operation == "update"
all_valid = True
for name, param in self.state_dict.items():
is_initialized = param._is_initialized()
@@ -177,12 +168,12 @@ class DynamicWeightManager:
if all_valid:
logger.info(f"💡 Model Parameter {operation} verified successfully")
else:
raise RuntimeError(
f"❌ Model Parameter {operation} verification failed")
raise RuntimeError(f"❌ Model Parameter {operation} verification failed")
@staticmethod
def _convert_ipc_meta_to_tensor(
ipc_meta: Dict[str, Any]) -> Dict[str, paddle.Tensor]:
ipc_meta: Dict[str, Any],
) -> Dict[str, paddle.Tensor]:
"""Convert IPC metadata to tensor dictionary."""
converted = {}
for name, meta in ipc_meta.items():
@@ -199,18 +190,18 @@ class DynamicWeightManager:
curr_alloc = paddle.device.cuda.memory_allocated() / (1024**3)
curr_reserved = paddle.device.cuda.memory_reserved() / (1024**3)
logger.warning(f"GPU memory usage {context}:"
f"max_allocated: {max_alloc:.2f}GB\n"
f"max_reserved: {max_reserved:.2f}GB\n"
f"current_allocated: {curr_alloc:.2f}GB\n"
f"current_reserved: {curr_reserved:.2f}GB")
logger.warning(
f"GPU memory usage {context}:"
f"max_allocated: {max_alloc:.2f}GB\n"
f"max_reserved: {max_reserved:.2f}GB\n"
f"current_allocated: {curr_alloc:.2f}GB\n"
f"current_reserved: {curr_reserved:.2f}GB"
)
def _update_shared_status(self, pid: int, status: int) -> None:
"""Update shared memory status flag for inter-process communication."""
array = np.zeros([1], dtype=np.int32)
shm = SharedMemory(create=False,
size=array.nbytes,
name=f"model_weights_status.{pid}")
shm = SharedMemory(create=False, size=array.nbytes, name=f"model_weights_status.{pid}")
value = np.ndarray(array.shape, dtype=array.dtype, buffer=shm.buf)
if self.rank == 0:
value[self.rank] = status
@@ -223,20 +214,17 @@ class DynamicWeightManager:
is_stop = 0
while model_weights_status.value[0] != 0:
if model_weights_status.value[0] == 1:
logger.info(
"infer engine stopped! start to load new checkpoint...")
logger.info("infer engine stopped! start to load new checkpoint...")
model_runner.update_parameters(pid)
elif model_weights_status.value[0] == -1:
logger.info(
"infer engine stopped! start to clear checkpoint...")
logger.info("infer engine stopped! start to clear checkpoint...")
model_runner.clear_parameters(pid)
while True:
if model_weights_status.value[0] == 0:
logger.info("finished loading new checkpoint")
break
elif is_stop == 1 or (model_weights_status.value[0] == -2
and is_stop == 0):
elif is_stop == 1 or (model_weights_status.value[0] == -2 and is_stop == 0):
if is_stop == 0:
logger.info("finished clearing checkpoint")
is_stop = 1