[fea] support dp-moe for zcc and global_expert_id (#11050)

* support dp-moe for zcc (#10539)

* [fix] zcc ema under non-pp when `acc=1`

* zcc-ema fix load-state-dict when dp-moe

* [BugFix] shard-reshard下dp间is_matched行为不一致，以dp0为准 (#9404)

---------

Co-authored-by: Ferrebo <kebo01@baidu.com>

* [mod] Sharding IO: Added DP-Meta gather for MoE

---------

Co-authored-by: Meiyim <chenxuyi@baidu.com>

Author: bo-ke

paddlenlp/trainer/utils/sharding_io.py

@@ -402,7 +402,7 @@ def _load_one_state_dict_from_checkpoint(self, resume_from_checkpoint, base_weig
         if not os.path.isfile(file_path):
             raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint}, no {file_path}")
 
-        logger.info(f"Loading model from {resume_from_checkpoint} .")
+        logger.info(f"Loading model from {file_path}.")
         # We load the model state dict on the CPU to avoid an OOM error.
         state_dict = paddle.load(file_path, return_numpy=True)
         if self.is_ema:
@@ -524,6 +524,11 @@ def load_optimizer_state_with_reshard(self, checkpoint, base_opt_name, model_wra
                 is_matched = reshard_util.sharding_v2.is_matched_optimizer_state_dict(
                     one_shard_opt_state_dict, self.optimizer, model_wrapped
                 )
+                is_matched = paddle.to_tensor([is_matched], dtype=paddle.int32)
+                dp_group = fleet.get_hybrid_communicate_group().get_data_parallel_group()
+                dp_src_rank = fleet.get_hybrid_communicate_group().get_data_parallel_group_src_rank()
+                dist.broadcast(is_matched, src=dp_src_rank, group=dp_group)
+                is_matched = bool(is_matched[0])
             else:
                 is_matched = True
 
@@ -904,6 +909,10 @@ def _gather_sharding_metas(self):
         sharding_meta["param_meta_keys"] = ["shape", "dtype", "is_distributed", "no_sync"]
         sharding_meta["sharding_strategy"] = sharding_strategy
         sharding_meta["enable_overlap"] = pp_overlap
+        dp_metas_list = self._all_gather_simple_object(sharding_meta, self.hcg.get_data_parallel_group())
+        for e in dp_metas_list:
+            for key in ["structure_name_mapping", "param_meta"]:
+                sharding_meta[key].update(e[key])
         suffix = self._sharding_meta_suffix()
         sharding_metas[suffix] = sharding_meta
         sharding_metas_list = self._all_gather_simple_object(sharding_metas, self.hcg.get_model_parallel_group())

paddlenlp/trainer/utils/zero_cost_checkpoint.py

@@ -220,36 +220,34 @@ def ema_state_dict(self):
                 ema_state_dict[k] = tensor
             ema_state_dict_master_weights = {}
             for k, meta in self.optimizer_fusion_storage_helper.master_weights_meta.items():
-                t = self.ema_buffer._slice(
-                    meta["start"] - self.master_min_offset, meta["end"] - self.master_min_offset
-                ).clone()
+                s = meta["start"] - self.master_min_offset
+                e = meta["end"] - self.master_min_offset
+                t = self.ema_buffer._slice(s, e).clone()
                 t.get_tensor()._set_dims(meta["shape"])
                 t.name = meta["name"]
                 ema_state_dict_master_weights[k] = t
             ema_state_dict["master_weights"] = ema_state_dict_master_weights
         return ema_state_dict
 
-    def load_ema_state_dict(self, path):
-        with device_guard("cpu"):
-            logger.info(f"[ZCC EMA] load state dict from {path}")
-            state_dict = paddle.load(path)
-            for k, tensor_meta in self.param_fusion_storage_helper.model_weights_metas.items():
-                logger.info(f"[ZCC EMA] load model weight key={k}")
-                start = tensor_meta["start"]
-                end = tensor_meta["end"]
-                if tensor_meta["buffer_index"] not in self.ema_buffer_model_params:
-                    continue  # non fp32 has no `self.ema_buffer_model_params`
+    def load_ema_state_dict(self, state_dict):
+        for k, tensor_meta in self.param_fusion_storage_helper.model_weights_metas.items():
+            logger.info(f"[ZCC EMA] load model weight key={k}")
+            start = tensor_meta["start"]
+            end = tensor_meta["end"]
+            if tensor_meta["buffer_index"] not in self.ema_buffer_model_params:
+                continue  # non fp32 has no `self.ema_buffer_model_params`
+            if k in state_dict:
                 cpu_buffer = self.ema_buffer_model_params[tensor_meta["buffer_index"]]
                 tensor = state_dict[k].flatten()
                 cpu_buffer[start:end] = tensor
 
-            ema_master = state_dict["master_weights"]
-            for k, meta in self.optimizer_fusion_storage_helper.master_weights_meta.items():
-                logger.info(f"[ZCC EMA] load optimizer weight key={k}")
-                s = meta["start"] - self.master_min_offset
-                e = meta["end"] - self.master_min_offset
-                self.ema_buffer[s:e] = ema_master[k]
-            logger.info("[ZCC EMA] done loading")
+        ema_master = state_dict["master_weights"]
+        for k, meta in self.optimizer_fusion_storage_helper.master_weights_meta.items():
+            logger.info(f"[ZCC EMA] load optimizer weight key={k}")
+            s = meta["start"] - self.master_min_offset
+            e = meta["end"] - self.master_min_offset
+            if k in ema_master:  # state-dict is filtered
+                self.ema_buffer[s:e] = ema_master[k].flatten()
 
 
 class ParamFusionStorageHelper:
@@ -408,11 +406,6 @@ def on_optimizer_begin(self, args, state, control, **kwargs):
             logger.info("[ZCC manager] Synced checkpoints.")
 
     def on_step_end(self, args, state, control, model, lr_scheduler, optimizer, **kwargs):
-        if not isinstance(model, PipelineLayer):
-            self.manager.zcc_pipeline_hook(0)
-        # logger.info(
-        #     f"check coef: {args.zcc_save_ema_coef} {control.should_save}, {state.global_step}, {self.zcc_ema_interval}"
-        # )
         if not control.should_save:
             if args.zcc_save_ema_coef is not None and state.global_step % self.zcc_ema_interval == 0:
                 self.maybe_update_zcc_worker(args, model, optimizer, state.global_step)
@@ -425,6 +418,8 @@ def on_step_end(self, args, state, control, model, lr_scheduler, optimizer, **kw
             non_cached_objects = (lr_scheduler.state_dict(), state, self.get_rng_states(args))
             self.manager.get_idle_worker_for_saving((save_infos, non_cached_objects))
             self.runtime_timer.stop()
+        if not isinstance(model, PipelineLayer):
+            self.manager.zcc_pipeline_hook(0)
 
     def get_rng_states(self, args):
         if not args.save_rng_states:
@@ -959,7 +954,15 @@ def run(self):
                             self.optimizer_fusion_storage_helper, self.param_fusion_storage_helper, self.ema_coef
                         )
                         if ema_ckpt_path is not None:  # update ema if needed
-                            self.zcc_ema_processor.load_ema_state_dict(ema_ckpt_path)
+                            logger.info(f"[ZCC EMA] load state dict from {ema_ckpt_path}")
+                            with device_guard("cpu"):
+                                state_dict = paddle.load(ema_ckpt_path)
+                                if self.use_expert_parallel and self.dp_rank > 0:
+                                    state_dict = self._filter_moe_no_sync_optimizer_params(
+                                        self.model_meta_content, state_dict
+                                    )
+                                self.zcc_ema_processor.load_ema_state_dict(state_dict)
+                            logger.info("[ZCC EMA] done loading")
                         ema_ckpt_path = None
                 elif task_type == ZCCTaskType.PREPARE:
                     start_time = time.time()