[fea] moe support

Author: bo-ke

paddlenlp/trainer/trainer.py

@@ -143,6 +143,7 @@
 from .utils import reshard as reshard_util
 from .utils.helper import (  # nested_truncate,
     broadcast_dp_optimizer,
+    broadcast_moe_optimizer,
     distributed_concat,
     distributed_file,
     distributed_isfile,
@@ -930,22 +931,14 @@ def _inner_training_loop(
                     self.control = self.callback_handler.on_step_begin(args, self.state, self.control)
                     self.timers and self.timers("forward-backward").start()
 
-                dp_enabled = (
-                    self.args.data_parallel_degree > 1 if self.args.use_hybrid_parallel else args.local_rank != -1
-                )
-                forbidden_no_sync = False
                 # stage2 and stage3 should not no_sync, because the is no DDP wrapper and no_sync API
                 # hybrid_parallel (tp or pp or sharding stage 1) should not no_sync
-                if self.args.use_hybrid_parallel:
-                    forbidden_no_sync = True
-
-                availiable_no_sync = dp_enabled and not forbidden_no_sync
-
+                availiable_no_sync = hasattr(model, "no_sync")
                 is_no_sync = (
-                    ((step_control + 1) % args.gradient_accumulation_steps != 0)
-                    and availiable_no_sync
-                    and args._no_sync_in_gradient_accumulation
-                ) or (args.recompute and availiable_no_sync)
+                    (((step + 1) % args.gradient_accumulation_steps != 0) and args._no_sync_in_gradient_accumulation)
+                    or args.recompute
+                    or args.use_moe
+                ) and availiable_no_sync
                 # sharding
                 # stage1. the same as ddp
                 # stage2. manualy collect gradient on dp group
@@ -965,6 +958,14 @@ def _inner_training_loop(
 
                 tr_loss += tr_loss_step
 
+                def fused_allreduce_gradients_no_sync(paramlist, hcg):
+                    paramlist = list(paramlist)
+                    nonmoe_list = [p for p in paramlist if not getattr(p, "no_sync", False)]
+                    moelist = [p for p in paramlist if getattr(p, "no_sync", False)]
+                    if moelist and not self.args.use_moe:
+                        logger.warning("found `no sync` param when `use_moe=False`")
+                    fused_allreduce_gradients(nonmoe_list, hcg)
+
                 if (step_control + 1) % args.gradient_accumulation_steps == 0 or (
                     # last step in epoch but step is always smaller than gradient_accumulation_steps
                     steps_in_epoch <= args.gradient_accumulation_steps
@@ -983,12 +984,12 @@ def _inner_training_loop(
 
                     # Case 1: Use recompute and dp / sharding stage1,
                     # manualy collect gradient for dp.
-                    if args.recompute and availiable_no_sync:
-                        fused_allreduce_gradients(list(model.parameters()), None)
+                    if (args.recompute or args.use_moe) and availiable_no_sync:
+                        fused_allreduce_gradients_no_sync(list(model.parameters()), None)
 
                     # Case 2: hack dp with master_grad
-                    if dp_master_grad and not (args.recompute and availiable_no_sync):
-                        fused_allreduce_gradients(list(model.parameters()), None)
+                    elif dp_master_grad:
+                        fused_allreduce_gradients_no_sync(list(model.parameters()), None)
 
                     # Pipeline parallel mode,  handle gradient reduce here to overlap
                     pipeline_parallel_config = (
@@ -1007,8 +1008,7 @@ def _inner_training_loop(
                                 self.optimizer._inner_opt.reduce_gradients(list(parameters_list), self.optimizer._hcg)
 
                             if self.optimizer._dp_enable or getattr(self.optimizer, "_sep_enable", False):
-                                fused_allreduce_gradients(list(parameters_list), self.optimizer._hcg)
-
+                                fused_allreduce_gradients_no_sync(list(parameters_list), self.optimizer._hcg)
                     self.timers and self.timers("all-reduce").stop()
                     self.timers and self.timers("optimizer-step").start()
 
@@ -1028,7 +1028,9 @@ def _inner_training_loop(
                     )
                     optimizer_was_run = True
                     if self.do_grad_scaling:
-                        scale_before = paddle.assign(self.scaler._scale)
+                        if args.pipeline_parallel_degree > 1:
+                            assert not self.args.use_moe, "pipline moe not work under fp16"
+                        scale_before = self.scaler._scale.numpy()
                         self.scaler.step(self.optimizer)
                         self.scaler.update()
                         scale_after = self.scaler._scale
@@ -2042,7 +2044,6 @@ def training_step(self, model: nn.Layer, inputs: Dict[str, Union[paddle.Tensor,
 
         model.train()
         inputs = self._prepare_inputs(inputs)
-
         with self.autocast_smart_context_manager():
             loss = self.compute_loss(model, inputs)
 
@@ -2053,7 +2054,6 @@ def training_step(self, model: nn.Layer, inputs: Dict[str, Union[paddle.Tensor,
             self.scaler.scale(loss).backward()
         else:
             loss.backward()
-
         return loss.detach()
 
     def training_pipeline_step(self, model: nn.Layer, inputs: Dict[str, Union[paddle.Tensor, Any]]) -> paddle.Tensor:
@@ -2143,6 +2143,20 @@ def save_model(self, output_dir: Optional[str] = None, merge_tensor_parallel: Op
                 # For ckpt integrity
                 paddle.save(self.state.global_step, os.path.join(output_dir, ".model_done"))
 
+    def _save_moe_weights(
+        self,
+        output_dir: Optional[str] = None,
+        merge_tensor_parallel: Optional[bool] = False,
+    ):
+        # save moe optimizer and model state # TODO 默认为冗余存储
+
+        self._save(output_dir=output_dir, merge_tensor_parallel=merge_tensor_parallel)
+        optimizer_name = _add_variant(OPTIMIZER_NAME, self.args.optimizer_name_suffix)
+        saved_signal_path = os.path.join(output_dir, f"saved_signal_{dist.get_rank()}")
+        paddle.save(self.optimizer.state_dict(), os.path.join(output_dir, optimizer_name))
+        with open(saved_signal_path, mode="w+") as f:
+            f.write("1")
+
     def _save_checkpoint(self, model, metrics=None):
         # assert unwrap_model(model) is self.model, "internal model should be a reference to self.model"
         self.runtime_timer.start("checkpoint saving time")
@@ -2245,6 +2259,8 @@ def _save_checkpoint(self, model, metrics=None):
             os.makedirs(output_dir, exist_ok=True)
             paddle.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
 
+        if self.args.use_moe and self.args.data_parallel_rank > 0:
+            self._save_moe_weights(output_dir)
         # Maybe delete some older checkpoints.
         # For hybrid parallel training, the checkpoint files maybe on different node.
         need_to_rotate_checkpoints = False
@@ -2476,7 +2492,10 @@ def _load_optimizer_and_scheduler(self, checkpoint):
         # broadcast optimizer state in dp group
         if self.args.local_rank != -1:
             dist.barrier()
-        opt_state_dict = broadcast_dp_optimizer(opt_state_dict)
+        if not self.args.use_moe:
+            opt_state_dict = broadcast_dp_optimizer(opt_state_dict)
+        else:
+            opt_state_dict = broadcast_moe_optimizer(opt_state_dict)
 
         if opt_state_dict is not None:
             # Load in optimizer and scheduler states

paddlenlp/trainer/training_args.py

@@ -803,6 +803,10 @@ class TrainingArguments:
         default=False,
         metadata={"help": "whether to run distributed training in auto parallel mode"},
     )
+    use_moe: Optional[bool] = field(
+        default=False,
+        metadata={"help": "开启moe训练"},
+    )
 
     def __post_init__(self):
         env_local_rank = int(os.environ.get("PADDLE_RANK_IN_NODE", -1))
@@ -1149,6 +1153,8 @@ def is_segment_parallel_supported():
                         order = ["dp", "sharding", "pp", "sep", "mp"]
                     else:
                         order = ["dp", "sharding", "pp", "mp"]
+                if self.use_moe:
+                    order = order[1:-1] + ["dp", "mp"]
 
                 if is_segment_parallel_supported():
                     hybrid_configs = {
@@ -1640,8 +1646,12 @@ def optimizer_name_suffix(self):
                 name.append(self._format_name("pp", self.pipeline_parallel_rank, self.pipeline_parallel_degree))
             if self.sharding_parallel_degree > 1:
                 name.append(self._format_name("shard", self.sharding_parallel_rank, self.sharding_parallel_degree))
+            if self.use_moe:
+                name.append(self._format_name("moe", self.data_parallel_rank, self.data_parallel_degree))
             return "_".join(name)
         else:
+            if self.use_moe:
+                return self._format_name("moe", self.data_parallel_rank, self.data_parallel_degree)
             return None
 
     @property
@@ -1652,12 +1662,16 @@ def weight_name_suffix(self):
                 name.append(self._format_name("tp", self.tensor_parallel_rank, self.tensor_parallel_degree))
             if self.pipeline_parallel_degree > 1:
                 name.append(self._format_name("pp", self.pipeline_parallel_rank, self.pipeline_parallel_degree))
+            if self.use_moe:
+                name.append(self._format_name("moe", self.data_parallel_rank, self.data_parallel_degree))
             return "_".join(name)
 
         else:
+            if self.use_moe:
+                return self._format_name("moe", self.data_parallel_rank, self.data_parallel_degree)
             return None
 
-    def sharded_name_suffix(self, shard_id=None, pp_id=None):
+    def sharded_name_suffix(self, shard_id=None, pp_id=None, moe_id=None):
         if self.use_hybrid_parallel:
             name = []
             if self.tensor_parallel_degree > 1:
@@ -1672,8 +1686,17 @@ def sharded_name_suffix(self, shard_id=None, pp_id=None):
                     shard_id = self.sharding_parallel_rank
                 assert isinstance(shard_id, int)
                 name.append(self._format_name("shard", shard_id, self.sharding_parallel_degree))
+            if self.use_moe:
+                if moe_id is None:
+                    moe_id = self.data_parallel_rank
+                assert isinstance(moe_id, int)
+                name.append(self._format_name("moe", moe_id, self.data_parallel_degree))
             return "_".join(name)
         else:
+            if self.use_moe:
+                if moe_id is None:
+                    moe_id = self.data_parallel_rank
+                return self._format_name("moe", moe_id, self.data_parallel_degree)
             return None
 
     @property

paddlenlp/trainer/utils/helper.py

@@ -226,3 +226,59 @@ def broadcast_dp_optimizer(state_dict):
     state_dict = nested_broadcast_tensor(state_dict, src=src_rank, group=dp_group)
 
     return state_dict
+
+
+def broadcast_moe_optimizer(state_dict):
+
+    try:
+        hcg = fleet.get_hybrid_communicate_group()
+        dp_group = hcg.get_data_parallel_group()
+        src_rank = hcg.get_data_parallel_group_src_rank()
+        data_parallel_rank = hcg.get_data_parallel_rank()
+        # Don't broadcast optimizer for dp rank is 1.
+        if dp_group.nranks <= 1:
+            return state_dict
+    except:
+        dp_group = None
+        src_rank = 0
+        data_parallel_rank = 0
+
+    def _broadcast_moe_optimizer_state(state_dict):
+        # boardcast_keys
+        base_state_dict = {"master_weights": {}}
+        buf = [
+            {i: j.shape for i, j in state_dict.items() if i not in ["master_weights", "LR_Scheduler"]},
+            {i: j.shape for i, j in state_dict["master_weights"].items()},
+            {"LR_Scheduler": state_dict.get("LR_Scheduler", {})},
+        ]
+
+        dist.broadcast_object_list(buf, src=src_rank, group=dp_group)
+        # logger.info(f"moe-optimizer-gather-keys{buf}")
+        for k, s in buf[0].items():
+            v = state_dict.get(k, paddle.zeros(s, "float32")).cuda()
+            v.name = k
+            # k = k.replace("_fp32_master_0", "")
+            dist.broadcast(v, src=src_rank, group=dp_group)
+            logger.info(f"broadcast moe optimizer {k} from {src_rank}")
+            base_state_dict[k] = v.cpu()
+        for k, s in buf[1].items():
+            v = state_dict["master_weights"].get(k, paddle.zeros(s, "float32")).cuda()
+            v.name = k
+            dist.broadcast(v, src=src_rank, group=dp_group)
+            logger.info(f"broadcast moe optimizer-master_weights {k} from {src_rank}")
+            base_state_dict["master_weights"][k] = v.cpu()
+        base_state_dict.update(buf[2])
+        return base_state_dict
+
+    base_state_dict = _broadcast_moe_optimizer_state(state_dict)
+    if data_parallel_rank > 0:
+        master_weight = state_dict.pop("master_weights", {})
+        base_state_dict.update(state_dict)
+        if master_weight:
+            if "master_weights" in base_state_dict:
+                base_state_dict["master_weights"].update(master_weight)
+            else:
+                base_state_dict["master_weights"] = master_weight
+        state_dict = base_state_dict
+        del base_state_dict
+    return state_dict

paddlenlp/trainer/utils/reshard/common.py

@@ -266,6 +266,16 @@ def _opt_name_to_tname(tensor_names, opt_names):
             all_names.extend(opt_names)
             all_names.sort()
             pre_t_name = ""
+            suffix = [
+                "_fp32_master_0_beta1_pow_acc_0",
+                "_fp32_master_0_beta2_pow_acc_0",
+                "_fp32_master_0_moment1_0",
+                "_fp32_master_0_moment2_0",
+                "_beta1_pow_acc_0",
+                "_beta2_pow_acc_0",
+                "_moment1_0",
+                "_moment2_0",
+            ]
             opt_to_t = {}
             for n in all_names:
                 if n in tensor_names:
@@ -274,6 +284,24 @@ def _opt_name_to_tname(tensor_names, opt_names):
                 else:
                     assert pre_t_name
                     opt_to_t[n] = pre_t_name
+
+            for t in opt_names:
+                _find = False
+                for s in suffix:
+                    if t.endswith(s):
+                        logger.info(f"{t}-{t[:-len(s)]}--{t[:-len(s)] in tensor_names}")
+                        opt_to_t[t] = t[: -len(s)]
+                        _find = True
+                        break
+                assert _find
+            # opt_to_t = {}
+            # for n in all_names:
+            #     if n in tensor_names:
+            #         # we get a param
+            #         pre_t_name = n
+            #     else:
+            #         assert pre_t_name
+            #         opt_to_t[n] = pre_t_name
             return opt_to_t
 
         if structure_name_mapping is not None:
@@ -291,7 +319,7 @@ def _opt_name_to_tname(tensor_names, opt_names):
         (self._model_weights, model_weights_tmp) = (model_weights_tmp, self._model_weights)
         for k in list(model_weights_tmp.keys()):
             t_name = structure_name_mapping[k]
-            self._model_weights[(k, t_name)] = model_weights_tmp[k].cpu()
+            self._model_weights[(k, t_name)] = paddle.to_tensor(model_weights_tmp[k]).cpu()
             del model_weights_tmp[k]
 
         # opt

paddlenlp/trainer/utils/sharding_io.py

@@ -67,11 +67,14 @@ def filter_sharded_params(state_dict, optimizer, sharding_group):
     if reshard_util.get_sharding_strategy(optimizer) == reshard_util.SHARDING_STRATEGY_V1:
         optimizer = unwrap_optimizer(optimizer, DygraphShardingOptimizer)
         for (k, v) in state_dict.items():
-            assert v.name in optimizer._param2rank
-            sharded_rank = optimizer._param2rank[v.name]
-            if sharded_rank != sharding_rank:
-                continue
-            filtered_state_dict[k] = v
+            if v.name in optimizer._param2rank:
+                sharded_rank = optimizer._param2rank[v.name]
+                if sharded_rank != sharding_rank:
+                    continue
+                filtered_state_dict[k] = v
+            else:
+                if sharding_rank == 0:
+                    filtered_state_dict[k] = v
     else:
         optimizer = unwrap_optimizer(optimizer, DygraphShardingOptimizerV2)
         parameters = optimizer._parameter_list
@@ -352,7 +355,7 @@ def manipulate_state_dict_and_config(self, model_to_save, merge_tensor_parallel=
             )
             logger.info(
                 "param_names_in_master_weights len:{}, bf16 state_dict len:{}, :{}".format(
-                    len(param_names_in_master_weights), len(state_dict), state_dict
+                    len(param_names_in_master_weights), len(state_dict), state_dict.keys()
                 )
             )
         return state_dict, config_to_save, weight_name_suffix
@@ -444,12 +447,17 @@ def filter_func(name):
 
         master_weights = reshard_util.all_gather_state_dict(master_weights, filter_func, self.sharding_group)
         model_state_dict = self.model.state_dict()
+        logger.info(f"state-dict-keys: {state_dict.keys()}, nums: {len(state_dict.keys())}")
         logger.info("before recover, model_state_dict number: {}".format(len(model_state_dict)))
         for key, param in model_state_dict.items():
             if param.name in master_weights:
                 assert param.shape == master_weights[param.name].shape
-                paddle.assign(master_weights[param.name].cuda(), model_state_dict[key])
-
+                paddle.assign(paddle.cast(master_weights[param.name].cuda(), paddle.bfloat16), model_state_dict[key])
+            elif key in state_dict:
+                logger.info(f"key: {key} is in state_dict, but not in master_weights")
+                paddle.assign(state_dict[key], model_state_dict[key])
+            else:
+                logger.info(f"key: {key} is not in state_dict and master_weights")
         logger.info("after recover, casted model_state_dict number: {}".format(len(model_state_dict)))
         state_dict.update(model_state_dict)
         return state_dict