Cherry pick some changes from incubate branch (#8862)

* Fix sharding reshard bug and support reshard in different partition ways (#8837)

* Fix sharding reshard bug and support reshard in different partition ways

* fix some bugs

* refine codes

* revert some useless codes

* add log

* Fix TP sync (#8846)

* fix sharding reshard compatiblity for wrong master weight name (#8851)

* change print to logger.info

* remove useless f-string

paddlenlp/trainer/trainer.py

@@ -195,7 +195,17 @@
 g_cpu_optimizer_state_dict = {}
 
 
-def _save_func(obj, path, saved_signal_path, protocol):
+def _save_func(obj, name_mapping, path, saved_signal_path, protocol):
+    if isinstance(obj, dict):
+        for k, v in obj.items():
+            if k == "master_weights" and isinstance(v, dict):
+                for kk, vv in v.items():
+                    if isinstance(vv, paddle.Tensor):
+                        vv.name = name_mapping["master_weights"][kk]
+            else:
+                if k in name_mapping and isinstance(v, paddle.Tensor):
+                    v.name = name_mapping[k]
+
     paddle.save(obj, path, protocol)
     # dump savd_siganl
     with open(saved_signal_path, mode="w+") as f:
@@ -228,17 +238,18 @@ def clear_async_save_task_queue():
 def async_save_optimizer(optimizer_state_dict, path, saved_signal_path, protocol=4):
     global g_cpu_optimizer_state_dict
     g_cpu_optimizer_state_dict.clear()
+    name_mapping = {"master_weights": {}}
     for k, v in optimizer_state_dict.items():
         if k == "master_weights":
             g_cpu_optimizer_state_dict[k] = {}
             for kk, vv in v.items():
-                tensor_name = vv.name
                 g_cpu_optimizer_state_dict[k][kk] = vv.pin_memory()
-                g_cpu_optimizer_state_dict[k][kk].name = tensor_name
+                name_mapping[k][kk] = vv.name
         elif k == "LR_Scheduler":
             g_cpu_optimizer_state_dict[k] = copy.deepcopy(v)
         else:
             g_cpu_optimizer_state_dict[k] = v.pin_memory()
+            name_mapping[k] = v.name
         paddle.device.synchronize()
     clear_async_save_task_queue()
 
@@ -248,7 +259,9 @@ def async_save_optimizer(optimizer_state_dict, path, saved_signal_path, protocol
     def start_process():
         nonlocal attempt
         try:
-            p = ctx.Process(target=_save_func, args=(g_cpu_optimizer_state_dict, path, saved_signal_path, protocol))
+            p = ctx.Process(
+                target=_save_func, args=(g_cpu_optimizer_state_dict, name_mapping, path, saved_signal_path, protocol)
+            )
             p.start()
             return p
         except Exception as e:

paddlenlp/trainer/training_args.py

@@ -1171,15 +1171,23 @@ def split_parallel_config(parallel_config):
                         # sync_param_name = [""] matches any parameter name.
                         # If sync_param, sync_grad and sync_moment are not set, the default value in Paddle is :
                         # sync_param = True, sync_grad = False, sync_moment = False, sync_param_name = ["embedding", "layer_norm", ".b_"].
+
+                        if sync_param or sync_grad or sync_moment:
+                            logger.info("setting sync_param_name")
+                            strategy.sync_param_name = [""]
+
                         if sync_param:
+                            logger.info("setting sync_param")
                             strategy.hybrid_configs["mp_configs"].sync_param = True
-                            strategy.hybrid_configs["mp_configs"].sync_param_name = [""]
+
                         if sync_grad:
+                            logger.info("setting sync_grad")
                             strategy.hybrid_configs["mp_configs"].sync_grad = True
-                            strategy.hybrid_configs["mp_configs"].sync_grad_name = [""]
+
                         if sync_moment:
+                            logger.info("setting sync_moment")
                             strategy.hybrid_configs["mp_configs"].sync_moment = True
-                            strategy.hybrid_configs["mp_configs"].sync_moment_name = [""]
+
                     except:
                         warnings.warn(
                             "The enable_mp_async_allreduce, enable_mp_skip_c_identity and enable_mp_fused_linear_param_grad_add are not supported "

paddlenlp/trainer/utils/reshard/sharding_v2.py

@@ -14,11 +14,14 @@
 
 import numpy as np
 import paddle
+import paddle.distributed.fleet as fleet
 from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer import (
     HybridParallelOptimizer,
 )
 from paddle.distributed.fleet.model import PipelineParallel
 
+from paddlenlp.utils.log import logger
+
 from ....transformers.model_utils import unwrap_optimizer
 
 try:
@@ -29,6 +32,9 @@
     DygraphShardingOptimizerV2 = None
 
 
+from paddle.distributed.communication.reduce import ReduceOp
+
+
 def shard(node_model_state, model, optimizer, hcg):
     assert DygraphShardingOptimizerV2 is not None
     group = hcg.get_sharding_parallel_group()
@@ -137,7 +143,7 @@ def slice_tensor(tensor, begin, end):
     return tensor[begin:end]
 
 
-def collect_split_info(optimizer, model):
+def collect_split_info(optimizer, model, only_return_lengths=False):
     split_infos = {}
 
     def gather_infos(comm_buffer):
@@ -146,7 +152,13 @@ def gather_infos(comm_buffer):
             padded_size = v._padded_size
             buffer_size = v._param_buffer._numel()
             has_slice_grad = v._slice_grad is not None
-            split_infos[k] = (index, padded_size, buffer_size, has_slice_grad)
+            if only_return_lengths:
+                if v._param_begin < v._param_end:
+                    split_infos[k] = v._param_end - v._param_begin
+                else:
+                    split_infos[k] = None
+            else:
+                split_infos[k] = (index, padded_size, buffer_size, has_slice_grad)
 
     if isinstance(model, PipelineParallel) and model._sharding_comm_overlap > 0:
         optimizer = unwrap_optimizer(optimizer, HybridParallelOptimizer)
@@ -167,6 +179,51 @@ def gather_infos(comm_buffer):
     return split_infos
 
 
+def is_matched_optimizer_state_dict(opt_state_dict, optimizer, model, hcg=None, need_allgather=True):
+    split_infos = collect_split_info(optimizer, model, only_return_lengths=True)
+    master_weights = opt_state_dict.get("master_weights", None)
+
+    def get_matched_length(name):
+        if master_weights and name in master_weights:
+            tensor = master_weights[name]
+        else:
+            moment_name = name + "_moment1_0"
+            if moment_name not in opt_state_dict:
+                return None
+
+            tensor = opt_state_dict[moment_name]
+            if isinstance(tensor, (list, tuple)):
+                assert len(tensor) == 2, tensor
+                assert isinstance(tensor[0], str), tensor[0]
+                tensor = tensor[1]
+        shape = tensor.shape
+        assert len(shape) == 1, shape
+        length = shape[0]
+        return length
+
+    is_matched = 1
+    for k, length in split_infos.items():
+        matched_length = get_matched_length(k)
+        if length != matched_length:
+            is_matched = 0
+            break
+
+    if need_allgather:
+        if hcg is None:
+            hcg = fleet.get_hybrid_communicate_group()
+        group = hcg.get_sharding_parallel_group()
+        if group is not None and group.nranks > 1:
+            x = paddle.to_tensor([is_matched], dtype=paddle.int32)
+            paddle.distributed.stream.all_reduce(x, op=ReduceOp.MIN, group=group, sync_op=True, use_calc_stream=True)
+            global_is_matched = int(x.numpy()[0])
+    else:
+        global_is_matched = is_matched
+
+    global_is_matched = True if global_is_matched else False
+    logger.info(f"Sharding reshard checkpoint: local_match = {is_matched} , global_match = {global_is_matched}")
+    return global_is_matched
+
+
 def is_bata(name):
     if "_beta1_pow_acc_" in name:
         return True

paddlenlp/trainer/utils/sharding_io.py

@@ -40,7 +40,7 @@
 from paddlenlp.utils.log import logger
 
 from . import reshard as reshard_util
-from .reshard import SHARDING_STRATEGY_V1, pp_reshard
+from .reshard import SHARDING_STRATEGY_V1, SHARDING_STRATEGY_V2, pp_reshard
 
 # Name of the files used for checkpointing
 TRAINING_ARGS_NAME = "training_args.bin"
@@ -204,10 +204,21 @@ def _load_optimizer_state_of_one_shard(self, checkpoint, base_opt_name, optimize
         path = os.path.join(checkpoint, optimizer_name)
         logger.info(f"load optimizer state from {path}")
         if os.path.isfile(path):
-            return paddlenlp_load(path, map_location="cpu")
+            return self._modify_ckpt_for_compatibility(paddlenlp_load(path, map_location="cpu"))
         logger.info(f"{path} not exists")
         return None
 
+    def _modify_ckpt_for_compatibility(self, ckpt):
+        master_weights = ckpt.get("master_weights", None)
+        if master_weights:
+            for k, v in master_weights.items():
+                assert isinstance(v, paddle.Tensor), v
+                if not v.name.startswith(k):
+                    new_name = k + "_fp32_master_0"
+                    logger.info(f"Modify master weights {v.name} -> {new_name}")
+                    v.name = new_name
+        return ckpt
+
     def _need_reshard(self, checkpoint):
         if self._need_reshard_pp(checkpoint):
             return True
@@ -253,10 +264,6 @@ def _need_reshard_pp(self, checkpoint):
     def load_optimizer_state_with_reshard(self, checkpoint, base_opt_name, model_wrapped):
         """load state_dict of multiple shard from_checkpoint, Only load model state dict."""
 
-        if not self._need_reshard(checkpoint):
-            logger.info("do not need reshard")
-            return self._load_optimizer_state_of_one_shard(checkpoint, base_opt_name, self.args.optimizer_name_suffix)
-        logger.info("reshard optimizer state")
         parallel_config = self._load_distributed_strategy(checkpoint)
         sharding_meta = self._load_sharding_meta(checkpoint, 0)
         pp_degree = parallel_config["pp_degree"]
@@ -276,16 +283,41 @@ def load_optimizer_state_with_reshard(self, checkpoint, base_opt_name, model_wra
         cur_sharding_degree = self.args.sharding_parallel_degree
         cur_sharding_strategy = reshard_util.get_sharding_strategy(self.optimizer)
 
+        if not self._need_reshard(checkpoint):
+            one_shard_opt_state_dict = self._load_optimizer_state_of_one_shard(
+                checkpoint, base_opt_name, self.args.optimizer_name_suffix
+            )
+
+            if sharding_strategy == SHARDING_STRATEGY_V2 and cur_sharding_strategy == SHARDING_STRATEGY_V2:
+                is_matched = reshard_util.sharding_v2.is_matched_optimizer_state_dict(
+                    one_shard_opt_state_dict, self.optimizer, model_wrapped
+                )
+            else:
+                is_matched = True
+
+            if is_matched:
+                logger.info("do not need reshard")
+                return one_shard_opt_state_dict
+        else:
+            one_shard_opt_state_dict = None
+
+        logger.info("reshard optimizer state")
+
         def load_model_slices():
             model_state = reshard_util.NodeModelState()
             for j in range(self.args.pipeline_parallel_rank, pp_degree, cur_pp_degree):
                 cur_sharding_meta = self._load_sharding_meta(checkpoint, j)
                 assert "structure_name_mapping" in cur_sharding_meta
                 structure_name_map = cur_sharding_meta["structure_name_mapping"]
                 for i in range(self.args.sharding_parallel_rank, sharding_degree, cur_sharding_degree):
-                    tmp = self._load_optimizer_state_of_one_shard(
-                        checkpoint, base_opt_name, self.args.sharded_name_suffix(i, j)
-                    )
+                    sharded_name_suffix = self.args.sharded_name_suffix(i, j)
+                    if one_shard_opt_state_dict is None:
+                        tmp = self._load_optimizer_state_of_one_shard(checkpoint, base_opt_name, sharded_name_suffix)
+                    else:
+                        assert (
+                            self.args.optimizer_name_suffix == sharded_name_suffix
+                        ), f"{self.args.optimizer_name_suffix} vs {sharded_name_suffix}"
+                        tmp = one_shard_opt_state_dict
                     node_model_state_tmp = reshard_util.NodeModelState()
                     node_model_state_tmp.add_opts(tmp)
                     node_model_state_tmp.pack_keys(structure_name_map)