fix(moe): fix moe compatibility for fsdp and memory profiling (#417)

* fix moe compatibility for fsdp and memory profiling

* update moe config

configs/7B_MoE4_sft.py

@@ -4,7 +4,7 @@ DO_ALERT = False
 SEQ_LEN = 2048
 HIDDEN_SIZE = 4096
 NUM_ATTENTION_HEAD = 32
-MLP_RATIO = 8 / 3
+MLP_RATIO = 4 / 3
 NUM_LAYER = 32
 VOCAB_SIZE = 103168
 
@@ -30,6 +30,14 @@ ckpt = dict(
     # 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all"
     # 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, now only 'normal' type is supported.
     load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("model",), ckpt_type="internlm"),
+    # 'auto_resume' is designed to automatically load the latest checkpoint from 'save_ckpt_folder' when encountering
+    # training interruptions/hangs caused by hardware failures, using a scheduling system (such as k8s/slurm)
+    # with an automatic restart mechanism upon training reboot.
+    # Please be aware that if `auto_resume` is not set (its default value is True), it will not load the checkpoint
+    # path specified in `load_ckpt_info` by default.
+    # If you want to initialize your model weights from another model, you must set `auto_resume` to False.
+    # If you want to train from scratch, please set `auto_resume` to False and 'load_ckpt_info' to None.
+    auto_resume=True,
     checkpoint_every=CHECKPOINT_EVERY,
     async_upload=True,  # async ckpt upload. (only work for boto3 ckpt)
     async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/",  # path for temporarily files during asynchronous upload.
@@ -43,7 +51,7 @@ data = dict(
     # micro_num means the number of micro_batch contained in one gradient update
     micro_num=4,
     # packed_length = micro_bsz * SEQ_LEN
-    micro_bsz=1,
+    micro_bsz=2,
     # defaults to the value of micro_num
     valid_micro_num=4,
     # defaults to 0, means disable evaluate
@@ -81,8 +89,8 @@ grad_scaler = dict(
 
 hybrid_zero_optimizer = dict(
     # Enable low_level_optimzer overlap_communication
-    overlap_sync_grad=True,
-    overlap_sync_param=True,
+    overlap_sync_grad=False,
+    overlap_sync_param=False,
     # bucket size for nccl communication params
     reduce_bucket_size=512 * 1024 * 1024,
     # grad clipping
@@ -133,7 +141,7 @@ model = dict(
     layer_norm_epsilon=1e-5,
     use_flash_attn=True,
     num_chunks=1,  # if num_chunks > 1, interleaved pipeline scheduler is used.
-    num_experts=4,
+    num_experts=8,
     moe_use_residual=False,
     moe_gate_k=2,
 )
@@ -150,8 +158,8 @@ pipeline parallel (dict):
 tensor parallel: tensor parallel size, usually the number of GPUs per node.
 """
 parallel = dict(
-    zero1=-1,
-    tensor=2,
+    zero1=dict(size=-1, fsdp=False),
+    tensor=1,
     pipeline=dict(size=1, interleaved_overlap=True),
     sequence_parallel=False,
 )

internlm/initialize/launch.py

@@ -349,7 +349,7 @@ def args_sanity_check():
         assert (
             not optim_ckpt.overlap_sync_grad & optim_ckpt.overlap_sync_param
         ), "not support overlap and moe at the same time"
-        assert gpc.config.parallel.zero1 == -1, "moe only support zero1, set zero1=-1 can fix this"
+        assert gpc.config.parallel.zero1.size == -1, "moe only support zero1, set zero1=-1 can fix this"
 
 
 def launch(

internlm/utils/simple_memory_profiler.py

@@ -424,7 +424,9 @@ class SimpleMemoryProfiler:
             layer_name, output.element_size() * output.nelement(), flush=False
         )
 
-    def _activation_trace_hook_forward(self, chunk_id: int, model: Any, inputs: Any, output: torch.Tensor) -> None:
+    def _activation_trace_hook_forward(
+        self, chunk_id: int, model: Any, inputs: Any, output: Any  # pylint: disable=W0613
+    ) -> None:
         """
         Hook function to trace the activation memory usage for a forward pass.
 
@@ -437,7 +439,6 @@ class SimpleMemoryProfiler:
             None
         """
         del model, inputs
-        assert isinstance(output, torch.Tensor), f"invalid output type: {type(output)}"
 
         if self._stoped:
             return