Add MoE

configs/125M-moe.yml

@@ -0,0 +1,103 @@
+# GPT-2 pretraining setup
+{
+   # Have 4 experts per layer (every 2 layers by default)
+   # So with 12 layers total:
+   # 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
+   # Experts would be in layers:
+   # 0, 2, 4, 6, 8, 10
+   "num_experts": 4,
+
+   # parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages
+   # across the node boundaries )
+   "pipe_parallel_size": 1,
+   "model_parallel_size": 1,
+   "moe_expert_parallel_size": 1,
+
+   # model settings
+   "num_layers": 12,
+   "hidden_size": 768,
+   "num_attention_heads": 12,
+   "seq_length": 2048,
+   "max_position_embeddings": 2048,
+   "norm": "layernorm",
+   "pos_emb": "rotary",
+   "no_weight_tying": true,
+   "gpt_j_residual": false,
+   "output_layer_parallelism": "column",
+
+   # these should provide some speedup but takes a while to build, set to true if desired
+   "scaled_upper_triang_masked_softmax_fusion": false,
+   "bias_gelu_fusion": false,
+   "rope_fusion": false,
+
+   # init methods
+   "init_method": "small_init",
+   "output_layer_init_method": "wang_init",
+
+
+   # optimizer settings
+   "optimizer": {
+     "type": "Adam",
+     "params": {
+       "lr": 0.0006,
+       "betas": [0.9, 0.95],
+       "eps": 1.0e-8,
+     }
+   },
+   "min_lr": 0.00006,
+
+   # for all zero_optimization options, see https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training
+   "zero_optimization": {
+    "stage": 1,
+    "allgather_partitions": True,
+    "allgather_bucket_size": 500000000,
+    "overlap_comm": True,
+    "reduce_scatter": True,
+    "reduce_bucket_size": 500000000,
+    "contiguous_gradients": True,
+  },
+
+   # batch / data settings
+   "train_micro_batch_size_per_gpu": 4,
+   "data_impl": "mmap",
+
+   # activation checkpointing
+   "checkpoint_activations": true,
+   "checkpoint_num_layers": 1,
+   "partition_activations": true,
+   "synchronize_each_layer": true,
+
+   # regularization
+   "gradient_clipping": 1.0,
+   "weight_decay": 0.1,
+   "hidden_dropout": 0.0,
+   "attention_dropout": 0.0,
+
+   # precision settings
+   "fp16": {
+     "enabled": true,
+     "loss_scale": 0,
+     "loss_scale_window": 1000,
+     "hysteresis": 2,
+     "min_loss_scale": 1
+   },
+
+   # misc. training settings
+   "train_iters": 320000,
+   "lr_decay_iters": 320000,
+   "distributed_backend": "nccl",
+   "lr_decay_style": "cosine",
+   "warmup": 0.01,
+   "checkpoint_factor": 10000,
+   "eval_interval": 1000,
+   "eval_iters": 10,
+
+   # logging
+   "log_interval": 10,
+   "steps_per_print": 10,
+   "keep_last_n_checkpoints": 4,
+   "wall_clock_breakdown": true,
+
+  #  networking
+  "hostfile": "/mock_path"
+}

configs/neox_arguments.md

@@ -111,7 +111,7 @@ Logging Arguments
 
 - **git_hash**: str
 
-    Default = 2a3c4e1
+    Default = ae06be5
 
     current git hash of repository
 
@@ -1007,6 +1007,14 @@ Parallelism Arguments
 
 
 
+- **expert_interval**: int
+
+    Default = 2
+
+    Have one MoE layer every expert_interval layers
+
+
+
 ## NeoXArgsTemplate
 
 NeoXArgsTemplate()
@@ -1128,6 +1136,94 @@ Text Generation arguments
 
 
 
+- **moe_top_k**: int
+
+    Default = 1
+
+    Activate top K experts in MoE
+
+
+
+- **use_tutel**: bool
+
+    Default = False
+
+    Use Tutel optimizations in MoE
+
+
+
+- **num_experts**: int
+
+    Default = 1
+
+    Number of MoE experts
+
+
+
+- **moe_loss_coeff**: float
+
+    Default = 0.1
+
+    Coefficient for MoE loss
+
+
+
+- **moe_train_capacity_factor**: float
+
+    Default = 1.0
+
+    The capacity of the expert at train time
+
+
+
+- **moe_eval_capacity_factor**: float
+
+    Default = 1.0
+
+    The capacity of the expert at eval time
+
+
+
+- **moe_min_capacity**: int
+
+    Default = 4
+
+    The minimum capacity per expert regardless of the capacity_factor
+
+
+
+- **moe_token_dropping**: bool
+
+    Default = True
+
+    Whether to drop tokens when exceeding capacity
+
+
+
+- **create_moe_param_group**: bool
+
+    Default = True
+
+    Whether to create a separate parameter group for MoE parameters
+
+
+
+- **moe_use_residual**: bool
+
+    Default = True
+
+    Whether to use residual in MoE
+
+
+
+- **moe_expert_parallel_size**: int
+
+    Default = 1
+
+    Number of parallel experts in MoE
+
+
+
 ## NeoXArgsTokenizer
 
 Tokenizer Arguments