Add DS inference

configs/125M-moe.yml

@@ -0,0 +1,98 @@
+# GPT-2 pretraining setup
+{
+   # Have 2 experts per layer (every 2 layers by default)
+   "num_experts": 2,
+
+   # 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,
+
+   # 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"
+}

megatron/model/transformer.py

@@ -43,6 +43,7 @@
     bias_dropout_add_fused_inference,
 )
 from megatron.model.utils import configure_sparse_attention
+from deepspeed.moe.layer import MoE
 
 # flags required to enable jit fusion kernels
 torch._C._jit_set_profiling_mode(False)
@@ -82,7 +83,13 @@ class ParallelMLP(nn.Module):
     """
 
     def __init__(
-        self, neox_args, init_method, output_layer_init_method, parallel_output=False
+        self,
+        neox_args,
+        init_method,
+        output_layer_init_method,
+        parallel_output=False,
+        MOE=False,
+        MoE_mp_size=1,
     ):
         super().__init__()
 
@@ -104,6 +111,8 @@ def __init__(
             gather_output=False,
             init_method=init_method,
             skip_bias_add=True,
+            MOE=MOE,
+            MoE_mp_size=MoE_mp_size,
         )
         ff_dim_in = ff_dim // 2 if self.activation_type == "geglu" else ff_dim
         # Project back to h.
@@ -113,8 +122,10 @@ def __init__(
             output_size=neox_args.hidden_size,
             input_is_parallel=True,
             init_method=output_layer_init_method,
-            skip_bias_add=True,
             parallel_output=parallel_output,
+            skip_bias_add=True,
+            MOE=MOE,
+            MoE_mp_size=MoE_mp_size,
         )
 
     def forward(self, hidden_states):
@@ -817,6 +828,44 @@ def __init__(
         else:
             raise KeyError(neox_args.mlp_type)
 
+        self.num_experts = neox_args.num_experts
+        args = neox_args
+        if self.num_experts <= 1:
+            self.mlp = ParallelMLP(
+                neox_args=neox_args,
+                init_method=init_method,
+                output_layer_init_method=output_layer_init_method,
+                parallel_output=self.gpt_j_residual,
+            )
+        else:
+            from torch import distributed as dist
+
+            if neox_args.ds_inference or self.num_experts > dist.get_world_size():
+                moe_mp_size = 1
+            else:
+                moe_mp_size = dist.get_world_size() // self.num_experts
+
+            self.mlp = MoE(
+                args.hidden_size,
+                ParallelMLP(
+                    neox_args=neox_args,
+                    init_method=init_method,
+                    output_layer_init_method=output_layer_init_method,
+                    parallel_output=self.gpt_j_residual,
+                    MOE=True,
+                    MoE_mp_size=moe_mp_size,
+                ),
+                num_experts=self.num_experts,
+                ep_size=args.moe_expert_parallel_size,
+                k=args.topk,
+                use_residual=(args.mlp_type == "residual"),
+                capacity_factor=args.moe_train_capacity_factor,
+                eval_capacity_factor=args.moe_eval_capacity_factor,
+                min_capacity=args.moe_min_capacity,
+                drop_tokens=args.moe_token_dropping,
+                use_tutel=args.use_tutel,
+            )
+
         self.layer_past = None  # used to cache k/v pairs in inference
 
     def _get_bias_dropout(self):
@@ -913,10 +962,19 @@ def forward(self, x, attention_mask, layer_past=None):
                     )
 
             # output = x + mlp(ln2(x))
-            mlp_output, mlp_bias = self.mlp(
-                self.post_attention_layernorm(attention_output)
+            layernorm_output = self.post_attention_layernorm(attention_output)
+            moe_loss = torch.tensor(
+                0.0, device=layernorm_output.device, dtype=layernorm_output.dtype
+            )
+            mlp_bias = torch.tensor(
+                0.0, device=layernorm_output.device, dtype=layernorm_output.dtype
             )
 
+            if self.num_experts == 1:
+                mlp_output, mlp_bias = self.mlp(layernorm_output)
+            else:
+                mlp_output, moe_loss, _ = self.mlp(layernorm_output)
+
             with torch.enable_grad():
                 if self.mlp_type == "llama":
                     # No dropout either
@@ -930,7 +988,7 @@ def forward(self, x, attention_mask, layer_past=None):
                         prob=self.hidden_dropout,
                     )
 
-        return output
+        return output, moe_loss
 
 
 class ParallelTransformerLayerPipe(ParallelTransformerLayer):
@@ -942,7 +1000,7 @@ def forward(self, args):
         ), "ParallelTransformerLayerPipe expects 2 arguments - hidden_states and attention_mask"
         hidden_states, attention_mask = args
         # we are returning just [hidden_states, mask]
-        return super().forward(hidden_states, attention_mask), attention_mask
+        return super().forward(hidden_states, attention_mask)[0], attention_mask
 
 
 class ParallelLinearPipe(ParallelLinear):

megatron/model/utils.py

@@ -28,8 +28,12 @@ def get_params_for_weight_decay_optimization(module, neox_args):
     """Divide params into with-weight-decay and without-weight-decay groups.
     Layernorms and biases will have no weight decay but the rest will.
     """
-    weight_decay_params = {"params": []}
-    no_weight_decay_params = {"params": [], "weight_decay": 0.0}
+    weight_decay_params = {"params": [], "name": "weight_decay_params"}
+    no_weight_decay_params = {
+        "params": [],
+        "weight_decay": 0.0,
+        "name": "no_weight_decay_params",
+    }
     for module_ in module.modules():
         if any(
             [

megatron/mpu/__init__.py

@@ -31,6 +31,7 @@
 from .initialize import get_pipe_parallel_group
 from .initialize import get_pipe_parallel_rank
 from .initialize import get_pipe_parallel_world_size
+from .initialize import get_tensor_model_parallel_world_size
 from .initialize import get_io_parallel_group
 from .initialize import initialize_model_parallel
 from .initialize import model_parallel_is_initialized

megatron/mpu/initialize.py

@@ -266,6 +266,12 @@ def get_pipe_parallel_world_size():
     return torch.distributed.get_world_size(group=get_pipe_parallel_group())
 
 
+# Needed for MOE. True tensor parallelism todo.
+def get_tensor_model_parallel_world_size():
+    """Return world size for the tensor model parallel group."""
+    return torch.distributed.get_world_size()
+
+
 def destroy_model_parallel():
     """Set the groups to none."""
     global _MODEL_PARALLEL_GROUP

megatron/mpu/layers.py

@@ -413,6 +413,8 @@ def __init__(
         stride=1,
         keep_master_weight_for_test=False,
         skip_bias_add=False,
+        MOE=False,
+        MoE_mp_size=1,
         mup_rescale_parameters=False,
     ):
         super(ColumnParallelLinear, self).__init__()
@@ -422,7 +424,7 @@ def __init__(
         self.output_size = output_size
         self.gather_output = gather_output
         # Divide the weight matrix along the last dimension.
-        world_size = get_model_parallel_world_size()
+        world_size = MoE_mp_size if MOE else get_model_parallel_world_size()
         self.output_size_per_partition = divide(output_size, world_size)
         self.skip_bias_add = skip_bias_add
         self.init_method = init_method
@@ -605,6 +607,8 @@ def __init__(
         stride=1,
         keep_master_weight_for_test=False,
         skip_bias_add=False,
+        MOE=False,
+        MoE_mp_size=1,
         parallel_output=False,
         mup_rescale_parameters=False,
     ):
@@ -615,7 +619,7 @@ def __init__(
         self.output_size = output_size
         self.input_is_parallel = input_is_parallel
         # Divide the weight matrix along the last dimension.
-        world_size = get_model_parallel_world_size()
+        world_size = MoE_mp_size if MOE else get_model_parallel_world_size()
         self.input_size_per_partition = divide(input_size, world_size)
         self.skip_bias_add = skip_bias_add
         self.parallel_output = parallel_output

megatron/neox_arguments/arguments.py

@@ -1032,7 +1032,13 @@ def calculate_derived(self):
         # Update 'is pipe parallel' flag
         # if we set pipe_parallel_size to 0 or 1, GPT2ModelPipe.to_sequential() is called, and we run training with
         # the sequential model without the PipelineModule wrapper to avoid the overhead it incurs
-        self.update_value("is_pipe_parallel", self.pipe_parallel_size >= 1)
+        self.update_value(
+            "is_pipe_parallel", self.pipe_parallel_size > 1 and self.num_experts == 1
+        )
+        assert not (
+            (self.is_pipe_parallel or self.pipe_parallel_size > 1)
+            and self.num_experts > 1
+        ), "MoE not supported with pipeline parallelism"
 
         # Attention config
         if self.attention_config is None:

megatron/neox_arguments/neox_args.py

@@ -83,6 +83,11 @@ class NeoXArgsParallelism(NeoXArgsTemplate):
     according to pipeline parallel size.
     """
 
+    expert_interval: int = 2
+    """
+    Have one MoE layer every expert_interval layers
+    """
+
 
 @dataclass
 class NeoXArgsModel(NeoXArgsTemplate):
@@ -1150,7 +1155,62 @@ class NeoXArgsTextgen(NeoXArgsTemplate):
     prefix to which to save evaluation results - final fp will be {eval_results_prefix}_eval_results_yy-mm-dd-HH-MM.json
     """
 
+    ds_inference: bool = False
+    """
+    Use DeepSpeed inference.
+    """
+
+    moe_type: str = "standard"
+    """
+    Specify the type of MoE layer. We have two types of MoE layer: standard and residual.
+    """
+
     eval_tasks: list = None
     """
     Tasks to evaluate on using lm_eval_harness
     """
+
+    topk: int = 2
+    """
+    Activate top K experts in MoE
+    """
+
+    use_tutel: bool = False
+    """
+    Use Tutel optimizations in MoE
+    """
+
+    num_experts: int = 1
+    """
+    Number of MoE experts
+    """
+
+    moe_train_capacity_factor: float = 1.0
+    """
+    The capacity of the expert at train time
+    """
+
+    moe_eval_capacity_factor: float = 1.0
+    """
+    The capacity of the expert at eval time
+    """
+
+    moe_min_capacity: int = 4
+    """
+    The minimum capacity per expert regardless of the capacity_factor
+    """
+
+    moe_token_dropping: bool = True
+    """
+    Whether to drop tokens when exceeding capacity
+    """
+
+    create_moe_param_group: bool = True
+    """
+    Whether to create a separate parameter group for MoE parameters
+    """
+
+    moe_expert_parallel_size: int = 1
+    """
+    Number of parallel experts in MoE
+    """