fix(*)/all-reduce for norm in sequence parallel (#443)

* fix all-reduce norm grad

* change the order of dp and sp all-reduce

* fix lint

internlm/core/context/__init__.py

@@ -1,4 +1,5 @@
 from .parallel_context import (
+    IS_SEQUENCE_PARALLEL,
     IS_TENSOR_PARALLEL,
     Config,
     ParallelContext,
@@ -29,6 +30,7 @@ from .random import (
 __all__ = [
     "Config",
     "IS_TENSOR_PARALLEL",
+    "IS_SEQUENCE_PARALLEL",
     "global_context",
     "ParallelContext",
     "ParallelMode",

internlm/core/context/parallel_context.py

@@ -25,6 +25,7 @@ from .process_group_initializer import ParallelMode
 from .random import add_seed, get_seeds, set_mode
 
 IS_TENSOR_PARALLEL = "is_tensor_parallel"
+IS_SEQUENCE_PARALLEL = "is_sequence_parallel"
 
 logger = get_logger(__file__)
 

internlm/model/modeling_internlm.py

@@ -9,7 +9,7 @@ from flash_attn.modules.embedding import ParallelGPT2Embeddings
 from flash_attn.modules.mlp import ParallelFusedMLP
 from torch import nn
 
-from internlm.core.context import IS_TENSOR_PARALLEL, ParallelMode
+from internlm.core.context import IS_SEQUENCE_PARALLEL, IS_TENSOR_PARALLEL, ParallelMode
 from internlm.core.context.parallel_context import global_context as gpc
 from internlm.initialize.initialize_tensor import normal_, scaled_init_method_normal
 from internlm.model.embedding import Embedding1D
@@ -134,6 +134,12 @@ class PackedFlashBaseLayer1D(nn.Module):
         for _, param in self.mlp.named_parameters():
             if gpc.get_world_size(ParallelMode.TENSOR) > 1:
                 setattr(param, IS_TENSOR_PARALLEL, True)
+        for param in self.norm1.parameters():
+            if gpc.config.parallel.sequence_parallel is True:
+                setattr(param, IS_SEQUENCE_PARALLEL, True)
+        for param in self.norm2.parameters():
+            if gpc.config.parallel.sequence_parallel is True:
+                setattr(param, IS_SEQUENCE_PARALLEL, True)
 
         self.dropout2 = nn.Dropout(drop_rate)
         self.use_swiglu = use_swiglu
@@ -356,6 +362,10 @@ class PackedFlashInternLm1D(nn.Module):
                 normal_(std=0.0052)(param)
                 if gpc.get_world_size(ParallelMode.TENSOR) > 1:
                     setattr(param, IS_TENSOR_PARALLEL, True)
+            for param in self.norm.parameters():
+                if gpc.config.parallel.sequence_parallel is True:
+                    setattr(param, IS_SEQUENCE_PARALLEL, True)
+
         self.parallel_output = parallel_output
 
     def forward(self, hidden_states=None, cu_seqlens=None, input_ids=None, indexes=None, inference_params=None):

internlm/solver/optimizer/hybrid_zero_optim.py

@@ -8,7 +8,7 @@ import torch
 import torch.distributed as dist
 from torch.optim import Optimizer
 
-from internlm.core.context import Config, ParallelMode
+from internlm.core.context import IS_SEQUENCE_PARALLEL, Config, ParallelMode
 from internlm.core.context import global_context as gpc
 from internlm.monitor import send_alert_message
 from internlm.solver.optimizer.store import (
@@ -297,6 +297,15 @@ class HybridZeroOptimizer(BaseOptimizer):
                             reduce_rank=reduce_rank,
                         )
 
+                        def reduction_sp_func():
+                            handle = reduce_tensor(
+                                param.grad,
+                                dtype=None,
+                                dst_rank=reduce_rank,
+                                parallel_mode=ParallelMode.TENSOR,
+                            )
+                            handle.wait()
+
                         # define hook
                         # NOT IMPORTANT BUT GOOD TO KNOW:
                         # args here is not grad, but allow_unreacable and accumulate_grad
@@ -304,6 +313,18 @@ class HybridZeroOptimizer(BaseOptimizer):
                             if self.skip_grad_reduce is False:
                                 reduction_func()
 
+                        # define hook for sequence_parallel
+                        def reduce_grad_hook_sp(*args):  # pylint: disable=W0613
+                            if self.skip_grad_reduce is False:
+                                reduction_sp_func()
+
+                        # if sequence_parallel is True,
+                        # the grad of norm should be all-reduce across the tp process group
+                        if gpc.config.parallel.sequence_parallel is True:
+                            if hasattr(param, IS_SEQUENCE_PARALLEL) and getattr(param, IS_SEQUENCE_PARALLEL) is True:
+                                accum_grad_obj_sp = get_grad_accumulate_object(param)
+                                accum_grad_obj_sp.register_hook(reduce_grad_hook_sp)
+
                         accum_grad_obj.register_hook(reduce_grad_hook)
 
                     _define_and_attach(param, reduce_rank)