feat(parallel_context.py): remove useless gqa process group (#390)

internlm/core/context/parallel_context.py

@@ -662,19 +662,6 @@ def init_parallel_groups(self):
         group_configs = generate_parallel_group_configs(parallel_strategy, parallel_sizes, enable_moe)
         group_results = create_parallel_process_groups(world_size, rank, group_configs, with_cpu_group=False)
 
-        # process group for extra gqa tensor parallel.
-        if (
-            "num_kv_attention_heads" in self.config.model
-            and self.config.model.num_kv_attention_heads < self.tensor_parallel_size
-        ):
-            group_results.append(
-                create_single_process_group(
-                    world_size,
-                    rank,
-                    GroupConfig(ParallelMode.GQA, self.tensor_parallel_size // self.num_kv_attention_heads),
-                )
-            )
-
         # process group for network test.
         group_results.append(
             create_single_process_group(

internlm/core/context/process_group_initializer.py

@@ -74,9 +74,6 @@ class ParallelMode(Enum):
     # real data parallel for isp
     ISP_DATA = "isp_data"
 
-    # grouped query attention
-    GQA = "gqa"
-
     # sequence 2D parallel
     HEAD = "head"
     CONTEXT = "context"
@@ -1454,84 +1451,6 @@ def init_dist_group(self, use_cpu: bool = False):
         return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
 
 
-class Initializer_GQA(ProcessGroupInitializer):
-    """A ProcessGroupInitializer for allreduce kv gradients with common attention head.
-
-    Args:
-        rank (int): The rank of current process.
-        world_size (int): Size of whole communication world.
-        weight_parallel_size (int): Size of model weight parallel.
-        weight_data_parallel_size (int): Size of data parallel for common weight.
-        sequence_parallel_size (int): Size of data sequence parallel.
-        data_parallel_size (int): Size of data parallel.
-        pipeline_parallel_size (int): Size of pipeline parallel.
-        tensor_parallel_size (int): Size of tensor parallel.
-        zero1_parallel_size (int): Size of zero1 parallel.
-        nettest_parallel_size (int): Size of net testing parallel.
-        expert_parallel_size (int): Size of expert parallel.
-    """
-
-    def __init__(self, *args, **kwargs):
-        self.num_attention_heads = kwargs.pop("num_attention_heads")
-        self.num_kv_attention_heads = kwargs.pop("num_kv_attention_heads")
-        super().__init__(*args, **kwargs)
-        self.kv_head_repeats_num = self.tensor_parallel_size // self.num_kv_attention_heads
-        self.num_kv_group_per_tp = self.num_kv_attention_heads
-        self.num_kv_groups = self.num_kv_group_per_tp * self.data_parallel_size
-
-        assert self.world_size % self.tensor_parallel_size == 0
-        assert self.world_size % (self.pipeline_parallel_size * self.tensor_parallel_size) == 0
-        assert self.pipeline_parallel_size == 1
-
-    def init_dist_group(self, use_cpu: bool = False):
-        """Initialize weight's data parallel groups, and assign local_ranks and groups to each gpu.
-
-        Returns:
-            Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
-                A WEIGHT_DATA parallelism's information tuple.
-
-        n=128 sp=32 wp=64 zo1=1 with nopp
-        sp groups: [0-31] [32-63] [64-95] [96-127]
-        wp groups: [0-63] [64-127]
-        kv_head groups: [0,1,2,3] [4,5,6,7] [8,9,10,11] [12,13,14,15]
-                        [16,17,18,19] [20,21,22,23] [24,25,26,27] [28,29,30,31]
-                        ...
-                        ...
-                        ...
-        """
-        local_rank = None
-        ranks_in_group = None
-        process_group = None
-        cpu_group = None
-        group_world_size = None
-        mode = ParallelMode.GQA
-
-        for i in range(self.data_parallel_size):
-            for j in range(self.num_kv_group_per_tp):
-                ranks = [
-                    i * self.tensor_parallel_size + j * self.kv_head_repeats_num + k
-                    for k in range(self.kv_head_repeats_num)
-                ]
-                group = dist.new_group(ranks, timeout=LLM_NCCL_TIMEOUT)
-                if use_cpu:
-                    group_cpu = (
-                        dist.new_group(ranks, backend="gloo", timeout=LLM_NCCL_TIMEOUT)
-                        if dist.get_backend() != "gloo"
-                        else group
-                    )
-                else:
-                    group_cpu = None
-
-                if self.rank in ranks:
-                    local_rank = ranks.index(self.rank)
-                    group_world_size = len(ranks)
-                    process_group = group
-                    cpu_group = group_cpu
-                    ranks_in_group = ranks
-
-        return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
-
-
 class Initializer_2D_SEQUENCE_PARALLEL(ProcessGroupInitializer):
     """
     A ProcessGroupInitializer for 2D sequence parallel.