fix and update readme

gen_profiler_data.py

@@ -1,5 +1,4 @@
-
 from internlm.simulator.profiler.perf_comm import gen_perf
 
 if __name__ == "__main__":
-    gen_perf()
+    gen_perf()

internlm/core/context/parallel_context.py

@@ -18,13 +18,12 @@
 import torch.distributed as dist
 
 from internlm.accelerator import get_accelerator
-from internlm.core.context.process_group_initializer_simplified import Initializer, ParallelMeta
 from internlm.utils.common import SingletonMeta
 from internlm.utils.logger import get_logger
 from internlm.utils.timeout import LLM_NCCL_TIMEOUT
 
 from . import process_group_initializer as pgroup_initializer
-from .process_group_initializer_simplified import ParallelMode
+from .process_group_initializer import ParallelMode
 from .random import add_seed, get_seeds, set_mode
 from internlm.utils.common import get_args
 
@@ -422,20 +421,6 @@ def init_global_dist(
            use_cpu (bool): whether to set up cpu process group.
         """
 
-        # find   cluster info
-        if "clusters" not in self.config:
-            nv_info = {
-                "rank_range": [0, 8],
-                "peak_tflops": 320,
-                "capacity": 80 * 1024**3,
-                "intra_bw": 150,
-                "inter_bw": 100,
-            }
-            self.set_cluster_info("nv_cluster", nv_info)
-        else:
-            for cluster in self.config.clusters:
-                self.clusters.append(ClusterInfo(**cluster))
-
         # initialize the default process group
         if not fake_mode:
             init_method = f"tcp://[{host}]:{port}"
@@ -667,7 +652,7 @@ def _init_pg(self, rank, world_size, parallel_config):
         initializers.append(pgroup_initializer.Initializer_ISP_Data(*initializer_args))
         if (
             isinstance(parallel_config["tensor"], dict)
-            and parallel_config["tensor"]["mode"] == TensorParallelMode.isp.name
+            and parallel_config["tensor"]["mode"] == "isp"
         ):
             initializers.append(pgroup_initializer.Initializer_Zero1_ISP(*initializer_args))
         else:
@@ -688,6 +673,8 @@ def _init_pg(self, rank, world_size, parallel_config):
                 self._register_dist(*parallel_setting)
 
     def _init_use_simplified_pg(self, rank, world_size, parallel_config):
+        from internlm.core.context.process_group_initializer_simplified import InitializerParallelMeta
+
         try:
             self.tensor_mode = parallel_config["tensor"]["mode"]
         except AttributeError:
@@ -861,14 +848,14 @@ def check_pg_is_intra(self, parallel_mode: ParallelMode):
             return (max_rank - min_rank) <= 7
 
     def same_group_in_one_node(self, parallel_mode: ParallelMode):
-        """获得一个节点内有多少个相同类型的PG, 在跨节点通信时会存在带宽竞争
-        这里返回的相同PG的数量会乘上每个rank的通信数据量大小
+        """Get the number of the same type of PG within a node. There will be bandwidth competition during cross-node communication.
+        The number of the same PG returned here will be multiplied by the communication data size of each rank.
 
         Args:
             parallel_mode (ParallelMode):
 
         Returns:
-            int: 一个节点内相同类型的PG的数量
+            int: The number of the same type of PG within a node.
         """
         pg_group_ranks = self.get_ranks_in_group(parallel_mode)
         pg_group_ranks = sorted(pg_group_ranks)
@@ -881,68 +868,7 @@ def same_group_in_one_node(self, parallel_mode: ParallelMode):
             else:
                 return stride
 
-    # def set_cluster_info(self, name: str, info: dict):
-    #     self.clusters[name] = ClusterInfo(**info)
-
-    def get_cluster_info(self, name: str):
-        return self.clusters[name]
-
-    def get_cluster_name_from_ip(self):
-        """
-        node_ip_list = [
-            'metax-c500-1',
-            'metax-c500-2',
-            'nvidia-node-1',
-            'nvidia-node-2',
-        ]
-        """
-        hostname = socket.gethostname()
-        cluster_name = hostname.split("-")[0]
-        return cluster_name
-
-    def sort_rank_based_on_ip_and_capacity(self):
-        Capacity = []
-
-        def sort_rank(x, y):
-            x_name = self.get_cluster_name_from_ip(x)
-            y_name = self.get_cluster_name_from_ip(y)
-            if x_name == y_name:
-                return x_name > y_name
-            else:
-                x_c = self.clusters[x_name]["capacity"]
-                y_c = self.clusters[y_name]["capacity"]
-                return x_c > y_c
 
-        for cluster_name, cluster_info in self.clusters.items():
-            peak_tflops.append(cluster_info["peak_tflops"])
-            # Alpha.append(cluster_info.rank_range[-1] - cluster_info.rank_range[-1] + 1)
-            Capacity.append(cluster_info["capacity"])
-
-    def switch_topology_aware_rank_scheduling():
-        """
-        Switch topology-aware rank scheduling can optimize the performance of small-scale
-        collective communications. Currently only supported in Alibaba Cloud.
-        """
-
-        local_rank = int(os.environ["LOCAL_RANK"])
-        cluster_name = get_cluster_name_from_ip()
-
-        try:
-            if cluster_name == "Ali":
-                pass
-            else:
-                rank = int(os.environ["MLP_WORKER_RACK_RANK_INDEX"]) * 8 + local_rank
-        except Exception as e:
-            logger.error(
-                f"The switch topology awareness error is reported, the reason is: {e}",
-                "but don’t worry, this error will not affect normal training.",
-                "If you train on Alibaba or Volcano Cloud, please contact wangguoteng or lijiaxing",
-            )
-        else:
-            # If there is no any error, hack torch rank.
-            os.environ["RANK"] = str(rank)
-            if local_rank == 0:
-                logger.info("Successfully bound node switch affinity!")
 
 
 global_context = ParallelContext()

internlm/core/context/process_group_initializer.py

@@ -62,6 +62,10 @@ class ParallelMode(Enum):
 
     # grouped query attention
     GQA = "gqa"
+
+    INTRA_DP_SZIE = "intra_dp"
+
+    INTER_DP_SZIE = "inter_dp"
 
 
 class ProcessGroupInitializer(ABC):

internlm/core/context/process_group_initializer_simplified.py

@@ -2,13 +2,11 @@
 # -*- encoding: utf-8 -*-
 
 from copy import deepcopy
-from enum import Enum
 
 import torch
 import torch.distributed as dist
 
 from internlm.utils.timeout import LLM_NCCL_TIMEOUT
-from internlm.core.context.process_group_initializer import ParallelMode
 
 class ParallelMeta:
     def __init__(self, parallel_size, mode) -> None:

internlm/initialize/launch.py

@@ -12,7 +12,7 @@
 from internlm.accelerator import AcceleratorType, get_accelerator
 from internlm.core.context import Config
 from internlm.core.context import global_context as gpc
-from internlm.core.context.process_group_initializer_simplified import ParallelMode
+from internlm.core.context.process_group_initializer import ParallelMode
 from internlm.utils.common import get_master_node
 from internlm.utils.gputest import warmup_process_group
 from internlm.utils.logger import get_logger
@@ -86,7 +86,9 @@ def add_simulator_arguments(parser):
     group.add_argument(
         "--pre_profiling_data_path", type=str, help="The path to pre-profiled performance data on the target cluster."
     )
+    group.add_argument("--use_simplified_gp_init", action="store_true", default=True)
     group.add_argument("--use_simplified_gp_init", action="store_true", default=False)
+
     return parser
 
 

internlm/model/ops/linear.py

@@ -14,10 +14,6 @@
 
 from internlm.accelerator import AcceleratorType, get_accelerator
 from internlm.core.context import global_context as gpc
-from internlm.simulator.ops.linear import (
-    _fake_linear_bwdward_op,
-    _fake_linear_forward_op,
-)
 
 try:
     from fused_dense_lib import linear_bias_wgrad as _flash_linear_backward_op

internlm/simulator/README.md

@@ -0,0 +1,54 @@
+# InternLM Simulator
+
+
+## 1. Introduction
+The solver mainly consists of two components:
+1. `profiling`: Collects the time consumption of each stage during the model training process in advance and saves it as data files and image files.
+2. `simulation`: Simulates the model training process based on the collected data files and outputs the time consumption of each stage during the training process.
+
+## 2. Usage
+
+### 2.1 Generate profiling data
+
+There are two types of profiling data:
+1. '`linear`' profiling data, include: [`LINEAR`]
+2. '`Communication`' profiling data, include: [`ALL2ALL`, `ALLREDUCE`, `REDUCESCATTER`, `ALLGATHER`, `BROADCAST`]
+
+
+Note:
+1. It is recommended to use more than 64 GPUs for data collection to ensure more accurate communication data.
+2. `Flash Attention` information is not collected in advance but is collected on the fly during the simulation and stored in the cache. This is because there are many variables that affect the performance of flash attention, and collecting in advance cannot cover all variables.
+
+```python
+# generate profiling data
+torchrun --nproc-per-node=8  gen_profiler_data.py
+
+# the profiling data will be saved in the following path
+./prof_data
+├── data.pt
+└── pics
+    ├── cal
+    │   └── linear.jpg
+    └── comm
+        ├── all2all_intra_2_inter_1.jpg
+        ├── all2all_intra_4_inter_1.jpg
+        ├── all_gather_intra_2_inter_1.jpg
+        ├── all_gather_intra_4_inter_1.jpg
+        ├── all_reduce_intra_2_inter_1.jpg
+        ├── all_reduce_intra_4_inter_1.jpg
+        ├── broadcast_intra_2_inter_1.jpg
+        ├── broadcast_intra_4_inter_1.jpg
+        ├── reduce_scatter_intra_2_inter_1.jpg
+        └── reduce_scatter_intra_4_inter_1.jpg
+
+```
+
+### 2.2 Run simulation
+```python
+python simulation_train_formulaic.py --pre_profiling_data_path ./data/profiling_data.json  --config configs/exp_simluator.py
+
+```
+
+
+
+## 4. 贡献