[autoparallel] integrate device mesh initialization into autoparallelize (#2393)

* [autoparallel] integrate device mesh initialization into autoparallelize

* add megatron solution

* update gpt autoparallel examples with latest api

* adapt beta value to fit the current computation cost

colossalai/auto_parallel/tensor_shard/initialize.py

@@ -59,18 +59,6 @@ def extract_meta_args_from_dataloader(data_loader: torch.utils.data.DataLoader,
     pass
 
 
-def search_best_logical_mesh_shape(world_size: int, alpha_beta_dict: Dict[Tuple[int], Tuple[float]]):
-    '''
-    This method is used to search the best logical mesh shape for the given world size
-    based on the alpha_beta_dict.
-
-    For example:
-        if the world_size is 8, and the possible logical shape will be (1, 8), (2, 4), (4, 2), (8, 1).
-    '''
-    # TODO: implement this function
-    return (world_size, 1)
-
-
 def extract_alpha_beta_for_device_mesh(alpha_beta_dict: Dict[Tuple[int], Tuple[float]], logical_mesh_shape: Tuple[int]):
     '''
     This method is used to extract the mesh_alpha and mesh_beta for the given logical_mesh_shape
@@ -127,39 +115,56 @@ def transform_to_sharded_model(gm: GraphModule, solution: List[int], device_mesh
 
 
 def initialize_device_mesh(world_size: int = -1,
+                           physical_devices: List[int] = None,
                            alpha_beta_dict: Dict[Tuple[int], Tuple[float]] = None,
-                           logical_mesh_shape: Tuple[int] = None):
+                           logical_mesh_shape: Tuple[int] = None,
+                           logical_mesh_id: torch.Tensor = None):
     '''
     This method is used to initialize the device mesh.
 
     Args:
-        world_size(optional): the size of device mesh. If the world_size is -1,
+        world_size: the size of device mesh. If the world_size is -1,
             the world size will be set to the number of GPUs in the current machine.
+        physical_devices: the physical devices used to initialize the device mesh.
         alpha_beta_dict(optional): the alpha_beta_dict contains the alpha and beta values
             for each devices. if the alpha_beta_dict is None, the alpha_beta_dict will be
             generated by profile_alpha_beta function.
         logical_mesh_shape(optional): the logical_mesh_shape is used to specify the logical
-            mesh shape. If the logical_mesh_shape is None, the logical_mesh_shape will be
-            generated by search_best_logical_mesh_shape function.
+            mesh shape.
+        logical_mesh_id(optional): the logical_mesh_id is used to specify the logical mesh id.
     '''
     # if world_size is not set, use the world size from torch.distributed
     if world_size == -1:
         world_size = dist.get_world_size()
-    device1d = [i for i in range(world_size)]
+
+    if physical_devices is None:
+        physical_devices = [i for i in range(world_size)]
+    physical_mesh = torch.tensor(physical_devices)
 
     if alpha_beta_dict is None:
         # if alpha_beta_dict is not given, use a series of executions to profile alpha and beta values for each device
-        alpha_beta_dict = profile_alpha_beta(device1d)
+        ab_profiler = AlphaBetaProfiler(physical_devices)
+        alpha_beta_dict = ab_profiler.alpha_beta_dict
+    else:
+        ab_profiler = AlphaBetaProfiler(physical_devices, alpha_beta_dict=alpha_beta_dict)
 
-    if logical_mesh_shape is None:
+    if logical_mesh_shape is None and logical_mesh_id is None:
         # search for the best logical mesh shape
-        logical_mesh_shape = search_best_logical_mesh_shape(world_size, alpha_beta_dict)
+        logical_mesh_id = ab_profiler.search_best_logical_mesh()
+        logical_mesh_id = torch.Tensor(logical_mesh_id).to(torch.int)
+        logical_mesh_shape = logical_mesh_id.shape
+
+        # extract alpha and beta values for the chosen logical mesh shape
+        mesh_alpha, mesh_beta = ab_profiler.extract_alpha_beta_for_device_mesh()
+
+    elif logical_mesh_shape is not None and logical_mesh_id is None:
+        logical_mesh_id = physical_mesh.reshape(logical_mesh_shape)
+
+        # extract alpha and beta values for the chosen logical mesh shape
+        mesh_alpha, mesh_beta = extract_alpha_beta_for_device_mesh(alpha_beta_dict, logical_mesh_id)
 
-    # extract alpha and beta values for the chosen logical mesh shape
-    mesh_alpha, mesh_beta = extract_alpha_beta_for_device_mesh(alpha_beta_dict, logical_mesh_shape)
-    physical_mesh = torch.tensor(device1d)
     device_mesh = DeviceMesh(physical_mesh_id=physical_mesh,
-                             mesh_shape=logical_mesh_shape,
+                             logical_mesh_id=logical_mesh_id,
                              mesh_alpha=mesh_alpha,
                              mesh_beta=mesh_beta,
                              init_process_group=True)
@@ -224,6 +229,7 @@ def autoparallelize(model: nn.Module,
                     data_process_func: callable = None,
                     alpha_beta_dict: Dict[Tuple[int], Tuple[float]] = None,
                     logical_mesh_shape: Tuple[int] = None,
+                    logical_mesh_id: torch.Tensor = None,
                     save_solver_solution: bool = False,
                     load_solver_solution: bool = False,
                     solver_solution_path: str = None,
@@ -245,6 +251,7 @@ def autoparallelize(model: nn.Module,
         logical_mesh_shape(optional): the logical_mesh_shape is used to specify the logical
             mesh shape. If the logical_mesh_shape is None, the logical_mesh_shape will be
             generated by search_best_logical_mesh_shape function.
+        logical_mesh_id(optional): the logical_mesh_id is used to specify the logical mesh id.
         save_solver_solution(optional): if the save_solver_solution is True, the solution will be saved
             to the solution_path.
         load_solver_solution(optional): if the load_solver_solution is True, the solution will be loaded
@@ -254,7 +261,9 @@ def autoparallelize(model: nn.Module,
         memory_budget(optional): the max cuda memory could be used. If the memory budget is -1.0,
             the memory budget will be infinity.
     '''
-    device_mesh = initialize_device_mesh(alpha_beta_dict=alpha_beta_dict, logical_mesh_shape=logical_mesh_shape)
+    device_mesh = initialize_device_mesh(alpha_beta_dict=alpha_beta_dict,
+                                         logical_mesh_shape=logical_mesh_shape,
+                                         logical_mesh_id=logical_mesh_id)
     if meta_args is None:
         meta_args = extract_meta_args_from_dataloader(data_loader, data_process_func)
 
@@ -263,7 +272,7 @@ def autoparallelize(model: nn.Module,
                                      device_mesh,
                                      save_solver_solution=save_solver_solution,
                                      load_solver_solution=load_solver_solution,
-                                     solver_solution_path=solver_solution_path,
+                                     solution_path=solver_solution_path,
                                      return_solution=return_solution,
                                      memory_budget=memory_budget)
 

colossalai/device/alpha_beta_profiler.py

@@ -381,6 +381,8 @@ class AlphaBetaProfiler:
         first_latency, first_bandwidth = _extract_alpha_beta(first_axis, first_axis_process_group)
         second_latency, second_bandwidth = _extract_alpha_beta(second_axis, second_axis_process_group)
         mesh_alpha = [first_latency, second_latency]
-        mesh_beta = [1 / first_bandwidth, 1 / second_bandwidth]
+        # The beta values have been enlarged by 1e10 times temporarilly because the computation cost
+        # is still estimated in the unit of TFLOPs instead of time. We will remove this factor in future.
+        mesh_beta = [1e10 / first_bandwidth, 1e10 / second_bandwidth]
 
         return mesh_alpha, mesh_beta

colossalai/device/device_mesh.py

 import operator
 from functools import reduce
+from typing import List, Tuple
 
 import torch
 import torch.distributed as dist
@@ -15,7 +16,8 @@ class DeviceMesh:
 
     Arguments:
         physical_mesh_id (torch.Tensor): physical view of the devices in global rank.
-        mesh_shape (torch.Size): shape of logical view.
+        logical_mesh_id (torch.Tensor): logical view of the devices in global rank.
+        mesh_shape (torch.Size, optional): shape of logical view.
         mesh_alpha (List[float], optional): coefficients used for computing
             communication cost (default: None)
         mesh_beta (List[float], optional): coefficients used for computing
@@ -28,15 +30,21 @@ class DeviceMesh:
     """
 
     def __init__(self,
-                 physical_mesh_id,
-                 mesh_shape,
-                 mesh_alpha=None,
-                 mesh_beta=None,
-                 init_process_group=False,
-                 need_flatten=True):
+                 physical_mesh_id: torch.Tensor,
+                 mesh_shape: torch.Size = None,
+                 logical_mesh_id: torch.Tensor = None,
+                 mesh_alpha: List[float] = None,
+                 mesh_beta: List[float] = None,
+                 init_process_group: bool = False,
+                 need_flatten: bool = True):
         self.physical_mesh_id = physical_mesh_id
-        self.mesh_shape = mesh_shape
-        self._logical_mesh_id = self.physical_mesh_id.reshape(self.mesh_shape)
+        if logical_mesh_id is None:
+            self.mesh_shape = mesh_shape
+            self._logical_mesh_id = self.physical_mesh_id.reshape(self.mesh_shape)
+        else:
+            self._logical_mesh_id = logical_mesh_id
+            self.mesh_shape = self._logical_mesh_id.shape
+
         # map global rank into logical rank
         self.convert_map = {}
         self._global_rank_to_logical_rank_map(self._logical_mesh_id, [])
@@ -54,8 +62,8 @@ class DeviceMesh:
         if self.need_flatten and self._logical_mesh_id.dim() > 1:
             self.flatten_device_mesh = self.flatten()
             # Create a new member `flatten_device_meshes` to distinguish from original flatten methods (Because I'm not sure if there are functions that rely on the self.flatten())
-            self.flatten_device_meshes = FlattenDeviceMesh(self.physical_mesh_id, self.mesh_shape, self.mesh_alpha,
-                                                           self.mesh_beta)
+            # self.flatten_device_meshes = FlattenDeviceMesh(self.physical_mesh_id, self.mesh_shape, self.mesh_alpha,
+            #                                                self.mesh_beta)
 
     @property
     def shape(self):

examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py

@@ -16,14 +16,14 @@ from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch_from_torch
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 
-BATCH_SIZE = 8
-SEQ_LENGTH = 128
-HIDDEN_DIM = 3072
+BATCH_SIZE = 16
+SEQ_LENGTH = 1024
+HIDDEN_DIM = 4096
 NUM_HEADS = 16
-NUM_LAYERS = 1
+NUM_LAYERS = 4
 VOCAB_SIZE = 50257
 NUM_STEPS = 10
-FP16 = False
+FP16 = True
 
 
 def get_cpu_mem():
@@ -40,7 +40,7 @@ def get_mem_info(prefix=''):
 
 def get_tflops(model_numel, batch_size, seq_len, step_time):
     # Tflops_per_GPU = global_batch * global_numel * seq_len * 8 / #gpu
-    return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) / 4
+    return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) / 8
 
 
 # Randomly Generated Data
@@ -66,13 +66,7 @@ def main():
         'attention_mask': torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64).to('meta'),
     }
 
-    # Both device mesh initialization and model initialization will be integrated into autoparallelize
-    physical_mesh_id = torch.arange(0, 4)
-    mesh_shape = (2, 2)
-    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
-
-    # Enable auto-parallel
-    gm, solution = initialize_model(model, meta_input_sample, device_mesh, return_solution=True)
+    gm, solution = autoparallelize(model, meta_input_sample, return_solution=True)
 
     # print solution on rank 0
     if gpc.get_global_rank() == 0: