[Tensor] remove ParallelAction, use ComputeSpec instread (#1166)

colossalai/nn/_ops/addmm.py

@@ -1,7 +1,7 @@
 import torch
 from colossalai.tensor.op_wrapper import colo_op_impl
 from colossalai.nn.layer.parallel_1d._utils import reduce_input, reduce_grad
-from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor
+from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ComputeSpec, ColoTensor
 from colossalai.tensor import distspec
 from colossalai.context import ParallelMode
 from ._utils import GeneralTensor, Number, convert_to_colo_tensor
@@ -29,13 +29,13 @@ def colo_addmm_1Drow(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTenso
 def colo_addmm_1Dcol(input_tensor: ColoTensor, mat1: ColoTensor, mat2: ColoTensor, beta: Number,
                      alpha: Number) -> ColoTensor:
     # mat1:B x mat2:S[1] + input:S[1] = Output:S[1]
-    parallel_action = mat2.spec.parallel_action
+    parallel_action = mat2.spec.compute_spec
     mat1 = mat1.convert_to_dist_spec(distspec.replicate(mat2.spec.get_process_group()))
     mat1 = reduce_grad(mat1, ParallelMode.PARALLEL_1D)
 
     output_parallel = torch.addmm(input_tensor, mat1, mat2, beta=beta, alpha=alpha)
     output_spec = TensorSpec(distspec.shard(mat2.spec.get_process_group(), [-1], [mat2.spec.get_process_group_size()]),
-                             ParallelAction(ComputePattern.TP1D))
+                             ComputeSpec(ComputePattern.TP1D))
     output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
 
     # TODO(jiaruifang) addam is special case

colossalai/nn/_ops/embedding.py

@@ -3,7 +3,7 @@ from typing import Optional
 from colossalai.tensor.op_wrapper import colo_op_impl
 from colossalai.nn.layer.parallel_1d._utils import reduce_input
 from colossalai.core import global_context as gpc
-from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, distspec
+from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ComputeSpec, ColoTensor, distspec
 from colossalai.context import ParallelMode
 from ._utils import GeneralTensor, convert_to_colo_tensor
 
@@ -28,7 +28,7 @@ def colo_embedding_1Dcol(input_tensor: ColoTensor,
                                   sparse=sparse)
     output_spec = TensorSpec(
         distspec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group_size()]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
     return output.to_replicate()
 

colossalai/nn/_ops/embedding_bag.py

@@ -2,7 +2,7 @@ import torch.nn.functional as F
 from typing import Optional
 from torch import Tensor
 from colossalai.tensor.op_wrapper import colo_op_impl
-from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, distspec
+from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ComputeSpec, ColoTensor, distspec
 from ._utils import GeneralTensor, convert_to_colo_tensor
 
 
@@ -34,7 +34,7 @@ def colo_embedding_bag_1Dcol(input_tensor: ColoTensor,
                                       padding_idx=padding_idx)
     output_spec = TensorSpec(
         distspec.shard(weight.spec.get_process_group(), [-1], [weight.spec.get_process_group_size()]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     output = ColoTensor.from_torch_tensor(output_parallel, spec=output_spec)
 
     return output.to_replicate()

colossalai/nn/_ops/linear.py

@@ -3,7 +3,7 @@ from typing import Optional
 from ._utils import GeneralTensor, convert_to_colo_tensor
 from colossalai.tensor.op_wrapper import colo_op_impl
 from colossalai.nn.layer.parallel_1d._utils import reduce_input, reduce_grad
-from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, distspec
+from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ComputeSpec, ColoTensor, distspec
 from colossalai.context import ParallelMode
 from colossalai.nn.graph import register_colo_graph, GraphOpNode, GraphGlobalEnv
 
@@ -32,7 +32,7 @@ def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: Option
     # Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
     # All-Gather(Output)
     # Input:B
-    parallel_action = weight.spec.parallel_action
+    parallel_action = weight.spec.compute_spec
     input_tensor = input_tensor.convert_to_dist_spec(distspec.replicate(weight.spec.get_process_group()))
     input_parallel = reduce_grad(input_tensor, ParallelMode.PARALLEL_1D)
 
@@ -41,7 +41,7 @@ def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: Option
                                           spec=TensorSpec(
                                               distspec.shard(weight.spec.get_process_group(), [-1],
                                                              [weight.spec.get_process_group_size()]),
-                                              ParallelAction(ComputePattern.TP1D)))
+                                              ComputeSpec(ComputePattern.TP1D)))
     return output.to_replicate()
 
 

colossalai/nn/parallel/layers/module_utils.py

@@ -1,5 +1,5 @@
 from typing import Dict
-from colossalai.tensor import ColoParameter, ParallelAction, TensorSpec
+from colossalai.tensor import ColoParameter, ComputeSpec, TensorSpec
 from . import ColoModule
 import torch
 
@@ -39,7 +39,7 @@ def check_colo_module(module: torch.nn.Module, recursive=True):
             if not isinstance(param, ColoParameter):
                 raise Exception(f'Invalid ColoParameter spec: {param} in {module} is not a ColoParameter.')
             if param.has_spec():
-                cur_compute_pattern = param.spec.parallel_action.compute_pattern
+                cur_compute_pattern = param.spec.compute_spec.compute_pattern
                 if compute_pattern is None:
                     compute_pattern = cur_compute_pattern
                 else:
@@ -79,11 +79,11 @@ def check_colo_module(module: torch.nn.Module, recursive=True):
             check_colo_module(submodule, recursive=True)
 
 
-def init_colo_module(module: torch.nn.Module, parallel_action: ParallelAction, recursive=True, mode='default'):
+def init_colo_module(module: torch.nn.Module, parallel_action: ComputeSpec, recursive=True, mode='default'):
     compute_pattern = parallel_action.compute_pattern
     if is_colo_module(module):
         # for each param
-        # set DistSpec and ParallelAction
+        # set DistSpec and ComputeSpec
         colo_module = get_colo_module(module)
         colo_module.register(compute_pattern)
         if not colo_module.has_compute_pattern_with_mode(compute_pattern, mode=mode):

colossalai/tensor/__init__.py

@@ -1,14 +1,14 @@
-from .spec import ComputePattern, ParallelAction, TensorSpec
+from .tensor_spec import TensorSpec
-
+from .compute_spec import ComputeSpec, ComputePattern
 from .colo_tensor import ColoTensor
 from .colo_parameter import ColoParameter
 from .utils import convert_parameter, named_params_with_colotensor
-from . import distspec
 from .dist_spec_mgr import DistSpecManager
 from .param_op_hook import ParamOpHook, ParamOpHookManager
 from .chunk import ChunkManager, TensorState
+from . import distspec
 
 __all__ = [
-    'ColoTensor', 'convert_parameter', 'ComputePattern', 'TensorSpec', 'ParallelAction', 'named_params_with_colotensor',
+    'ColoTensor', 'convert_parameter', 'ComputePattern', 'TensorSpec', 'ComputeSpec', 'named_params_with_colotensor',
     'ColoParameter', 'distspec', 'DistSpecManager', 'ParamOpHook', 'ParamOpHookManager', 'ChunkManager', 'TensorState'
 ]

colossalai/tensor/colo_parameter.py

@@ -1,10 +1,12 @@
+import torch
+
+from typing import Optional
+from copy import copy
+
 from colossalai.tensor.colo_tensor import ColoTensor
 from colossalai.tensor.const import TensorType
-import torch
 from colossalai.tensor import TensorSpec, distspec
-from copy import copy
 from colossalai.tensor.param_op_hook import ParamOpHookManager
-from typing import Optional
 
 
 def filter_args(func, *args):

colossalai/tensor/colo_tensor.py

@@ -66,7 +66,7 @@ class ColoTensor(torch.Tensor):
         self._tensor_spec = spec
 
     def has_spec(self) -> bool:
-        return self._tensor_spec.parallel_action is not None
+        return self._tensor_spec.compute_spec is not None
 
     def is_model_data(self) -> bool:
         return self._type == TensorType.MODEL

colossalai/tensor/compute_spec.py

@@ -0,0 +1,23 @@
+from enum import Enum
+
+
+class ComputePattern(Enum):
+    TP1D = 0
+    TP2D = 1
+    TP2P5D = 2
+    TP3D = 3
+
+
+class ComputeSpec(object):
+    """ComputeSpec 
+    The Specification for compuattion pattern
+    Args:
+        compute_pattern (ComputePattern): an Enum instance for compute pattern.
+    """
+
+    def __init__(self, compute_pattern: ComputePattern) -> None:
+        assert isinstance(compute_pattern, ComputePattern)
+        self.compute_pattern = compute_pattern
+
+    def __repr__(self):
+        return f'compute pattern: {self.compute_pattern}'

colossalai/tensor/tensor_spec.py

@@ -1,24 +1,7 @@
 import torch.distributed as dist
-from enum import Enum
+from typing import Optional
-from typing import List, Optional
 from colossalai.tensor.distspec import _DistSpec, DistPlacementPattern
-
+from .compute_spec import ComputeSpec, ComputePattern
-
-class ComputePattern(Enum):
-    TP1D = 0
-    TP2D = 1
-    TP2P5D = 2
-    TP3D = 3
-
-
-class ParallelAction(object):
-
-    def __init__(self, compute_pattern: ComputePattern) -> None:
-        assert isinstance(compute_pattern, ComputePattern)
-        self.compute_pattern = compute_pattern
-
-    def __repr__(self):
-        return f'compute pattern: {self.compute_pattern}'
 
 
 class TensorSpec(object):
@@ -26,12 +9,12 @@ class TensorSpec(object):
     The specification of the ColoTensor.
     Args:
         dist_spec (_DistSpec): descriping the layout among processes.
-        parallel_action (Optional[ParallelAction], optional): actions conducted on the tensor after initialization if it's a model data tensor. 
+        parallel_action (Optional[ComputeSpec], optional): actions conducted on the tensor after initialization if it's a model data tensor. 
         Defaults to None.
     """
 
-    def __init__(self, dist_spec: _DistSpec, parallel_action: Optional[ParallelAction] = None):
+    def __init__(self, dist_spec: _DistSpec, compute_spec: Optional[ComputeSpec] = None):
-        self.parallel_action = parallel_action
+        self.compute_spec = compute_spec
         self.dist_spec = dist_spec
 
     def get_process_group(self):
@@ -58,7 +41,7 @@ class TensorSpec(object):
             and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == 0
 
     def has_compute_pattern(self, compute_pattern: ComputePattern):
-        return self.parallel_action.compute_pattern == compute_pattern
+        return self.compute_spec.compute_pattern == compute_pattern
 
     def __repr__(self):
-        return f'parallel action: {self.parallel_action}, dist_spec: {self.dist_spec}'
+        return f'parallel action: {self.compute_spec}, dist_spec: {self.dist_spec}'

docker/Dockerfile

@@ -14,4 +14,4 @@ RUN git clone https://github.com/hpcaitech/ColossalAI.git \
  && pip install -v --no-cache-dir .
 
 # install titans
-RUN pip install -no-cache-dir titans
+RUN pip install --no-cache-dir titans

tests/test_tensor/test_addmm_tp.py

@@ -5,7 +5,7 @@ import torch.nn as nn
 import torch.multiprocessing as mp
 from colossalai.tensor import ColoTensor
 from colossalai.tensor import distspec
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
 from colossalai.context import ParallelMode
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils import free_port
@@ -41,7 +41,7 @@ class Conv1D(nn.Module):
 def init_1d_row(weight, bias):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 
@@ -49,7 +49,7 @@ def init_1d_row(weight, bias):
 def init_1d_col(weight, bias):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
         bias.set_spec(spec)

tests/test_tensor/test_embedding_bag_tp.py

@@ -11,14 +11,14 @@ import torch.multiprocessing as mp
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils import free_port
 from colossalai.core import global_context as gpc
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
 from _utils import tensor_equal, tensor_shard_equal
 
 
 def init_1d_col(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 

tests/test_tensor/test_embedding_tp.py

@@ -11,14 +11,14 @@ import torch.multiprocessing as mp
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils import free_port
 from colossalai.core import global_context as gpc
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
 from _utils import tensor_equal, tensor_shard_equal
 
 
 def init_1d_row(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 
@@ -26,7 +26,7 @@ def init_1d_row(weight):
 def init_1d_col(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 

tests/test_tensor/test_gpt.py

@@ -6,7 +6,7 @@ from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils.cuda import get_current_device
 from colossalai.utils import free_port
 from colossalai.utils.model.colo_init_context import ColoInitContext
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager, distspec
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec
 from colossalai.core import global_context as gpc
 from functools import partial
 from _utils import tensor_equal, tensor_shard_equal, set_seed
@@ -18,7 +18,7 @@ from colossalai.nn.parallel.data_parallel import ColoDDP
 def init_1d_row_spec(model):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         for n, p in model.named_parameters():
             if 'weight' in n and 'ln' not in n:
@@ -28,7 +28,7 @@ def init_1d_row_spec(model):
 def init_1d_col_spec(model):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         for n, p in model.named_parameters():
             if 'ln' not in n and ('weight' in n or 'bias' in n):

tests/test_tensor/test_hybrid_device.py

@@ -1,7 +1,7 @@
 from colossalai.utils import free_port, get_current_device
 from colossalai.utils.model.colo_init_context import ColoInitContext
 from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.tensor import ComputePattern, ParallelAction
+from colossalai.tensor import ComputePattern, ComputeSpec
 
 from functools import partial
 from colossalai.core import global_context as gpc
@@ -46,7 +46,7 @@ def run_hybrid_device(use_ddp, mode):
 
     print(f'embedding weight size: {real_model.embed.weight.size()} | device: {real_model.embed.weight.device}')
     #print(f'linear weight size: {real_model.proj.weight.size()} | device: {real_model.proj.weight.device}')
-    parallel_action = ParallelAction(ComputePattern.TP1D)
+    parallel_action = ComputeSpec(ComputePattern.TP1D)
     init_colo_module(model, parallel_action, recursive=True, mode=mode)
 
     # use cpu gloo to handle embedding
@@ -63,6 +63,7 @@ def run_hybrid_device(use_ddp, mode):
     out.sum().backward()
     optimizer.step()
 
+
 def run_dist(rank, world_size, port, use_ddp, mode):
     if use_ddp and world_size == 1:
         return
@@ -71,6 +72,7 @@ def run_dist(rank, world_size, port, use_ddp, mode):
     colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     run_hybrid_device(use_ddp, mode)
 
+
 @pytest.mark.dist
 @pytest.mark.parametrize('world_size', [1, 4])
 @pytest.mark.parametrize('use_ddp', [False, True])
@@ -78,7 +80,7 @@ def run_dist(rank, world_size, port, use_ddp, mode):
 @rerun_if_address_is_in_use()
 # Working for simulate the embedding(CPU DP+TP) -> nn(GPU DP+TP)
 def _test_hybrid_device(world_size, use_ddp, mode):
-    run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp ,mode=mode)
+    run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp, mode=mode)
     mp.spawn(run_func, nprocs=world_size)
 
 

tests/test_tensor/test_linear_tp.py

@@ -12,14 +12,14 @@ import torch.nn.functional as F
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils import free_port
 from colossalai.core import global_context as gpc
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager
 from _utils import tensor_equal, tensor_shard_equal
 
 
 def init_1d_row(weight, bias):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 
@@ -27,7 +27,7 @@ def init_1d_row(weight, bias):
 def init_1d_col(weight, bias):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
         bias.set_spec(spec)

tests/test_tensor/test_model.py

@@ -10,7 +10,7 @@ from colossalai.utils.cuda import get_current_device
 from colossalai.utils import free_port
 from colossalai.utils.model.colo_init_context import ColoInitContext
 from colossalai.tensor import distspec, TensorSpec, ComputePattern, \
-    ParallelAction, ColoTensor, DistSpecManager
+    ComputeSpec, ColoTensor, DistSpecManager
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.nn.optimizer import ColoOptimizer
@@ -21,7 +21,7 @@ from _utils import tensor_equal, tensor_shard_equal, set_seed
 def init_1d_row_linear(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 
@@ -29,7 +29,7 @@ def init_1d_row_linear(weight):
 def init_1d_col_linear(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 
@@ -37,7 +37,7 @@ def init_1d_col_linear(weight):
 def init_1d_row_embedding(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 
@@ -45,7 +45,7 @@ def init_1d_row_embedding(weight):
 def init_1d_col_embedding(weight):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         weight.set_spec(spec)
 

tests/test_tensor/test_module_spec.py

@@ -5,7 +5,7 @@ from functools import partial
 import torch
 import torch.multiprocessing as mp
 
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec
 from colossalai.nn.parallel.layers import init_colo_module, check_colo_module
 from _utils import tensor_equal, tensor_shard_equal, set_seed
 
@@ -40,7 +40,7 @@ def run_model_with_spec(mode, model_name):
         for p1, p2 in zip(model.parameters(), model_seq.parameters()):
             p2.data.copy_(p1.data)
 
-    parallel_action = ParallelAction(ComputePattern.TP1D)
+    parallel_action = ComputeSpec(ComputePattern.TP1D)
     # Not all layers in Bert can be mod by 4.
     # e.g. row shard for all layers is invalid because the first dim of some layer is the classification type size 2.
     if 'bert' == model_name:
@@ -114,7 +114,7 @@ def run_linear_with_spec(mode):
 
     model_handy = copy(model)
 
-    parallel_action = ParallelAction(ComputePattern.TP1D)
+    parallel_action = ComputeSpec(ComputePattern.TP1D)
     init_colo_module(model, parallel_action, recursive=True, mode=mode)
 
     x = torch.rand(2, 4).cuda()
@@ -148,7 +148,7 @@ def run_check_shared_param():
         model = BertForMaskedLM(config)
 
     model = model.cuda()
-    parallel_action = ParallelAction(ComputePattern.TP1D)
+    parallel_action = ComputeSpec(ComputePattern.TP1D)
     # model.cls.predictions.decoder and model.cls.predictions share the bias, so they should have the same spec
     assert len(model.cls.predictions.decoder.bias.shared_param_modules) == 2
     # They are all Linear, so both row is allowed. This should pass check.
@@ -156,7 +156,7 @@ def run_check_shared_param():
     # This should be detected by check because you can not set weight as row while set bias as col.
     col_spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     model.cls.predictions.bias.set_spec(col_spec)
     try:
         check_colo_module(model.cls.predictions.decoder, recursive=False)

tests/test_tensor/test_zero_optim.py

@@ -19,7 +19,7 @@ from colossalai.zero import ZeroOptimizer
 from colossalai.testing import parameterize
 from colossalai.amp import convert_to_apex_amp
 from colossalai.gemini.gemini_mgr import GeminiManager
-from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, DistSpecManager, distspec
+from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, distspec
 
 
 def check_param_equal(model, torch_model):
@@ -47,7 +47,7 @@ def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
 def init_1d_row_spec(model):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         for n, p in model.named_parameters():
             if 'weight' in n and 'ln' not in n:
@@ -57,7 +57,7 @@ def init_1d_row_spec(model):
 def init_1d_col_spec(model):
     spec = TensorSpec(
         distspec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
-        ParallelAction(ComputePattern.TP1D))
+        ComputeSpec(ComputePattern.TP1D))
     with DistSpecManager.no_grad():
         for n, p in model.named_parameters():
             if 'ln' not in n and ('weight' in n or 'bias' in n):