moved env variables to global variables; (#215)

added branch context;
added vocab parallel layers;
moved split_batch from load_batch to tensor parallel embedding layers;
updated gpt model;
updated unit test cases;
fixed few collective communicator bugs

.gitignore

@@ -137,8 +137,4 @@ dmypy.json
 .DS_Store
 #data/
 
-# launcher setting
-tests/launcher/log
-tests/launcher/personal
-
 docs/.build

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     - id: yapf
       args: ['--style=google', '--parallel', '--in-place']
   - repo: https://github.com/pycqa/flake8
-    rev: ''
+    rev: '4.0.1'
     hooks:
     - id: flake8
   - repo: https://github.com/pre-commit/mirrors-clang-format

colossalai/amp/apex_amp/apex_amp.py

@@ -4,8 +4,9 @@
 import torch.nn as nn
 try:
     import apex.amp as apex_amp
-except:
-    pass
+except ImportError:
+    raise ImportError('Cannot import apex.amp correctly.')
+
 from torch import Tensor
 
 from colossalai.nn.optimizer import ColossalaiOptimizer

colossalai/communication/collective.py

@@ -30,7 +30,7 @@ def all_gather(tensor: Tensor, dim: int, parallel_mode: ParallelMode, async_op:
     """
     depth = gpc.get_world_size(parallel_mode)
     if depth == 1:
-        out = [tensor]
+        out = tensor
         work = None
     else:
         shape = list(tensor.shape)
@@ -96,34 +96,40 @@ def all_reduce(tensor: Tensor,
                async_op: bool = False) -> Tensor:
     depth = gpc.get_world_size(parallel_mode)
     if depth == 1:
+        out = tensor
         work = None
     else:
-        work = dist.all_reduce(tensor.contiguous(), op=op, group=gpc.get_group(parallel_mode), async_op=async_op)
+        out = tensor.contiguous()
+        work = dist.all_reduce(out, op=op, group=gpc.get_group(parallel_mode), async_op=async_op)
     if async_op:
-        return tensor, work
+        return out, work
     else:
-        return tensor
+        return out
 
 
 def broadcast(tensor: Tensor, src: int, parallel_mode: ParallelMode, async_op: bool = False):
     depth = gpc.get_world_size(parallel_mode)
     if depth == 1:
+        out = tensor
         work = None
     else:
-        work = dist.broadcast(tensor.contiguous(), src=src, group=gpc.get_group(parallel_mode), async_op=async_op)
+        out = tensor.contiguous()
+        work = dist.broadcast(out, src=src, group=gpc.get_group(parallel_mode), async_op=async_op)
     if async_op:
-        return tensor, work
+        return out, work
     else:
-        return tensor
+        return out
 
 
 def reduce(tensor: Tensor, dst: int, parallel_mode: ParallelMode, op: ReduceOp = ReduceOp.SUM, async_op: bool = False):
     depth = gpc.get_world_size(parallel_mode)
     if depth == 1:
+        out = tensor
         work = None
     else:
-        work = dist.reduce(tensor.contiguous(), dst=dst, op=op, group=gpc.get_group(parallel_mode), async_op=async_op)
+        out = tensor.contiguous()
+        work = dist.reduce(out, dst=dst, op=op, group=gpc.get_group(parallel_mode), async_op=async_op)
     if async_op:
-        return tensor, work
+        return out, work
     else:
-        return tensor
+        return out

colossalai/constants.py

@@ -19,23 +19,12 @@ INITIALIZER_MAPPING = {
     'moe': 'Initializer_Moe'
 }
 
-# 1D parallel
-PARALLEL_INPUT_1D = 'parallel_input_1d'
+# 3D parallelism groups
+INPUT_GROUP_3D = 'input_group_3d'
+WEIGHT_GROUP_3D = 'weight_group_3d'
+OUTPUT_GROUP_3D = 'output_group_3d'
 
-# 2D paralllel
-SUMMA_DIM = 'SUMMA_DIM'
-
-# 2.5D paralllel
-TESSERACT_DIM = 'TESSERACT_DIM'
-TESSERACT_DEP = 'TESSERACT_DEP'
-
-# 3D parallel
-DEPTH_3D = 'DEPTH_3D'
-INPUT_GROUP_3D = 'PARALLEL_3D_INPUT'
-WEIGHT_GROUP_3D = 'PARALLEL_3D_WEIGHT'
-OUTPUT_GROUP_3D = 'PARALLEL_3D_OUTPUT'
-
-# Tensor parallel attributes
+# Attributes of tensor parallel parameters 
 IS_TENSOR_PARALLEL = 'is_tensor_parallel'
 NUM_PARTITIONS = 'num_partitions'
 TENSOR_PARALLEL_ATTRIBUTES = [IS_TENSOR_PARALLEL, NUM_PARTITIONS]

colossalai/context/parallel_context.py

@@ -8,14 +8,15 @@ from typing import Union
 import numpy as np
 import torch
 import torch.distributed as dist
-from colossalai.constants import ALLOWED_MODES, INITIALIZER_MAPPING, TENSOR_PARALLEL_MODE
+from colossalai.constants import ALLOWED_MODES, INITIALIZER_MAPPING
 from colossalai.context.config import Config
+from colossalai.global_variables import moe_env
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.logging import get_dist_logger
 from colossalai.registry import DIST_GROUP_INITIALIZER
 
 from .parallel_mode import ParallelMode
 from .random import add_seed, get_seeds, set_mode
-from colossalai.global_variables import moe_env
 
 
 class ParallelContext:
@@ -307,7 +308,6 @@ class ParallelContext:
                          port: int
                          ):
         """Initializes the global distributed environment
-
         :param rank: rank for the default process group
         :type rank: int
         :param world_size: world size of the default process group
@@ -389,7 +389,8 @@ class ParallelContext:
         if parallel_config is not None and 'tensor' in parallel_config and 'mode' in parallel_config['tensor']:
             tensor_parallel_mode = parallel_config['tensor']['mode']
         assert tensor_parallel_mode in ALLOWED_MODES, f"mode in the parallel config must be set to one of {ALLOWED_MODES}"
-        os.environ[TENSOR_PARALLEL_MODE] = str(tensor_parallel_mode)
+        env.mode = tensor_parallel_mode
+        
         self.check_sanity()
 
         pg_init = []

colossalai/context/process_group_initializer/initializer_1d.py

@@ -1,22 +1,18 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
-import os
-import torch.distributed as dist
 
-from colossalai.context import Config
+import torch.distributed as dist
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.registry import DIST_GROUP_INITIALIZER
-from .process_group_initializer import ProcessGroupInitializer
+
 from ..parallel_mode import ParallelMode
-from colossalai.constants import PARALLEL_INPUT_1D
+from .process_group_initializer import ProcessGroupInitializer
 
 
 @DIST_GROUP_INITIALIZER.register_module
 class Initializer_1D(ProcessGroupInitializer):
-    """A ProcessGroupInitializer for 1d tensor parallelism.
-
-    :param args: Args used to initialize ProcessGroupInitializer
-    :param kwargs: Kwargs used to initialize ProcessGroupInitializer
-    """
+    '''A ProcessGroupInitializer for 1d tensor parallelism.
+    '''
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -24,7 +20,7 @@ class Initializer_1D(ProcessGroupInitializer):
 
     def init_dist_group(self):
         """Initialize 1D tensor parallel groups, and assign local_ranks and groups to each gpu.
-
+        
         :return: (local_rank, group_world_size, process_group, ranks_in_group, mode)
         :rtype: Tuple
         """
@@ -33,7 +29,7 @@ class Initializer_1D(ProcessGroupInitializer):
         process_group = None
         group_world_size = None
         mode = ParallelMode.PARALLEL_1D
-        os.environ[PARALLEL_INPUT_1D] = ''
+        env.parallel_input_1d = False
 
         for i in range(self.num_group):
             ranks = [i * self.tensor_parallel_size + j for j in range(self.tensor_parallel_size)]

colossalai/context/process_group_initializer/initializer_2d.py

@@ -1,34 +1,31 @@
 import math
-import os
 
 import torch.distributed as dist
 
-from colossalai.constants import SUMMA_DIM
 from colossalai.registry import DIST_GROUP_INITIALIZER
 from .process_group_initializer import ProcessGroupInitializer
 from ..parallel_mode import ParallelMode
+from colossalai.global_variables import tensor_parallel_env as env
 
 
 def _check_summa_env_var(summa_dim):
     # check environment variable for SUMMA
-    env_summa_dim = os.environ.get(SUMMA_DIM, None)
+    env_summa_dim = env.summa_dim
 
     if env_summa_dim:
         assert int(env_summa_dim) == summa_dim, \
             'SUMMA_DIM has been set in the current environment and ' \
             'does not match with the value passed to this initialized'
     else:
-        os.environ[SUMMA_DIM] = str(summa_dim)
+        env.summa_dim = summa_dim
 
 
 class Initializer_2D_Row(ProcessGroupInitializer):
     """2d tensor parallel initialization among rows.
-
     :param num_group: The number of all tensor groups
     :param summa_dim: The dimension of SUMMA
     :param args: Args used to initialize base class
     :param kwargs: Kwargs used to initialize base class
-
     :type num_group: int
     :type summa_dim: int
     """
@@ -132,7 +129,7 @@ class Initializer_2D(ProcessGroupInitializer):
 
     def init_dist_group(self):
         """Initialize 2D tensor row and col parallel groups, and assign local_ranks and groups to each gpu.
-
+        
         :return: 2D tensor parallelism's information
         :rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
         """

colossalai/context/process_group_initializer/initializer_2p5d.py

@@ -2,22 +2,21 @@
 # -*- encoding: utf-8 -*-
 
 import math
-import os
 
 import torch.distributed as dist
-
-from colossalai.constants import TESSERACT_DIM, TESSERACT_DEP
 from colossalai.context import Config
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.registry import DIST_GROUP_INITIALIZER
-from .process_group_initializer import ProcessGroupInitializer
+
 from ..parallel_mode import ParallelMode
+from .process_group_initializer import ProcessGroupInitializer
 
 
 def _check_tesseract_env_var(tesseract_dim: int,
                              tesseract_dep: int):
-    # check environment variable for TESSERACT
-    env_tesseract_dim = os.environ.get(TESSERACT_DIM, None)
-    env_tesseract_dep = os.environ.get(TESSERACT_DEP, None)
+    # check global variable for TESSERACT
+    env_tesseract_dim = env.tesseract_dim
+    env_tesseract_dep = env.tesseract_dep
 
     if env_tesseract_dim and env_tesseract_dep:
         assert int(env_tesseract_dim) == tesseract_dim, \
@@ -27,8 +26,8 @@ def _check_tesseract_env_var(tesseract_dim: int,
             'TESSERACT_DEP has been set in the current environment and ' \
             'does not match with the value passed to this initialized'
     else:
-        os.environ[TESSERACT_DIM] = str(tesseract_dim)
-        os.environ[TESSERACT_DEP] = str(tesseract_dep)
+        env.tesseract_dim = tesseract_dim
+        env.tesseract_dep = tesseract_dep
 
 
 # i row j col k dep
@@ -245,7 +244,6 @@ class Initializer_2p5D(ProcessGroupInitializer):
     :param pipeline_parallel_size: Size of pipeline parallel
     :param tensor_parallel_size: Size of tensor parallel
     :param depth: The depth of 2p5d parallel
-
     :type rank: int
     :type world_size: int
     :type config: Config
@@ -281,7 +279,7 @@ class Initializer_2p5D(ProcessGroupInitializer):
 
     def init_dist_group(self):
         """Initialize 2p5D tensor row, col, depth, and colXdepth parallel groups, and assign local_ranks and groups to each gpu.
-
+        
         :return: Whole 2p5D tensor parallelism's information
         :rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
         """

colossalai/context/process_group_initializer/initializer_3d.py

@@ -2,10 +2,9 @@
 # -*- encoding: utf-8 -*-
 
 import math
-import os
 
 import torch.distributed as dist
-from colossalai.constants import DEPTH_3D, INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
@@ -13,15 +12,15 @@ from .process_group_initializer import ProcessGroupInitializer
 
 
 def _check_depth_env_var(depth):
-    # check environment variable for SUMMA
-    env_depth = os.environ.get(DEPTH_3D, None)
+    # check global variable
+    env_depth = env.depth_3d
 
     if env_depth:
         assert int(env_depth) == depth, \
             'DEPTH_3D has been set in the current environment and ' \
             'does not match with the value passed to this initialized'
     else:
-        os.environ[DEPTH_3D] = str(depth)
+        env.depth_3d = depth
 
 
 class Initializer_3D_Input(ProcessGroupInitializer):
@@ -34,6 +33,7 @@ class Initializer_3D_Input(ProcessGroupInitializer):
     :type num_group: int
     :type depth: int
     """
+
     def __init__(self, num_group: int, depth: int, *args):
         super().__init__(*args)
         self.num_group = num_group
@@ -50,15 +50,12 @@ class Initializer_3D_Input(ProcessGroupInitializer):
         process_group = None
         group_world_size = None
         mode = ParallelMode.PARALLEL_3D_INPUT
-        os.environ[INPUT_GROUP_3D] = INPUT_GROUP_3D
+        env.input_group_3d = mode
 
         for h in range(self.num_group):
             for i in range(self.depth):
                 for k in range(self.depth):
-                    ranks = [
-                        h * self.depth**3 + i + self.depth *
-                        (j + self.depth * k) for j in range(self.depth)
-                    ]
+                    ranks = [h * self.depth**3 + i + self.depth * (j + self.depth * k) for j in range(self.depth)]
                     group = dist.new_group(ranks)
 
                     if self.rank in ranks:
@@ -97,15 +94,12 @@ class Initializer_3D_Weight(ProcessGroupInitializer):
         process_group = None
         group_world_size = None
         mode = ParallelMode.PARALLEL_3D_WEIGHT
-        os.environ[WEIGHT_GROUP_3D] = WEIGHT_GROUP_3D
+        env.weight_group_3d = mode
 
         for h in range(self.num_group):
             for k in range(self.depth):
                 for j in range(self.depth):
-                    ranks = [
-                        h * self.depth**3 + i + self.depth *
-                        (j + self.depth * k) for i in range(self.depth)
-                    ]
+                    ranks = [h * self.depth**3 + i + self.depth * (j + self.depth * k) for i in range(self.depth)]
                     group = dist.new_group(ranks)
 
                     if self.rank in ranks:
@@ -118,7 +112,7 @@ class Initializer_3D_Weight(ProcessGroupInitializer):
 
 
 class Initializer_3D_Output(ProcessGroupInitializer):
-    """3D tensor parallel initialization among weight.
+    """3D tensor parallel initialization among output.
 
     :param num_group: The number of all tensor groups
     :param depth: Depth of 3D parallelism
@@ -144,15 +138,12 @@ class Initializer_3D_Output(ProcessGroupInitializer):
         process_group = None
         group_world_size = None
         mode = ParallelMode.PARALLEL_3D_OUTPUT
-        os.environ[OUTPUT_GROUP_3D] = OUTPUT_GROUP_3D
+        env.output_group_3d = mode
 
         for h in range(self.num_group):
             for i in range(self.depth):
                 for j in range(self.depth):
-                    ranks = [
-                        h * self.depth**3 + i + self.depth *
-                        (j + self.depth * k) for k in range(self.depth)
-                    ]
+                    ranks = [h * self.depth**3 + i + self.depth * (j + self.depth * k) for k in range(self.depth)]
                     group = dist.new_group(ranks)
 
                     if self.rank in ranks:
@@ -170,6 +161,7 @@ class Initializer_3D(ProcessGroupInitializer):
 
     :param args: Args used to initialize ProcessGroupInitializer
     """
+
     def __init__(self, *args):
         super().__init__(*args)
         self.num_group = self.world_size // self.tensor_parallel_size
@@ -178,16 +170,13 @@ class Initializer_3D(ProcessGroupInitializer):
             f'3D depth ({self.depth}) if not cube root of tensor parallel size ({self.tensor_parallel_size})'
         _check_depth_env_var(self.depth)
 
-        self.input_initializer = Initializer_3D_Input(self.num_group,
-                                                      self.depth, *args)
-        self.weight_initializer = Initializer_3D_Weight(
-            self.num_group, self.depth, *args)
-        self.output_initializer = Initializer_3D_Output(
-            self.num_group, self.depth, *args)
+        self.input_initializer = Initializer_3D_Input(self.num_group, self.depth, *args)
+        self.weight_initializer = Initializer_3D_Weight(self.num_group, self.depth, *args)
+        self.output_initializer = Initializer_3D_Output(self.num_group, self.depth, *args)
 
     def init_dist_group(self):
         """Initialize 3D tensor parallel groups, and assign local_ranks and groups to each gpu.
-
+        
         :return: 3D tensor parallelism's information
         :rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
         """

colossalai/engine/gradient_handler/__init__.py

@@ -9,4 +9,4 @@ from ._sequence_parallel_gradient_handler import SequenceParallelGradientHandler
 
 __all__ = ['BaseGradientHandler', 'DataParallelGradientHandler',
            'ZeROGradientHandler', 'PipelineSharedModuleGradientHandler',
-           'MoeGradientHandler', 'SequenceParallelGradientHandler']
+           'MoeGradientHandler', 'SequenceParallelGradientHandler']

colossalai/engine/schedule/_base_schedule.py

@@ -9,7 +9,6 @@ from typing import Iterable, Callable
 from .._base_engine import Engine
 from colossalai.logging import get_dist_logger
 from colossalai.utils import get_current_device
-from colossalai.nn.layer import split_batch
 
 
 class BaseSchedule(ABC):
@@ -69,7 +68,6 @@ class BaseSchedule(ABC):
             self.batch_size = data.size(0)
         else:
             self.batch_size = next(iter(data.values())).size(0)
-        data, label = split_batch(data), split_batch(label)
         if to_gpu:
             return self._move_to_device(data), self._move_to_device(label)
         return data, label

colossalai/global_variables.py

@@ -1,3 +1,51 @@
+from typing import Optional
+
+
+class TensorParallelEnv(object):
+
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            cls._instance = object.__new__(cls, *args, **kwargs)
+        return cls._instance
+
+    def __init__(self, *args, **kwargs):
+        self.load(*args, **kwargs)
+
+    def load(self,
+             mode: Optional[str] = None,
+             vocab_parallel: bool = False,
+             parallel_input_1d: bool = False,
+             summa_dim: int = None,
+             tesseract_dim: int = None,
+             tesseract_dep: int = None,
+             depth_3d: int = None,
+             input_group_3d=None,
+             weight_group_3d=None,
+             output_group_3d=None):
+        self.mode = mode
+        self.vocab_parallel = vocab_parallel
+        self.parallel_input_1d = parallel_input_1d
+        self.summa_dim = summa_dim
+        self.tesseract_dim = tesseract_dim
+        self.tesseract_dep = tesseract_dep
+        self.depth_3d = depth_3d
+        self.input_group_3d = input_group_3d
+        self.weight_group_3d = weight_group_3d
+        self.output_group_3d = output_group_3d        
+
+    def save(self):
+        return dict(mode=self.mode,
+                    vocab_parallel=self.vocab_parallel,
+                    parallel_input_1d=self.parallel_input_1d,
+                    summa_dim=self.summa_dim,
+                    tesseract_dim=self.tesseract_dim,
+                    tesseract_dep=self.tesseract_dep,
+                    depth_3d=self.depth_3d,
+                    input_group_3d=self.input_group_3d,
+                    weight_group_3d=self.weight_group_3d,
+                    output_group_3d=self.output_group_3d)
 
 
 class MoeEnv:
@@ -33,4 +81,6 @@ class MoeEnv:
         return self.aux_loss
 
 
+tensor_parallel_env = TensorParallelEnv()
+
 moe_env = MoeEnv()

colossalai/kernel/cuda_native/layer_norm.py

@@ -37,17 +37,17 @@ class FusedLayerNormAffineFunction(torch.autograd.Function):
         input_, weight_, bias_, mean, invvar = ctx.saved_tensors
         grad_input = grad_weight = grad_bias = None
         grad_input, grad_weight, grad_bias \
-            = colossal_layer_norm_cuda.backward_affine(
-                grad_output.contiguous(), mean, invvar,
-                input_, ctx.normalized_shape,
-                weight_, bias_, ctx.eps)
+          = colossal_layer_norm_cuda.backward_affine(
+            grad_output.contiguous(), mean, invvar,
+            input_, ctx.normalized_shape,
+            weight_, bias_, ctx.eps)
 
         return grad_input, grad_weight, grad_bias, None, None
 
 
 class MixedFusedLayerNorm(torch.nn.Module):
 
-    def __init__(self, normalized_shape, eps=1e-5):
+    def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None):
         super(MixedFusedLayerNorm, self).__init__()
 
         global colossal_layer_norm_cuda
@@ -61,8 +61,8 @@ class MixedFusedLayerNorm(torch.nn.Module):
             normalized_shape = (normalized_shape,)
         self.normalized_shape = torch.Size(normalized_shape)
         self.eps = eps
-        self.weight = Parameter(torch.Tensor(*normalized_shape))
-        self.bias = Parameter(torch.Tensor(*normalized_shape))
+        self.weight = Parameter(torch.empty(*normalized_shape, device=device, dtype=dtype))
+        self.bias = Parameter(torch.empty(*normalized_shape, device=device, dtype=dtype))
         self.reset_parameters()
 
     def reset_parameters(self):

colossalai/nn/layer/colossalai_layer/__init__.py

@@ -1,7 +1,7 @@
-from ._utils import split_batch
+from ._utils import partition_batch
 from .dropout import Dropout
 from .embedding import Embedding, PatchEmbedding
 from .linear import Classifier, Linear
 from .normalization import LayerNorm
 
-__all__ = ['Linear', 'Classifier', 'Embedding', 'PatchEmbedding', 'LayerNorm', 'Dropout', 'split_batch']
+__all__ = ['Linear', 'Classifier', 'Embedding', 'PatchEmbedding', 'LayerNorm', 'Dropout', 'partition_batch']

colossalai/nn/layer/colossalai_layer/_utils.py

@@ -2,13 +2,13 @@ from torch import Tensor
 
 from ..parallel_2d._operation import split_tensor_2d
 from ..parallel_2p5d._operation import split_tensor_2p5d
-from ..parallel_3d._operation import split_tensor_3d
+from ..parallel_3d._operation import split_batch_3d
 from ..utils import get_tensor_parallel_mode
 
-_parallel_split_batch = {'2d': split_tensor_2d, '2.5d': split_tensor_2p5d, '3d': split_tensor_3d}
+_parallel_split_batch = {'2d': split_tensor_2d, '2.5d': split_tensor_2p5d, '3d': split_batch_3d}
 
 
-def split_batch(input_) -> Tensor:
+def partition_batch(input_) -> Tensor:
     tensor_parallel_mode = get_tensor_parallel_mode()
     if tensor_parallel_mode in _parallel_split_batch:
         if isinstance(input_, dict):

colossalai/nn/layer/colossalai_layer/dropout.py

@@ -1,8 +1,5 @@
-from contextlib import nullcontext
-
 import torch.nn as nn
 from colossalai.context import ParallelMode, seed
-from colossalai.utils import conditional_context
 
 from ..parallel_1d import *
 from ..utils import get_tensor_parallel_mode
@@ -26,6 +23,8 @@ class Dropout(nn.Module):
             self.drop = nn.Dropout(p, inplace)
 
     def forward(self, *args):
-        cm = nullcontext() if self.tensor_parallel in ['None', '1d'] else seed(ParallelMode.TENSOR)
-        with cm:
+        if self.tensor_parallel in [None, '1d']:
             return self.drop(*args)
+        else:
+            with seed(ParallelMode.TENSOR):
+                return self.drop(*args)

colossalai/nn/layer/colossalai_layer/embedding.py

@@ -1,5 +1,5 @@
 import math
-from typing import Callable, Optional
+from typing import Callable
 
 from colossalai.utils import get_current_device
 from torch import dtype, nn
@@ -12,10 +12,21 @@ from ..parallel_3d import *
 from ..utils import get_tensor_parallel_mode
 from ..vanilla import *
 
-_parallel_embedding = {'1d': Embedding1D, '2d': Embedding2D, '2.5d': Embedding2p5D, '3d': Embedding3D}
+_parallel_embedding = {
+    '2d': Embedding2D,
+    '2.5d': Embedding2p5D,
+    '3d': Embedding3D,
+}
+
+_vocab_parallel_embedding = {
+    '1d': VocabParallelEmbedding1D,
+    '2d': VocabParallelEmbedding2D,
+    '2.5d': VocabParallelEmbedding2p5D,
+    '3d': VocabParallelEmbedding3D
+}
 
 _parallel_patchembedding = {
-    'None': VanillaPatchEmbedding,
+    None: VanillaPatchEmbedding,
     '1d': VanillaPatchEmbedding,
     '2d': PatchEmbedding2D,
     '2.5d': PatchEmbedding2p5D,
@@ -40,26 +51,23 @@ class Embedding(nn.Module):
     :param args: Args used in F.embedding
     :param kwargs: Kwargs used in F.embedding
     """
+
     def __init__(self,
                  num_embeddings: int,
                  embedding_dim: int,
                  padding_idx: int = None,
                  dtype: dtype = None,
                  weight_initializer: Callable = init.normal_(),
+                 vocab_parallel_limit: int = 2048,
                  *args,
                  **kwargs) -> None:
         super().__init__()
         tensor_parallel = get_tensor_parallel_mode()
-        if tensor_parallel == 'None':
-            self.embed = nn.Embedding(num_embeddings,
-                                      embedding_dim,
-                                      padding_idx=padding_idx,
-                                      device=get_current_device(),
-                                      dtype=dtype,
-                                      *args,
-                                      **kwargs)
+        if tensor_parallel is None or (tensor_parallel == '1d' and num_embeddings <= vocab_parallel_limit):
+            self.embed = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx, *args,
+                                      **kwargs).to(dtype).to(get_current_device())
             weight_initializer(self.embed.weight, fan_in=num_embeddings, fan_out=embedding_dim)
-        else:
+        elif num_embeddings <= vocab_parallel_limit:
             self.embed = _parallel_embedding[tensor_parallel](
                 num_embeddings,
                 embedding_dim,
@@ -69,6 +77,16 @@ class Embedding(nn.Module):
                 *args,
                 **kwargs,
             )
+        else:
+            self.embed = _vocab_parallel_embedding[tensor_parallel](
+                num_embeddings,
+                embedding_dim,
+                padding_idx=padding_idx,
+                dtype=dtype,
+                weight_initializer=weight_initializer,
+                *args,
+                **kwargs,
+            )
 
     @property
     def weight(self):
@@ -101,16 +119,19 @@ class PatchEmbedding(nn.Module):
     :param position_embed_initializer: The intializer of position embedding, defaults to zero
     :type position_embed_initializer: typing.Callable, optional
     """
-    def __init__(self,
-                 img_size: int,
-                 patch_size: int,
-                 in_chans: int,
-                 embed_size: int,
-                 dtype: dtype = None,
-                 flatten: bool = True,
-                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
-                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
-                 position_embed_initializer: Callable = init.zeros_()) -> None:
+
+    def __init__(
+        self,
+        img_size: int,
+        patch_size: int,
+        in_chans: int,
+        embed_size: int,
+        dtype: dtype = None,
+        flatten: bool = True,
+        weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+        bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
+        position_embed_initializer: Callable = init.zeros_()
+    ) -> None:
         super().__init__()
         tensor_parallel = get_tensor_parallel_mode()
         self.embed = _parallel_patchembedding[tensor_parallel](

colossalai/nn/layer/colossalai_layer/linear.py

@@ -1,7 +1,6 @@
 import math
-from typing import Callable, Optional
+from typing import Callable
 
-from colossalai.nn.layer.parallel_1d.layers import Classifier1D
 from colossalai.utils import get_current_device
 from torch import dtype, nn
 
@@ -16,13 +15,20 @@ from ..vanilla import *
 _parallel_linear = {'1d': Linear1D, '2d': Linear2D, '2.5d': Linear2p5D, '3d': Linear3D}
 
 _parallel_classifier = {
-    'None': VanillaClassifier,
+    None: VanillaClassifier,
     '1d': Classifier1D,
     '2d': Classifier2D,
     '2.5d': Classifier2p5D,
     '3d': Classifier3D
 }
 
+_vocab_parallel_classifier = {
+    '1d': VocabParallelClassifier1D,
+    '2d': VocabParallelClassifier2D,
+    '2.5d': VocabParallelClassifier2p5D,
+    '3d': VocabParallelClassifier3D
+}
+
 
 class Linear(nn.Module):
     """
@@ -40,8 +46,9 @@ class Linear(nn.Module):
     :type weight_initializer: typing.Callable, optional
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
-    :param kwargs: Kwargs used for initialization
+    :param kwargs: Kwargs used for particular parallelisms
     """
+
     def __init__(self,
                  in_features: int,
                  out_features: int,
@@ -52,10 +59,10 @@ class Linear(nn.Module):
                  **kwargs) -> None:
         super().__init__()
         tensor_parallel = get_tensor_parallel_mode()
-        if tensor_parallel == 'None':
-            self.layer = nn.Linear(in_features, out_features, bias=bias, device=get_current_device(), dtype=dtype)
+        if tensor_parallel is None:
+            self.layer = nn.Linear(in_features, out_features, bias=bias).to(dtype).to(get_current_device())
             weight_initializer(self.layer.weight, fan_in=in_features, fan_out=out_features)
-            if bias:
+            if self.layer.bias is not None:
                 bias_initializer(self.layer.bias, fan_in=in_features)
         else:
             self.layer = _parallel_linear[tensor_parallel](
@@ -97,26 +104,38 @@ class Classifier(nn.Module):
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
     """
-    def __init__(
-        self,
-        in_features: int,
-        num_classes: int,
-        weight: nn.Parameter = None,
-        bias: bool = True,
-        dtype: dtype = None,
-        weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
-        bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)
-    ) -> None:
+
+    def __init__(self,
+                 in_features: int,
+                 num_classes: int,
+                 weight: nn.Parameter = None,
+                 bias: bool = True,
+                 dtype: dtype = None,
+                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
+                 vocab_parallel_limit: int = 2048) -> None:
         super().__init__()
-        self.layer = _parallel_classifier[get_tensor_parallel_mode()](
-            in_features,
-            num_classes,
-            weight=weight,
-            bias=bias,
-            dtype=dtype,
-            weight_initializer=weight_initializer,
-            bias_initializer=bias_initializer,
-        )
+        tensor_parallel = get_tensor_parallel_mode()
+        if num_classes <= vocab_parallel_limit or tensor_parallel is None:
+            self.layer = _parallel_classifier[tensor_parallel](
+                in_features,
+                num_classes,
+                weight=weight,
+                bias=bias,
+                dtype=dtype,
+                weight_initializer=weight_initializer,
+                bias_initializer=bias_initializer,
+            )
+        else:
+            self.layer = _vocab_parallel_classifier[tensor_parallel](
+                in_features,
+                num_classes,
+                weight=weight,
+                bias=bias,
+                dtype=dtype,
+                weight_initializer=weight_initializer,
+                bias_initializer=bias_initializer,
+            )
 
     @property
     def weight(self):

colossalai/nn/layer/colossalai_layer/normalization.py

@@ -1,7 +1,6 @@
-from typing import Optional
-
 from colossalai.utils import get_current_device
 from torch import nn
+from colossalai import kernel
 
 from ... import init as init
 from ..parallel_1d import *
@@ -11,7 +10,12 @@ from ..parallel_3d import *
 from ..utils import get_tensor_parallel_mode
 from ..vanilla import *
 
-_parallel_layernorm = {'2d': LayerNorm2D, '2.5d': LayerNorm2p5D, '3d': LayerNorm3D}
+_parallel_layernorm = {
+    '1d': kernel.LayerNorm,
+    '2d': LayerNorm2D,
+    '2.5d': LayerNorm2p5D,
+    '3d': LayerNorm3D
+}
 
 
 class LayerNorm(nn.Module):
@@ -28,11 +32,12 @@ class LayerNorm(nn.Module):
     :param dtype: The dtype of parameters, defaults to None
     :type dtype: torch.dtype, optional
     """
+
     def __init__(self, normalized_shape: int, eps=1e-05, dtype=None) -> None:
         super().__init__()
         tensor_parallel = get_tensor_parallel_mode()
-        if tensor_parallel in ['None', '1d']:
-            self.norm = nn.LayerNorm(normalized_shape, eps=eps, device=get_current_device(), dtype=dtype)
+        if tensor_parallel is None:
+            self.norm = nn.LayerNorm(normalized_shape, eps=eps).to(dtype).to(get_current_device())
         else:
             self.norm = _parallel_layernorm[tensor_parallel](normalized_shape, eps=eps, dtype=dtype)
 

colossalai/nn/layer/parallel_1d/__init__.py

@@ -1,4 +1,7 @@
-from .layers import Dropout1D, Embedding1D, Linear1D, Linear1D_Col, Linear1D_Row
-from .layers import MixedFusedLayerNorm1D as LayerNorm1D
+from .layers import (Classifier1D, Dropout1D, Embedding1D, Linear1D, Linear1D_Col, Linear1D_Row,
+                     VocabParallelClassifier1D, VocabParallelEmbedding1D)
 
-__all__ = ['Linear1D', 'Linear1D_Col', 'Linear1D_Row', 'LayerNorm1D', 'Embedding1D', 'Dropout1D']
+__all__ = [
+    'Linear1D', 'Linear1D_Col', 'Linear1D_Row', 'Embedding1D', 'Dropout1D', 'Classifier1D', 'VocabParallelClassifier1D',
+    'VocabParallelEmbedding1D'
+]

colossalai/nn/layer/parallel_1d/_utils.py

@@ -1,21 +1,20 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
-import os
 
 import torch
 import torch.distributed as dist
-from colossalai.constants import PARALLEL_INPUT_1D
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 
 from ..utils import divide
 
 
 def set_parallel_input(input_parallel: bool):
-    os.environ[PARALLEL_INPUT_1D] = 'true' if input_parallel else ''
+    env.parallel_input_1d = input_parallel
 
 
 def get_parallel_input():
-    return bool(os.environ[PARALLEL_INPUT_1D])
+    return env.parallel_input_1d
 
 
 def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank):

colossalai/nn/layer/parallel_1d/layers.py

@@ -2,8 +2,6 @@
 # -*- encoding: utf-8 -*-
 
 import math
-import numbers
-from contextlib import nullcontext
 from typing import Callable, Tuple
 
 import torch
@@ -11,17 +9,17 @@ import torch.nn.functional as F
 from colossalai.communication import broadcast
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.nn import init as init
 from colossalai.registry import LAYERS
-from colossalai.utils import get_current_device
-from torch import Tensor, dtype
+from colossalai.utils.cuda import get_current_device
+from torch import Tensor
 from torch.nn.parameter import Parameter
 
 from ..base_layer import ParallelLayer
 from ..utils import divide, set_tensor_parallel_attribute_by_partition
-from ._operation import FusedLayerNormAffineFunction1D
-from ._utils import (gather_forward_split_backward, get_parallel_input, reduce_grad, reduce_input, set_parallel_input,
-                     split_forward_gather_backward)
+from ._utils import (gather_forward_split_backward, get_parallel_input, reduce_grad,
+                     reduce_input, set_parallel_input, split_forward_gather_backward)
 
 
 @LAYERS.register_module
@@ -44,6 +42,7 @@ class Linear1D(torch.nn.Module):
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  in_features: int,
                  out_features: int,
@@ -106,12 +105,13 @@ class Classifier1D(ParallelLayer):
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  in_features: int,
                  num_classes: int,
                  weight: Parameter = None,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         super().__init__()
@@ -139,6 +139,7 @@ class Classifier1D(ParallelLayer):
             self.reset_parameters(weight_initializer, bias_initializer)
         self._set_tensor_parallel_attributes()
         set_parallel_input(False)
+        env.vocab_parallel = False
 
     def reset_parameters(self, weight_initializer, bias_initializer) -> None:
         fan_in, fan_out = self.in_features, self.num_classes
@@ -167,6 +168,84 @@ class Classifier1D(ParallelLayer):
         return output
 
 
+@LAYERS.register_module
+class VocabParallelClassifier1D(ParallelLayer):
+    """ColLinear with given weight
+    Classifier of 1D parallelism
+    
+    :param in_features: size of input features
+    :type in_features: int
+    :param num_classes: number of classes in the dataset
+    :type num_classes: int
+    :param weight: weight of the classifier, defaults to True
+    :type weight: torch.nn.Parameter, optional
+    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
+    :type bias: bool, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
+    :type weight_initializer: typing.Callable, optional
+    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
+    :type bias_initializer: typing.Callable, optional
+    """
+
+    def __init__(self,
+                 in_features: int,
+                 num_classes: int,
+                 weight: Parameter = None,
+                 bias: bool = True,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
+        super().__init__()
+        self.in_features = in_features
+        self.num_classes = num_classes
+        self.parallel_input = get_parallel_input()
+
+        # Divide the weight matrix along the last dimension.
+        self.num_classes_per_partition = divide(num_classes, gpc.tensor_parallel_size)
+
+        # Parameters.
+        # Initialize weight.
+        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
+        if weight is not None:
+            self.weight = weight
+            self.has_weight = False
+        else:
+            self.weight = Parameter(torch.empty(self.num_classes_per_partition, self.in_features, **factory_kwargs))
+            self.has_weight = True
+        if bias:
+            self.bias = Parameter(torch.empty(self.num_classes_per_partition, **factory_kwargs))
+        else:
+            self.bias = None
+        with seed(ParallelMode.TENSOR):
+            self.reset_parameters(weight_initializer, bias_initializer)
+        self._set_tensor_parallel_attributes()
+        set_parallel_input(False)
+        env.vocab_parallel = True
+
+    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
+        fan_in, fan_out = self.in_features, self.num_classes
+        if self.has_weight:
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+        if self.bias is not None:
+            bias_initializer(self.bias, fan_in=fan_in)
+
+    def _set_tensor_parallel_attributes(self):
+        num_partition = gpc.get_world_size(ParallelMode.TENSOR)
+        if self.has_weight:
+            set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
+        if self.bias is not None:
+            set_tensor_parallel_attribute_by_partition(self.bias, num_partition)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        # Set up backprop all-reduce.
+        input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D)
+        # Matrix multiply.
+        output = F.linear(input_parallel, self.weight, self.bias)
+        return output
+
+
 @LAYERS.register_module
 class Linear1D_Col(ParallelLayer):
     """Linear layer with column parallelism.
@@ -324,7 +403,7 @@ class Linear1D_Row(ParallelLayer):
         weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
         if self.bias is not None:
             bias_initializer(self.bias, fan_in=fan_in)
-        broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
+            broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
 
     def _set_tensor_parallel_attributes(self):
         num_partition = gpc.get_world_size(ParallelMode.TENSOR)
@@ -341,45 +420,13 @@ class Linear1D_Row(ParallelLayer):
         output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
 
         if not self.skip_bias_add:
-            output = output + self.bias
+            if self.bias is not None:
+                output = output + self.bias
             return output
         else:
             return output, self.bias
 
 
-@LAYERS.register_module
-class MixedFusedLayerNorm1D(torch.nn.Module):
-    r"""
-    Layer Normalization for 1D parallelism
-
-    :param normalized_shape: input shape from an expected input
-        of size. :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
-        If a single integer is used, it is treated as a singleton list, and this module will
-        normalize over the last dimension which is expected to be of that specific size.
-    :type normalized_shape: int
-    :param eps: a value added to the denominator for numerical stability, defaults to 1e-05
-    :type eps: float, optional
-    """
-
-    def __init__(self, normalized_shape, eps=1e-5):
-        super(MixedFusedLayerNorm1D, self).__init__()
-
-        if isinstance(normalized_shape, numbers.Integral):
-            normalized_shape = (normalized_shape, )
-        self.normalized_shape = torch.Size(normalized_shape)
-        self.eps = eps
-        self.weight = Parameter(torch.Tensor(*normalized_shape))
-        self.bias = Parameter(torch.Tensor(*normalized_shape))
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        init.ones_(self.weight)
-        init.zeros_(self.bias)
-
-    def forward(self, input):
-        return FusedLayerNormAffineFunction1D.apply(input, self.weight, self.bias, self.normalized_shape, self.eps)
-
-
 @LAYERS.register_module
 class Embedding1D(ParallelLayer):
     """
@@ -398,11 +445,12 @@ class Embedding1D(ParallelLayer):
     :param args: Args used in F.embedding
     :param kwargs: Kwargs used in F.embedding
     """
+
     def __init__(self,
                  num_embeddings: int,
                  embedding_dim: int,
                  padding_idx: int = None,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.normal_(),
                  *args,
                  **kwargs):
@@ -446,6 +494,84 @@ class Embedding1D(ParallelLayer):
         return output
 
 
+@LAYERS.register_module
+class VocabParallelEmbedding1D(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    :param num_embeddings: number of embeddings
+    :type num_embeddings: int
+    :param embedding_dim: dimension of embedding
+    :type embedding_dim: int
+    :param padding_idx: index of padding, defaults to None
+    :type padding_idx: int, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to normal initializer
+    :type weight_initializer: typing.Callable, optional
+    :param args: Args used in F.embedding
+    :param kwargs: Kwargs used in F.embedding
+    """
+
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 padding_idx: int = None,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.normal_(),
+                 *args,
+                 **kwargs):
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embed_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.embed_args = args
+        self.embed_kwargs = kwargs
+
+        tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
+        tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+        self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size)
+        self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
+        self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
+
+        self.weight = Parameter(
+            torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=get_current_device(), dtype=dtype))
+
+        self.reset_parameters(weight_initializer)
+        self._set_tensor_parallel_attributes()
+        set_parallel_input(False)
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self):
+        set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size)
+
+    def reset_parameters(self, weight_initializer) -> None:
+        with seed(ParallelMode.TENSOR):
+            fan_in, fan_out = self.num_embeddings, self.embed_dim
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+            self._fill_padding_idx_with_zero()
+
+    def _fill_padding_idx_with_zero(self) -> None:
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.weight[self.padding_idx].fill_(0)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        # Build the mask.
+        input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
+        # Mask the input.
+        masked_input = input_.clone() - self.vocab_start_index
+        masked_input[input_mask] = 0
+
+        output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
+                                      **self.embed_kwargs)
+
+        # Mask the output embedding.
+        output_parallel[input_mask, :] = 0.
+        # Reduce across all the model parallel GPUs.
+        output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
+        return output
+
+
 @LAYERS.register_module
 class Dropout1D(ParallelLayer):
     """
@@ -456,6 +582,7 @@ class Dropout1D(ParallelLayer):
     :param inplace: If set to ``True``, will do this operation in-place, defaults tp ``False``
     :type inplace: bool, optional
     """
+
     def __init__(self, p: float = 0.5, inplace: bool = False):
         super().__init__()
         self.parallel_input = get_parallel_input()
@@ -463,7 +590,9 @@ class Dropout1D(ParallelLayer):
         self.inplace = inplace
 
     def forward(self, input_: Tensor) -> Tensor:
-        cm = nullcontext() if not self.parallel_input else seed(ParallelMode.TENSOR)
-        with cm:
+        if self.parallel_input:
+            with seed(ParallelMode.TENSOR):
+                output = F.dropout(input_, self.p, self.training, self.inplace)
+        else:
             output = F.dropout(input_, self.p, self.training, self.inplace)
         return output

colossalai/nn/layer/parallel_2d/__init__.py

@@ -1,6 +1,8 @@
 from ._operation import reduce_by_batch_2d, split_tensor_2d
-from .layers import Classifier2D, Embedding2D, LayerNorm2D, Linear2D, PatchEmbedding2D
+from .layers import (Classifier2D, Embedding2D, LayerNorm2D, Linear2D, PatchEmbedding2D, VocabParallelClassifier2D,
+                     VocabParallelEmbedding2D)
 
 __all__ = [
-    'split_tensor_2d', 'reduce_by_batch_2d', 'Linear2D', 'LayerNorm2D', 'Classifier2D', 'PatchEmbedding2D', 'Embedding2D'
+    'split_tensor_2d', 'reduce_by_batch_2d', 'Linear2D', 'LayerNorm2D', 'Classifier2D', 'PatchEmbedding2D',
+    'Embedding2D', 'VocabParallelEmbedding2D', 'VocabParallelClassifier2D'
 ]

colossalai/nn/layer/parallel_2d/_operation.py

@@ -8,6 +8,7 @@ from colossalai.core import global_context as gpc
 from colossalai.utils import get_current_device
 from torch import Tensor
 from torch.cuda.amp import custom_bwd, custom_fwd
+from colossalai.global_variables import tensor_parallel_env as env
 
 
 def matmul_2d(
@@ -22,6 +23,7 @@ def matmul_2d(
 ):
     """
     Matrix multiplication for 2D parallelism
+
     :param a: matrix :math:`A`
     :type a: torch.tensor
     :param b: matrix :math:`B`
@@ -56,37 +58,7 @@ def matmul_2d(
                         data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size, tensor_parallel_size)
 
 
-class classifier_2d(torch.autograd.Function):
-    """
-    Classifier
-
-    :param a: matrix :math:`A`
-    :type a: torch.tensor
-    :param b: matrix :math:`B`
-    :type b: torch.tensor
-    :param bias: matrix of bias
-    :type bias: torch.tensor, optional
-    :param summa_dim: dimension of SUMMA fo 2D parallelism
-    :type summa_dim: int
-    :param out_shape: shape of output tensor
-    :type out_shape: tuple
-    :param row_rank: the rank of row
-    :type row_rank: int
-    :param col_rank: the rank of column
-    :type col_rank: int
-    :param row_parallel_mode: row parallel mode
-    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param col_parallel_mode: column parallel mode
-    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param data_parallel_rank: data parallel rank
-    :type data_parallel_rank: int
-    :param pipeline_parallel_rank: pipeline parallel rank
-    :type pipeline_parallel_rank: int
-    :param pipeline_parallel_size: pipeline parallel size
-    :type pipeline_parallel_size: int
-    :param tensor_parallel_size: tensor parallel size
-    :type tensor_parallel_size: int
-    """
+class _Classifier2D(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(
@@ -150,14 +122,54 @@ class classifier_2d(torch.autograd.Function):
             B_grad = torch.matmul(output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), A)
             B_grad = reduce_scatter(B_grad, -1, ctx.col_parallel_mode)
             B_grad = B_grad.reshape(ctx.B_shape)
-            bias_grad = None
             if ctx.use_bias:
                 bias_grad = torch.sum(output_grad, dim=tuple(range(output_grad.ndim - 1)))
                 bias_grad = all_reduce(bias_grad, ctx.col_parallel_mode)
+            else:
+                bias_grad = None
 
         return A_grad, B_grad, bias_grad, None, None, None, None, None, None, None, None, None, None
 
 
+def classifier_2d(A: Tensor, B: Tensor, bias: Optional[Tensor], summa_dim: int, out_shape: Tuple[int, ...],
+                  row_rank: int, col_rank: int, row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode,
+                  data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
+                  tensor_parallel_size: int) -> Tensor:
+    """
+    2D parallel classifier
+
+    :param a: matrix :math:`A`
+    :type a: torch.tensor
+    :param b: matrix :math:`B`
+    :type b: torch.tensor
+    :param bias: matrix of bias
+    :type bias: torch.tensor, optional
+    :param summa_dim: dimension of SUMMA fo 2D parallelism
+    :type summa_dim: int
+    :param out_shape: shape of output tensor
+    :type out_shape: tuple
+    :param row_rank: the rank of row
+    :type row_rank: int
+    :param col_rank: the rank of column
+    :type col_rank: int
+    :param row_parallel_mode: row parallel mode
+    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param col_parallel_mode: column parallel mode
+    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param data_parallel_rank: data parallel rank
+    :type data_parallel_rank: int
+    :param pipeline_parallel_rank: pipeline parallel rank
+    :type pipeline_parallel_rank: int
+    :param pipeline_parallel_size: pipeline parallel size
+    :type pipeline_parallel_size: int
+    :param tensor_parallel_size: tensor parallel size
+    :type tensor_parallel_size: int
+    """
+    return _Classifier2D.apply(A, B, bias, summa_dim, out_shape, row_rank, col_rank, row_parallel_mode,
+                               col_parallel_mode, data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size,
+                               tensor_parallel_size)
+
+
 class Matmul_AB_2D(torch.autograd.Function):
     """
     Matrix multiplication for :math:`C = AB`
@@ -230,9 +242,9 @@ class Matmul_AB_2D(torch.autograd.Function):
         col_group = gpc.get_group(col_parallel_mode)
 
         src_a = summa_dim * row_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
-            pipeline_parallel_rank * tensor_parallel_size
+                pipeline_parallel_rank * tensor_parallel_size
         src_b = col_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
-            pipeline_parallel_rank * tensor_parallel_size
+                pipeline_parallel_rank * tensor_parallel_size
 
         opa = [None] * 2
         opb = [None] * 2
@@ -361,9 +373,9 @@ class Matmul_ABT_2D(torch.autograd.Function):
         col_group = gpc.get_group(col_parallel_mode)
 
         src_b = col_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
-            pipeline_parallel_rank * tensor_parallel_size
+                pipeline_parallel_rank * tensor_parallel_size
         src_c = summa_dim * row_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
-            pipeline_parallel_rank * tensor_parallel_size
+                pipeline_parallel_rank * tensor_parallel_size
 
         opb = [None] * 2
         opr = [None] * 2
@@ -501,9 +513,9 @@ class Matmul_ATB_2D(torch.autograd.Function):
         col_group = gpc.get_group(col_parallel_mode)
 
         src_a = summa_dim * row_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
-            pipeline_parallel_rank * tensor_parallel_size
+                pipeline_parallel_rank * tensor_parallel_size
         src_c = col_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
-            pipeline_parallel_rank * tensor_parallel_size
+                pipeline_parallel_rank * tensor_parallel_size
 
         opa = [None] * 2
         opr = [None] * 2
@@ -572,35 +584,7 @@ class Matmul_ATB_2D(torch.autograd.Function):
         return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None
 
 
-class add_bias_2d(torch.autograd.Function):
-    """
-    Matrix add bias: :math:`C = A + b`
-
-    :param input_: matrix :math:`A`
-    :type input_: torch.tensor
-    :param bias: matrix :math:`b`
-    :type bias: torch.tensor
-    :param output_size_per_partition: size of ouput per partition
-    :type output_size_per_partition: int
-    :param row_rank: the rank of row
-    :type row_rank: int
-    :param col_rank: the rank of column
-    :type col_rank: int
-    :param row_parallel_mode: row parallel mode
-    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param col_parallel_mode: column parallel mode
-    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion
-    :type skip_bias_add: bool
-    :param data_parallel_rank: data parallel rank
-    :type data_parallel_rank: int
-    :param pipeline_parallel_rank: pipeline parallel rank
-    :type pipeline_parallel_rank: int
-    :param pipeline_parallel_size: pipeline parallel size
-    :type pipeline_parallel_size: int
-    :param tensor_parallel_size: tensor parallel size
-    :type tensor_parallel_size: int
-    """
+class _Add_Bias_2D(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(
@@ -651,31 +635,47 @@ class add_bias_2d(torch.autograd.Function):
             return output_grad, grad, None, None, None, None, None, None, None, None, None, None
 
 
-class layernorm_2d(torch.autograd.Function):
+def add_bias_2d(input_: Tensor, bias: Tensor, output_size_per_partition: int, row_rank: int, col_rank: int,
+                row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode, skip_bias_add: bool,
+                data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
+                tensor_parallel_size: int) -> Tensor:
     """
-    Layernorm
+    Matrix add bias: :math:`C = A + b`
 
-    :param input_: input maxtrix
+    :param input_: matrix :math:`A`
     :type input_: torch.tensor
-    :param E_x: mean
-    :type E_x: torch.tensor
-    :param Var_x: variance
-    :type Var_x: torch.tensor
-    :param hidden_size: hidden size
-    :type hidden_size: int
+    :param bias: matrix :math:`b`
+    :type bias: torch.tensor
+    :param output_size_per_partition: size of ouput per partition
+    :type output_size_per_partition: int
+    :param row_rank: the rank of row
+    :type row_rank: int
+    :param col_rank: the rank of column
+    :type col_rank: int
     :param row_parallel_mode: row parallel mode
     :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     :param col_parallel_mode: column parallel mode
     :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion
+    :type skip_bias_add: bool
+    :param data_parallel_rank: data parallel rank
+    :type data_parallel_rank: int
+    :param pipeline_parallel_rank: pipeline parallel rank
+    :type pipeline_parallel_rank: int
+    :param pipeline_parallel_size: pipeline parallel size
+    :type pipeline_parallel_size: int
+    :param tensor_parallel_size: tensor parallel size
+    :type tensor_parallel_size: int
     """
+    return _Add_Bias_2D.apply(input_, bias, output_size_per_partition, row_rank, col_rank, row_parallel_mode,
+                              col_parallel_mode, skip_bias_add, data_parallel_rank, pipeline_parallel_rank,
+                              pipeline_parallel_size, tensor_parallel_size)
+
+
+class _Layernorm_2D(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float32)
-    def forward(ctx: Any, 
-                input_: Tensor, 
-                E_x: Tensor, 
-                Var_x: Tensor, 
-                hidden_size: int, 
-                row_parallel_mode: ParallelMode,
+    def forward(ctx: Any, input_: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int, row_parallel_mode: ParallelMode,
                 col_parallel_mode: ParallelMode) -> Tensor:
         input_ = input_ - E_x
         # in here, input = x - E[x], Var_x = 1 / sqrt(Var[x] + eps)
@@ -709,76 +709,64 @@ class layernorm_2d(torch.autograd.Function):
         return input_grad, None, None, None, None, None
 
 
-class all_gather_weight_2d(torch.autograd.Function):
+def layernorm_2d(input_: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int, row_parallel_mode: ParallelMode,
+                 col_parallel_mode: ParallelMode) -> Tensor:
     """
-    all gather the weight of 2D parallelism
+    Layernorm
 
-    :param inputs: input maxtrix
-    :type inputs: torch.tensor
-    :param dim: dimension of all gather
-    :type dim: int
-    :param summa_dim: dimension of SUMMA fo 2D parallelism
-    :type summa_dim: int
+    :param input_: input maxtrix
+    :type input_: torch.tensor
+    :param E_x: mean
+    :type E_x: torch.tensor
+    :param Var_x: variance
+    :type Var_x: torch.tensor
+    :param hidden_size: hidden size
+    :type hidden_size: int
+    :param row_parallel_mode: row parallel mode
+    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     :param col_parallel_mode: column parallel mode
     :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     """
+    return _Layernorm_2D.apply(input_, E_x, Var_x, hidden_size, row_parallel_mode, col_parallel_mode)
+
+
+class _AllGatherTensor2D(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
-    def forward(ctx: Any, inputs: Tensor, dim: int, summa_dim: int, col_parallel_mode: ParallelMode) -> Tensor:
+    def forward(ctx: Any, inputs: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
         ctx.dim = dim
-        ctx.summa_dim = summa_dim
-        ctx.row_rank = gpc.get_local_rank(col_parallel_mode)
+        ctx.parallel_mode = parallel_mode
 
-        outputs = all_gather(inputs, dim, col_parallel_mode)
+        outputs = all_gather(inputs, dim, parallel_mode)
         return outputs
 
     @staticmethod
     @custom_bwd
     def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
-        grad = output_grad.chunk(ctx.summa_dim, dim=ctx.dim)[ctx.row_rank]
-        return grad.contiguous(), None, None, None
+        grad = reduce_scatter(output_grad, ctx.dim, ctx.parallel_mode)
+        return grad.contiguous(), None, None
 
 
-class SplitFirst(torch.autograd.Function):
+def all_gather_tensor_2d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
     """
+    All gather the tensor of 2D parallelism
+
     :param inputs: input maxtrix
     :type inputs: torch.tensor
-    :param summa_dim: dimension of SUMMA fo 2D parallelism
-    :type summa_dim: int
-    :param col_parallel_mode: column parallel mode
-    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param dim: dimension to gather
+    :type dim: int
+    :param parallel_mode: parallel mode
+    :type parallel_mode: colossalai.context.parallel_mode.ParallelMode
     """
-    @staticmethod
-    @custom_fwd(cast_inputs=torch.float16)
-    def forward(ctx: Any, inputs: Tensor, summa_dim: int, col_parallel_mode: ParallelMode) -> Tensor:
-        ctx.summa_dim = summa_dim
-        ctx.batch_size = inputs.size(0)
-        ctx.para_mode = col_parallel_mode
-        row_rank = gpc.get_local_rank(col_parallel_mode)
-
-        outputs = inputs.chunk(summa_dim, dim=0)[row_rank]
-        return outputs
-
-    @staticmethod
-    @custom_bwd
-    def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
-        grad_shape = (ctx.batch_size, ) + output_grad.shape[1:]
-        grad = torch.empty(grad_shape, dtype=output_grad.dtype, device=get_current_device())
-        dist.all_gather(list(grad.chunk(ctx.summa_dim, dim=0)),
-                        output_grad.contiguous(),
-                        group=gpc.get_group(ctx.para_mode))
-        return grad, None, None
+    return _AllGatherTensor2D.apply(tensor, dim, parallel_mode)
 
 
 def split_tensor_2d(input_: Tensor, dim: int = 0) -> Tensor:
     """Splits 2D tensor in specified dimension across cols
-
     :param input_: Input tensor
     :param dim: Specified dimension in which to split
-
     :type input_: torch.Tensor
     :type dim: int, optional
-
     :return output: Splitted tensor
     :rtype output: torch.Tensor
     """
@@ -788,9 +776,50 @@ def split_tensor_2d(input_: Tensor, dim: int = 0) -> Tensor:
                        dim=dim)[gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)].contiguous()
 
 
-class reduce_by_batch_2d(torch.autograd.Function):
-    """All-reduce the input from the model parallel region.
+class _ReduceTensor2D(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input_, parallel_mode):
+        return all_reduce(input_, parallel_mode)
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        return output_grad, None
+
+
+def reduce_tensor_2d(input_: Tensor, parallel_mode: ParallelMode) -> Tensor:
     """
+    All-reduce the input.
+    
+    :param input_: input tensor
+    :param parallel_mode: parallel mode
+    """
+    return _ReduceTensor2D.apply(input_, parallel_mode)
+
+
+class _ReduceScatterTensor2D(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input_, dim, parallel_mode):
+        ctx.dim = dim
+        ctx.parallel_mode = parallel_mode
+        return reduce_scatter(input_, dim, parallel_mode)
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        return all_gather(output_grad, ctx.dim, ctx.parallel_mode), None, None
+
+
+def reduce_scatter_tensor_2d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
+    """
+    Reduce-scatter the input.
+    
+    :param tensor: Input tensor
+    :param dim: Dimension to scatter
+    :param parallel_mode: Parallel mode
+    """
+    return _ReduceScatterTensor2D.apply(tensor, dim, parallel_mode)
+
+
+class _ReduceByBatch2D(torch.autograd.Function):
     @staticmethod
     def symbolic(graph, input_, reduce_mean: bool = False):
         output = all_reduce(input_, ParallelMode.PARALLEL_2D_COL)
@@ -802,12 +831,6 @@ class reduce_by_batch_2d(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float32)
     def forward(ctx, input_, reduce_mean: bool = False):
-        """
-        :param input_: input maxtrix
-        :type input_: torch.tensor
-        :param reduce_mean:  If set to ``True``, it will divide the output by column parallel size, default to False
-        :type reduce_mean: int, optional
-        """
         output = all_reduce(input_, ParallelMode.PARALLEL_2D_COL)
         ctx.reduce_mean = reduce_mean
         if reduce_mean:
@@ -823,3 +846,14 @@ class reduce_by_batch_2d(torch.autograd.Function):
             return output_grad / ctx.reduce_size, None
         else:
             return output_grad, None
+
+
+def reduce_by_batch_2d(input_, reduce_mean: bool = False) -> Tensor:
+    """All-reduce the input from the model parallel region.
+
+    :param input_: input maxtrix
+    :type input_: torch.tensor
+    :param reduce_mean:  If set to ``True``, it will divide the output by column parallel size, default to False
+    :type reduce_mean: bool, optional
+    """
+    return _ReduceByBatch2D.apply(input_, reduce_mean)

colossalai/nn/layer/parallel_2d/_utils.py

@@ -1,14 +1,11 @@
-import os
-
 from colossalai.context.parallel_mode import ParallelMode
-from colossalai.context.process_group_initializer.initializer_2d import SUMMA_DIM
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 
 
 def get_summa_dim_from_env() -> int:
     try:
-        summa_dim = os.environ[SUMMA_DIM]
-        summa_dim = int(summa_dim)
+        summa_dim = env.summa_dim
         assert summa_dim > 0, 'SUMMA_DIM must be larger than zero'
         return summa_dim
 

colossalai/nn/layer/parallel_2d/layers.py

@@ -7,15 +7,16 @@ import torch.nn.functional as F
 from colossalai.communication import broadcast
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.nn import init as init
 from colossalai.registry import LAYERS
-from colossalai.utils import get_current_device
-from torch import Tensor, dtype
+from colossalai.utils.cuda import get_current_device
+from torch import Tensor
 from torch.nn import Parameter
 
-from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
 from ..base_layer import ParallelLayer
-from ._operation import Matmul_AB_2D, add_bias_2d, all_gather_weight_2d, classifier_2d, layernorm_2d
+from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
+from ._operation import *
 from ._utils import assert_summa_initialization, get_summa_dim_from_env
 
 
@@ -43,7 +44,7 @@ class Linear2D(ParallelLayer):
                  in_features: int,
                  out_features: int,
                  bias: bool = True,
-                 dtype=None,
+                 dtype: torch.dtype = None,
                  skip_bias_add: bool = False,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
@@ -101,16 +102,16 @@ class Linear2D(ParallelLayer):
 
         if self.bias is not None:
             if self.skip_bias_add:
-                bias = add_bias_2d.apply(None, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
-                                         ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
-                                         self.data_parallel_rank, self.pipeline_parallel_rank,
-                                         self.pipeline_parallel_size, self.tensor_parallel_size)
+                bias = add_bias_2d(None, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
+                                   ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
+                                   self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                                   self.tensor_parallel_size)
                 return output, bias
             else:
-                output = add_bias_2d.apply(output, self.bias, self.hidden_size_per_partition, self.row_rank,
-                                           self.col_rank, ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
-                                           False, self.data_parallel_rank, self.pipeline_parallel_rank,
-                                           self.pipeline_parallel_size, self.tensor_parallel_size)
+                output = add_bias_2d(output, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
+                                     ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, False,
+                                     self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                                     self.tensor_parallel_size)
                 return output
         else:
             return output
@@ -174,16 +175,14 @@ class LayerNorm2D(ParallelLayer):
             # this time 1/sqrt(Var_x + epsilon)
             Var_x = 1.0 / torch.sqrt(Var_x + self.variance_epsilon)
 
-        output = layernorm_2d.apply(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2D_ROW,
-                                    ParallelMode.PARALLEL_2D_COL)
-        bias = add_bias_2d.apply(None, self.beta, self.partitioned_partition, self.row_rank, self.col_rank,
-                                 ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
-                                 self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                 self.tensor_parallel_size)
-        scale = add_bias_2d.apply(None, self.gamma, self.partitioned_partition, self.row_rank, self.col_rank,
-                                  ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
-                                  self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                  self.tensor_parallel_size)
+        output = layernorm_2d(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2D_ROW,
+                              ParallelMode.PARALLEL_2D_COL)
+        bias = add_bias_2d(None, self.beta, self.partitioned_partition, self.row_rank, self.col_rank,
+                           ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True, self.data_parallel_rank,
+                           self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
+        scale = add_bias_2d(None, self.gamma, self.partitioned_partition, self.row_rank, self.col_rank,
+                            ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True, self.data_parallel_rank,
+                            self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
         output = torch.addcmul(bias, scale, output)
         return output
 
@@ -217,8 +216,8 @@ class PatchEmbedding2D(ParallelLayer):
                  patch_size: int,
                  in_chans: int,
                  embed_size: int,
-                 dtype: dtype = None,
                  flatten: bool = True,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                  position_embed_initializer: Callable = init.zeros_()):
@@ -268,19 +267,21 @@ class PatchEmbedding2D(ParallelLayer):
             position_embed_initializer(self.pos_embed)
 
     def forward(self, input_: Tensor) -> Tensor:
+        input_ = split_tensor_2d(input_)
+
         B, C, H, W = input_.shape
         assert H == self.img_size[0] and W == self.img_size[1], \
             f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
 
-        weight = all_gather_weight_2d.apply(self.weight, 0, self.summa_dim, ParallelMode.PARALLEL_2D_COL)
-        bias = all_gather_weight_2d.apply(self.bias, 0, self.summa_dim, ParallelMode.PARALLEL_2D_COL)
+        weight = all_gather_tensor_2d(self.weight, 0, ParallelMode.PARALLEL_2D_COL)
+        bias = all_gather_tensor_2d(self.bias, 0, ParallelMode.PARALLEL_2D_COL)
 
         output = F.conv2d(input_, weight, bias, stride=self.patch_size)
         if self.flatten:
             output = output.flatten(2).transpose(1, 2)  # BCHW -> BNC
 
-        cls_token = all_gather_weight_2d.apply(self.cls_token, -1, self.summa_dim, ParallelMode.PARALLEL_2D_COL)
-        pos_embed = all_gather_weight_2d.apply(self.pos_embed, -1, self.summa_dim, ParallelMode.PARALLEL_2D_COL)
+        cls_token = all_gather_tensor_2d(self.cls_token, -1, ParallelMode.PARALLEL_2D_COL)
+        pos_embed = all_gather_tensor_2d(self.pos_embed, -1, ParallelMode.PARALLEL_2D_COL)
         cls_token = cls_token.expand(output.shape[0], -1, -1)
         output = torch.cat((cls_token, output), dim=1)
         output = output + pos_embed
@@ -310,7 +311,7 @@ class Embedding2D(ParallelLayer):
                  num_embeddings: int,
                  embedding_dim: int,
                  padding_idx: int = None,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.normal_(),
                  *args,
                  **kwargs):
@@ -347,13 +348,90 @@ class Embedding2D(ParallelLayer):
                 self.weight[self.padding_idx].fill_(0)
 
     def forward(self, input_: Tensor) -> Tensor:
-        weight = all_gather_weight_2d.apply(self.weight, -1, self.summa_dim, ParallelMode.PARALLEL_2D_COL)
+        input_ = split_tensor_2d(input_)
 
+        weight = all_gather_tensor_2d(self.weight, -1, ParallelMode.PARALLEL_2D_COL)
         output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
 
         return output
 
 
+@LAYERS.register_module
+class VocabParallelEmbedding2D(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    :param num_embeddings: number of embeddings
+    :type num_embeddings: int
+    :param embedding_dim: dimension of embedding
+    :type embedding_dim: int
+    :param padding_idx: index of padding, defaults to None
+    :type padding_idx: int, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to normal initializer
+    :type weight_initializer: typing.Callable, optional
+    :param args: Args used in F.embedding
+    :param kwargs: Kwargs used in F.embedding
+    """
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 padding_idx: int = None,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.normal_(),
+                 *args,
+                 **kwargs):
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embed_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.embed_args = args
+        self.embed_kwargs = kwargs
+
+        assert_summa_initialization()
+        self.summa_dim = get_summa_dim_from_env()
+        self.num_embeddings_per_partition = divide(self.num_embeddings, self.summa_dim)
+        self.embed_dim_per_partition = divide(self.embed_dim, self.summa_dim)
+        tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+        self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
+        self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
+
+        self.weight = Parameter(
+            torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
+                        device=get_current_device(),
+                        dtype=dtype))
+
+        self.reset_parameters(weight_initializer)
+        self._set_tensor_parallel_attributes()
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self):
+        set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
+
+    def reset_parameters(self, weight_initializer) -> None:
+        with seed(ParallelMode.TENSOR):
+            fan_in, fan_out = self.num_embeddings, self.embed_dim
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+            self._fill_padding_idx_with_zero()
+
+    def _fill_padding_idx_with_zero(self) -> None:
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.weight[self.padding_idx].fill_(0)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
+        masked_input = input_.clone() - self.vocab_start_index
+        masked_input[input_mask] = 0
+
+        output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
+                                      **self.embed_kwargs)
+
+        output_parallel[input_mask, :] = 0.
+        output = reduce_scatter_tensor_2d(output_parallel, 0, ParallelMode.PARALLEL_2D_COL)
+        return output
+
+
 @LAYERS.register_module
 class Classifier2D(ParallelLayer):
     """
@@ -379,7 +457,7 @@ class Classifier2D(ParallelLayer):
                  num_classes: int,
                  weight: Parameter = None,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         super().__init__()
@@ -429,7 +507,101 @@ class Classifier2D(ParallelLayer):
     def forward(self, input_: Tensor) -> Tensor:
         out_shape = input_.shape[:-1] + (self.num_classes, )
 
-        return classifier_2d.apply(input_, self.weight, self.bias, self.summa_dim, out_shape, self.row_rank,
-                                   self.col_rank, ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
-                                   self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                   self.tensor_parallel_size)
+        return classifier_2d(input_, self.weight, self.bias, self.summa_dim, out_shape, self.row_rank, self.col_rank,
+                             ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
+                             self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
+
+
+@LAYERS.register_module
+class VocabParallelClassifier2D(ParallelLayer):
+    """
+    Vocab parallel classifier layer for 2D parallelism
+
+    :param in_features: size of each input sample
+    :type in_features: int
+    :param num_classes: number of classes
+    :type num_classes: int
+    :param weight: weight of the classifier, defaults to True
+    :type weight: torch.nn.Parameter, optional
+    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
+    :type bias: bool, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
+    :type weight_initializer: typing.Callable, optional
+    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
+    :type bias_initializer: typing.Callable, optional
+    """
+    def __init__(self,
+                 in_features: int,
+                 num_classes: int,
+                 weight: Parameter = None,
+                 bias: bool = True,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
+        super().__init__()
+
+        self.in_features = in_features
+        self.num_classes = num_classes
+
+        # parallel setting
+        assert_summa_initialization()
+        self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+        self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+        self.summa_dim = get_summa_dim_from_env()
+
+        # partitioning dimension
+        self.input_size_per_partition = divide(in_features, self.summa_dim)
+        self.output_size_per_partition = divide(num_classes, self.summa_dim)
+
+        # create weight, shape: [k/q, h/q]
+        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
+        if weight is not None:
+            self.weight = weight
+            self.has_weight = False
+        else:
+            self.weight = Parameter(
+                torch.empty(self.output_size_per_partition, self.input_size_per_partition, **factory_kwargs))
+            self.has_weight = True
+        # create bias, shape: [h/q]
+        if bias:
+            self.bias = Parameter(torch.empty(divide(self.num_classes, self.summa_dim**2), **factory_kwargs))
+        else:
+            self.bias = None
+
+        # initialize parameters
+        with seed(ParallelMode.TENSOR):
+            self.reset_parameters(weight_initializer, bias_initializer)
+        self._set_tensor_parallel_attributes()
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self):
+        if self.has_weight:
+            set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
+        if self.bias is not None:
+            set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
+
+    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
+        fan_in, fan_out = self.in_features, self.num_classes
+        if self.has_weight:
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+        if self.bias is not None:
+            bias_initializer(self.bias, fan_in=fan_in)
+
+    def forward(self, x: Tensor) -> Tensor:
+        # input: [m/q, n/q, k/q]
+        # output: [m/q, n/q, h/q]
+        out_shape = x.shape[:-1] + (self.output_size_per_partition, )
+
+        output = Matmul_ABT_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
+                                     ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
+                                     self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                                     self.tensor_parallel_size)
+
+        if self.bias is not None:
+            output = add_bias_2d(output, self.bias, self.output_size_per_partition, self.row_rank, self.col_rank,
+                                 ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, False,
+                                 self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                                 self.tensor_parallel_size)
+        return output

colossalai/nn/layer/parallel_2p5d/__init__.py

@@ -1,7 +1,8 @@
 from ._operation import reduce_by_batch_2p5d, split_tensor_2p5d
-from .layers import Classifier2p5D, Embedding2p5D, LayerNorm2p5D, Linear2p5D, PatchEmbedding2p5D
+from .layers import (Classifier2p5D, Embedding2p5D, LayerNorm2p5D, Linear2p5D, PatchEmbedding2p5D,
+                     VocabParallelClassifier2p5D, VocabParallelEmbedding2p5D)
 
 __all__ = [
     'split_tensor_2p5d', 'reduce_by_batch_2p5d', 'Linear2p5D', 'LayerNorm2p5D', 'Classifier2p5D', 'PatchEmbedding2p5D',
-    'Embedding2p5D'
+    'Embedding2p5D', 'VocabParallelClassifier2p5D', 'VocabParallelEmbedding2p5D'
 ]

colossalai/nn/layer/parallel_2p5d/_operation.py

@@ -22,42 +22,7 @@ def get_parallel_rank(parallel_mode: ParallelMode):
     return gpc.get_local_rank(parallel_mode)
 
 
-def split_tensor_2p5d(input_: Tensor, dim: int = 0) -> Tensor:
-    return torch.chunk(input_, gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL),
-                       dim=dim)[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)].contiguous()
-
-
-class classifier_2p5d(torch.autograd.Function):
-    """
-    Classifier
-
-    :param a: matrix :math:`A`
-    :type a: torch.tensor
-    :param b: matrix :math:`B`
-    :type b: torch.tensor
-    :param bias: matrix of bias
-    :type bias: torch.tensor, optional
-    :param tesseract_dim: dimension of TESSERACT fo 2.5D parallelism
-    :type tesseract_dim: int
-    :param out_shape: shape of output tensor
-    :type out_shape: tuple
-    :param row_rank: the rank of row
-    :type row_rank: int
-    :param col_rank: the rank of column
-    :type col_rank: int
-    :param row_parallel_mode: row parallel mode
-    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param col_parallel_mode: column parallel mode
-    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param data_parallel_rank: data parallel rank
-    :type data_parallel_rank: int
-    :param pipeline_parallel_rank: pipeline parallel rank
-    :type pipeline_parallel_rank: int
-    :param pipeline_parallel_size: pipeline parallel size
-    :type pipeline_parallel_size: int
-    :param tensor_parallel_size: tensor parallel size
-    :type tensor_parallel_size: int
-    """
+class _Classifier2p5D(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(
@@ -122,12 +87,54 @@ class classifier_2p5d(torch.autograd.Function):
             B_grad = reduce_scatter(B_grad, -1, ctx.col_parallel_mode)
             B_grad = B_grad.reshape(ctx.B_shape)
 
-            bias_grad = torch.sum(output_grad, dim=tuple(range(output_grad.ndim - 1)))
-            bias_grad = all_reduce(bias_grad, ctx.col_parallel_mode)
+            if ctx.use_bias:
+                bias_grad = torch.sum(output_grad, dim=tuple(range(output_grad.ndim - 1)))
+                bias_grad = all_reduce(bias_grad, ctx.col_parallel_mode)
+            else:
+                bias_grad = None
 
         return A_grad, B_grad, bias_grad, None, None, None, None, None, None, None, None, None, None
 
 
+def classifier_2p5d(A: Tensor, B: Tensor, bias, tesseract_dim: int, out_shape: Tuple[int,
+                                                                                     ...], row_rank: int, col_rank: int,
+                    row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode, data_parallel_rank: int,
+                    pipeline_parallel_rank: int, pipeline_parallel_size: int, tensor_parallel_size: int) -> Tensor:
+    """
+    Classifier
+
+    :param a: matrix :math:`A`
+    :type a: torch.tensor
+    :param b: matrix :math:`B`
+    :type b: torch.tensor
+    :param bias: matrix of bias
+    :type bias: torch.tensor, optional
+    :param tesseract_dim: dimension of TESSERACT fo 2.5D parallelism
+    :type tesseract_dim: int
+    :param out_shape: shape of output tensor
+    :type out_shape: tuple
+    :param row_rank: the rank of row
+    :type row_rank: int
+    :param col_rank: the rank of column
+    :type col_rank: int
+    :param row_parallel_mode: row parallel mode
+    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param col_parallel_mode: column parallel mode
+    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param data_parallel_rank: data parallel rank
+    :type data_parallel_rank: int
+    :param pipeline_parallel_rank: pipeline parallel rank
+    :type pipeline_parallel_rank: int
+    :param pipeline_parallel_size: pipeline parallel size
+    :type pipeline_parallel_size: int
+    :param tensor_parallel_size: tensor parallel size
+    :type tensor_parallel_size: int
+    """
+    return _Classifier2p5D.apply(A, B, bias, tesseract_dim, out_shape, row_rank, col_rank, row_parallel_mode,
+                                 col_parallel_mode, data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size,
+                                 tensor_parallel_size)
+
+
 class Matmul_AB_2p5D(torch.autograd.Function):
     """
     Matrix multiplication for :math:`C = AB`
@@ -522,37 +529,7 @@ class Matmul_ATB_2p5D(torch.autograd.Function):
         return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None, None, None, None
 
 
-class Add_Bias_2p5D(torch.autograd.Function):
-    """
-    Matrix add bias: :math:`C = A + b`
-
-    :param input: matrix :math:`A`
-    :type input: torch.tensor
-    :param bias: matrix :math:`b`
-    :type bias: torch.tensor
-    :param output_size_per_partition: output size in each partition
-    :type output_size_per_partition: int
-    :param tesseract_dim: dimension of TESSERACT fo 2.5D parallelism
-    :type tesseract_dim: int
-    :param row_rank: the rank of row
-    :type row_rank: int
-    :param col_rank: the rank of column
-    :type col_rank: int
-    :param row_parallel_mode: row parallel mode
-    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param col_parallel_mode: column parallel mode
-    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion
-    :type skip_bias_add: bool
-    :param data_parallel_rank: data parallel rank
-    :type data_parallel_rank: int
-    :param pipeline_parallel_rank: pipeline parallel rank
-    :type pipeline_parallel_rank: int
-    :param pipeline_parallel_size: pipeline parallel size
-    :type pipeline_parallel_size: int
-    :param tensor_parallel_size: tensor parallel size
-    :type tensor_parallel_size: int
-    """
+class _Add_Bias_2p5D(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(ctx: Any, input: Tensor, bias: Tensor, output_size_per_partition: int, tesseract_dim: int,
@@ -621,7 +598,46 @@ class Add_Bias_2p5D(torch.autograd.Function):
                 return output_grad, reduce_tmp, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None
 
 
-class layernorm_2p5d(torch.autograd.Function):
+def add_bias_2p5d(input: Tensor, bias: Tensor, output_size_per_partition: int, tesseract_dim: int, row_rank: int,
+                  col_rank: int, dep_rank: int, col_parallel_mode: ParallelMode, skip_bias_add: bool,
+                  data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
+                  tensor_parallel_size: int) -> Tensor:
+    """
+    Matrix add bias: :math:`C = A + b`
+
+    :param input: matrix :math:`A`
+    :type input: torch.tensor
+    :param bias: matrix :math:`b`
+    :type bias: torch.tensor
+    :param output_size_per_partition: output size in each partition
+    :type output_size_per_partition: int
+    :param tesseract_dim: dimension of TESSERACT fo 2.5D parallelism
+    :type tesseract_dim: int
+    :param row_rank: the rank of row
+    :type row_rank: int
+    :param col_rank: the rank of column
+    :type col_rank: int
+    :param row_parallel_mode: row parallel mode
+    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param col_parallel_mode: column parallel mode
+    :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param skip_bias_add: If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion
+    :type skip_bias_add: bool
+    :param data_parallel_rank: data parallel rank
+    :type data_parallel_rank: int
+    :param pipeline_parallel_rank: pipeline parallel rank
+    :type pipeline_parallel_rank: int
+    :param pipeline_parallel_size: pipeline parallel size
+    :type pipeline_parallel_size: int
+    :param tensor_parallel_size: tensor parallel size
+    :type tensor_parallel_size: int
+    """
+    return _Add_Bias_2p5D.apply(input, bias, output_size_per_partition, tesseract_dim, row_rank, col_rank, dep_rank,
+                                col_parallel_mode, skip_bias_add, data_parallel_rank, pipeline_parallel_rank,
+                                pipeline_parallel_size, tensor_parallel_size)
+
+
+class _Layernorm2p5D(torch.autograd.Function):
     """
     Layernorm
 
@@ -671,7 +687,43 @@ class layernorm_2p5d(torch.autograd.Function):
         return input_grad, None, None, None, None, None, None
 
 
-class all_gather_weight_2p5d(torch.autograd.Function):
+def layernorm_2p5d(input: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int,
+                   row_parallel_mode: ParallelMode) -> Tensor:
+    """
+    Layernorm
+
+    :param input: input maxtrix
+    :type input: torch.tensor
+    :param E_x: mean
+    :type E_x: torch.tensor
+    :param Var_x: variance
+    :type Var_x: torch.tensor
+    :param hidden_size: hidden size
+    :type hidden_size: int
+    :param row_parallel_mode: row parallel mode
+    :type row_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    """
+    return _Layernorm2p5D.apply(input, E_x, Var_x, hidden_size, row_parallel_mode)
+
+
+class _AllGatherTensor2p5D(torch.autograd.Function):
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float16)
+    def forward(ctx: Any, inputs: Tensor, dim: int, col_parallel_mode: ParallelMode) -> Tensor:
+        ctx.dim = dim
+        ctx.col_parallel_mode = col_parallel_mode
+
+        outputs = all_gather(inputs, dim, col_parallel_mode)
+        return outputs
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
+        grad = reduce_scatter(output_grad, ctx.dim, ctx.col_parallel_mode)
+        return grad.contiguous(), None, None
+
+
+def all_gather_tensor_2p5d(inputs: Tensor, dim: int, col_parallel_mode: ParallelMode) -> Tensor:
     """
     all gather the weight of 2.5D parallelism
 
@@ -684,21 +736,7 @@ class all_gather_weight_2p5d(torch.autograd.Function):
     :param col_parallel_mode: column parallel mode
     :type col_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     """
-    @staticmethod
-    @custom_fwd(cast_inputs=torch.float16)
-    def forward(ctx: Any, inputs: Tensor, dim: int, tesseract_dim: int, col_parallel_mode: ParallelMode) -> Tensor:
-        ctx.dim = dim
-        ctx.tesseract_dim = tesseract_dim
-        ctx.row_rank = gpc.get_local_rank(col_parallel_mode)
-
-        outputs = all_gather(inputs, dim, col_parallel_mode)
-        return outputs
-
-    @staticmethod
-    @custom_bwd
-    def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
-        grad = output_grad.chunk(ctx.tesseract_dim, dim=ctx.dim)[ctx.row_rank]
-        return grad.contiguous(), None, None, None
+    return _AllGatherTensor2p5D.apply(inputs, dim, col_parallel_mode)
 
 
 class SplitFirst(torch.autograd.Function):
@@ -737,10 +775,10 @@ def split_tensor_2p5d(input_: Tensor, dim: int = 0) -> Tensor:
 
     :param input_: Input tensor
     :param dim: Specified dimension in which to split
-
+    
     :type input_: torch.Tensor
     :type dim: int, optional
-
+    
     :return output: Splitted tensor
     :rtype output: torch.Tensor
     """
@@ -750,9 +788,49 @@ def split_tensor_2p5d(input_: Tensor, dim: int = 0) -> Tensor:
                        dim=dim)[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)].contiguous()
 
 
-class reduce_by_batch_2p5d(torch.autograd.Function):
-    """All-reduce the input from the model parallel region.
+class _ReduceTensor2p5D(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input_, parallel_mode):
+        return all_reduce(input_, parallel_mode)
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        return output_grad, None
+
+
+def reduce_tensor_2p5d(input_: Tensor, parallel_mode: ParallelMode) -> Tensor:
     """
+    All-reduce the input.
+    
+    :param input_: input tensor
+    :param parallel_mode: parallel mode
+    """
+    return _ReduceTensor2p5D.apply(input_, parallel_mode)
+
+
+class _ReduceScatterTensor2p5D(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input_, dim, parallel_mode):
+        ctx.dim = dim
+        ctx.parallel_mode = parallel_mode
+        return reduce_scatter(input_, dim, parallel_mode)
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        return all_gather(output_grad, ctx.dim, ctx.parallel_mode), None, None
+
+
+def reduce_scatter_tensor_2p5d(input_: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
+    """
+    Reduce-scatter the input.
+    
+    :param input_: input tensor
+    :param parallel_mode: parallel mode
+    """
+    return _ReduceScatterTensor2p5D.apply(input_, dim, parallel_mode)
+
+
+class _RreduceByBatch2p5D(torch.autograd.Function):
     @staticmethod
     def symbolic(graph, input_, reduce_mean: bool = False):
         output = all_reduce(input_, ParallelMode.PARALLEL_2P5D_COL)
@@ -764,12 +842,6 @@ class reduce_by_batch_2p5d(torch.autograd.Function):
     @staticmethod
     @custom_fwd(cast_inputs=torch.float32)
     def forward(ctx, input_, reduce_mean: bool = False):
-        """
-        :param input_: input maxtrix
-        :type input_: torch.tensor
-        :param reduce_mean:  If set to ``True``, it will divide the output by column parallel size, default to False
-        :type reduce_mean: int, optional
-        """
         output = all_reduce(input_, ParallelMode.PARALLEL_2P5D_COL)
         ctx.reduce_mean = reduce_mean
         if reduce_mean:
@@ -785,3 +857,15 @@ class reduce_by_batch_2p5d(torch.autograd.Function):
             return output_grad / ctx.reduce_size, None
         else:
             return output_grad, None
+
+
+def reduce_by_batch_2p5d(input_, reduce_mean: bool = False) -> Tensor:
+    """
+    All-reduce the input from the model parallel region.
+
+    :param input_: input maxtrix
+    :type input_: torch.tensor
+    :param reduce_mean:  If set to ``True``, it will divide the output by column parallel size, default to False
+    :type reduce_mean: bool, optional
+    """
+    return _RreduceByBatch2p5D.apply(input_, reduce_mean)

colossalai/nn/layer/parallel_2p5d/_utils.py

@@ -1,13 +1,12 @@
-import os
-
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 
 
 def get_tesseract_dim_dep_from_env():
     try:
-        tesseract_dim = int(os.environ['TESSERACT_DIM'])
-        tesseract_dep = int(os.environ['TESSERACT_DEP'])
+        tesseract_dim = env.tesseract_dim
+        tesseract_dep = env.tesseract_dep
         assert tesseract_dim > 0, 'TESSERACT_DIM must be larger than zero'
         assert tesseract_dep > 0, 'TESSERACT_DEP must be larger than zero'
         return tesseract_dim, tesseract_dep

colossalai/nn/layer/parallel_2p5d/layers.py

@@ -7,16 +7,18 @@ import torch.nn.functional as F
 from colossalai.communication import broadcast
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.nn import init as init
 from colossalai.registry import LAYERS
-from colossalai.utils import get_current_device
-from torch import Tensor, dtype
+from colossalai.utils.cuda import get_current_device
+from torch import Tensor
 from torch.nn import Parameter
 
 from ..base_layer import ParallelLayer
-from ..utils import (divide, set_tensor_parallel_attribute_by_partition, to_2tuple)
-from ._operation import (Add_Bias_2p5D, Matmul_AB_2p5D, all_gather_weight_2p5d, classifier_2p5d, layernorm_2p5d)
-from ._utils import (assert_tesseract_initialization, get_tesseract_dim_dep_from_env)
+from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
+from ._operation import (add_bias_2p5d, Matmul_AB_2p5D, Matmul_ABT_2p5D, all_gather_tensor_2p5d, classifier_2p5d,
+                         layernorm_2p5d, reduce_scatter_tensor_2p5d, split_tensor_2p5d)
+from ._utils import assert_tesseract_initialization, get_tesseract_dim_dep_from_env
 
 
 @LAYERS.register_module
@@ -41,7 +43,7 @@ class Linear2p5D(ParallelLayer):
                  in_features: int,
                  out_features: int,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  skip_bias_add: bool = False,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
@@ -112,17 +114,16 @@ class Linear2p5D(ParallelLayer):
 
         if self.bias is not None:
             if self.skip_bias_add:
-                bias = Add_Bias_2p5D.apply(None, self.bias, self.hidden_size_per_partition, self.tesseract_dim,
-                                           self.row_rank, self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL,
-                                           True, self.data_parallel_rank, self.pipeline_parallel_rank,
-                                           self.pipeline_parallel_size, self.tensor_parallel_size)
+                bias = add_bias_2p5d(None, self.bias, self.hidden_size_per_partition, self.tesseract_dim, self.row_rank,
+                                     self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
+                                     self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                                     self.tensor_parallel_size)
                 return output, bias
             else:
-                output = Add_Bias_2p5D.apply(output, self.bias, self.hidden_size_per_partition, self.tesseract_dim,
-                                             self.row_rank, self.col_rank, self.dep_rank,
-                                             ParallelMode.PARALLEL_2P5D_COL, False, self.data_parallel_rank,
-                                             self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                             self.tensor_parallel_size)
+                output = add_bias_2p5d(output, self.bias, self.hidden_size_per_partition, self.tesseract_dim,
+                                       self.row_rank, self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL,
+                                       False, self.data_parallel_rank, self.pipeline_parallel_rank,
+                                       self.pipeline_parallel_size, self.tensor_parallel_size)
                 return output
         else:
             return output
@@ -187,15 +188,15 @@ class LayerNorm2p5D(ParallelLayer):
             # this time 1/sqrt(Var_x + epsilon)
             Var_x = 1.0 / torch.sqrt(Var_x + self.variance_epsilon)
 
-        output = layernorm_2p5d.apply(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2P5D_ROW)
-        bias = Add_Bias_2p5D.apply(None, self.beta, self.partitioned_partition, self.tesseract_dim, self.row_rank,
-                                   self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
-                                   self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                   self.tensor_parallel_size)
-        scale = Add_Bias_2p5D.apply(None, self.gamma, self.partitioned_partition, self.tesseract_dim, self.row_rank,
-                                    self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
-                                    self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                    self.tensor_parallel_size)
+        output = layernorm_2p5d(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2P5D_ROW)
+        bias = add_bias_2p5d(None, self.beta, self.partitioned_partition, self.tesseract_dim, self.row_rank,
+                             self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
+                             self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                             self.tensor_parallel_size)
+        scale = add_bias_2p5d(None, self.gamma, self.partitioned_partition, self.tesseract_dim, self.row_rank,
+                              self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
+                              self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                              self.tensor_parallel_size)
         output = torch.addcmul(bias, scale, output)
         return output
 
@@ -229,8 +230,8 @@ class PatchEmbedding2p5D(ParallelLayer):
                  patch_size: int,
                  in_chans: int,
                  embed_size: int,
-                 dtype: dtype = None,
                  flatten: bool = True,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                  position_embed_initializer: Callable = init.zeros_()):
@@ -280,19 +281,21 @@ class PatchEmbedding2p5D(ParallelLayer):
             position_embed_initializer(self.pos_embed)
 
     def forward(self, input_: Tensor) -> Tensor:
+        input_ = split_tensor_2p5d(input_, 0)
+
         B, C, H, W = input_.shape
         assert H == self.img_size[0] and W == self.img_size[1], \
             f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
 
-        weight = all_gather_weight_2p5d.apply(self.weight, 0, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
-        bias = all_gather_weight_2p5d.apply(self.bias, 0, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
+        weight = all_gather_tensor_2p5d(self.weight, 0, ParallelMode.PARALLEL_2P5D_COL)
+        bias = all_gather_tensor_2p5d(self.bias, 0, ParallelMode.PARALLEL_2P5D_COL)
 
         output = F.conv2d(input_, weight, bias, stride=self.patch_size)
         if self.flatten:
             output = output.flatten(2).transpose(1, 2)  # BCHW -> BNC
 
-        cls_token = all_gather_weight_2p5d.apply(self.cls_token, -1, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
-        pos_embed = all_gather_weight_2p5d.apply(self.pos_embed, -1, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
+        cls_token = all_gather_tensor_2p5d(self.cls_token, -1, ParallelMode.PARALLEL_2P5D_COL)
+        pos_embed = all_gather_tensor_2p5d(self.pos_embed, -1, ParallelMode.PARALLEL_2P5D_COL)
         cls_token = cls_token.expand(output.shape[0], -1, -1)
         output = torch.cat((cls_token, output), dim=1)
         output = output + pos_embed
@@ -322,7 +325,7 @@ class Embedding2p5D(ParallelLayer):
                  num_embeddings: int,
                  embedding_dim: int,
                  padding_idx: int = None,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.normal_(),
                  *args,
                  **kwargs):
@@ -359,13 +362,95 @@ class Embedding2p5D(ParallelLayer):
                 self.weight[self.padding_idx].fill_(0)
 
     def forward(self, input_: Tensor) -> Tensor:
-        weight = all_gather_weight_2p5d.apply(self.weight, -1, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
+        input_ = split_tensor_2p5d(input_, 0)
+
+        weight = all_gather_tensor_2p5d(self.weight, -1, ParallelMode.PARALLEL_2P5D_COL)
 
         output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
 
         return output
 
 
+@LAYERS.register_module
+class VocabParallelEmbedding2p5D(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    :param num_embeddings: number of embeddings
+    :type num_embeddings: int
+    :param embedding_dim: dimension of embedding
+    :type embedding_dim: int
+    :param padding_idx: index of padding, defaults to None
+    :type padding_idx: int, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to normal initializer
+    :type weight_initializer: typing.Callable, optional
+    :param args: Args used in F.embedding
+    :param kwargs: Kwargs used in F.embedding
+    """
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 padding_idx: int = None,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.normal_(),
+                 *args,
+                 **kwargs):
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embed_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.embed_args = args
+        self.embed_kwargs = kwargs
+
+        assert_tesseract_initialization()
+        self.tesseract_dim, self.tesseract_dep = get_tesseract_dim_dep_from_env()
+        self.num_embeddings_per_partition = divide(self.num_embeddings, self.tesseract_dim)
+        self.embed_dim_per_partition = divide(self.embed_dim, self.tesseract_dim)
+        tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+        self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
+        self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
+
+        self.weight = Parameter(
+            torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
+                        device=get_current_device(),
+                        dtype=dtype))
+
+        self.reset_parameters(weight_initializer)
+        self._set_tensor_parallel_attributes()
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self):
+        set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
+
+    def reset_parameters(self, weight_initializer) -> None:
+        with seed(ParallelMode.TENSOR):
+            fan_in, fan_out = self.num_embeddings, self.embed_dim
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+            self._fill_padding_idx_with_zero()
+
+    def _fill_padding_idx_with_zero(self) -> None:
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.weight[self.padding_idx].fill_(0)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        # Build the mask.
+        input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
+        # Mask the input.
+        masked_input = input_.clone() - self.vocab_start_index
+        masked_input[input_mask] = 0
+
+        output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
+                                      **self.embed_kwargs)
+
+        # Mask the output embedding.
+        output_parallel[input_mask, :] = 0.
+        # Reduce across all the model parallel GPUs.
+        output = reduce_scatter_tensor_2p5d(output_parallel, 0, ParallelMode.PARALLEL_2P5D_COL)
+        return output
+
+
 @LAYERS.register_module
 class Classifier2p5D(ParallelLayer):
     """
@@ -391,7 +476,7 @@ class Classifier2p5D(ParallelLayer):
                  num_classes: int,
                  weight: Parameter = None,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         super().__init__()
@@ -442,7 +527,114 @@ class Classifier2p5D(ParallelLayer):
     def forward(self, input_: Tensor) -> Tensor:
         out_shape = input_.shape[:-1] + (self.num_classes, )
 
-        return classifier_2p5d.apply(input_, self.weight, self.bias, self.tesseract_dim, out_shape, self.row_rank,
-                                     self.col_rank, ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL,
-                                     self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
-                                     self.tensor_parallel_size)
+        return classifier_2p5d(input_, self.weight, self.bias, self.tesseract_dim, out_shape, self.row_rank,
+                               self.col_rank, ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL,
+                               self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                               self.tensor_parallel_size)
+
+
+@LAYERS.register_module
+class VocabParallelClassifier2p5D(ParallelLayer):
+    """
+    Vocab parallel classifier layer for 2.5D parallelism
+
+    :param in_features: size of each input sample
+    :type in_features: int
+    :param num_classes: number of classes
+    :type num_classes: int
+    :param weight: weight of the classifier, defaults to True
+    :type weight: torch.nn.Parameter, optional
+    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
+    :type bias: bool, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
+    :type weight_initializer: typing.Callable, optional
+    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
+    :type bias_initializer: typing.Callable, optional
+    """
+    def __init__(self,
+                 in_features: int,
+                 num_classes: int,
+                 weight: Parameter = None,
+                 bias: bool = True,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
+        super().__init__()
+
+        self.in_features = in_features
+        self.num_classes = num_classes
+
+        # parallel setting
+        assert_tesseract_initialization()
+        self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+        self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+        self.dep_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+        self.tesseract_dim, _ = get_tesseract_dim_dep_from_env()
+
+        # partitioning dimension
+        self.input_size_per_partition = divide(in_features, self.tesseract_dim)
+        self.hidden_size_per_partition = divide(num_classes, self.tesseract_dim)
+
+        # create weight, shape: [k/q, h/q]
+        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
+        if weight is not None:
+            self.weight = weight
+            self.has_weight = False
+        else:
+            self.weight = Parameter(
+                torch.empty(self.hidden_size_per_partition, self.input_size_per_partition, **factory_kwargs))
+            self.has_weight = True
+        # create bias, shape: [h/q]
+        if bias:
+            self.bias = Parameter(torch.empty(self.hidden_size_per_partition, **factory_kwargs))
+        else:
+            self.bias = None
+
+        # initialize parameters
+        with seed(ParallelMode.TENSOR):
+            self.reset_parameters(weight_initializer, bias_initializer)
+        self._set_tensor_parallel_attributes()
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self):
+        if self.has_weight:
+            set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
+        if self.bias is not None:
+            set_tensor_parallel_attribute_by_partition(self.bias, self.tesseract_dim)
+
+    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
+        fan_in, fan_out = self.in_features, self.num_classes
+        if self.has_weight:
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+        if self.bias is not None:
+            bias_initializer(self.bias, fan_in=fan_in)
+
+    def forward(self, x: Tensor) -> Tensor:
+        # input: [m/dq, n/q, k/q]
+        # output: [m/dq, n/q, h/q]
+        out_shape = x.shape[:-1] + (self.hidden_size_per_partition, )
+
+        output = Matmul_ABT_2p5D.apply(
+            x,
+            self.weight,
+            self.tesseract_dim,
+            out_shape,
+            self.row_rank,
+            self.col_rank,
+            self.dep_rank,
+            ParallelMode.PARALLEL_2P5D_ROW,
+            ParallelMode.PARALLEL_2P5D_COL,
+            self.data_parallel_rank,
+            self.pipeline_parallel_rank,
+            self.pipeline_parallel_size,
+            self.tensor_parallel_size,
+        )
+
+        if self.bias is not None:
+            output = add_bias_2p5d(output, self.bias, self.hidden_size_per_partition, self.tesseract_dim, self.row_rank,
+                                   self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, False,
+                                   self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
+                                   self.tensor_parallel_size)
+        return output

colossalai/nn/layer/parallel_3d/__init__.py

@@ -1,6 +1,8 @@
-from ._operation import reduce_by_batch_3d, split_tensor_3d
-from .layers import Classifier3D, Embedding3D, LayerNorm3D, Linear3D, PatchEmbedding3D
+from ._operation import reduce_by_batch_3d, split_batch_3d, split_tensor_3d
+from .layers import (Classifier3D, Embedding3D, LayerNorm3D, Linear3D, PatchEmbedding3D, VocabParallelClassifier3D,
+                     VocabParallelEmbedding3D)
 
 __all__ = [
-    'reduce_by_batch_3d', 'split_tensor_3d', 'Linear3D', 'LayerNorm3D', 'PatchEmbedding3D', 'Classifier3D', 'Embedding3D'
+    'reduce_by_batch_3d', 'split_tensor_3d', 'split_batch_3d', 'Linear3D', 'LayerNorm3D', 'PatchEmbedding3D',
+    'Classifier3D', 'Embedding3D', 'VocabParallelEmbedding3D', 'VocabParallelClassifier3D'
 ]

colossalai/nn/layer/parallel_3d/_operation.py

@@ -4,36 +4,20 @@
 from typing import Optional, Tuple
 
 import torch
-from colossalai.communication import all_gather, all_reduce, reduce_scatter, broadcast, reduce
+from colossalai.communication import (all_gather, all_reduce, broadcast, reduce, reduce_scatter)
+from colossalai.context import parallel_mode
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
 from torch import Tensor
 from torch.cuda.amp import custom_bwd, custom_fwd
+from ._utils import get_parallel_mode_from_env
+from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
+
+from colossalai.nn.layer.base_layer import ParallelLayer
 
 
-class linear_3d(torch.autograd.Function):
-    """
-    Linear layer for 3D parallelism
+class _Linear3D(torch.autograd.Function):
 
-    :param input_: matrix of input
-    :type input_: torch.tensor
-    :param weight: matrix of weight
-    :type weight: torch.tensor
-    :param bias: matrix of bias
-    :type bias: torch.tensor, optional
-    :param input_parallel_mode: input parallel mode
-    :type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param weight_parallel_mode: weight parallel mode
-    :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param output_parallel_mode: output parallel mode
-    :type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-    :param input_dim: dimension of input, defaults to 0
-    :type input_dim: int, optional
-    :param weight_dim: dimension of weight, defaults to -1
-    :type weight_dim: int, optional
-    :param output_dim: dimension of output, defaults to 0
-    :type output_dim: int, optional
-    """
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(ctx,
@@ -87,6 +71,8 @@ class linear_3d(torch.autograd.Function):
                 bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
                 bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
                 async_ops.append(op)
+            else:
+                bias_grad = None
 
             for op in async_ops:
                 if op is not None:
@@ -95,9 +81,17 @@ class linear_3d(torch.autograd.Function):
         return input_grad, weight_grad, bias_grad, None, None, None, None, None, None
 
 
-class classifier_3d(torch.autograd.Function):
+def linear_3d(input_: Tensor,
+              weight: Tensor,
+              bias: Optional[Tensor],
+              input_parallel_mode: ParallelMode,
+              weight_parallel_mode: ParallelMode,
+              output_parallel_mode: ParallelMode,
+              input_dim: int = 0,
+              weight_dim: int = -1,
+              output_dim: int = 0) -> Tensor:
     """
-    Classifier
+    Linear layer for 3D parallelism
 
     :param input_: matrix of input
     :type input_: torch.tensor
@@ -111,7 +105,19 @@ class classifier_3d(torch.autograd.Function):
     :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     :param output_parallel_mode: output parallel mode
     :type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param input_dim: dimension of input, defaults to 0
+    :type input_dim: int, optional
+    :param weight_dim: dimension of weight, defaults to -1
+    :type weight_dim: int, optional
+    :param output_dim: dimension of output, defaults to 0
+    :type output_dim: int, optional
     """
+    return _Linear3D.apply(input_, weight, bias, input_parallel_mode, weight_parallel_mode, output_parallel_mode,
+                           input_dim, weight_dim, output_dim)
+
+
+class _Classifier3D(torch.autograd.Function):
+
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(ctx, input_: Tensor, weight: Tensor, bias: Optional[Tensor], input_parallel_mode: ParallelMode,
@@ -156,6 +162,8 @@ class classifier_3d(torch.autograd.Function):
                 bias_grad = all_reduce(bias_grad, ctx.input_parallel_mode)
                 bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
                 async_ops.append(op)
+            else:
+                bias_grad = None
 
             input_grad = torch.matmul(output_grad, weight)
 
@@ -166,23 +174,17 @@ class classifier_3d(torch.autograd.Function):
         return input_grad, weight_grad, bias_grad, None, None, None, None, None, None
 
 
-class layernorm_3d(torch.autograd.Function):
+def classifier_3d(input_: Tensor, weight: Tensor, bias: Optional[Tensor], input_parallel_mode: ParallelMode,
+                  weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
     """
-    Layernorm
+    3D parallel classifier
 
-    :param input_: input maxtrix
+    :param input_: matrix of input
     :type input_: torch.tensor
     :param weight: matrix of weight
     :type weight: torch.tensor
     :param bias: matrix of bias
-    :type bias: torch.tensor
-    :param normalized_shape: input shape from an expected input
-        of size. :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
-        If a single integer is used, it is treated as a singleton list, and this module will
-        normalize over the last dimension which is expected to be of that specific size.
-    :type normalized_shape: int
-    :param eps: a value added to the denominator for numerical stability
-    :type eps: float
+    :type bias: torch.tensor, optional
     :param input_parallel_mode: input parallel mode
     :type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     :param weight_parallel_mode: weight parallel mode
@@ -190,6 +192,11 @@ class layernorm_3d(torch.autograd.Function):
     :param output_parallel_mode: output parallel mode
     :type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     """
+    return _Classifier3D.apply(input_, weight, bias, input_parallel_mode, weight_parallel_mode, output_parallel_mode)
+
+
+class _Layernorm3D(torch.autograd.Function):
+
     @staticmethod
     @custom_fwd(cast_inputs=torch.float32)
     def forward(ctx, input_: Tensor, weight: Tensor, bias: Tensor, normalized_shape: int, eps: float,
@@ -236,27 +243,78 @@ class layernorm_3d(torch.autograd.Function):
         return input_grad, weight_grad, bias_grad, None, None, None, None, None
 
 
-def split_tensor_3d(input_: Tensor,
-                   dim: int = 0,
-                   input_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_INPUT,
-                   weight_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_WEIGHT) -> Tensor:
-    """Splits 3D tensor in specified dimension
+def layernorm_3d(input_: Tensor, weight: Tensor, bias: Tensor, normalized_shape: int, eps: float,
+                 input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
+                 output_parallel_mode: ParallelMode) -> Tensor:
+    """
+    3D parallel Layernorm
 
+    :param input_: input maxtrix
+    :type input_: torch.tensor
+    :param weight: matrix of weight
+    :type weight: torch.tensor
+    :param bias: matrix of bias
+    :type bias: torch.tensor
+    :param normalized_shape: input shape from an expected input
+        of size. :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
+        If a single integer is used, it is treated as a singleton list, and this module will
+        normalize over the last dimension which is expected to be of that specific size.
+    :type normalized_shape: int
+    :param eps: a value added to the denominator for numerical stability
+    :type eps: float
+    :param input_parallel_mode: input parallel mode
+    :type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param weight_parallel_mode: weight parallel mode
+    :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param output_parallel_mode: output parallel mode
+    :type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    """
+    return _Layernorm3D.apply(input_, weight, bias, normalized_shape, eps, input_parallel_mode, weight_parallel_mode,
+                              output_parallel_mode)
+
+
+def split_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
+    """Splits 3D parallel tensor in specified dimension
+
+    :param tensor: Input tensor
+    :param dim: Specified dimension in which to split
+    :param parallel_mode: Parallel mode
+    :param weight_parallel_mode: Weight parallel mode
+
+    :type tensor: torch.Tensor
+    :type dim: int
+    :type parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    
+    :return output: Splitted tensor
+    :rtype output: torch.Tensor
+    """
+    if tensor.size(dim) <= 1:
+        return tensor
+    output = torch.chunk(tensor, gpc.get_world_size(parallel_mode),
+                         dim=dim)[gpc.get_local_rank(parallel_mode)].contiguous()
+    return output
+
+
+def split_batch_3d(input_: Tensor,
+                    dim: int = 0,
+                    input_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_INPUT,
+                    weight_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_WEIGHT) -> Tensor:
+    """Splits 3D tensor in batch
     :param input_: Input tensor
     :param dim: Specified dimension in which to split
     :param input_parallel_mode: Input parallel mode
     :param weight_parallel_mode: Weight parallel mode
-
     :type input_: torch.Tensor
     :type dim: int, optional
     :type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode, optional
     :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode, optional
-
     :return output: Splitted tensor
     :rtype output: torch.Tensor
     """
     if input_.size(dim) <= 1:
         return input_
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
     output = torch.chunk(input_, gpc.get_world_size(weight_parallel_mode),
                          dim=dim)[gpc.get_local_rank(weight_parallel_mode)].contiguous()
     output = torch.chunk(output, gpc.get_world_size(input_parallel_mode),
@@ -264,9 +322,77 @@ def split_tensor_3d(input_: Tensor,
     return output
 
 
-class reduce_by_batch_3d(torch.autograd.Function):
-    """All-reduce the input from the model parallel region.
+class _ReduceTensor3D(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, input_, parallel_mode):
+        return all_reduce(input_, parallel_mode)
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        return output_grad, None
+
+
+def reduce_tensor_3d(tensor: Tensor, parallel_mode: ParallelMode) -> Tensor:
     """
+    All-reduce the input.
+    
+    :param tensor: Input tensor
+    :param parallel_mode: Parallel mode
+    """
+    return _ReduceTensor3D.apply(tensor, parallel_mode)
+
+
+class _ReduceGrad3D(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, input_, parallel_mode):
+        ctx.parallel_mode = parallel_mode
+        return input_
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        input_grad = all_reduce(output_grad, ctx.parallel_mode)
+        return input_grad, None
+
+
+def reduce_grad_3d(tensor: Tensor, parallel_mode: ParallelMode) -> Tensor:
+    """
+    All-reduce the gradient in backward pass.
+    
+    :param tensor: Input tensor
+    :param parallel_mode: Parallel mode
+    """
+    return _ReduceGrad3D.apply(tensor, parallel_mode)
+
+
+class _ReduceScatterTensor3D(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, input_, dim, parallel_mode):
+        ctx.dim = dim
+        ctx.parallel_mode = parallel_mode
+        return reduce_scatter(input_, dim, parallel_mode)
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        input_grad = all_gather(output_grad, ctx.dim, ctx.parallel_mode)
+        return input_grad, None, None
+
+
+def reduce_scatter_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
+    """
+    Reduce-scatter the input.
+    
+    :param tensor: Input tensor
+    :param dim: Dimension to scatter
+    :param parallel_mode: Parallel mode
+    """
+    return _ReduceScatterTensor3D.apply(tensor, dim, parallel_mode)
+
+
+class _ReduceByBatch3D(torch.autograd.Function):
+
     @staticmethod
     @custom_fwd(cast_inputs=torch.float32)
     def forward(ctx,
@@ -274,16 +400,6 @@ class reduce_by_batch_3d(torch.autograd.Function):
                 input_parallel_mode: ParallelMode,
                 weight_parallel_mode: ParallelMode,
                 reduce_mean: bool = False) -> Tensor:
-        """
-        :param input_: input maxtrix
-        :type input_: torch.tensor
-        :param input_parallel_mode: input parallel mode
-        :type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-        :param weight_parallel_mode: weight parallel mode
-        :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
-        :param reduce_mean:  If set to ``True``, it will divide the output by (input parallel size * weight parallel size), default to False
-        :type reduce_mean: int, optional
-        """
         output = all_reduce(input_, input_parallel_mode)
         output = all_reduce(output, weight_parallel_mode)
         ctx.reduce_mean = reduce_mean
@@ -302,7 +418,26 @@ class reduce_by_batch_3d(torch.autograd.Function):
             return output_grad, None, None, None
 
 
-class broadcast_weight_3d_from_diagonal(torch.autograd.Function):
+def reduce_by_batch_3d(tensor: Tensor,
+                       input_parallel_mode: ParallelMode,
+                       weight_parallel_mode: ParallelMode,
+                       reduce_mean: bool = False) -> Tensor:
+    """
+    All-reduce the input from the model parallel region.
+
+    :param input_: input maxtrix
+    :type input_: torch.tensor
+    :param input_parallel_mode: input parallel mode
+    :type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param weight_parallel_mode: weight parallel mode
+    :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
+    :param reduce_mean:  If set to ``True``, it will divide the output by (input parallel size * weight parallel size), default to False
+    :type reduce_mean: int, optional
+    """
+    return _ReduceByBatch3D.apply(tensor, input_parallel_mode, weight_parallel_mode, reduce_mean)
+
+
+class _BroadcastWeight3D_FromDiagonal(torch.autograd.Function):
     """
     broadcast weight from diagonal
 
@@ -315,6 +450,7 @@ class broadcast_weight_3d_from_diagonal(torch.autograd.Function):
     :param weight_parallel_mode: output parallel mode
     :type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
     """
+
     @staticmethod
     @custom_fwd(cast_inputs=torch.float16)
     def forward(ctx, input_: Tensor, input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
@@ -337,3 +473,9 @@ class broadcast_weight_3d_from_diagonal(torch.autograd.Function):
         else:
             input_grad = None
         return input_grad, None, None, None
+
+
+def broadcast_weight_3d_from_diagonal(tensor: Tensor, input_parallel_mode: ParallelMode,
+                                      weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
+    return _BroadcastWeight3D_FromDiagonal.apply(tensor, input_parallel_mode, weight_parallel_mode,
+                                                 output_parallel_mode)

colossalai/nn/layer/parallel_3d/_utils.py

@@ -1,31 +1,25 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-import os
-
-from colossalai.constants import (DEPTH_3D, INPUT_GROUP_3D, OUTPUT_GROUP_3D,
-                                  WEIGHT_GROUP_3D)
+from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 from torch import Tensor
 
 
 def get_depth_from_env() -> int:
     try:
-        depth = os.environ[DEPTH_3D]
-        depth = int(depth)
+        depth = env.depth_3d
         assert depth > 0, 'DEPTH must be greater than zero'
         return depth
 
     except KeyError as e:
-        raise EnvironmentError(
-            'DEPTH is not found in the current environment, '
-            'please make sure that you have used the correct process group initializer'
-        )
+        raise EnvironmentError('DEPTH is not found in the current environment, '
+                               'please make sure that you have used the correct process group initializer')
 
 
 def get_parallel_mode_from_env(group):
-    return getattr(ParallelMode, os.environ[group])
+    assert group in [INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D], \
+        f'{group} is not valid for 3D tensor parallelism.'
+    return getattr(env, group)
 
 
 def get_last_group(a, b):
@@ -35,8 +29,7 @@ def get_last_group(a, b):
         ParallelMode.PARALLEL_3D_OUTPUT: 'C',
     }
 
-    res = chr(
-        ord('A') + ord('B') + ord('C') - ord(mapping[a]) - ord(mapping[b]))
+    res = chr(ord('A') + ord('B') + ord('C') - ord(mapping[a]) - ord(mapping[b]))
 
     if res == 'A':
         return ParallelMode.PARALLEL_3D_INPUT
@@ -47,8 +40,7 @@ def get_last_group(a, b):
 
 
 def swap_in_out_group():
-    os.environ[INPUT_GROUP_3D], os.environ[OUTPUT_GROUP_3D] = \
-        os.environ[OUTPUT_GROUP_3D], os.environ[INPUT_GROUP_3D]
+    env.input_group_3d, env.output_group_3d = env.output_group_3d, env.input_group_3d
 
 
 def dbg_check_shape(tensor: Tensor, shape: tuple):

colossalai/nn/layer/parallel_3d/layers.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
 import math
 from typing import Callable
 
@@ -10,11 +8,12 @@ from colossalai.communication import all_reduce, broadcast
 from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.nn import init as init
 from colossalai.nn.layer.base_layer import ParallelLayer
 from colossalai.registry import LAYERS
-from colossalai.utils import get_current_device
-from torch import Tensor, dtype
+from colossalai.utils.cuda import get_current_device
+from torch import Tensor
 from torch.nn import Parameter
 
 from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
@@ -37,7 +36,8 @@ class LayerNorm3D(ParallelLayer):
     :param dtype: The dtype of parameters, defaults to None
     :type dtype: torch.dtype, optional
     """
-    def __init__(self, normalized_shape: int, eps: float = 1e-12, dtype: dtype = None):
+
+    def __init__(self, normalized_shape: int, eps: float = 1e-12, dtype=None):
         super().__init__()
         self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
         self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
@@ -62,8 +62,8 @@ class LayerNorm3D(ParallelLayer):
         init.ones_()(self.weight)
 
     def forward(self, input_: Tensor) -> Tensor:
-        return layernorm_3d.apply(input_, self.weight, self.bias, self.normalized_shape, self.variance_epsilon,
-                                  self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode)
+        return layernorm_3d(input_, self.weight, self.bias, self.normalized_shape, self.variance_epsilon,
+                            self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode)
 
 
 @LAYERS.register_module
@@ -84,11 +84,12 @@ class Linear3D(ParallelLayer):
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  in_features: int,
                  out_features: int,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         super().__init__()
@@ -136,8 +137,8 @@ class Linear3D(ParallelLayer):
                 broadcast(self.bias, output_src_rank, self.output_parallel_mode)
 
     def forward(self, input_: Tensor) -> Tensor:
-        return linear_3d.apply(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
-                               self.output_parallel_mode)
+        return linear_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
+                         self.output_parallel_mode)
 
 
 @LAYERS.register_module
@@ -160,12 +161,13 @@ class Classifier3D(ParallelLayer):
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  in_features: int,
                  num_classes: int,
                  weight: Parameter = None,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         super().__init__()
@@ -214,8 +216,94 @@ class Classifier3D(ParallelLayer):
                 broadcast(self.bias, input_src_rank, self.input_parallel_mode)
 
     def forward(self, input_: Tensor) -> Tensor:
-        return classifier_3d.apply(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
-                                   self.output_parallel_mode)
+        return classifier_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
+                             self.output_parallel_mode)
+
+
+@LAYERS.register_module
+class VocabParallelClassifier3D(ParallelLayer):
+    """
+    Vocab parallel classifier layer for 2D parallelism
+
+    :param in_features: size of each input sample
+    :type in_features: int
+    :param num_classes: number of classes
+    :type num_classes: int
+    :param weight: weight of the classifier, defaults to True
+    :type weight: torch.nn.Parameter, optional
+    :param bias: If set to ``False``, the layer will not learn an additive bias, defaults to ``True``
+    :type bias: bool, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
+    :type weight_initializer: typing.Callable, optional
+    :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
+    :type bias_initializer: typing.Callable, optional
+    """
+
+    def __init__(self,
+                 in_features: int,
+                 num_classes: int,
+                 weight: Parameter = None,
+                 bias: bool = True,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
+        super().__init__()
+        self.in_features = in_features
+        self.num_classes = num_classes
+        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
+        self.depth = get_depth_from_env()
+        self.in_features_per_partition = divide(in_features, self.depth)
+        self.out_features_per_partition = divide(num_classes, self.depth)
+
+        if weight is not None:
+            self.weight = weight
+            self.has_weight = False
+        else:
+            self.weight = Parameter(
+                torch.empty(self.out_features_per_partition,
+                            self.in_features_per_partition,
+                            device=get_current_device(),
+                            dtype=dtype))
+            self.has_weight = True
+        if bias:
+            self.bias = Parameter(torch.zeros(self.out_features_per_partition, device=get_current_device(),
+                                              dtype=dtype))
+        else:
+            self.bias = None
+
+        self.reset_parameters(weight_initializer, bias_initializer)
+        self._set_tensor_parallel_attributes()
+        swap_in_out_group()
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self) -> None:
+        if self.has_weight:
+            set_tensor_parallel_attribute_by_partition(self.weight, self.depth**2)
+        if self.bias is not None:
+            set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
+
+    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
+        with seed(ParallelMode.TENSOR):
+            fan_in, fan_out = self.in_features, self.num_classes
+            weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
+            output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
+
+            if self.has_weight:
+                weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+                broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
+
+            if self.bias is not None:
+                bias_initializer(self.bias, fan_in=fan_in)
+                broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
+                broadcast(self.bias, output_src_rank, self.output_parallel_mode)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        return linear_3d(input_, self.weight.transpose(0, 1), self.bias, self.input_parallel_mode,
+                         self.weight_parallel_mode, self.output_parallel_mode)
 
 
 @LAYERS.register_module
@@ -242,13 +330,14 @@ class PatchEmbedding3D(ParallelLayer):
     :param position_embed_initializer: The intializer of position embedding, defaults to zero
     :type position_embed_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  img_size: int,
                  patch_size: int,
                  in_chans: int,
                  embed_size: int,
-                 dtype: dtype = None,
                  flatten: bool = True,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                  position_embed_initializer: Callable = init.zeros_()):
@@ -284,8 +373,8 @@ class PatchEmbedding3D(ParallelLayer):
         set_tensor_parallel_attribute_by_partition(self.cls_token, self.depth)
         set_tensor_parallel_attribute_by_partition(self.pos_embed, self.depth)
 
-    def _sync_grad_hook(self, grad) -> None:
-        grad = all_reduce(grad, self.input_parallel_mode)
+    def _sync_grad_hook(self, grad) -> Tensor:
+        grad = all_reduce(grad.clone(), self.input_parallel_mode)
         grad = all_reduce(grad, self.weight_parallel_mode)
         return grad
 
@@ -302,17 +391,19 @@ class PatchEmbedding3D(ParallelLayer):
         broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
         broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
         broadcast(self.pos_embed, weight_src_rank, self.weight_parallel_mode)
+        broadcast(self.weight, input_src_rank, self.input_parallel_mode)
         broadcast(self.bias, input_src_rank, self.input_parallel_mode)
         broadcast(self.pos_embed, input_src_rank, self.input_parallel_mode)
 
+        self.weight.register_hook(self._sync_grad_hook)
         self.bias.register_hook(self._sync_grad_hook)
         self.cls_token.register_hook(self._sync_grad_hook)
         self.pos_embed.register_hook(self._sync_grad_hook)
 
     def forward(self, input_: Tensor) -> Tensor:
-        weight = broadcast_weight_3d_from_diagonal.apply(self.weight, self.input_parallel_mode,
-                                                         self.weight_parallel_mode, self.output_parallel_mode)
-        output = F.conv2d(input_, weight, self.bias, stride=self.patch_size)
+        input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
+        input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
+        output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
         if self.flatten:
             output = output.flatten(2).transpose(1, 2)  # BCHW -> BNC
 
@@ -341,11 +432,12 @@ class Embedding3D(ParallelLayer):
     :param args: Args used in F.embedding
     :param kwargs: Kwargs used in F.embedding
     """
+
     def __init__(self,
                  num_embeddings: int,
                  embedding_dim: int,
                  padding_idx: int = None,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.normal_(),
                  *args,
                  **kwargs):
@@ -385,8 +477,95 @@ class Embedding3D(ParallelLayer):
                 self.weight[self.padding_idx].fill_(0)
 
     def forward(self, input_: Tensor) -> Tensor:
-        weight = broadcast_weight_3d_from_diagonal.apply(self.weight, self.input_parallel_mode,
-                                                         self.weight_parallel_mode, self.output_parallel_mode)
+        input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
+        input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
+        weight = broadcast_weight_3d_from_diagonal(self.weight, self.input_parallel_mode, self.weight_parallel_mode,
+                                                   self.output_parallel_mode)
         output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
 
         return output
+
+
+@LAYERS.register_module
+class VocabParallelEmbedding3D(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    :param num_embeddings: number of embeddings
+    :type num_embeddings: int
+    :param embedding_dim: dimension of embedding
+    :type embedding_dim: int
+    :param padding_idx: index of padding, defaults to None
+    :type padding_idx: int, optional
+    :param dtype: The dtype of parameters, defaults to None
+    :type dtype: torch.dtype, optional
+    :param weight_initializer: The intializer of weight, defaults to normal initializer
+    :type weight_initializer: typing.Callable, optional
+    :param args: Args used in F.embedding
+    :param kwargs: Kwargs used in F.embedding
+    """
+
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 padding_idx: int = None,
+                 dtype: torch.dtype = None,
+                 weight_initializer: Callable = init.normal_(),
+                 *args,
+                 **kwargs):
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embed_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.embed_args = args
+        self.embed_kwargs = kwargs
+
+        self.depth = get_depth_from_env()
+        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+        self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
+        self.num_embeddings_per_partition = divide(self.num_embeddings, self.depth)
+        self.embed_dim_per_partition = divide(self.embed_dim, self.depth)
+        vocab_parallel_rank = gpc.get_local_rank(self.input_parallel_mode)
+        self.vocab_start_index = vocab_parallel_rank * self.num_embeddings_per_partition
+        self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
+
+        self.weight = Parameter(
+            torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
+                        device=get_current_device(),
+                        dtype=dtype))
+
+        self.reset_parameters(weight_initializer)
+        self._set_tensor_parallel_attributes()
+        env.vocab_parallel = True
+
+    def _set_tensor_parallel_attributes(self):
+        set_tensor_parallel_attribute_by_partition(self.weight, self.depth**2)
+
+    def reset_parameters(self, weight_initializer) -> None:
+        with seed(ParallelMode.TENSOR):
+            fan_in, fan_out = self.num_embeddings, self.embed_dim
+            weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+            self._fill_padding_idx_with_zero()
+        weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
+        broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
+
+    def _fill_padding_idx_with_zero(self) -> None:
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.weight[self.padding_idx].fill_(0)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
+
+        input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
+        masked_input = input_.clone() - self.vocab_start_index
+        masked_input[input_mask] = 0
+
+        weight = reduce_grad_3d(self.weight, self.weight_parallel_mode)
+
+        output_parallel = F.embedding(masked_input, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
+
+        output_parallel[input_mask, :] = 0.
+        output = reduce_scatter_tensor_3d(output_parallel, 0, self.input_parallel_mode)
+
+        return output

colossalai/nn/layer/utils/common.py

@@ -2,12 +2,12 @@
 # -*- encoding: utf-8 -*-
 
 import collections.abc
-import os
 from itertools import repeat
 
 import numpy as np
 import torch
-from colossalai.constants import (IS_TENSOR_PARALLEL, NUM_PARTITIONS, TENSOR_PARALLEL_MODE)
+from colossalai.constants import IS_TENSOR_PARALLEL, NUM_PARTITIONS
+from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.utils import checkpoint
 from torch import Tensor, nn
 
@@ -38,7 +38,7 @@ class CheckpointModule(nn.Module):
 
 def divide(numerator, denominator):
     """Only allow exact division
-
+    
     :param numerator: Numerator of the division
     :param denominator: Denominator of the division
     """
@@ -65,7 +65,7 @@ def set_tensor_parallel_attribute_by_partition(param, num_partitions):
 
 
 def get_tensor_parallel_mode():
-    return os.environ[TENSOR_PARALLEL_MODE]
+    return env.mode
 
 
 # From PyTorch internals

colossalai/nn/layer/vanilla/layers.py

@@ -3,14 +3,14 @@ from typing import Callable
 
 import torch
 import torch.nn.functional as F
+from colossalai.context import seed
 from colossalai.nn import init as init
 from colossalai.registry import LAYERS
-from colossalai.utils import get_current_device
-from torch import Tensor, dtype
+from colossalai.utils.cuda import get_current_device
+from torch import Tensor
 from torch import nn as nn
 
 from ..utils import to_2tuple
-from colossalai.context import seed
 
 
 def drop_path(x, drop_prob: float = 0., training: bool = False):
@@ -36,6 +36,7 @@ class DropPath(nn.Module):
     Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
     Adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
     """
+
     def __init__(self, drop_prob=None):
         super(DropPath, self).__init__()
         self.drop_prob = drop_prob
@@ -47,6 +48,7 @@ class DropPath(nn.Module):
 class WrappedDropout(nn.Module):
     """Same as torch.nn.Dropout. But it is wrapped with the context of seed manager.
     """
+
     def __init__(self, p: float = 0.5, inplace: bool = False, mode=None):
         super().__init__()
         if p < 0 or p > 1:
@@ -75,6 +77,7 @@ class WrappedDropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
     Here, it is wrapped with the context of seed manager.
     """
+
     def __init__(self, p: float = 0., mode=None):
         super().__init__()
         self.p = p
@@ -120,13 +123,14 @@ class VanillaPatchEmbedding(nn.Module):
     :param position_embed_initializer: The intializer of position embedding, defaults to zero
     :type position_embed_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  img_size: int,
                  patch_size: int,
                  in_chans: int,
                  embed_size: int,
-                 dtype: dtype = None,
                  flatten: bool = True,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
                  position_embed_initializer: Callable = init.zeros_()):
@@ -142,8 +146,9 @@ class VanillaPatchEmbedding(nn.Module):
         self.weight = nn.Parameter(
             torch.empty((embed_size, in_chans, *self.patch_size), device=get_current_device(), dtype=dtype))
         self.bias = nn.Parameter(torch.empty(embed_size, device=get_current_device(), dtype=dtype))
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_size))
-        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_size))
+        self.cls_token = nn.Parameter(torch.zeros((1, 1, embed_size), device=get_current_device(), dtype=dtype))
+        self.pos_embed = nn.Parameter(
+            torch.zeros((1, self.num_patches + 1, embed_size), device=get_current_device(), dtype=dtype))
 
         self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
 
@@ -170,7 +175,7 @@ class VanillaPatchEmbedding(nn.Module):
 @LAYERS.register_module
 class VanillaClassifier(nn.Module):
     """
-    Classifier for ViT
+    Dense linear classifier
 
     :param in_features: size of each input sample
     :type in_features: int
@@ -187,12 +192,13 @@ class VanillaClassifier(nn.Module):
     :param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
     :type bias_initializer: typing.Callable, optional
     """
+
     def __init__(self,
                  in_features: int,
                  num_classes: int,
                  weight: nn.Parameter = None,
                  bias: bool = True,
-                 dtype: dtype = None,
+                 dtype: torch.dtype = None,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         super().__init__()

colossalai/nn/loss/__init__.py

@@ -1,25 +1,37 @@
+from colossalai.global_variables import tensor_parallel_env as env
+from colossalai.nn.layer.utils import get_tensor_parallel_mode
 from torch import nn
 from torch.nn.modules.loss import *
 from torch.nn.modules.loss import _Loss
 
-from colossalai.nn.layer.utils import get_tensor_parallel_mode 
-from .loss_2d import CrossEntropyLoss2D
-from .loss_2p5d import CrossEntropyLoss2p5D
-from .loss_3d import CrossEntropyLoss3D
+from .loss_1d import VocabParallelCrossEntropyLoss1D
+from .loss_2d import CrossEntropyLoss2D, VocabParallelCrossEntropyLoss2D
+from .loss_2p5d import CrossEntropyLoss2p5D, VocabParallelCrossEntropyLoss2p5D
+from .loss_3d import CrossEntropyLoss3D, VocabParallelCrossEntropyLoss3D
 from .loss_moe import MoeCrossEntropyLoss, MoeLoss
 
 _parallel_cross_entropy = {
     '2d': CrossEntropyLoss2D,
     '2.5d': CrossEntropyLoss2p5D,
-    '3d': CrossEntropyLoss3D
+    '3d': CrossEntropyLoss3D,
+}
+
+_vocab_parallel_cross_entropy = {
+    '1d': VocabParallelCrossEntropyLoss1D,
+    '2d': VocabParallelCrossEntropyLoss2D,
+    '2.5d': VocabParallelCrossEntropyLoss2p5D,
+    '3d': VocabParallelCrossEntropyLoss3D,
 }
 
 
 class CrossEntropyLoss(_Loss):
+
     def __init__(self, reduction: bool = True, *args, **kwargs):
         super().__init__()
         tensor_parallel = get_tensor_parallel_mode()
-        if tensor_parallel in ['None', '1d']:
+        if tensor_parallel is not None and env.vocab_parallel:
+            self.loss = _vocab_parallel_cross_entropy[tensor_parallel](reduction=reduction, *args, **kwargs)
+        elif tensor_parallel is None or tensor_parallel == '1d':
             reduction = 'mean' if reduction else 'none'
             self.loss = nn.CrossEntropyLoss(reduction=reduction, *args, **kwargs)
         else:

colossalai/nn/loss/loss_1d.py

@@ -0,0 +1,110 @@
+import torch
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.registry import LOSSES
+from torch.cuda.amp import custom_bwd, custom_fwd
+from torch.nn.modules.loss import _Loss
+
+
+class _VocabParallelCrossEntropy1D(torch.autograd.Function):
+
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(ctx, vocab_parallel_logits, targets):
+
+        # Maximum value along vocab dimension across all GPUs.
+        logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
+        torch.distributed.all_reduce(logits_max,
+                                     op=torch.distributed.ReduceOp.MAX,
+                                     group=gpc.get_group(ParallelMode.PARALLEL_1D))
+        # Subtract the maximum value.
+        vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
+
+        # Get the partition's vocab indecies
+        partition_vocab_size = vocab_parallel_logits.size()[-1]
+        rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+        vocab_start_index = partition_vocab_size * rank
+        vocab_end_index = vocab_start_index + partition_vocab_size
+
+        # Create a mask of valid vocab ids (1 means it needs to be masked).
+        target_mask = (targets < vocab_start_index) | (targets >= vocab_end_index)
+        masked_target = targets.clone() - vocab_start_index
+        masked_target[target_mask] = 0
+
+        # Get predicted-logits = logits[target].
+        # For Simplicity, we convert logits to a 2-D tensor with size
+        # [*, partition-vocab-size] and target to a 1-D tensor of size [*].
+        logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
+        masked_target_1d = masked_target.view(-1)
+        arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
+        predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
+        predicted_logits_1d = predicted_logits_1d.clone().contiguous()
+        predicted_logits = predicted_logits_1d.view_as(targets)
+        predicted_logits[target_mask] = 0.0
+        # All reduce is needed to get the chunks from other GPUs.
+        torch.distributed.all_reduce(predicted_logits,
+                                     op=torch.distributed.ReduceOp.SUM,
+                                     group=gpc.get_group(ParallelMode.PARALLEL_1D))
+
+        # Sum of exponential of logits along vocab dimension across all GPUs.
+        exp_logits = vocab_parallel_logits
+        torch.exp(vocab_parallel_logits, out=exp_logits)
+        sum_exp_logits = exp_logits.sum(dim=-1)
+        torch.distributed.all_reduce(sum_exp_logits,
+                                     op=torch.distributed.ReduceOp.SUM,
+                                     group=gpc.get_group(ParallelMode.PARALLEL_1D))
+
+        # Loss = log(sum(exp(logits))) - predicted-logit.
+        loss = torch.log(sum_exp_logits) - predicted_logits
+        # Store softmax, target-mask and masked-target for backward pass.
+        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+        ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
+        return loss
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, grad_output):
+
+        # Retreive tensors from the forward path.
+        softmax, target_mask, masked_target_1d = ctx.saved_tensors
+
+        # All the inputs have softmax as thier gradient.
+        grad_input = softmax
+        # For simplicity, work with the 2D gradient.
+        partition_vocab_size = softmax.size()[-1]
+        grad_2d = grad_input.view(-1, partition_vocab_size)
+
+        # Add the gradient from matching classes.
+        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
+        grad_2d[arange_1d, masked_target_1d] -= (1.0 - target_mask.view(-1).float())
+
+        # Finally elementwise multiplication with the output gradients.
+        grad_input.mul_(grad_output.unsqueeze(dim=-1))
+
+        return grad_input, None
+
+
+@LOSSES.register_module
+class VocabParallelCrossEntropyLoss1D(_Loss):
+    """
+    Vocab parallel cross entropy loss for 1D parallelism
+
+    :param reduction: whether to average the loss, defaults to True
+
+    :type reduction: bool, optional
+    """
+
+    def __init__(self, reduction=True):
+        super().__init__()
+        self.reduction_mean = reduction
+
+    def forward(self, logits, targets):
+        """Calculate loss between logits and targets
+
+        :param logits: Output logits of model
+        :param targets: True targets from data
+        """
+        loss = _VocabParallelCrossEntropy1D.apply(logits, targets)
+        if self.reduction_mean:
+            loss = loss.mean()
+        return loss

colossalai/nn/loss/loss_2d.py

@@ -1,6 +1,12 @@
-from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d
+import torch
+import torch.distributed as dist
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d, split_tensor_2d
 from colossalai.nn.layer.parallel_2d._utils import assert_summa_initialization
 from colossalai.registry import LOSSES
+from colossalai.utils import get_current_device
+from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.nn.functional import cross_entropy
 from torch.nn.modules.loss import _Loss
 
@@ -16,6 +22,7 @@ class CrossEntropyLoss2D(_Loss):
 
     :type reduction: bool, optional
     """
+
     def __init__(self, reduction=True, *args, **kwargs):
         super().__init__()
         assert_summa_initialization()
@@ -29,8 +36,110 @@ class CrossEntropyLoss2D(_Loss):
         :param logits: Output logits of model
         :param targets: True targets from data
         """
+        targets = split_tensor_2d(targets)
         loss = cross_entropy(logits, targets, reduction='none', *self.loss_args, **self.loss_kwargs)
         if self.reduction_mean:
             loss = loss.mean()
-            loss = reduce_by_batch_2d.apply(loss, True)
+            loss = reduce_by_batch_2d(loss, True)
+        return loss
+
+
+class _VocabParallelCrossEntropy2D(torch.autograd.Function):
+    ### Modified based on megatron.mpu.cross_entropy ###
+
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(ctx, logits, targets):
+        # logits: [b/q, h/q]
+        # labels: [b/q]
+        # loss: [b/q]
+        # vocab_parallel_logits: [b/q, s, v/q]
+        # target: [b/q, s]
+        logits_max = torch.max(logits, dim=-1)[0]
+        torch.distributed.all_reduce(logits_max,
+                                     op=torch.distributed.ReduceOp.MAX,
+                                     group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
+        # Subtract the maximum value.
+        # vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
+        logits = logits - logits_max.unsqueeze(dim=-1)
+
+        vocab_size = logits.size(-1)
+        rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+        vocab_start = rank * (vocab_size)
+        vocab_end = (rank + 1) * (vocab_size) - 1
+
+        target_mask = (targets < vocab_start) | (targets > vocab_end)
+
+        masked_target = targets.clone() - vocab_start
+        masked_target[target_mask] = 0
+        arange_1d = torch.arange(
+            start=0,
+            end=logits.size()[0],
+        )
+        predicted_logits = logits[arange_1d, masked_target]
+        predicted_logits[target_mask] = 0.
+        dist.all_reduce(predicted_logits, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
+
+        exp_logits = torch.exp(logits)
+        sum_exp_logits = exp_logits.sum(dim=1)
+        dist.all_reduce(sum_exp_logits, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
+
+        loss = torch.log(sum_exp_logits) - predicted_logits
+
+        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+        ctx.save_for_backward(exp_logits, target_mask, masked_target)
+
+        return loss
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, output_grad):
+        # Retreive tensors from the forward path.
+        softmax, target_mask, masked_target = ctx.saved_tensors
+
+        # All the inputs have softmax as their gradient.
+        grad_input = softmax
+
+        # For simplicity, work with the 2D gradient.
+        partition_vocab_size = softmax.size()[-1]
+        grad_2d = grad_input.view(-1, partition_vocab_size)
+
+        # Add the gradient from matching classes.
+        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=get_current_device())
+        grad_2d[arange_1d, masked_target] -= (1.0 - target_mask.view(-1).float())
+
+        # Finally elementwise multiplication with the output gradients.
+        grad_input.mul_(output_grad.unsqueeze(dim=-1))
+
+        return grad_input, None
+
+
+@LOSSES.register_module
+class VocabParallelCrossEntropyLoss2D(_Loss):
+    """
+    Vocab parallel cross entropy loss for 2D parallelism
+
+    :param reduction: whether to average the loss, defaults to True
+
+    :type reduction: bool, optional
+    """
+
+    def __init__(self, reduction=True):
+        super().__init__()
+        self.reduction_mean = reduction
+
+    def forward(self, logits, targets):
+        """Calculate loss between logits and targets
+
+        :param logits: Output logits of model
+        :param targets: True targets from data
+        """
+        targets = split_tensor_2d(targets)
+        loss = _VocabParallelCrossEntropy2D.apply(
+            logits,
+            targets,
+        )
+        if self.reduction_mean:
+            loss = loss.mean()
+            loss = reduce_by_batch_2d(loss, True)
         return loss

colossalai/nn/loss/loss_2p5d.py

@@ -1,6 +1,12 @@
-from colossalai.nn.layer.parallel_2p5d import reduce_by_batch_2p5d
+import torch
+import torch.distributed as dist
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.nn.layer.parallel_2p5d import reduce_by_batch_2p5d, split_tensor_2p5d
 from colossalai.nn.layer.parallel_2p5d._utils import assert_tesseract_initialization
 from colossalai.registry import LOSSES
+from colossalai.utils import get_current_device
+from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.nn.functional import cross_entropy
 from torch.nn.modules.loss import _Loss
 
@@ -9,7 +15,7 @@ from torch.nn.modules.loss import _Loss
 class CrossEntropyLoss2p5D(_Loss):
     """
     Cross entropy loss for 2.5D parallelism
-    
+
     :param reduction: whether to average the loss, defaults to True
     :param args: Args for loss function
     :param kwargs: Kwargs for loss function
@@ -29,8 +35,104 @@ class CrossEntropyLoss2p5D(_Loss):
         :param logits: Output logits of model
         :param targets: True targets from data
         """
+        targets = split_tensor_2p5d(targets)
         loss = cross_entropy(logits, targets, reduction='none', *self.loss_args, **self.loss_kwargs)
         if self.reduction_mean:
             loss = loss.mean()
-            loss = reduce_by_batch_2p5d.apply(loss, True)
+            loss = reduce_by_batch_2p5d(loss, True)
+        return loss
+
+
+class _VocabParallelCrossEntropy2p5D(torch.autograd.Function):
+    ### Modified based on megatron.mpu.cross_entropy ###
+
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(ctx, logits, targets):
+        # logits: [b/dq, h/q]
+        # loss: [b/dq]
+        # targets: [b/dq, h/q]
+        logits_max = torch.max(logits, dim=-1)[0]
+        torch.distributed.all_reduce(logits_max,
+                                     op=torch.distributed.ReduceOp.MAX,
+                                     group=gpc.get_group(ParallelMode.PARALLEL_2P5D_ROW))
+        # Subtract the maximum value.
+        logits = logits - logits_max.unsqueeze(dim=-1)
+
+        vocab_size = logits.size(-1)
+        rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+        vocab_start = rank * (vocab_size)
+        vocab_end = (rank + 1) * (vocab_size) - 1
+
+        target_mask = (targets < vocab_start) | (targets > vocab_end)
+
+        masked_target = targets.clone() - vocab_start
+        masked_target[target_mask] = 0
+        arange_1d = torch.arange(
+            start=0,
+            end=logits.size()[0],
+        )
+        predicted_logits = logits[arange_1d, masked_target]
+        predicted_logits[target_mask] = 0.
+        dist.all_reduce(predicted_logits, group=gpc.get_group(ParallelMode.PARALLEL_2P5D_ROW))
+
+        exp_logits = torch.exp(logits)
+        sum_exp_logits = exp_logits.sum(dim=1)
+        dist.all_reduce(sum_exp_logits, group=gpc.get_group(ParallelMode.PARALLEL_2P5D_ROW))
+
+        loss = torch.log(sum_exp_logits) - predicted_logits
+
+        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+        ctx.save_for_backward(exp_logits, target_mask, masked_target)
+
+        return loss
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, output_grad):
+        # Retreive tensors from the forward path.
+        softmax, target_mask, masked_target = ctx.saved_tensors
+
+        # All the inputs have softmax as their gradient.
+        grad_input = softmax
+
+        # For simplicity, work with the 2D gradient.
+        partition_vocab_size = softmax.size()[-1]
+        grad_2d = grad_input.view(-1, partition_vocab_size)
+
+        # Add the gradient from matching classes.
+        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=get_current_device())
+        grad_2d[arange_1d, masked_target] -= (1.0 - target_mask.view(-1).float())
+
+        # Finally elementwise multiplication with the output gradients.
+        grad_input.mul_(output_grad.unsqueeze(dim=-1))
+
+        return grad_input, None
+
+
+@LOSSES.register_module
+class VocabParallelCrossEntropyLoss2p5D(_Loss):
+    """
+    Vocab parallel cross entropy loss for 2.5D parallelism
+
+    :param reduction: whether to average the loss, defaults to True
+
+    :type reduction: bool, optional
+    """
+    def __init__(self, reduction=True):
+        super().__init__()
+        self.reduction_mean = reduction
+
+    def forward(self, logits, targets):
+        """Calculate loss between logits and targets
+
+        :param logits: Output logits of model
+        :param targets: True targets from data
+        """
+        targets = split_tensor_2p5d(targets)
+        loss = _VocabParallelCrossEntropy2p5D.apply(logits, targets)
+        if self.reduction_mean:
+            loss = loss.mean()
+            loss = reduce_by_batch_2p5d(loss, True)
+
         return loss

colossalai/nn/loss/loss_3d.py

@@ -1,23 +1,28 @@
-from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
-from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d
+import torch
+import torch.distributed as dist
+from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D
+from colossalai.core import global_context as gpc
+from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
 from colossalai.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
 from colossalai.registry import LOSSES
+from colossalai.utils import get_current_device
+from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.nn.functional import cross_entropy
 from torch.nn.modules.loss import _Loss
 
+
 @LOSSES.register_module
 class CrossEntropyLoss3D(_Loss):
     """
     Cross entropy loss for 3D parallelism
 
-    :param depth: depth for 3D parallelism
-    :type depth: int
     :param reduction: whether to average the loss, defaults to True
-    :type reduction: bool, optional
-
     :param args: Args for loss function
     :param kwargs: Kwargs for loss function
+
+    :type reduction: bool, optional
     """
+
     def __init__(self, reduction=True, *args, **kwargs):
         super().__init__()
         self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
@@ -32,8 +37,103 @@ class CrossEntropyLoss3D(_Loss):
         :param logits: Output logits of model
         :param targets: True targets from data
         """
+        targets = split_tensor_3d(targets, 0, self.weight_parallel_mode)
+        targets = split_tensor_3d(targets, 0, self.input_parallel_mode)
         loss = cross_entropy(logits, targets, reduction='none', *self.loss_args, **self.loss_kwargs)
         if self.reduction_mean:
             loss = loss.mean()
-            loss = reduce_by_batch_3d.apply(loss, self.input_parallel_mode, self.weight_parallel_mode, True)
+            loss = reduce_by_batch_3d(loss, self.input_parallel_mode, self.weight_parallel_mode, True)
+        return loss
+
+
+class _VocabParallelCrossEntropy3D(torch.autograd.Function):
+    # Adapted from megatron.mpu.cross_entropy
+    # loss[i] = -logits[i][targets] + log(sum(exp(logits[i])))
+
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(ctx, logits, targets, output_parallel_mode):
+        # logits: [b/q^2, c/q]
+        # labels: [b/q^2]
+        # loss: [b/q^2]
+        logits_max = torch.max(logits, dim=-1)[0]
+        dist.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=gpc.get_group(output_parallel_mode))
+        # Subtract the maximum value.
+        logits = logits - logits_max.unsqueeze(dim=-1)
+
+        vocab_size_per_partition = logits.size()[-1]
+        rank = gpc.get_local_rank(output_parallel_mode)
+        vocab_start = rank * vocab_size_per_partition
+        vocab_end = (rank + 1) * vocab_size_per_partition - 1
+
+        # loss[i] = 0 if targets[i] < vocab_start or targets[i] > vocab_end
+        target_mask = (targets < vocab_start) | (targets > vocab_end)
+        masked_target = targets.clone() - vocab_start
+        masked_target[target_mask] = 0
+        arange_1d = torch.arange(start=0, end=logits.size()[0], device=get_current_device())
+        predicted_logits = logits[arange_1d, masked_target]
+        predicted_logits = predicted_logits.clone().contiguous().view_as(targets)
+        predicted_logits[target_mask] = 0.
+        dist.all_reduce(predicted_logits, group=gpc.get_group(output_parallel_mode))
+
+        # Loss = log(sum(exp(logits))) - predicted-logit.
+        exp_logits = torch.exp(logits)
+        sum_exp_logits = exp_logits.sum(dim=-1)
+        dist.all_reduce(sum_exp_logits, group=gpc.get_group(output_parallel_mode))
+        loss = torch.log(sum_exp_logits) - predicted_logits
+
+        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+        ctx.save_for_backward(exp_logits, target_mask, masked_target)
+
+        return loss
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, output_grad):
+        # Retreive tensors from the forward path.
+        softmax, target_mask, masked_target = ctx.saved_tensors
+
+        # All the inputs have softmax as thier gradient.
+        input_grad = softmax
+        # For simplicity, work with the 2D gradient.
+        partition_vocab_size = softmax.size()[-1]
+        grad_2d = input_grad.view(-1, partition_vocab_size)
+
+        # Add the gradient from matching classes.
+        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=get_current_device())
+        grad_2d[arange_1d, masked_target] -= (1.0 - target_mask.view(-1).float())
+        input_grad.mul_(output_grad.unsqueeze(dim=-1))
+
+        return input_grad, None, None, None
+
+
+@LOSSES.register_module
+class VocabParallelCrossEntropyLoss3D(_Loss):
+    """
+    Vocab parallel cross entropy loss for 2D parallelism
+
+    :param reduction: whether to average the loss, defaults to True
+
+    :type reduction: bool, optional
+    """
+
+    def __init__(self, reduction=True):
+        super().__init__()
+        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+        self.output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+        self.reduction_mean = reduction
+
+    def forward(self, logits, targets):
+        """Calculate loss between logits and targets
+
+        :param logits: Output logits of model
+        :param targets: True targets from data
+        """
+        targets = split_tensor_3d(targets, 0, self.weight_parallel_mode)
+        targets = split_tensor_3d(targets, 0, self.input_parallel_mode)
+        loss = _VocabParallelCrossEntropy3D.apply(logits, targets, self.output_parallel_mode)
+        if self.reduction_mean:
+            loss = loss.mean()
+            loss = reduce_by_batch_3d(loss, self.input_parallel_mode, self.weight_parallel_mode, True)
         return loss

colossalai/nn/metric/__init__.py

@@ -17,7 +17,7 @@ class Accuracy(nn.Module):
     def __init__(self):
         super().__init__()
         tensor_parallel = get_tensor_parallel_mode()
-        if tensor_parallel in ['None', '1d']:
+        if tensor_parallel not in _parallel_accuracy:
             self.acc = calc_acc
         else:
             self.acc = _parallel_accuracy[tensor_parallel]()

colossalai/nn/metric/accuracy_2d.py

@@ -1,5 +1,5 @@
 import torch
-from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d
+from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d, split_tensor_2d
 from torch import nn
 
 from ._utils import calc_acc
@@ -18,6 +18,7 @@ class Accuracy2D(nn.Module):
         :param targets: True labels from data
         """
         with torch.no_grad():
+            targets = split_tensor_2d(targets)
             correct = calc_acc(logits, targets)
-            correct = reduce_by_batch_2d.apply(correct)
+            correct = reduce_by_batch_2d(correct)
         return correct

colossalai/nn/metric/accuracy_2p5d.py

@@ -18,6 +18,7 @@ class Accuracy2p5D(nn.Module):
         :param targets: True labels from data
         """
         with torch.no_grad():
+            targets = split_tensor_2p5d(targets)
             correct = calc_acc(logits, targets)
-            correct = reduce_by_batch_2p5d.apply(correct)
+            correct = reduce_by_batch_2p5d(correct)
         return correct

colossalai/nn/metric/accuracy_3d.py

@@ -1,6 +1,6 @@
 import torch
 from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
-from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d
+from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
 from colossalai.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
 from torch import nn
 
@@ -22,6 +22,8 @@ class Accuracy3D(nn.Module):
          :param targets: True labels from data
          """
         with torch.no_grad():
+            targets = split_tensor_3d(targets, 0, self.weight_parallel_mode)
+            targets = split_tensor_3d(targets, 0, self.input_parallel_mode)
             correct = calc_acc(logits, targets)
-            correct = reduce_by_batch_3d.apply(correct, self.input_parallel_mode, self.weight_parallel_mode)
+            correct = reduce_by_batch_3d(correct, self.input_parallel_mode, self.weight_parallel_mode)
         return correct

colossalai/trainer/hooks/_log_hook.py

@@ -224,7 +224,7 @@ class LogTimingByEpochHook(LogByEpochHook):
         super().__init__(logger=logger, interval=interval, priority=priority)
         self._timer = timer
         self._log_eval = log_eval
-        self._is_rank_to_log = is_dp_rank_0() and is_tp_rank_0()
+        self._is_rank_to_log = is_dp_rank_0() and is_tp_rank_0() and is_no_pp_or_last_stage()
 
         # extra handling to avoid the unstable readings of the first
         # few training steps to affect the history mean time
@@ -256,7 +256,7 @@ class LogTimingByEpochHook(LogByEpochHook):
         """
         if self._is_epoch_to_log(trainer) and self._is_rank_to_log:
             msg = self._get_message('Train')
-            self.logger.info(f'[Epoch {trainer.cur_epoch} / Train]: {msg}, #steps/epoch = {trainer.steps_per_epoch}')
+            self.logger.info(f'[Epoch {trainer.cur_epoch} / Train]: {msg} | #steps/epoch = {trainer.steps_per_epoch}')
 
     def after_test_epoch(self, trainer):
         """Writes log after finishing a testing epoch.

colossalai/trainer/hooks/_metric_hook.py

@@ -317,24 +317,29 @@ class ThroughputMetric(Metric):
     :param epoch_only: epoch only
     :type epoch_only: bool
     """
-    def __init__(self, epoch_only: bool):
+    def __init__(self, epoch_only: bool, ignored_steps: int = 0):
         super().__init__(epoch_only=epoch_only)
+        self.ignored_steps = ignored_steps
+        self.cur_steps = 0
         self.accumulated_num_samples = torch.zeros(1, device=get_current_device())
         self.accumulated_used_time = torch.zeros(1, device=get_current_device())
         self.last_step_num_samples = torch.zeros(1, device=get_current_device())
         self.last_step_used_time = torch.zeros(1, device=get_current_device())
 
     def reset(self) -> None:
+        # self.cur_steps = 0
         self.accumulated_num_samples.zero_()
         self.accumulated_used_time.zero_()
         self.last_step_num_samples.zero_()
         self.last_step_used_time.zero_()
 
     def update(self, num_samples, time) -> None:
+        self.cur_steps += 1
         self.last_step_num_samples.fill_(num_samples)
         self.last_step_used_time.fill_(time)
-        self.accumulated_num_samples += self.last_step_num_samples
-        self.accumulated_used_time += self.last_step_used_time
+        if self.cur_steps >= self.ignored_steps:
+            self.accumulated_num_samples += self.last_step_num_samples
+            self.accumulated_used_time += self.last_step_used_time
 
     def get_last_step_value(self):
         self.last_step_used_time = all_reduce(self.last_step_used_time, ParallelMode.DATA) / \
@@ -360,13 +365,14 @@ class ThroughputHook(MetricHook):
     :param priority: priority of throughput hook, defaults to 10
     :type priority: int, optional
     """
-    def __init__(self, priority: int = 10):
+    def __init__(self, ignored_steps: int = 0, priority: int = 10):
         super().__init__(priority)
+        self.ignored_steps = ignored_steps
 
     def after_hook_is_attached(self, trainer):
         self._check_metric_states_initialization(trainer)
         if self._is_stage_to_compute:
-            self.metric = ThroughputMetric(epoch_only=True)
+            self.metric = ThroughputMetric(epoch_only=True, ignored_steps=self.ignored_steps)
 
             # register the metric
             trainer.states['metrics']['train']['Throughput'] = self.metric

colossalai/utils/__init__.py

@@ -1,8 +1,9 @@
 from .activation_checkpoint import checkpoint
 from .common import (clip_grad_norm_fp32, conditional_context, copy_tensor_parallel_attributes, count_zeros_fp32,
                      free_port, is_dp_rank_0, is_model_parallel_parameter, is_no_pp_or_last_stage, is_tp_rank_0,
-                     is_using_ddp, is_using_pp, is_using_sequence, multi_tensor_applier, param_is_not_tensor_parallel_duplicate,
-                     print_rank_0, switch_virtual_pipeline_parallel_rank, sync_model_param)
+                     is_using_ddp, is_using_pp, is_using_sequence, model_branch_context, multi_tensor_applier,
+                     param_is_not_tensor_parallel_duplicate, print_rank_0, switch_virtual_pipeline_parallel_rank,
+                     sync_model_param)
 from .cuda import empty_cache, get_current_device, set_to_cuda, synchronize
 from .data_sampler import DataParallelSampler, get_dataloader
 from .gradient_accumulation import accumulate_gradient
@@ -11,9 +12,9 @@ from .timer import MultiTimer, Timer
 
 __all__ = [
     'checkpoint', 'free_port', 'print_rank_0', 'sync_model_param', 'is_dp_rank_0', 'is_tp_rank_0',
-    'is_no_pp_or_last_stage', 'is_using_ddp', 'is_using_pp', 'is_using_sequence', 'conditional_context',
-    'is_model_parallel_parameter', 'clip_grad_norm_fp32', 'count_zeros_fp32', 'copy_tensor_parallel_attributes',
-    'param_is_not_tensor_parallel_duplicate', 'get_current_device', 'synchronize', 'empty_cache', 'set_to_cuda',
-    'report_memory_usage', 'Timer', 'MultiTimer', 'multi_tensor_applier', 'accumulate_gradient', 'DataParallelSampler',
-    'get_dataloader', 'switch_virtual_pipeline_parallel_rank'
+    'is_no_pp_or_last_stage', 'is_using_ddp', 'is_using_pp', 'is_using_sequence', 'model_branch_context',
+    'conditional_context', 'is_model_parallel_parameter', 'clip_grad_norm_fp32', 'count_zeros_fp32',
+    'copy_tensor_parallel_attributes', 'param_is_not_tensor_parallel_duplicate', 'get_current_device', 'synchronize',
+    'empty_cache', 'set_to_cuda', 'report_memory_usage', 'Timer', 'MultiTimer', 'multi_tensor_applier',
+    'accumulate_gradient', 'DataParallelSampler', 'get_dataloader', 'switch_virtual_pipeline_parallel_rank'
 ]

colossalai/utils/common.py

@@ -6,8 +6,6 @@ import socket
 import torch
 from torch._six import inf
 
-import colossalai.context.parallel_mode
-
 try:
     import colossal_C
 except:
@@ -20,6 +18,7 @@ from colossalai.constants import IS_TENSOR_PARALLEL, NUM_PARTITIONS, TENSOR_PARA
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.global_variables import moe_env
+from colossalai.global_variables import tensor_parallel_env as env
 
 from .multi_tensor_apply import multi_tensor_applier
 
@@ -62,8 +61,7 @@ def sync_model_param(model, parallel_mode):
     if gpc.is_initialized(parallel_mode) and gpc.get_world_size(parallel_mode) > 1:
         for param in model.parameters():
             ranks = gpc.get_ranks_in_group(parallel_mode)
-            dist.broadcast(
-                param, src=ranks[0], group=gpc.get_group(parallel_mode))
+            dist.broadcast(param, src=ranks[0], group=gpc.get_group(parallel_mode))
 
 
 def is_dp_rank_0():
@@ -99,6 +97,15 @@ def conditional_context(context_manager, enable=True):
         yield
 
 
+class model_branch_context(object):
+
+    def __enter__(self):
+        self.env_status = env.save()
+
+    def __exit__(self, *exc_info):
+        env.load(**self.env_status)
+
+
 def is_model_parallel_parameter(p):
     return hasattr(p, IS_TENSOR_PARALLEL) and getattr(p, IS_TENSOR_PARALLEL)
 
@@ -124,9 +131,10 @@ def _calc_lp(grads, norm_type):
     norm = 0.0
     for grad in grads:
         grad_norm = torch.norm(grad, norm_type)
-        norm += grad_norm ** norm_type
+        norm += grad_norm**norm_type
     return norm
 
+
 # ======== Gradient Clipping =========
 
 
@@ -183,7 +191,7 @@ def clip_grad_norm_fp32(parameters, max_norm, norm_type=2):
         moe_parallel_grads = []  # used to collect moe tensor parallel gradients
         for p in params:
             if is_model_parallel_parameter(p):
-                reductor = (gpc.get_world_size(ParallelMode.TENSOR) / getattr(p, NUM_PARTITIONS)) ** (1 / norm_type)
+                reductor = (gpc.get_world_size(ParallelMode.TENSOR) / getattr(p, NUM_PARTITIONS))**(1 / norm_type)
                 tensor_parallel_grads.append(p.grad.data / reductor)
             elif is_moe_parallel_parameter(p):
                 moe_parallel_grads.append(p.grad.data)
@@ -191,32 +199,24 @@ def clip_grad_norm_fp32(parameters, max_norm, norm_type=2):
                 no_tensor_parallel_grads.append(p.grad.data)
 
         if norm_type == 2.0:
-            tensor_parallel_norm = _calc_l2_norm(
-                tensor_parallel_grads) ** norm_type
-            no_tensor_parallel_norm = _calc_l2_norm(
-                no_tensor_parallel_grads) ** norm_type
-            moe_parallel_norm = _calc_l2_norm(
-                moe_parallel_grads) ** norm_type
+            tensor_parallel_norm = _calc_l2_norm(tensor_parallel_grads)**norm_type
+            no_tensor_parallel_norm = _calc_l2_norm(no_tensor_parallel_grads)**norm_type
+            moe_parallel_norm = _calc_l2_norm(moe_parallel_grads)**norm_type
         else:
             tensor_parallel_norm = _calc_lp(tensor_parallel_grads, norm_type)
-            no_tensor_parallel_norm = _calc_lp(
-                no_tensor_parallel_grads, norm_type)
+            no_tensor_parallel_norm = _calc_lp(no_tensor_parallel_grads, norm_type)
             moe_parallel_norm = _calc_lp(moe_parallel_grads, norm_type)
         # Sum across all model-parallel GPUs.
         if gpc.is_initialized(ParallelMode.TENSOR) and len(tensor_parallel_grads) > 0:
-            dist.all_reduce(tensor_parallel_norm,
-                            op=dist.ReduceOp.SUM,
-                            group=gpc.get_group(ParallelMode.TENSOR))
+            dist.all_reduce(tensor_parallel_norm, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.TENSOR))
         # Sum across all moe-tensor-parallel GPUs
         if len(moe_parallel_grads) > 0:
             dist.all_reduce(moe_parallel_norm, group=gpc.get_group(ParallelMode.MOE_MODEL))
             no_tensor_parallel_norm += moe_parallel_norm
         total_norm = tensor_parallel_norm + no_tensor_parallel_norm
         if gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1:
-            dist.all_reduce(total_norm,
-                            op=dist.ReduceOp.SUM,
-                            group=gpc.get_group(ParallelMode.PIPELINE))
-        total_norm = total_norm ** (1.0 / norm_type)
+            dist.all_reduce(total_norm, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.PIPELINE))
+        total_norm = total_norm**(1.0 / norm_type)
         if type(total_norm) == 'torch.cuda.FloatTensor':
             total_norm = total_norm.item()
 
@@ -225,10 +225,7 @@ def clip_grad_norm_fp32(parameters, max_norm, norm_type=2):
     if clip_coeff < 1.0:
         grads = [p.grad.detach() for p in params]
         dummy_overflow_buf = torch.cuda.IntTensor([0])
-        multi_tensor_applier(colossal_C.multi_tensor_scale,
-                             dummy_overflow_buf,
-                             [grads, grads],
-                             clip_coeff)
+        multi_tensor_applier(colossal_C.multi_tensor_scale, dummy_overflow_buf, [grads, grads], clip_coeff)
 
     return total_norm
 
@@ -254,15 +251,14 @@ def count_zeros_fp32(parameters):
 
     # Sum across all model-parallel GPUs.
     ops = []
-    ops.append(dist.all_reduce(total_num_zeros,
-                               op=dist.ReduceOp.SUM,
-                               group=gpc.get_group(ParallelMode.TENSOR),
-                               async_op=True))
+    ops.append(
+        dist.all_reduce(total_num_zeros, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.TENSOR), async_op=True))
     if gpc.is_initialized(ParallelMode.PIPELINE):
-        ops.append(dist.all_reduce(total_num_zeros,
-                                   op=dist.ReduceOp.SUM,
-                                   group=gpc.get_group(ParallelMode.PIPELINE),
-                                   async_op=True))
+        ops.append(
+            dist.all_reduce(total_num_zeros,
+                            op=dist.ReduceOp.SUM,
+                            group=gpc.get_group(ParallelMode.PIPELINE),
+                            async_op=True))
 
     for req in ops:
         req.wait()
@@ -279,9 +275,8 @@ def copy_tensor_parallel_attributes(src_tensor, dst_tensor):
 
 
 def param_is_not_tensor_parallel_duplicate(param):
-    return (hasattr(param, IS_TENSOR_PARALLEL) and
-            getattr(param, IS_TENSOR_PARALLEL)) or (
-        gpc.get_local_rank(ParallelMode.TENSOR) == 0)
+    return (hasattr(param, IS_TENSOR_PARALLEL) and getattr(param, IS_TENSOR_PARALLEL)) or (gpc.get_local_rank(
+        ParallelMode.TENSOR) == 0)
 
 
 @contextmanager

model_zoo/gpt/gpt.py

@@ -3,12 +3,20 @@ from typing import Callable
 
 import torch
 from colossalai import nn as col_nn
-from colossalai.nn.layer.utils import CheckpointModule
-from colossalai.registry import LAYERS, MODELS, LOSSES
+from colossalai.builder.pipeline import partition_uniform
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.logging import get_dist_logger
+from colossalai.nn.layer.utils import CheckpointModule, divide
+from colossalai.nn.layer.wrapper import PipelineSharedModuleWrapper
+from colossalai.registry import LAYERS, LOSSES, MODELS
 from colossalai.utils import get_current_device
 from torch import dtype, nn
 
-__all__ = ['GPT', 'GPTLMLoss', 'gpt2_small', 'gpt2_medium', 'gpt2_large', 'gpt2_xl', 'gpt3']
+__all__ = [
+    'GPT', 'GPTLMLoss', 'gpt2_small', 'gpt2_medium', 'gpt2_large', 'gpt2_xl', 'gpt2_8B', 'gpt2_xl_pipeline',
+    'gpt2_8B_pipeline', 'gpt3', 'gpt3_pipeline'
+]
 
 
 @LAYERS.register_module
@@ -18,7 +26,7 @@ class GPTEmbedding(nn.Module):
                  vocab_size: int,
                  max_position_embeddings: int,
                  num_tokentypes: int = 0,
-                 padding_idx: int = 0,
+                 padding_idx: int = None,
                  dropout: float = 0.,
                  dtype: dtype = None) -> None:
         super().__init__()
@@ -34,7 +42,7 @@ class GPTEmbedding(nn.Module):
     def word_embedding_weight(self):
         return self.word_embeddings.weight
 
-    def forward(self, input_ids, position_ids=None, tokentype_ids=None):
+    def forward(self, input_ids, attention_mask=None, position_ids=None, tokentype_ids=None):
         seq_length = input_ids.size(1)
         if position_ids is None:
             position_ids = torch.arange(seq_length, dtype=torch.long, device=get_current_device()).unsqueeze(0)
@@ -42,7 +50,20 @@ class GPTEmbedding(nn.Module):
         if self.tokentype_embeddings is not None and tokentype_ids is not None:
             x = x + self.tokentype_embeddings(tokentype_ids)
         x = self.dropout(x)
-        return x
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # Adapted from huggingface
+        if attention_mask is not None:
+            batch_size = input_ids.shape[0]
+            attention_mask = attention_mask.view(batch_size, -1)
+            attention_mask = col_nn.partition_batch(attention_mask)
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = attention_mask.to(dtype=x.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * -10000.0
+
+        return x, attention_mask
 
 
 @LAYERS.register_module
@@ -53,20 +74,32 @@ class GPTSelfAttention(nn.Module):
                  attention_dropout: float,
                  dropout: float,
                  bias: bool = True,
+                 fuse_scale_mask_softmax: bool = False,
                  dtype: dtype = None) -> None:
         super().__init__()
-
-        self.attention_head_size = dim // num_heads
+        self.fuse_scale_mask_softmax = fuse_scale_mask_softmax
+        self.attention_head_size = divide(dim, num_heads)
         self.query_key_value = col_nn.Linear(dim, 3 * dim, dtype=dtype, bias=bias)
+        if fuse_scale_mask_softmax:
+            from colossalai.kernel import FusedScaleMaskSoftmax
+            from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
+            self.softmax = FusedScaleMaskSoftmax(input_in_fp16=True,
+                                                 input_in_bf16=False,
+                                                 attn_mask_type=AttnMaskType.causal,
+                                                 scaled_masked_softmax_fusion=True,
+                                                 mask_func=None,
+                                                 softmax_in_fp32=True,
+                                                 scale=math.sqrt(self.attention_head_size))
+        else:
+            self.softmax = nn.Softmax(dim=-1)
         self.attention_dropout = col_nn.Dropout(attention_dropout)
         self.dense = col_nn.Linear(dim, dim, dtype=dtype, bias=True)
         self.dropout = col_nn.Dropout(dropout)
-        self.softmax = nn.Softmax(dim=-1)
 
     def forward(self, x, attention_mask=None):
         qkv = self.query_key_value(x)
         all_head_size = qkv.shape[-1] // 3
-        num_attention_heads = all_head_size // self.attention_head_size
+        num_attention_heads = divide(all_head_size, self.attention_head_size)
         new_qkv_shape = qkv.shape[:-1] + \
             (num_attention_heads, 3 * self.attention_head_size)
         qkv = qkv.view(new_qkv_shape)
@@ -74,17 +107,20 @@ class GPTSelfAttention(nn.Module):
         q, k, v = torch.chunk(qkv, 3, dim=-1)
 
         x = torch.matmul(q, k.transpose(-1, -2))
-        x = x / math.sqrt(self.attention_head_size)
 
-        # causal mask
-        q_len, k_len = q.size(-2), k.size(-2)
-        causal_mask = torch.tril(torch.ones((q_len, k_len), dtype=torch.uint8,
-                                            device=get_current_device())).view(1, 1, q_len, k_len).bool()
-        x = torch.where(causal_mask, x, torch.tensor(-1e4, dtype=x.dtype, device=get_current_device()))
+        if self.fuse_scale_mask_softmax:
+            x = self.softmax(x, attention_mask)
+        else:
+            x = x / math.sqrt(self.attention_head_size)
+            # causal mask
+            q_len, k_len = q.size(-2), k.size(-2)
+            causal_mask = torch.tril(torch.ones((q_len, k_len), dtype=torch.uint8,
+                                                device=get_current_device())).view(1, 1, q_len, k_len).bool()
+            x = torch.where(causal_mask, x, torch.tensor(-1e4, dtype=x.dtype, device=get_current_device()))
+            if attention_mask is not None:
+                x = x + attention_mask
+            x = self.softmax(x)
 
-        if attention_mask is not None:
-            x = x + attention_mask
-        x = self.softmax(x)
         x = self.attention_dropout(x)
 
         x = torch.matmul(x, v)
@@ -102,15 +138,16 @@ class GPTSelfAttention(nn.Module):
 class GPTMLP(nn.Module):
     def __init__(self,
                  dim: int,
-                 mlp_ratio: int,
+                 mlp_ratio: float,
                  activation: Callable,
                  dropout: float,
                  dtype: dtype = None,
                  bias: bool = True):
         super().__init__()
-        self.dense_1 = col_nn.Linear(dim, mlp_ratio * dim, dtype=dtype, bias=bias)
+        intermediate_dim = int(dim * mlp_ratio)
+        self.dense_1 = col_nn.Linear(dim, intermediate_dim, dtype=dtype, bias=bias)
         self.activation = activation
-        self.dense_2 = col_nn.Linear(mlp_ratio * dim, dim, dtype=dtype, bias=bias)
+        self.dense_2 = col_nn.Linear(intermediate_dim, dim, dtype=dtype, bias=bias)
         self.dropout = col_nn.Dropout(dropout)
 
     def forward(self, x):
@@ -126,27 +163,44 @@ class GPTBlock(CheckpointModule):
     def __init__(self,
                  dim: int,
                  num_heads: int,
-                 mlp_ratio: int,
+                 mlp_ratio: float,
                  activation: Callable,
                  attention_dropout: float = 0.,
                  dropout: float = 0.,
+                 layernorm_epsilon: float = 1e-5,
                  dtype: dtype = None,
                  bias: bool = True,
+                 apply_post_layernorm: bool = False,
+                 fuse_scale_mask_softmax: bool = False,
                  checkpoint: bool = False):
-        super().__init__(checkpoint=checkpoint)
-        self.norm1 = col_nn.LayerNorm(normalized_shape=dim, eps=1e-6, dtype=dtype)
+        super().__init__(checkpoint)
+        self.apply_post_layernorm = apply_post_layernorm
+        self.norm1 = col_nn.LayerNorm(normalized_shape=dim, eps=layernorm_epsilon, dtype=dtype)
         self.attn = GPTSelfAttention(dim=dim,
                                      num_heads=num_heads,
                                      attention_dropout=attention_dropout,
                                      dropout=dropout,
                                      bias=bias,
+                                     fuse_scale_mask_softmax=fuse_scale_mask_softmax,
                                      dtype=dtype)
-        self.norm2 = col_nn.LayerNorm(normalized_shape=dim, eps=1e-6, dtype=dtype)
+        self.norm2 = col_nn.LayerNorm(normalized_shape=dim, eps=layernorm_epsilon, dtype=dtype)
         self.mlp = GPTMLP(dim=dim, mlp_ratio=mlp_ratio, activation=activation, dropout=dropout, dtype=dtype, bias=bias)
 
     def _forward(self, x, attention_mask=None):
-        x = x + self.attn(self.norm1(x), attention_mask)
-        x = x + self.mlp(self.norm2(x))
+        if not self.apply_post_layernorm:
+            residual = x
+        x = self.norm1(x)
+        if self.apply_post_layernorm:
+            residual = x
+        x = residual + self.attn(x, attention_mask)
+
+        if not self.apply_post_layernorm:
+            residual = x
+        x = self.norm2(x)
+        if self.apply_post_layernorm:
+            residual = x
+        x = residual + self.mlp(x)
+
         return x, attention_mask
 
 
@@ -161,6 +215,10 @@ class GPTLMHead(nn.Module):
         super().__init__()
         self.dense = col_nn.Classifier(dim, vocab_size, word_embeeding_weight, bias=bias, dtype=dtype)
 
+    @property
+    def weight(self):
+        return self.dense.weight
+
     def forward(self, x):
         x = self.dense(x)
         return x
@@ -187,18 +245,19 @@ class GPT(nn.Module):
                  dim: int = 768,
                  num_heads: int = 12,
                  depth: int = 12,
-                 mlp_ratio: int = 4,
+                 mlp_ratio: float = 4.0,
                  dropout: float = 0.1,
                  embedding_dropout: float = 0.1,
                  attention_dropout: float = 0.1,
                  layernorm_epsilon: float = 1e-5,
                  activation: Callable = nn.functional.gelu,
-                 checkpoint: bool = False,
+                 padding_idx: int = None,
                  dtype: dtype = None,
                  bias: bool = True,
-                 padding_idx: int = 0) -> None:
+                 apply_post_layernorm: bool = False,
+                 fuse_scale_mask_softmax: bool = False,
+                 checkpoint: bool = False) -> None:
         super().__init__()
-        self.dtype = dtype
         self.embed = GPTEmbedding(embedding_dim=dim,
                                   vocab_size=vocab_size,
                                   max_position_embeddings=max_position_embeddings,
@@ -213,8 +272,11 @@ class GPT(nn.Module):
                 activation=activation,
                 attention_dropout=attention_dropout,
                 dropout=dropout,
+                layernorm_epsilon=layernorm_epsilon,
                 dtype=dtype,
                 bias=bias,
+                apply_post_layernorm=apply_post_layernorm,
+                fuse_scale_mask_softmax=fuse_scale_mask_softmax,
                 checkpoint=checkpoint,
             ) for _ in range(depth)
         ])
@@ -224,22 +286,10 @@ class GPT(nn.Module):
         self.head = GPTLMHead(dim=dim,
                               vocab_size=vocab_size,
                               word_embeeding_weight=self.embed.word_embedding_weight,
-                              bias=bias,
                               dtype=dtype)
 
     def forward(self, input_ids, attention_mask=None):
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # Adapted from huggingface
-        if attention_mask is not None:
-            batch_size = input_ids.shape[0]
-            attention_mask = attention_mask.view(batch_size, -1)
-            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-            attention_mask = (1.0 - attention_mask) * -10000.0
-
-        x = self.embed(input_ids)
+        x, attention_mask = self.embed(input_ids, attention_mask)
 
         for block in self.blocks:
             x, attention_mask = block(x, attention_mask)
@@ -249,11 +299,103 @@ class GPT(nn.Module):
         return x
 
 
+class PipelineGPT(nn.Module):
+    def __init__(self,
+                 vocab_size: int = 50304,
+                 max_position_embeddings: int = 1024,
+                 dim: int = 768,
+                 num_heads: int = 12,
+                 depth: int = 12,
+                 mlp_ratio: float = 4.0,
+                 dropout: float = 0.1,
+                 embedding_dropout: float = 0.1,
+                 attention_dropout: float = 0.1,
+                 layernorm_epsilon: float = 1e-5,
+                 activation: Callable = nn.functional.gelu,
+                 padding_idx: int = None,
+                 dtype: dtype = None,
+                 bias: bool = True,
+                 apply_post_layernorm: bool = False,
+                 fuse_scale_mask_softmax: bool = False,
+                 checkpoint: bool = False,
+                 first: bool = False,
+                 last: bool = False):
+        super().__init__()
+        self.checkpoint = checkpoint
+        self.first = first
+        self.last = last
+        if first:
+            self.embed = GPTEmbedding(embedding_dim=dim,
+                                      vocab_size=vocab_size,
+                                      max_position_embeddings=max_position_embeddings,
+                                      padding_idx=padding_idx,
+                                      dropout=embedding_dropout,
+                                      dtype=dtype)
+        self.blocks = nn.ModuleList([
+            GPTBlock(
+                dim=dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                activation=activation,
+                attention_dropout=attention_dropout,
+                dropout=dropout,
+                layernorm_epsilon=layernorm_epsilon,
+                dtype=dtype,
+                bias=bias,
+                apply_post_layernorm=apply_post_layernorm,
+                fuse_scale_mask_softmax=fuse_scale_mask_softmax,
+                checkpoint=checkpoint,
+            ) for _ in range(depth)
+        ])
+        if self.last:
+            self.norm = col_nn.LayerNorm(normalized_shape=dim, eps=layernorm_epsilon, dtype=dtype)
+            self.head = GPTLMHead(dim=dim, vocab_size=vocab_size, dtype=dtype)
+
+    def forward(self, x=None, input_ids=None, attention_mask=None):
+        if self.first:
+            x, attention_mask = self.embed(input_ids, attention_mask)
+
+        for block in self.blocks:
+            x, attention_mask = block(x, attention_mask)
+
+        if self.last:
+            x = self.head(self.norm(x))
+
+        return x
+
+
 def _create_gpt_model(**model_kwargs):
     model = GPT(**model_kwargs)
     return model
 
 
+def _create_gpt_pipeline_model(depth=48, num_chunks=1, layer_partitions=None, **model_kwargs):
+    logger = get_dist_logger()
+    pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE)
+    pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
+    rank = gpc.get_global_rank()
+    wrapper = PipelineSharedModuleWrapper([0, pipeline_size - 1])
+    parts = partition_uniform(depth, pipeline_size,
+                              num_chunks)[pipeline_rank] if layer_partitions is None else layer_partitions
+    models = []
+    for start, end in parts:
+        model_kwargs['first'] = start == 0
+        model_kwargs['last'] = end == depth
+        model_kwargs['depth'] = end - start
+        chunk = PipelineGPT(**model_kwargs).to(get_current_device())
+        if start == 0:
+            wrapper.register_parameter(chunk.embed.word_embedding_weight)
+        elif end == depth:
+            wrapper.register_parameter(chunk.head.weight)
+        models.append(chunk)
+        logger.info(f'==> Rank {rank} built layer {start}-{end} / total {depth}')
+    if len(models) == 1:
+        model = models[0]
+    else:
+        model = nn.ModuleList(models)
+    return model
+
+
 @MODELS.register_module
 def gpt2_small(**kwargs):
     model_kwargs = dict(dim=768, depth=12, num_heads=12, **kwargs)
@@ -262,23 +404,47 @@ def gpt2_small(**kwargs):
 
 @MODELS.register_module
 def gpt2_medium(**kwargs):
-    model_kwargs = dict(dim=1024, depth=24, num_heads=16, **kwargs)
+    model_kwargs = dict(dim=1024, depth=24, num_heads=8, **kwargs)
     return _create_gpt_model(**model_kwargs)
 
 
 @MODELS.register_module
 def gpt2_large(**kwargs):
-    model_kwargs = dict(dim=1280, depth=36, num_heads=20, **kwargs)
+    model_kwargs = dict(dim=1536, depth=36, num_heads=12, **kwargs)
     return _create_gpt_model(**model_kwargs)
 
 
 @MODELS.register_module
 def gpt2_xl(**kwargs):
-    model_kwargs = dict(dim=1600, depth=48, num_heads=25, **kwargs)
+    model_kwargs = dict(dim=1600, depth=48, num_heads=16, **kwargs)
     return _create_gpt_model(**model_kwargs)
 
 
 @MODELS.register_module
-def gpt3(**kwargs):
-    model_kwargs = dict(dim=12288, max_position_embeddings=2048, depth=96, num_heads=96, **kwargs)
+def gpt2_8B(**kwargs):
+    model_kwargs = dict(dim=3072, depth=72, num_heads=24, **kwargs)
     return _create_gpt_model(**model_kwargs)
+
+
+@MODELS.register_module
+def gpt2_xl_pipeline(**kwargs):
+    model_kwargs = dict(dim=1600, depth=48, num_heads=20, **kwargs)
+    return _create_gpt_pipeline_model(**model_kwargs)
+
+
+@MODELS.register_module
+def gpt2_8B_pipeline(**kwargs):
+    model_kwargs = dict(dim=3072, depth=72, num_heads=24, **kwargs)
+    return _create_gpt_pipeline_model(**model_kwargs)
+
+
+@MODELS.register_module
+def gpt3(**kwargs):
+    model_kwargs = dict(dim=12288, depth=96, num_heads=96, **kwargs)
+    return _create_gpt_model(**model_kwargs)
+
+
+@MODELS.register_module
+def gpt3_pipeline(**kwargs):
+    model_kwargs = dict(dim=12288, depth=96, num_heads=96, **kwargs)
+    return _create_gpt_pipeline_model(**model_kwargs)

tests/test_layers/test_1d/checks_1d/check_layer_1d.py

@@ -1,12 +1,14 @@
 import torch
 import torch.distributed as dist
-from torch.nn import Parameter
-import time
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.nn import Linear1D_Col, Linear1D_Row
+from colossalai.global_variables import tensor_parallel_env as env
+from colossalai.nn import (Classifier1D, Embedding1D, Linear1D_Col, Linear1D_Row, VanillaClassifier,
+                           VocabParallelClassifier1D, VocabParallelCrossEntropyLoss1D, VocabParallelEmbedding1D)
 from colossalai.utils import get_current_device, print_rank_0
-from .common import HIDDEN_SIZE, DEPTH, BATCH_SIZE, SEQ_LENGTH, NUM_CLASSES, check_equal, IMG_SIZE
+from torch.nn import Parameter
+
+from .common import BATCH_SIZE, DEPTH, HIDDEN_SIZE, NUM_CLASSES, SEQ_LENGTH, VOCAB_SIZE, check_equal
 
 
 def check_linear_col():
@@ -144,3 +146,351 @@ def check_linear_row():
     check_equal(B_grad, layer.bias.grad)
 
     print_rank_0('linear_row backward: pass')
+
+
+def check_embed():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    embed = Embedding1D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[i]
+    embed.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = embed(A)
+
+    A_master = A_master.clone()
+    C_master = embed_master(A_master)
+    C = C_master.clone()
+    check_equal(out, C)
+    print_rank_0('embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = grad_master.clone()
+    out.backward(grad)
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[i]
+    check_equal(B_grad, embed.weight.grad)
+    print_rank_0('embed backward: pass')
+
+
+def check_vocab_parallel_embed():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    embed = VocabParallelEmbedding1D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=0)[i]
+    embed.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = embed(A)
+
+    A_master = A_master.clone()
+    C_master = embed_master(A_master)
+    C = C_master.clone()
+    check_equal(out, C)
+    print_rank_0('vocab parallel embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = grad_master.clone()
+    out.backward(grad)
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[i]
+    check_equal(B_grad, embed.weight.grad)
+    print_rank_0('vocab parallel embed backward: pass')
+
+
+def check_classifier_no_given_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    env.parallel_input_1d = False
+    parallel_input_1d = env.parallel_input_1d
+    layer = Classifier1D(HIDDEN_SIZE, NUM_CLASSES, bias=True)
+    layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, NUM_CLASSES, bias=True)
+    layer_master = layer_master.to(dtype).to(device)
+
+    W_master = layer_master.weight.data
+    dist.broadcast(W_master, src=0)
+    W = torch.chunk(W_master, DEPTH, dim=-1)[i]
+    layer.weight.data.copy_(W)
+    B_master = layer_master.bias.data
+    dist.broadcast(B_master, src=0)
+    B = B_master.clone()
+    layer.bias.data.copy_(B)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    dist.broadcast(A_master, src=0)
+    if parallel_input_1d:
+        A = torch.chunk(A_master, DEPTH, dim=-1)[i]
+        A = A.clone()
+    else:
+        A = A_master.clone()
+    A.requires_grad = True
+
+    out = layer(A)
+
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    C_master = layer_master(A_master)
+    C = C_master.clone()
+
+    check_equal(out, C)
+    print_rank_0('classifier (no given weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    dist.broadcast(grad_master, src=0)
+    grad = grad_master.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    if parallel_input_1d:
+        A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[i]
+    check_equal(A_grad, A.grad)
+
+    W_grad = layer_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[i]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = layer_master.bias.grad
+    check_equal(B_grad, layer.bias.grad)
+
+    print_rank_0('classifier (no given weight) backward: pass')
+
+
+def check_vocab_parallel_classifier_no_given_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    layer = VocabParallelClassifier1D(HIDDEN_SIZE, VOCAB_SIZE, bias=True)
+    layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, bias=True)
+    layer_master = layer_master.to(dtype).to(device)
+
+    W_master = layer_master.weight.data
+    dist.broadcast(W_master, src=0)
+    W = torch.chunk(W_master, DEPTH, dim=0)[i]
+    layer.weight.data.copy_(W)
+    B_master = layer_master.bias.data
+    dist.broadcast(B_master, src=0)
+    B = torch.chunk(B_master, DEPTH, dim=0)[i]
+    layer.bias.data.copy_(B)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    dist.broadcast(A_master, src=0)
+    A = A_master.clone()
+    A.requires_grad = True
+
+    out = layer(A)
+
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=-1)[i]
+
+    check_equal(out, C)
+    print_rank_0('vocab parallel classifier (no given weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    dist.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=-1)[i]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    check_equal(A_grad, A.grad)
+
+    W_grad = layer_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=0)[i]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = layer_master.bias.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[i]
+    check_equal(B_grad, layer.bias.grad)
+
+    print_rank_0('vocab parallel classifier (no given weight) backward: pass')
+
+
+def check_classifier_given_embed_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    embed = Embedding1D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[i]
+    embed.weight.data.copy_(weight)
+
+    env.parallel_input_1d = False
+    layer = Classifier1D(HIDDEN_SIZE, NUM_CLASSES, weight=embed.weight, bias=False)
+    layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, NUM_CLASSES, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(embed(A))
+
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = C_master.clone()
+    check_equal(out, C)
+    print_rank_0('classifier (given embed weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    dist.broadcast(grad_master, src=0)
+    grad = grad_master.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = embed_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[i]
+    check_equal(W_grad, embed.weight.grad)
+
+    print_rank_0('classifier (given embed weight) backward: pass')
+
+
+def check_vocab_parallel_classifier_given_embed_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    embed = VocabParallelEmbedding1D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=0)[i]
+    embed.weight.data.copy_(weight)
+
+    env.parallel_input_1d = False
+    layer = VocabParallelClassifier1D(HIDDEN_SIZE, NUM_CLASSES, weight=embed.weight, bias=False)
+    layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, NUM_CLASSES, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(embed(A))
+
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, DEPTH, dim=-1)[i]
+    check_equal(out, C)
+    print_rank_0('vocab parallel classifier (given embed weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    dist.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=-1)[i]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = embed_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=0)[i]
+    check_equal(W_grad, embed.weight.grad)
+
+    print_rank_0('vocab parallel classifier (given embed weight) backward: pass')
+
+
+def check_vocab_parallel_loss():
+    device = get_current_device()
+    dtype = torch.float32
+
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    criterion = VocabParallelCrossEntropyLoss1D()
+    criterion_master = torch.nn.CrossEntropyLoss()
+
+    out_shape = (BATCH_SIZE, SEQ_LENGTH, NUM_CLASSES)
+    out_master = torch.randn(out_shape, dtype=dtype, device=device)
+    target_master = torch.randint(NUM_CLASSES, (BATCH_SIZE, SEQ_LENGTH), dtype=torch.long, device=device)
+    torch.distributed.broadcast(out_master, src=0)
+    torch.distributed.broadcast(target_master, src=0)
+    out = torch.chunk(out_master, DEPTH, dim=-1)[i]
+    out = out.clone()
+    out.requires_grad = True
+
+    loss = criterion(out, target_master)
+
+    out_master = out_master.clone()
+    out_master.requires_grad = True
+    loss_master = criterion_master(out_master, target_master)
+    check_equal(loss, loss_master)
+    print_rank_0('vocab parallel loss forward: pass')
+
+    loss.backward()
+    loss_master.backward()
+
+    out_grad = out_master.grad
+    out_grad = torch.chunk(out_grad, DEPTH, dim=-1)[i]
+    check_equal(out_grad, out.grad)
+    print_rank_0('vocab parallel loss backward: pass')

tests/test_layers/test_1d/checks_1d/common.py

@@ -9,6 +9,7 @@ SEQ_LENGTH = 8
 IMG_SIZE = 16
 HIDDEN_SIZE = 8
 NUM_CLASSES = 8
+VOCAB_SIZE = 16
 
 def check_equal(A, B):
     assert torch.allclose(A, B, rtol=1e-3, atol=1e-1) == True

tests/test_layers/test_1d/test_1d.py

@@ -7,6 +7,7 @@ import pytest
 import torch
 import torch.multiprocessing as mp
 from colossalai.core import global_context as gpc
+from colossalai.logging import disable_existing_loggers
 from colossalai.initialize import launch
 from colossalai.utils import free_port
 
@@ -24,6 +25,7 @@ CONFIG = dict(
 
 
 def check_layer(rank, world_size, port):
+    disable_existing_loggers()
     launch(config=CONFIG,
            rank=rank,
            world_size=world_size,
@@ -33,6 +35,13 @@ def check_layer(rank, world_size, port):
 
     check_linear_col()
     check_linear_row()
+    check_embed()
+    check_vocab_parallel_embed()
+    check_classifier_no_given_weight()
+    check_vocab_parallel_classifier_no_given_weight()
+    check_classifier_given_embed_weight()
+    check_vocab_parallel_classifier_given_embed_weight()
+    check_vocab_parallel_loss()
 
     gpc.destroy()
     torch.cuda.empty_cache()

tests/test_layers/test_2d/checks_2d/check_layer_2d.py

@@ -1,11 +1,12 @@
 import torch
-from torch.nn import Parameter
-
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.nn import Linear2D, LayerNorm2D, Classifier2D
+from colossalai.nn import (Classifier2D, CrossEntropyLoss2D, Embedding2D, LayerNorm2D, Linear2D, PatchEmbedding2D,
+                           VanillaClassifier, VanillaPatchEmbedding, VocabParallelClassifier2D,
+                           VocabParallelCrossEntropyLoss2D, VocabParallelEmbedding2D)
 from colossalai.utils import get_current_device, print_rank_0
-from .common import HIDDEN_SIZE, DEPTH, BATCH_SIZE, SEQ_LENGTH, check_equal, NUM_CLASSES
+
+from .common import (BATCH_SIZE, DEPTH, HIDDEN_SIZE, IMG_SIZE, NUM_CLASSES, SEQ_LENGTH, VOCAB_SIZE, check_equal)
 
 
 def check_linear():
@@ -57,7 +58,6 @@ def check_linear():
     C = torch.chunk(C_master, DEPTH, dim=0)[i]
     C = torch.chunk(C, DEPTH, dim=-1)[j]
 
-    # print(f'Rank {gpc.get_global_rank()} A:\n{A}\nRank {gpc.get_global_rank()} W:\n{W}\nRank {gpc.get_global_rank()} b:\n{B}\nRank {gpc.get_global_rank()} C:\n{C}\nRank {gpc.get_global_rank()} out:\n{out}')
     check_equal(out, C)
     print_rank_0('linear forward: pass')
 
@@ -90,84 +90,6 @@ def check_linear():
     print_rank_0('linear backward: pass')
 
 
-def check_classifier():
-    device = get_current_device()
-    dtype = torch.float32
-    INPUT_SIZE = HIDDEN_SIZE
-    OUTPUT_SIZE = NUM_CLASSES
-
-    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
-    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
-
-    layer = Classifier2D(INPUT_SIZE, OUTPUT_SIZE)
-
-    A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
-    A_master = torch.randint(5, A_shape, dtype=dtype, device=device)
-    torch.distributed.broadcast(A_master, src=0)
-    A = torch.chunk(A_master, DEPTH, dim=0)[i]
-    A = torch.chunk(A, DEPTH, dim=-1)[j]
-    A = A.clone()
-    A.requires_grad = True
-
-    W_shape = (OUTPUT_SIZE, INPUT_SIZE)
-    W_master = torch.randint(5, W_shape, dtype=dtype, device=device)
-    torch.distributed.broadcast(W_master, src=0)
-    W = torch.chunk(W_master, DEPTH, dim=-1)[j]
-    W = torch.chunk(W, DEPTH, dim=-1)[i]
-    W = W.clone()
-    layer.weight.data.copy_(W)
-    # W.requires_grad = True
-
-    B_shape = (OUTPUT_SIZE,)
-    B_master = torch.randint(5, B_shape, dtype=dtype, device=device)
-    torch.distributed.broadcast(B_master, src=0)
-    # B = torch.chunk(B_master, DEPTH, dim=0)[j]
-    B = B_master.clone()
-    layer.bias.data.copy_(B)
-
-    out = layer(A)
-
-    A_master = A_master.clone()
-    A_master.requires_grad = True
-    W_master = W_master.clone()
-    W_master.requires_grad = True
-    B_master = B_master.clone()
-    B_master.requires_grad = True
-    C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
-    C = torch.chunk(C_master, DEPTH, dim=0)[i]
-    # C = torch.chunk(C, DEPTH, dim=-1)[j]
-
-    check_equal(out, C)
-    print_rank_0('classifier forward: pass')
-
-    grad_shape = C_master.shape
-    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
-    torch.distributed.broadcast(grad_master, src=0)
-    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
-    # grad = torch.chunk(grad, DEPTH, dim=-1)[j]
-    grad = grad.clone()
-    out.backward(grad)
-
-    grad_master = grad_master.clone()
-    C_master.backward(grad_master)
-    A_grad = A_master.grad
-    A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
-    A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j]
-    check_equal(A_grad, A.grad)
-
-    W_grad = W_master.grad
-    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[j]
-    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[i]
-    check_equal(W_grad, layer.weight.grad)
-
-    B_grad = B_master.grad
-    # B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j]
-    # if i == 0:
-    check_equal(B_grad, layer.bias.grad)
-
-    print_rank_0('classifier backward: pass')
-
-
 def check_layernorm():
     device = get_current_device()
     dtype = torch.float32
@@ -219,6 +141,497 @@ def check_layernorm():
     print_rank_0('layer norm backward: pass')
 
 
+def check_embed():
+    device = get_current_device()
+    dtype = torch.float32
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    embed = Embedding2D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[j]
+    weight = torch.chunk(weight, DEPTH, dim=-1)[i]
+    embed.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = embed(A)
+
+    A_master = A_master.clone()
+    C_master = embed_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[j]
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[i]
+    check_equal(B_grad, embed.weight.grad)
+    print_rank_0('embed backward: pass')
+
+
+def check_patch_embed():
+    device = get_current_device()
+    dtype = torch.float32
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    layer = PatchEmbedding2D(IMG_SIZE, 4, 3, HIDDEN_SIZE, dtype=dtype)
+    torch.nn.init.ones_(layer.cls_token)
+    torch.nn.init.ones_(layer.pos_embed)
+    layer = layer.to(device)
+
+    layer_master = VanillaPatchEmbedding(IMG_SIZE, 4, 3, HIDDEN_SIZE, dtype=dtype)
+    torch.nn.init.ones_(layer_master.cls_token)
+    torch.nn.init.ones_(layer_master.pos_embed)
+    layer_master = layer_master.to(device)
+
+    proj_weight_master = layer_master.weight.data
+    torch.distributed.broadcast(proj_weight_master, src=0)
+    proj_weight = torch.chunk(proj_weight_master, DEPTH, dim=0)[j]
+    proj_weight = torch.chunk(proj_weight, DEPTH, dim=0)[i]
+    layer.weight.data.copy_(proj_weight)
+    proj_bias_master = layer_master.bias.data
+    torch.distributed.broadcast(proj_bias_master, src=0)
+    proj_bias = torch.chunk(proj_bias_master, DEPTH, dim=0)[j]
+    proj_bias = torch.chunk(proj_bias, DEPTH, dim=0)[i]
+    layer.bias.data.copy_(proj_bias)
+
+    A_shape = (BATCH_SIZE, 3, IMG_SIZE, IMG_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(A)
+
+    A_master = A_master.clone()
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('patch embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    cls_grad_master = layer_master.cls_token.grad
+    cls_grad = torch.chunk(cls_grad_master, DEPTH, dim=-1)[j]
+    cls_grad = torch.chunk(cls_grad, DEPTH, dim=-1)[i]
+    check_equal(cls_grad, layer.cls_token.grad)
+
+    pos_grad_master = layer_master.pos_embed.grad
+    pos_grad = torch.chunk(pos_grad_master, DEPTH, dim=-1)[j]
+    pos_grad = torch.chunk(pos_grad, DEPTH, dim=-1)[i]
+    check_equal(pos_grad, layer.pos_embed.grad)
+
+    B_grad = layer_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j]
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[i]
+    check_equal(B_grad, layer.weight.grad)
+
+    bias_grad = layer_master.bias.grad
+    bias_grad = torch.chunk(bias_grad, DEPTH)[j]
+    bias_grad = torch.chunk(bias_grad, DEPTH)[i]
+    check_equal(bias_grad, layer.bias.grad)
+    print_rank_0('patch embed backward: pass')
+
+
+def check_vocab_parallel_embed():
+    device = get_current_device()
+    dtype = torch.float32
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    embed = VocabParallelEmbedding2D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[j]
+    weight = torch.chunk(weight, DEPTH, dim=0)[i]
+    embed.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = embed(A)
+
+    A_master = A_master.clone()
+    C_master = embed_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('vocab parallel embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[j]
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[i]
+    check_equal(B_grad, embed.weight.grad)
+    print_rank_0('vocab parallel embed backward: pass')
+
+
+def check_classifier_no_given_weight():
+    device = get_current_device()
+    dtype = torch.float32
+    INPUT_SIZE = HIDDEN_SIZE
+    OUTPUT_SIZE = NUM_CLASSES
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    layer = Classifier2D(INPUT_SIZE, OUTPUT_SIZE)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
+    A_master = torch.randint(5, A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = torch.chunk(A_master, DEPTH, dim=0)[i]
+    A = torch.chunk(A, DEPTH, dim=-1)[j]
+    A = A.clone()
+    A.requires_grad = True
+
+    W_shape = (OUTPUT_SIZE, INPUT_SIZE)
+    W_master = torch.randint(5, W_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(W_master, src=0)
+    W = torch.chunk(W_master, DEPTH, dim=-1)[j]
+    W = torch.chunk(W, DEPTH, dim=-1)[i]
+    W = W.clone()
+    layer.weight.data.copy_(W)
+    # W.requires_grad = True
+
+    B_shape = (OUTPUT_SIZE, )
+    B_master = torch.randint(5, B_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(B_master, src=0)
+    # B = torch.chunk(B_master, DEPTH, dim=0)[j]
+    B = B_master.clone()
+    layer.bias.data.copy_(B)
+
+    out = layer(A)
+
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    W_master = W_master.clone()
+    W_master.requires_grad = True
+    B_master = B_master.clone()
+    B_master.requires_grad = True
+    C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    # C = torch.chunk(C, DEPTH, dim=-1)[j]
+
+    check_equal(out, C)
+    print_rank_0('classifier (no given weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    # grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
+    A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j]
+    check_equal(A_grad, A.grad)
+
+    W_grad = W_master.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[j]
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[i]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = B_master.grad
+    # B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j]
+    # if i == 0:
+    check_equal(B_grad, layer.bias.grad)
+
+    print_rank_0('classifier (no given weight) backward: pass')
+
+
+def check_vocab_parallel_classifier_no_given_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    layer = VocabParallelClassifier2D(HIDDEN_SIZE, VOCAB_SIZE, bias=True)
+    layer = layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, bias=True)
+    layer_master = layer_master.to(dtype).to(device)
+
+    weight_master = layer_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=0)[i]
+    weight = torch.chunk(weight, DEPTH, dim=-1)[j]
+    layer.weight.data.copy_(weight)
+    bias_master = layer_master.bias.data
+    torch.distributed.broadcast(bias_master, src=0)
+    bias = torch.chunk(bias_master, DEPTH)[j]
+    bias = torch.chunk(bias, DEPTH)[i]
+    layer.bias.data.copy_(bias)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = torch.chunk(A_master, DEPTH, dim=0)[i]
+    A = torch.chunk(A, DEPTH, dim=-1)[j]
+    A = A.clone()
+    A.requires_grad = True
+    out = layer(A)
+
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('vocab parallel classifier (no given weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
+    A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[j]
+    check_equal(A_grad, A.grad)
+
+    W_grad = layer_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=0)[i]
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[j]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = layer_master.bias.grad
+    B_grad = torch.chunk(B_grad, DEPTH)[j]
+    B_grad = torch.chunk(B_grad, DEPTH)[i]
+    check_equal(B_grad, layer.bias.grad)
+    print_rank_0('vocab parallel classifier (no given weight) backward: pass')
+
+
+def check_classifier_given_embed_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    embed = Embedding2D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[j]
+    weight = torch.chunk(weight, DEPTH, dim=-1)[i]
+    embed.weight.data.copy_(weight)
+
+    layer = Classifier2D(HIDDEN_SIZE, VOCAB_SIZE, weight=embed.weight, bias=False)
+    layer = layer.to(dtype).to(device)
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(embed(A))
+
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    check_equal(out, C)
+    print_rank_0('classifier (given embed weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = embed_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[j]
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[i]
+    check_equal(W_grad, embed.weight.grad)
+    print_rank_0('classifier (given embed weight) backward: pass')
+
+
+def check_vocab_parallel_classifier_given_embed_weight():
+    device = get_current_device()
+    dtype = torch.float32
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    embed = VocabParallelEmbedding2D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[j]
+    weight = torch.chunk(weight, DEPTH, dim=0)[i]
+    embed.weight.data.copy_(weight)
+
+    layer = VocabParallelClassifier2D(HIDDEN_SIZE, VOCAB_SIZE, weight=embed.weight, bias=False)
+    layer = layer.to(dtype).to(device)
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(embed(A))
+
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('vocab parallel classifier (given embed weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = embed_master.weight.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[j]
+    W_grad = torch.chunk(W_grad, DEPTH, dim=0)[i]
+    check_equal(W_grad, embed.weight.grad)
+    print_rank_0('vocab parallel classifier (given embed weight) backward: pass')
+
+
+def check_loss():
+    device = get_current_device()
+    dtype = torch.float32
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    criterion = CrossEntropyLoss2D()
+    criterion_master = torch.nn.CrossEntropyLoss()
+
+    out_shape = (BATCH_SIZE, NUM_CLASSES)
+    out_master = torch.randn(out_shape, dtype=dtype, device=device)
+    target_master = torch.randint(NUM_CLASSES, (BATCH_SIZE, ), dtype=torch.long, device=device)
+    torch.distributed.broadcast(out_master, src=0)
+    torch.distributed.broadcast(target_master, src=0)
+    out = torch.chunk(out_master, DEPTH, dim=0)[i]
+    out = out.clone()
+    out.requires_grad = True
+    loss = criterion(out, target_master)
+
+    out_master = out_master.clone()
+    out_master.requires_grad = True
+    loss_master = criterion_master(out_master, target_master)
+    check_equal(loss, loss_master)
+    print_rank_0('cross entropy loss forward: pass')
+
+    loss.backward()
+    loss_master.backward()
+
+    out_grad = out_master.grad
+    out_grad = torch.chunk(out_grad, DEPTH, dim=0)[i]
+    check_equal(out_grad, out.grad)
+    print_rank_0('cross entropy loss backward: pass')
+
+
+def check_vocab_parallel_loss():
+    device = get_current_device()
+    dtype = torch.float32
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
+
+    criterion = VocabParallelCrossEntropyLoss2D()
+    criterion_master = torch.nn.CrossEntropyLoss()
+
+    out_shape = (BATCH_SIZE, NUM_CLASSES)
+    out_master = torch.randn(out_shape, dtype=dtype, device=device)
+    target_master = torch.randint(NUM_CLASSES, (BATCH_SIZE, ), dtype=torch.long, device=device)
+    torch.distributed.broadcast(out_master, src=0)
+    torch.distributed.broadcast(target_master, src=0)
+    out = torch.chunk(out_master, DEPTH, dim=0)[i]
+    out = torch.chunk(out, DEPTH, dim=-1)[j]
+    out = out.clone()
+    out.requires_grad = True
+    loss = criterion(out, target_master)
+
+    out_master = out_master.clone()
+    out_master.requires_grad = True
+    loss_master = criterion_master(out_master, target_master)
+    check_equal(loss, loss_master)
+    print_rank_0('vocab parallel cross entropy loss forward: pass')
+
+    loss.backward()
+    loss_master.backward()
+
+    out_grad = out_master.grad
+    out_grad = torch.chunk(out_grad, DEPTH, dim=0)[i]
+    out_grad = torch.chunk(out_grad, DEPTH, dim=-1)[j]
+    check_equal(out_grad, out.grad)
+    print_rank_0('vocab parallel cross entropy loss backward: pass')
+
+
 # def check_attention():
 #     device = get_current_device()
 #     dtype = torch.float32
@@ -257,7 +670,6 @@ def check_layernorm():
 #     assert A.grad.shape == A.shape
 #     print_rank_0('self attention backward: pass')
 
-
 # def check_mlp():
 #     device = get_current_device()
 #     dtype = torch.float32
@@ -291,7 +703,6 @@ def check_layernorm():
 #     assert A.grad.shape == A.shape
 #     print_rank_0('mlp backward: pass')
 
-
 # def check_transformerlayer():
 #     device = get_current_device()
 #     dtype = torch.float32

tests/test_layers/test_2d/checks_2d/common.py

@@ -8,6 +8,9 @@ BATCH_SIZE = 8
 SEQ_LENGTH = 8
 HIDDEN_SIZE = 8
 NUM_CLASSES = 8
+VOCAB_SIZE = 16
+IMG_SIZE = 16
+
 
 def check_equal(A, B):
-    assert torch.allclose(A, B, rtol=1e-3, atol=1e-2) == True
+    assert torch.allclose(A, B, rtol=1e-3, atol=1e-2)

tests/test_layers/test_2d/test_2d.py

@@ -8,20 +8,17 @@ import torch
 import torch.multiprocessing as mp
 from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
 from colossalai.utils import free_port
 
-from checks_2d.check_layer_2d import *
-from checks_2d.check_operation_2d import *
+from checks_2d.check_layer_2d import (check_classifier_given_embed_weight, check_classifier_no_given_weight,
+                                      check_embed, check_layernorm, check_linear, check_loss, check_patch_embed,
+                                      check_vocab_parallel_classifier_given_embed_weight,
+                                      check_vocab_parallel_classifier_no_given_weight, check_vocab_parallel_embed,
+                                      check_vocab_parallel_loss)
+from checks_2d.check_operation_2d import check_AB, check_ABT, check_ATB
 
-CONFIG = dict(
-    parallel=dict(
-        pipeline=dict(size=1),
-        tensor=dict(
-            size=4,
-            mode='2d'
-        )
-    ),
-)
+CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=4, mode='2d')), )
 
 
 def check_operations():
@@ -33,16 +30,24 @@ def check_operations():
 def check_layer():
     check_linear()
     check_layernorm()
-    check_classifier()
+    check_embed()
+    check_patch_embed()
+    check_vocab_parallel_embed()
+    check_classifier_no_given_weight()
+    check_vocab_parallel_classifier_no_given_weight()
+    check_classifier_given_embed_weight()
+    check_vocab_parallel_classifier_given_embed_weight()
+    check_loss()
+    check_vocab_parallel_loss()
+
 
 def check_layer_and_operation(rank, world_size, port):
-    launch(config=CONFIG,
-           rank=rank,
-           world_size=world_size,
-           host='localhost',
-           port=port,
-           backend='nccl')
+    disable_existing_loggers()
+    launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    torch.backends.cudnn.deterministic = True
     # check_operations()
     check_layer()
     gpc.destroy()

tests/test_layers/test_2p5d/checks_2p5d/check_layer_2p5d.py

@@ -1,11 +1,12 @@
 import torch
-from torch.nn import Parameter
-
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.nn import Linear2p5D, LayerNorm2p5D, Classifier2p5D
-from colossalai.utils import get_current_device
-from colossalai.utils import print_rank_0
+from colossalai.nn import (Classifier2p5D, CrossEntropyLoss2p5D, Embedding2p5D, LayerNorm2p5D, Linear2p5D,
+                           PatchEmbedding2p5D, VanillaClassifier, VanillaPatchEmbedding, VocabParallelClassifier2p5D,
+                           VocabParallelCrossEntropyLoss2p5D, VocabParallelEmbedding2p5D)
+from colossalai.utils import get_current_device, print_rank_0
+from torch.nn import Parameter
+
 from .common import *
 
 
@@ -19,11 +20,7 @@ def check_linear():
     j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
     k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
 
-    layer = Linear2p5D(
-        INPUT_SIZE,
-        OUTPUT_SIZE,
-        dtype=dtype,
-        skip_bias_add=False)
+    layer = Linear2p5D(INPUT_SIZE, OUTPUT_SIZE, dtype=dtype, skip_bias_add=False)
 
     A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
     A_master = torch.randn(A_shape, dtype=dtype, device=device)
@@ -94,86 +91,6 @@ def check_linear():
     print_rank_0('linear backward: pass')
 
 
-def check_classifier():
-    device = get_current_device()
-    dtype = torch.float32
-    INPUT_SIZE = HIDDEN_SIZE
-    OUTPUT_SIZE = NUM_CLASSES
-
-    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
-    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
-
-    layer = Classifier2p5D(INPUT_SIZE, OUTPUT_SIZE)
-
-    A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
-    A_master = torch.randint(5, A_shape, dtype=dtype, device=device)
-    torch.distributed.broadcast(A_master, src=0)
-    A = torch.chunk(A_master, TESSERACT_DIM, dim=0)[i]
-    A = torch.chunk(A, TESSERACT_DIM, dim=-1)[j]
-    A = A.clone()
-    A.requires_grad = True
-
-    W_shape = (OUTPUT_SIZE, INPUT_SIZE)
-    W_master = torch.randint(5, W_shape, dtype=dtype, device=device)
-    torch.distributed.broadcast(W_master, src=0)
-    # W = torch.chunk(W_master, TESSERACT_DIM, dim=-1)[j]
-    W = torch.chunk(W_master, TESSERACT_DIM, dim=-1)[j]
-    W = torch.chunk(W, TESSERACT_DIM, dim=-1)[i]
-    W = W.clone()
-    layer.weight.data.copy_(W)
-    # W.requires_grad = True
-
-    B_shape = (OUTPUT_SIZE,)
-    B_master = torch.randint(5, B_shape, dtype=dtype, device=device)
-    torch.distributed.broadcast(B_master, src=0)
-    # B = torch.chunk(B_master, TESSERACT_DIM, dim=0)[j]
-    B = B_master.clone()
-    layer.bias.data.copy_(B)
-
-
-    out = layer(A)
-
-    A_master = A_master.clone()
-    A_master.requires_grad = True
-    W_master = W_master.clone()
-    W_master.requires_grad = True
-    B_master = B_master.clone()
-    B_master.requires_grad = True
-    C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
-    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
-    # C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
-
-    check_equal(out, C)
-    print_rank_0('classifier forward: pass')
-
-    grad_shape = C_master.shape
-    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
-    torch.distributed.broadcast(grad_master, src=0)
-    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
-    # grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
-    grad = grad.clone()
-    out.backward(grad)
-
-    grad_master = grad_master.clone()
-    C_master.backward(grad_master)
-    A_grad = A_master.grad
-    A_grad = torch.chunk(A_grad, TESSERACT_DIM, dim=0)[i]
-    A_grad = torch.chunk(A_grad, TESSERACT_DIM, dim=-1)[j]
-    check_equal(A_grad, A.grad)
-
-    W_grad = W_master.grad
-    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[j]
-    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[i]
-    check_equal(W_grad, layer.weight.grad)
-
-    B_grad = B_master.grad
-    # B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=0)[j]
-    # if i == 0:
-    check_equal(B_grad, layer.bias.grad)
-
-    print_rank_0('classifier backward: pass')
-    
-
 def check_layernorm():
     device = get_current_device()
     dtype = torch.float32
@@ -184,9 +101,7 @@ def check_layernorm():
     j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
     k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
 
-    layernorm = LayerNorm2p5D(
-        INPUT_SIZE,
-        dtype=dtype)
+    layernorm = LayerNorm2p5D(INPUT_SIZE, dtype=dtype)
 
     A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
     A_master = torch.randn(A_shape, dtype=dtype, device=device)
@@ -228,6 +143,500 @@ def check_layernorm():
     print_rank_0('layer norm backward: pass')
 
 
+def check_embed():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    embed = Embedding2p5D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, TESSERACT_DIM, dim=-1)[j]
+    weight = torch.chunk(weight, TESSERACT_DIM, dim=-1)[i]
+    embed.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = embed(A)
+
+    A_master = A_master.clone()
+    C_master = embed_master(A_master)
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=-1)[j]
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=-1)[i]
+    check_equal(B_grad, embed.weight.grad)
+    print_rank_0('embed backward: pass')
+
+
+def check_patch_embed():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    layer = PatchEmbedding2p5D(IMG_SIZE, 4, 3, HIDDEN_SIZE, dtype=dtype)
+    torch.nn.init.ones_(layer.cls_token)
+    torch.nn.init.ones_(layer.pos_embed)
+    layer = layer.to(device)
+
+    layer_master = VanillaPatchEmbedding(IMG_SIZE, 4, 3, HIDDEN_SIZE, dtype=dtype)
+    torch.nn.init.ones_(layer_master.cls_token)
+    torch.nn.init.ones_(layer_master.pos_embed)
+    layer_master = layer_master.to(device)
+
+    proj_weight_master = layer_master.weight.data
+    torch.distributed.broadcast(proj_weight_master, src=0)
+    proj_weight = torch.chunk(proj_weight_master, TESSERACT_DIM, dim=0)[j]
+    proj_weight = torch.chunk(proj_weight, TESSERACT_DIM, dim=0)[i]
+    layer.weight.data.copy_(proj_weight)
+    proj_bias_master = layer_master.bias.data
+    torch.distributed.broadcast(proj_bias_master, src=0)
+    proj_bias = torch.chunk(proj_bias_master, TESSERACT_DIM, dim=0)[j]
+    proj_bias = torch.chunk(proj_bias, TESSERACT_DIM, dim=0)[i]
+    layer.bias.data.copy_(proj_bias)
+
+    A_shape = (BATCH_SIZE, 3, IMG_SIZE, IMG_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(A)
+
+    A_master = A_master.clone()
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('patch embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    cls_grad_master = layer_master.cls_token.grad
+    cls_grad = torch.chunk(cls_grad_master, TESSERACT_DIM, dim=-1)[j]
+    cls_grad = torch.chunk(cls_grad, TESSERACT_DIM, dim=-1)[i]
+    check_equal(cls_grad, layer.cls_token.grad)
+
+    pos_grad_master = layer_master.pos_embed.grad
+    pos_grad = torch.chunk(pos_grad_master, TESSERACT_DIM, dim=-1)[j]
+    pos_grad = torch.chunk(pos_grad, TESSERACT_DIM, dim=-1)[i]
+    check_equal(pos_grad, layer.pos_embed.grad)
+
+    B_grad = layer_master.weight.grad
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=0)[j]
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=0)[i]
+    check_equal(B_grad, layer.weight.grad)
+
+    bias_grad = layer_master.bias.grad
+    bias_grad = torch.chunk(bias_grad, TESSERACT_DIM)[j]
+    bias_grad = torch.chunk(bias_grad, TESSERACT_DIM)[i]
+    check_equal(bias_grad, layer.bias.grad)
+    print_rank_0('patch embed backward: pass')
+
+
+def check_vocab_parallel_embed():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    embed = VocabParallelEmbedding2p5D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, TESSERACT_DIM, dim=-1)[j]
+    weight = torch.chunk(weight, TESSERACT_DIM, dim=0)[i]
+    embed.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = embed(A)
+
+    A_master = A_master.clone()
+    C_master = embed_master(A_master)
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('vocab parallel embed forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=-1)[j]
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=0)[i]
+    check_equal(B_grad, embed.weight.grad)
+    print_rank_0('vocab parallel embed backward: pass')
+
+
+def check_classifier_no_given_weight():
+    device = get_current_device()
+    dtype = torch.float32
+    INPUT_SIZE = HIDDEN_SIZE
+    OUTPUT_SIZE = NUM_CLASSES
+
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+
+    layer = Classifier2p5D(INPUT_SIZE, OUTPUT_SIZE)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
+    A_master = torch.randint(5, A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = torch.chunk(A_master, TESSERACT_DIM, dim=0)[i]
+    A = torch.chunk(A, TESSERACT_DIM, dim=-1)[j]
+    A = A.clone()
+    A.requires_grad = True
+
+    W_shape = (OUTPUT_SIZE, INPUT_SIZE)
+    W_master = torch.randint(5, W_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(W_master, src=0)
+    # W = torch.chunk(W_master, TESSERACT_DIM, dim=-1)[j]
+    W = torch.chunk(W_master, TESSERACT_DIM, dim=-1)[j]
+    W = torch.chunk(W, TESSERACT_DIM, dim=-1)[i]
+    W = W.clone()
+    layer.weight.data.copy_(W)
+    # W.requires_grad = True
+
+    B_shape = (OUTPUT_SIZE, )
+    B_master = torch.randint(5, B_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(B_master, src=0)
+    # B = torch.chunk(B_master, TESSERACT_DIM, dim=0)[j]
+    B = B_master.clone()
+    layer.bias.data.copy_(B)
+
+    out = layer(A)
+
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    W_master = W_master.clone()
+    W_master.requires_grad = True
+    B_master = B_master.clone()
+    B_master.requires_grad = True
+    C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    # C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
+
+    check_equal(out, C)
+    print_rank_0('classifier (no given weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    # grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    A_grad = torch.chunk(A_grad, TESSERACT_DIM, dim=0)[i]
+    A_grad = torch.chunk(A_grad, TESSERACT_DIM, dim=-1)[j]
+    check_equal(A_grad, A.grad)
+
+    W_grad = W_master.grad
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[j]
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[i]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = B_master.grad
+    # B_grad = torch.chunk(B_grad, TESSERACT_DIM, dim=0)[j]
+    # if i == 0:
+    check_equal(B_grad, layer.bias.grad)
+
+    print_rank_0('classifier (no given weight) backward: pass')
+
+
+def check_vocab_parallel_classifier_no_given_weight():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    layer = VocabParallelClassifier2p5D(HIDDEN_SIZE, VOCAB_SIZE, bias=True)
+    layer = layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, bias=True)
+    layer_master = layer_master.to(dtype).to(device)
+
+    weight_master = layer_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, TESSERACT_DIM, dim=0)[i]
+    weight = torch.chunk(weight, TESSERACT_DIM, dim=-1)[j]
+    layer.weight.data.copy_(weight)
+    bias_master = layer_master.bias.data
+    torch.distributed.broadcast(bias_master, src=0)
+    bias = torch.chunk(bias_master, TESSERACT_DIM)[j]
+    layer.bias.data.copy_(bias)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, HIDDEN_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = torch.chunk(A_master, TESSERACT_DIM, dim=0)[i]
+    A = torch.chunk(A, TESSERACT_DIM, dim=-1)[j]
+    A = A.clone()
+    A.requires_grad = True
+    out = layer(A)
+
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('vocab parallel classifier (no given weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    A_grad = torch.chunk(A_grad, TESSERACT_DIM, dim=0)[i]
+    A_grad = torch.chunk(A_grad, TESSERACT_DIM, dim=-1)[j]
+    check_equal(A_grad, A.grad)
+
+    W_grad = layer_master.weight.grad
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=0)[i]
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[j]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = layer_master.bias.grad
+    B_grad = torch.chunk(B_grad, TESSERACT_DIM)[j]
+    if i == 0:
+        check_equal(B_grad, layer.bias.grad)
+    print_rank_0('vocab parallel classifier (no given weight) backward: pass')
+
+
+def check_classifier_given_embed_weight():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    embed = Embedding2p5D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, TESSERACT_DIM, dim=-1)[j]
+    weight = torch.chunk(weight, TESSERACT_DIM, dim=-1)[i]
+    embed.weight.data.copy_(weight)
+
+    layer = Classifier2p5D(HIDDEN_SIZE, VOCAB_SIZE, weight=embed.weight, bias=False)
+    layer = layer.to(dtype).to(device)
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(embed(A))
+
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    check_equal(out, C)
+    print_rank_0('classifier (given embed weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = embed_master.weight.grad
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[j]
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[i]
+    check_equal(W_grad, embed.weight.grad)
+    print_rank_0('classifier (given embed weight) backward: pass')
+
+
+def check_vocab_parallel_classifier_given_embed_weight():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    embed = VocabParallelEmbedding2p5D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, TESSERACT_DIM, dim=-1)[j]
+    weight = torch.chunk(weight, TESSERACT_DIM, dim=0)[i]
+    embed.weight.data.copy_(weight)
+
+    layer = VocabParallelClassifier2p5D(HIDDEN_SIZE, VOCAB_SIZE, weight=embed.weight, bias=False)
+    layer = layer.to(dtype).to(device)
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+    out = layer(embed(A))
+
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, TESSERACT_DIM, dim=0)[i]
+    C = torch.chunk(C, TESSERACT_DIM, dim=-1)[j]
+    check_equal(out, C)
+    print_rank_0('vocab parallel classifier (given embed weight) forward: pass')
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, TESSERACT_DIM, dim=0)[i]
+    grad = torch.chunk(grad, TESSERACT_DIM, dim=-1)[j]
+    grad = grad.clone()
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = embed_master.weight.grad
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=-1)[j]
+    W_grad = torch.chunk(W_grad, TESSERACT_DIM, dim=0)[i]
+    check_equal(W_grad, embed.weight.grad)
+    print_rank_0('vocab parallel classifier (given embed weight) backward: pass')
+
+
+def check_loss():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    criterion = CrossEntropyLoss2p5D()
+    criterion_master = torch.nn.CrossEntropyLoss()
+
+    out_shape = (BATCH_SIZE, NUM_CLASSES)
+    out_master = torch.randn(out_shape, dtype=dtype, device=device)
+    target_master = torch.randint(NUM_CLASSES, (BATCH_SIZE, ), dtype=torch.long, device=device)
+    torch.distributed.broadcast(out_master, src=0)
+    torch.distributed.broadcast(target_master, src=0)
+    out = torch.chunk(out_master, TESSERACT_DIM, dim=0)[i]
+    out = out.clone()
+    out.requires_grad = True
+    loss = criterion(out, target_master)
+
+    out_master = out_master.clone()
+    out_master.requires_grad = True
+    loss_master = criterion_master(out_master, target_master)
+    check_equal(loss, loss_master)
+    print_rank_0('cross entropy loss forward: pass')
+
+    loss.backward()
+    loss_master.backward()
+
+    out_grad = out_master.grad
+    out_grad = torch.chunk(out_grad, TESSERACT_DIM, dim=0)[i]
+    check_equal(out_grad, out.grad)
+    print_rank_0('cross entropy loss backward: pass')
+
+
+def check_vocab_parallel_loss():
+    device = get_current_device()
+    dtype = torch.float32
+    i = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
+    j = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
+    k = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
+
+    criterion = VocabParallelCrossEntropyLoss2p5D()
+    criterion_master = torch.nn.CrossEntropyLoss()
+
+    out_shape = (BATCH_SIZE, NUM_CLASSES)
+    out_master = torch.randn(out_shape, dtype=dtype, device=device)
+    target_master = torch.randint(NUM_CLASSES, (BATCH_SIZE, ), dtype=torch.long, device=device)
+    torch.distributed.broadcast(out_master, src=0)
+    torch.distributed.broadcast(target_master, src=0)
+    out = torch.chunk(out_master, TESSERACT_DIM, dim=0)[i]
+    out = torch.chunk(out, TESSERACT_DIM, dim=-1)[j]
+    out = out.clone()
+    out.requires_grad = True
+    loss = criterion(out, target_master)
+
+    out_master = out_master.clone()
+    out_master.requires_grad = True
+    loss_master = criterion_master(out_master, target_master)
+    check_equal(loss, loss_master)
+    print_rank_0('vocab parallel cross entropy loss forward: pass')
+
+    loss.backward()
+    loss_master.backward()
+
+    out_grad = out_master.grad
+    out_grad = torch.chunk(out_grad, TESSERACT_DIM, dim=0)[i]
+    out_grad = torch.chunk(out_grad, TESSERACT_DIM, dim=-1)[j]
+    check_equal(out_grad, out.grad)
+    print_rank_0('vocab parallel cross entropy loss backward: pass')
+
+
 # def check_attention():
 #     device = get_current_device()
 #     dtype = torch.float32
@@ -267,7 +676,6 @@ def check_layernorm():
 #     assert A.grad.shape == A.shape
 #     print_rank_0('self attention backward: pass')
 
-
 # def check_mlp():
 #     device = get_current_device()
 #     dtype = torch.float32
@@ -304,7 +712,6 @@ def check_layernorm():
 #     assert A.grad.shape == A.shape
 #     print_rank_0('mlp backward: pass')
 
-
 # def check_transformerlayer():
 #     device = get_current_device()
 #     dtype = torch.float32
@@ -344,4 +751,4 @@ def check_layernorm():
 
 #     out.backward(grad)
 #     assert A.grad.shape == A.shape
-#     print_rank_0('transformerlayer backward: pass')
+#     print_rank_0('transformerlayer backward: pass')

tests/test_layers/test_2p5d/checks_2p5d/common.py

@@ -5,8 +5,10 @@ TESSERACT_DEP = 2
 BATCH_SIZE = 8
 SEQ_LENGTH = 8
 HIDDEN_SIZE = 8
-NUM_CLASSES = 3
+NUM_CLASSES = 8
+VOCAB_SIZE = 16
+IMG_SIZE = 16
 
 
 def check_equal(A, B):
-    assert torch.allclose(A, B, rtol=1e-5, atol=1e-2) == True
+    assert torch.allclose(A, B, rtol=1e-5, atol=1e-2)

tests/test_layers/test_2p5d/test_2p5d.py

@@ -5,10 +5,10 @@ import torch
 import torch.multiprocessing as mp
 from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
 from colossalai.utils import free_port
 
-from checks_2p5d.check_layer_2p5d import (check_classifier, check_layernorm,
-                                          check_linear)
+from checks_2p5d.check_layer_2p5d import *
 from checks_2p5d.check_operation_2p5d import check_AB, check_ABT, check_ATB
 
 CONFIG = dict(
@@ -28,10 +28,19 @@ def check_operations():
 def check_layer():
     check_linear()
     check_layernorm()
-    check_classifier()
+    check_embed()
+    check_patch_embed()
+    check_vocab_parallel_embed()
+    check_classifier_no_given_weight()
+    check_vocab_parallel_classifier_no_given_weight()
+    check_classifier_given_embed_weight()
+    check_vocab_parallel_classifier_given_embed_weight()
+    check_loss()
+    check_vocab_parallel_loss()
 
 
 def check_layer_and_operation(rank, world_size, port):
+    disable_existing_loggers()
     launch(config=CONFIG,
            rank=rank,
            world_size=world_size,
@@ -39,6 +48,9 @@ def check_layer_and_operation(rank, world_size, port):
            port=port,
            backend='nccl')
 
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    torch.backends.cudnn.deterministic = True
     check_operations()
     check_layer()
     gpc.destroy()

tests/test_layers/test_3d/checks_3d/check_layer_3d.py

@@ -3,16 +3,17 @@
 
 import time
 
-from colossalai.constants import (INPUT_GROUP_3D, OUTPUT_GROUP_3D, WEIGHT_GROUP_3D)
+import torch
+from colossalai.constants import INPUT_GROUP_3D, OUTPUT_GROUP_3D, WEIGHT_GROUP_3D
 from colossalai.core import global_context
 from colossalai.logging import get_dist_logger
-from colossalai.nn import (Classifier3D, CrossEntropyLoss3D, LayerNorm3D, Linear3D, PatchEmbedding3D, VanillaClassifier,
-                           VanillaPatchEmbedding)
+from colossalai.nn import (Classifier3D, CrossEntropyLoss3D, Embedding3D, LayerNorm3D, Linear3D, PatchEmbedding3D,
+                           VanillaClassifier, VanillaPatchEmbedding, VocabParallelClassifier3D,
+                           VocabParallelCrossEntropyLoss3D, VocabParallelEmbedding3D)
 from colossalai.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
 from colossalai.utils import get_current_device, print_rank_0
 
-from .common import *
-import torch
+from .common import BATCH_SIZE, DEPTH, HIDDEN_SIZE, IMG_SIZE, NUM_CLASSES, SEQ_LENGTH, VOCAB_SIZE, check_equal
 
 
 def check_linear():
@@ -27,9 +28,9 @@ def check_linear():
     weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
     output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
 
-    j = A_rank = global_context.get_local_rank(input_parallel_mode)
-    i = B_rank = global_context.get_local_rank(weight_parallel_mode)
-    k = C_rank = global_context.get_local_rank(output_parallel_mode)
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
 
     layer = Linear3D(INPUT_SIZE, OUTPUT_SIZE, dtype=dtype, bias=True)
     layer = layer.to(device)
@@ -112,9 +113,9 @@ def check_layernorm():
     weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
     output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
 
-    j = A_rank = global_context.get_local_rank(input_parallel_mode)
-    i = B_rank = global_context.get_local_rank(weight_parallel_mode)
-    k = C_rank = global_context.get_local_rank(output_parallel_mode)
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
 
     norm = LayerNorm3D(INPUT_SIZE, eps=1e-6, dtype=dtype)
     norm = norm.to(device)
@@ -186,7 +187,7 @@ def check_layernorm():
     return fwd_end - fwd_start, bwd_end - bwd_start
 
 
-def check_classifier():
+def check_classifier_no_given_weight():
     rank = torch.distributed.get_rank()
     logger = get_dist_logger()
     device = get_current_device()
@@ -197,9 +198,9 @@ def check_classifier():
     weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
     output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
 
-    j = A_rank = global_context.get_local_rank(input_parallel_mode)
-    i = B_rank = global_context.get_local_rank(weight_parallel_mode)
-    k = C_rank = global_context.get_local_rank(output_parallel_mode)
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
 
     layer = Classifier3D(INPUT_SIZE, NUM_CLASSES, dtype=dtype, bias=True)
     layer = layer.to(device)
@@ -229,14 +230,14 @@ def check_classifier():
     torch.cuda.synchronize()
     fwd_end = time.time()
     print_rank_0(
-        'head forward: pass | {0} --> {1} | {2:.3f} s'.format(tuple(A.shape), tuple(out.shape), fwd_end - fwd_start),
-        logger)
+        'classifier (no given weight) forward: pass | {0} --> {1} | {2:.3f} s'.format(
+            tuple(A.shape), tuple(out.shape), fwd_end - fwd_start), logger)
     A_master = A_master.clone()
     A_master.requires_grad = True
     C_master = layer_master(A_master)
     C = torch.chunk(C_master, DEPTH, dim=0)[i]
     C = torch.chunk(C, DEPTH, dim=0)[j]
-    logger.info('Rank {} head forward: {}'.format(rank, check_equal(out, C)))
+    logger.info('Rank {} classifier (no given weight) forward: {}'.format(rank, check_equal(out, C)))
 
     grad_shape = C_master.shape
     grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
@@ -249,7 +250,7 @@ def check_classifier():
     out.backward(grad)
     torch.cuda.synchronize()
     bwd_end = time.time()
-    print_rank_0('head backward: pass | {:.3f} s'.format(bwd_end - bwd_start), logger)
+    print_rank_0('classifier (no given weight) backward: pass | {:.3f} s'.format(bwd_end - bwd_start), logger)
 
     grad_master = grad_master.clone()
     C_master.backward(grad_master)
@@ -257,23 +258,275 @@ def check_classifier():
     A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
     A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[k]
     A_grad = torch.chunk(A_grad, DEPTH, dim=0)[j]
-    logger.info('Rank {} head backward (input_grad): {}'.format(rank, check_equal(A_grad, A.grad)))
+    logger.info('Rank {} classifier (no given weight) backward (input_grad): {}'.format(
+        rank, check_equal(A_grad, A.grad)))
 
     B_grad = layer_master.weight.grad
     B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[k]
     if j == k:
-        logger.info('Rank {} head backward (weight_grad): {}'.format(rank,
-                                                                     check_equal(B_grad, layer.weight.grad)))
+        logger.info('Rank {} classifier (no given weight) backward (weight_grad): {}'.format(
+            rank, check_equal(B_grad, layer.weight.grad)))
     else:
-        logger.info('Rank {} head backward (weight_grad): {}'.format(rank, layer.weight.grad is None))
+        logger.info('Rank {} classifier (no given weight) backward (weight_grad): {}'.format(
+            rank, layer.weight.grad is None))
 
     bias_grad = layer_master.bias.grad
-    logger.info('Rank {} head backward (bias_grad): {}'.format(rank, check_equal(bias_grad, layer.bias.grad)))
+    logger.info('Rank {} classifier (no given weight) backward (bias_grad): {}'.format(
+        rank, check_equal(bias_grad, layer.bias.grad)))
 
     return fwd_end - fwd_start, bwd_end - bwd_start
 
 
-def check_embed():
+def check_vocab_parallel_classifier_no_given_weight():
+    rank = torch.distributed.get_rank()
+    logger = get_dist_logger()
+    device = get_current_device()
+    dtype = torch.float32
+    INPUT_SIZE = HIDDEN_SIZE
+
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
+
+    layer = VocabParallelClassifier3D(INPUT_SIZE, VOCAB_SIZE, bias=True)
+    layer = layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(INPUT_SIZE, VOCAB_SIZE, bias=True)
+    layer_master = layer_master.to(dtype).to(device)
+
+    weight_master = layer_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=0)[j]
+    weight = torch.chunk(weight, DEPTH, dim=-1)[k]
+    layer.weight.data.copy_(weight)
+    bias_master = layer_master.bias.data
+    torch.distributed.broadcast(bias_master, src=0)
+    bias = torch.chunk(bias_master, DEPTH)[j]
+    layer.bias.data.copy_(bias)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = torch.chunk(A_master, DEPTH, dim=0)[i]
+    A = torch.chunk(A, DEPTH, dim=-1)[k]
+    A = torch.chunk(A, DEPTH, dim=0)[j]
+    A = A.clone()
+    A.requires_grad = True
+
+    fwd_start = time.time()
+    out = layer(A)
+    torch.cuda.synchronize()
+    fwd_end = time.time()
+    print_rank_0(
+        'vocab parallel classifier (no given weight) forward: pass | {0} --> {1} | {2:.3f} s'.format(
+            tuple(A.shape), tuple(out.shape), fwd_end - fwd_start), logger)
+    A_master = A_master.clone()
+    A_master.requires_grad = True
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    C = torch.chunk(C, DEPTH, dim=0)[k]
+    logger.info('Rank {} vocab parallel classifier (no given weight) forward: {}'.format(rank, check_equal(out, C)))
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = torch.chunk(grad, DEPTH, dim=0)[k]
+    grad = grad.clone()
+
+    bwd_start = time.time()
+    out.backward(grad)
+    torch.cuda.synchronize()
+    bwd_end = time.time()
+    print_rank_0('vocab parallel classifier (no given weight) backward: pass | {:.3f} s'.format(bwd_end - bwd_start),
+                 logger)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+    A_grad = A_master.grad
+    A_grad = torch.chunk(A_grad, DEPTH, dim=0)[i]
+    A_grad = torch.chunk(A_grad, DEPTH, dim=-1)[k]
+    A_grad = torch.chunk(A_grad, DEPTH, dim=0)[j]
+    logger.info('Rank {} vocab parallel classifier (no given weight) backward (input_grad): {}'.format(
+        rank, check_equal(A_grad, A.grad)))
+
+    B_grad = layer_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j]
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[k]
+    logger.info('Rank {} vocab parallel classifier (no given weight) backward (weight_grad): {}'.format(
+        rank, check_equal(B_grad, layer.weight.grad)))
+
+    bias_grad = layer_master.bias.grad
+    bias_grad = torch.chunk(bias_grad, DEPTH)[j]
+    logger.info('Rank {} vocab parallel classifier (no given weight) backward (bias_grad): {}'.format(
+        rank, check_equal(bias_grad, layer.bias.grad)))
+
+    return fwd_end - fwd_start, bwd_end - bwd_start
+
+
+def check_classifier_given_embed_weight():
+    rank = torch.distributed.get_rank()
+    logger = get_dist_logger()
+    device = get_current_device()
+    dtype = torch.float32
+
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
+
+    embed = Embedding3D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[k]
+    embed.weight.data.copy_(weight)
+
+    layer = Classifier3D(HIDDEN_SIZE, VOCAB_SIZE, weight=embed.weight, bias=False)
+    layer = layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+
+    fwd_start = time.time()
+    out = layer(embed(A))
+    torch.cuda.synchronize()
+    fwd_end = time.time()
+    print_rank_0(
+        'classifier (given embed weight) forward: pass | {0} --> {1} | {2:.3f} s'.format(
+            tuple(A.shape), tuple(out.shape), fwd_end - fwd_start), logger)
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=0)[j]
+    logger.info('Rank {} classifier (given embed weight) forward: {}'.format(rank, check_equal(out, C)))
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=0)[j]
+    grad = grad.clone()
+
+    bwd_start = time.time()
+    out.backward(grad)
+    torch.cuda.synchronize()
+    bwd_end = time.time()
+    print_rank_0('classifier (given embed weight) backward: pass | {:.3f} s'.format(bwd_end - bwd_start), logger)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[k]
+    if j == k:
+        logger.info('Rank {} classifier (given embed weight) backward (weight_grad): {}'.format(
+            rank, check_equal(B_grad, embed.weight.grad)))
+    else:
+        logger.info('Rank {} classifier (given embed weight) backward (weight_grad): {}'.format(
+            rank, embed.weight.grad is None))
+
+    return fwd_end - fwd_start, bwd_end - bwd_start
+
+
+def check_vocab_parallel_classifier_given_embed_weight():
+    rank = torch.distributed.get_rank()
+    logger = get_dist_logger()
+    device = get_current_device()
+    dtype = torch.float32
+
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
+
+    embed = VocabParallelEmbedding3D(VOCAB_SIZE, HIDDEN_SIZE)
+    embed = embed.to(dtype).to(device)
+
+    embed_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    embed_master = embed_master.to(dtype).to(device)
+
+    weight_master = embed_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=0)[j]
+    weight = torch.chunk(weight, DEPTH, dim=-1)[k]
+    embed.weight.data.copy_(weight)
+
+    layer = VocabParallelClassifier3D(HIDDEN_SIZE, VOCAB_SIZE, weight=embed.weight, bias=False)
+    layer = layer.to(dtype).to(device)
+
+    layer_master = VanillaClassifier(HIDDEN_SIZE, VOCAB_SIZE, weight=embed_master.weight, bias=False)
+    layer_master = layer_master.to(dtype).to(device)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+
+    fwd_start = time.time()
+    out = layer(embed(A))
+    torch.cuda.synchronize()
+    fwd_end = time.time()
+    print_rank_0(
+        'vocab parallel classifier (given embed weight) forward: pass | {0} --> {1} | {2:.3f} s'.format(
+            tuple(A.shape), tuple(out.shape), fwd_end - fwd_start), logger)
+    A_master = A_master.clone()
+    C_master = layer_master(embed_master(A_master))
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[j]
+    C = torch.chunk(C, DEPTH, dim=0)[k]
+    logger.info('Rank {} vocab parallel classifier (given embed weight) forward: {}'.format(rank, check_equal(out, C)))
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[j]
+    grad = torch.chunk(grad, DEPTH, dim=0)[k]
+    grad = grad.clone()
+
+    bwd_start = time.time()
+    out.backward(grad)
+    torch.cuda.synchronize()
+    bwd_end = time.time()
+    print_rank_0('vocab parallel classifier (given embed weight) backward: pass | {:.3f} s'.format(bwd_end - bwd_start),
+                 logger)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    B_grad = embed_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j]
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[k]
+    logger.info('Rank {} vocab parallel embed backward (weight_grad): {}'.format(rank,
+                                                                                 check_equal(B_grad,
+                                                                                             embed.weight.grad)))
+
+    return fwd_end - fwd_start, bwd_end - bwd_start
+
+
+def check_patch_embed():
     rank = torch.distributed.get_rank()
     device = get_current_device()
     logger = get_dist_logger()
@@ -283,9 +536,9 @@ def check_embed():
     weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
     output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
 
-    j = A_rank = global_context.get_local_rank(input_parallel_mode)
-    i = B_rank = global_context.get_local_rank(weight_parallel_mode)
-    k = C_rank = global_context.get_local_rank(output_parallel_mode)
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
 
     layer = PatchEmbedding3D(IMG_SIZE, 4, 3, HIDDEN_SIZE, dtype=dtype)
     torch.nn.init.ones_(layer.cls_token)
@@ -310,18 +563,99 @@ def check_embed():
     A_master = torch.randn(A_shape, dtype=dtype, device=device)
     torch.distributed.broadcast(A_master, src=0)
     A = A_master.clone()
-    A.requires_grad = True
 
     fwd_start = time.time()
     out = layer(A)
     torch.cuda.synchronize()
     fwd_end = time.time()
     print_rank_0(
-        'embedding forward: pass | {0} --> {1} | {2:.3f} s'.format(tuple(A.shape), tuple(out.shape),
-                                                                   fwd_end - fwd_start), logger)
+        'patch embed forward: pass | {0} --> {1} | {2:.3f} s'.format(tuple(A.shape), tuple(out.shape),
+                                                                     fwd_end - fwd_start), logger)
+
+    A_master = A_master.clone()
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[k]
+    C = torch.chunk(C, DEPTH, dim=0)[j]
+    logger.info('Rank {} patch embed forward: {}'.format(rank, check_equal(out, C)))
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[k]
+    grad = torch.chunk(grad, DEPTH, dim=0)[j]
+    grad = grad.clone()
+
+    bwd_start = time.time()
+    out.backward(grad)
+    torch.cuda.synchronize()
+    bwd_end = time.time()
+    print_rank_0('patch embed backward: pass | {:.3f} s'.format(bwd_end - bwd_start), logger)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    cls_grad_master = layer_master.cls_token.grad
+    cls_grad = torch.chunk(cls_grad_master, DEPTH, dim=-1)[k]
+    logger.info('Rank {} patch embed backward (cls_grad): {}'.format(rank, check_equal(cls_grad, layer.cls_token.grad)))
+
+    pos_grad_master = layer_master.pos_embed.grad
+    pos_grad = torch.chunk(pos_grad_master, DEPTH, dim=-1)[k]
+    logger.info('Rank {} patch embed backward (pos_embed_grad): {}'.format(rank,
+                                                                           check_equal(pos_grad, layer.pos_embed.grad)))
+
+    B_grad = layer_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[k]
+    logger.info('Rank {} patch embed backward (proj_weight_grad): {}'.format(rank,
+                                                                             check_equal(B_grad, layer.weight.grad)))
+
+    bias_grad = layer_master.bias.grad
+    bias_grad = torch.chunk(bias_grad, DEPTH)[k]
+    logger.info('Rank {} patch embed backward (proj_bias_grad): {}'.format(rank,
+                                                                           check_equal(bias_grad, layer.bias.grad)))
+
+    return fwd_end - fwd_start, bwd_end - bwd_start
+
+
+def check_embed():
+    rank = torch.distributed.get_rank()
+    device = get_current_device()
+    logger = get_dist_logger()
+    dtype = torch.float32
+
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
+
+    layer = Embedding3D(VOCAB_SIZE, HIDDEN_SIZE)
+    layer = layer.to(dtype).to(device)
+    layer_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    layer_master = layer_master.to(dtype).to(device)
+
+    weight_master = layer_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=-1)[k]
+    layer.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+
+    fwd_start = time.time()
+    out = layer(A)
+    torch.cuda.synchronize()
+    fwd_end = time.time()
+    logger.info('embed forward: pass | {0} --> {1} | {2:.3f} s'.format(tuple(A.shape), tuple(out.shape),
+                                                                       fwd_end - fwd_start),
+                ranks=[0])
 
     A_master = A_master.clone()
-    A_master.requires_grad = True
     C_master = layer_master(A_master)
     C = torch.chunk(C_master, DEPTH, dim=0)[i]
     C = torch.chunk(C, DEPTH, dim=-1)[k]
@@ -329,7 +663,7 @@ def check_embed():
     logger.info('Rank {} embed forward: {}'.format(rank, check_equal(out, C)))
 
     grad_shape = C_master.shape
-    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
     torch.distributed.broadcast(grad_master, src=0)
     grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
     grad = torch.chunk(grad, DEPTH, dim=-1)[k]
@@ -339,30 +673,88 @@ def check_embed():
     out.backward(grad)
     torch.cuda.synchronize()
     bwd_end = time.time()
-    print_rank_0('embedding backward: pass | {:.3f} s'.format(bwd_end - bwd_start), logger)
+    logger.info('embed backward: pass | {:.3f} s'.format(bwd_end - bwd_start), ranks=[0])
 
     grad_master = grad_master.clone()
     C_master.backward(grad_master)
 
-    cls_grad_master = layer_master.cls_token.grad
-    cls_grad = torch.chunk(cls_grad_master, DEPTH, dim=-1)[k]
-    logger.info('Rank {} embed backward (cls_grad): {}'.format(rank, check_equal(cls_grad, layer.cls_token.grad)))
+    B_grad = layer_master.weight.grad
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[k]
+    if j == k:
+        logger.info('Rank {} embed backward (weight_grad): {}'.format(rank, check_equal(B_grad, layer.weight.grad)))
+    else:
+        logger.info('Rank {} embed backward (weight_grad): {}'.format(rank, layer.weight.grad is None))
 
-    pos_grad_master = layer_master.pos_embed.grad
-    pos_grad = torch.chunk(pos_grad_master, DEPTH, dim=-1)[k]
-    logger.info('Rank {} embed backward (pos_embed_grad): {}'.format(rank, check_equal(pos_grad, layer.pos_embed.grad)))
+    return fwd_end - fwd_start, bwd_end - bwd_start
+
+
+def check_vocab_parallel_embed():
+    rank = torch.distributed.get_rank()
+    device = get_current_device()
+    logger = get_dist_logger()
+    dtype = torch.float32
+
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
+
+    layer = VocabParallelEmbedding3D(VOCAB_SIZE, HIDDEN_SIZE)
+    layer = layer.to(dtype).to(device)
+    layer_master = torch.nn.Embedding(VOCAB_SIZE, HIDDEN_SIZE)
+    layer_master = layer_master.to(dtype).to(device)
+
+    weight_master = layer_master.weight.data
+    torch.distributed.broadcast(weight_master, src=0)
+    weight = torch.chunk(weight_master, DEPTH, dim=0)[j]
+    weight = torch.chunk(weight, DEPTH, dim=-1)[k]
+    layer.weight.data.copy_(weight)
+
+    A_shape = (BATCH_SIZE, SEQ_LENGTH)
+    A_master = torch.randint(VOCAB_SIZE, A_shape, device=device)
+    torch.distributed.broadcast(A_master, src=0)
+    A = A_master.clone()
+
+    fwd_start = time.time()
+    out = layer(A)
+    torch.cuda.synchronize()
+    fwd_end = time.time()
+    logger.info('vocab parallel embed forward: pass | {0} --> {1} | {2:.3f} s'.format(
+        tuple(A.shape), tuple(out.shape), fwd_end - fwd_start),
+                ranks=[0])
+
+    A_master = A_master.clone()
+    C_master = layer_master(A_master)
+    C = torch.chunk(C_master, DEPTH, dim=0)[i]
+    C = torch.chunk(C, DEPTH, dim=-1)[k]
+    C = torch.chunk(C, DEPTH, dim=0)[j]
+    logger.info('Rank {} vocab parallel embed forward: {}'.format(rank, check_equal(out, C)))
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=device)
+    torch.distributed.broadcast(grad_master, src=0)
+    grad = torch.chunk(grad_master, DEPTH, dim=0)[i]
+    grad = torch.chunk(grad, DEPTH, dim=-1)[k]
+    grad = torch.chunk(grad, DEPTH, dim=0)[j]
+    grad = grad.clone()
+    bwd_start = time.time()
+    out.backward(grad)
+    torch.cuda.synchronize()
+    bwd_end = time.time()
+    logger.info('vocab parallel embed backward: pass | {:.3f} s'.format(bwd_end - bwd_start), ranks=[0])
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
 
     B_grad = layer_master.weight.grad
-    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[k]
-    if j == k:
-        logger.info('Rank {} embed backward (proj_weight_grad): {}'.format(rank, check_equal(B_grad,
+    B_grad = torch.chunk(B_grad, DEPTH, dim=0)[j]
+    B_grad = torch.chunk(B_grad, DEPTH, dim=-1)[k]
+    logger.info('Rank {} vocab parallel embed backward (weight_grad): {}'.format(rank,
+                                                                                 check_equal(B_grad,
                                                                                              layer.weight.grad)))
-    else:
-        logger.info('Rank {} embed backward (proj_weight_grad): {}'.format(rank, layer.weight.grad is None))
-
-    bias_grad = layer_master.bias.grad
-    bias_grad = torch.chunk(bias_grad, DEPTH)[k]
-    logger.info('Rank {} embed backward (proj_bias_grad): {}'.format(rank, check_equal(bias_grad, layer.bias.grad)))
 
     return fwd_end - fwd_start, bwd_end - bwd_start
 
@@ -375,11 +767,9 @@ def check_loss():
 
     input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
     weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
-    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
 
-    j = A_rank = global_context.get_local_rank(input_parallel_mode)
-    i = B_rank = global_context.get_local_rank(weight_parallel_mode)
-    k = C_rank = global_context.get_local_rank(output_parallel_mode)
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
 
     criterion = CrossEntropyLoss3D()
     criterion_master = torch.nn.CrossEntropyLoss()
@@ -397,24 +787,79 @@ def check_loss():
     fwd_start = time.time()
     loss = criterion(out, target_master)
     fwd_end = time.time()
-    print_rank_0(
-        'loss forward: pass | {0} --> {1} | {2:.3f} s'.format(tuple(out.shape), tuple(loss.shape), fwd_end - fwd_start),
-        logger)
+    logger.info('cross entropy loss forward: pass | {0} --> {1} | {2:.3f} s'.format(tuple(out.shape), tuple(loss.shape),
+                                                                                    fwd_end - fwd_start),
+                ranks=[0])
 
     out_master = out_master.clone()
     out_master.requires_grad = True
     loss_master = criterion_master(out_master, target_master)
-    logger.info('Rank {} CrossEntropyLoss forward: {}'.format(rank, check_equal(loss, loss_master)))
+    logger.info('Rank {} cross entropy loss forward: {}'.format(rank, check_equal(loss, loss_master)))
 
     bwd_start = time.time()
     loss.backward()
     bwd_end = time.time()
-    print_rank_0('loss backward: pass | {:.3f} s'.format(bwd_end - bwd_start), logger)
+    logger.info('cross entropy loss backward: pass | {:.3f} s'.format(bwd_end - bwd_start), ranks=[0])
 
     loss_master.backward()
     out_grad = out_master.grad
     out_grad = torch.chunk(out_grad, DEPTH, dim=0)[i]
     out_grad = torch.chunk(out_grad, DEPTH, dim=0)[j]
-    logger.info('Rank {} CrossEntropyLoss backward: {}'.format(rank, check_equal(out_grad, out.grad)))
+    logger.info('Rank {} cross entropy loss backward: {}'.format(rank, check_equal(out_grad, out.grad)))
+
+    return fwd_end - fwd_start, bwd_end - bwd_start
+
+
+def check_vocab_parallel_loss():
+    rank = torch.distributed.get_rank()
+    logger = get_dist_logger()
+    device = get_current_device()
+    dtype = torch.float32
+
+    input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+    weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+    output_parallel_mode = get_parallel_mode_from_env(OUTPUT_GROUP_3D)
+
+    j = global_context.get_local_rank(input_parallel_mode)
+    i = global_context.get_local_rank(weight_parallel_mode)
+    k = global_context.get_local_rank(output_parallel_mode)
+
+    criterion = VocabParallelCrossEntropyLoss3D()
+    criterion_master = torch.nn.CrossEntropyLoss()
+
+    out_shape = (BATCH_SIZE, NUM_CLASSES)
+    out_master = torch.randn(out_shape, dtype=dtype, device=device)
+    target_master = torch.randint(NUM_CLASSES, (BATCH_SIZE, ), dtype=torch.long, device=device)
+    torch.distributed.broadcast(out_master, src=0)
+    torch.distributed.broadcast(target_master, src=0)
+    out = torch.chunk(out_master, DEPTH, dim=0)[i]
+    out = torch.chunk(out, DEPTH, dim=-1)[k]
+    out = torch.chunk(out, DEPTH, dim=0)[j]
+    out = out.clone()
+    out.requires_grad = True
+
+    fwd_start = time.time()
+    loss = criterion(out, target_master)
+    fwd_end = time.time()
+    logger.info('vocab parallel cross entropy loss forward: pass | {0} --> {1} | {2:.3f} s'.format(
+        tuple(out.shape), tuple(loss.shape), fwd_end - fwd_start),
+                ranks=[0])
+
+    out_master = out_master.clone()
+    out_master.requires_grad = True
+    loss_master = criterion_master(out_master, target_master)
+    logger.info('Rank {} vocab parallel cross entropy loss forward: {}'.format(rank, check_equal(loss, loss_master)))
+
+    bwd_start = time.time()
+    loss.backward()
+    bwd_end = time.time()
+    logger.info('vocab parallel cross entropy loss backward: pass | {:.3f} s'.format(bwd_end - bwd_start), ranks=[0])
+
+    loss_master.backward()
+    out_grad = out_master.grad
+    out_grad = torch.chunk(out_grad, DEPTH, dim=0)[i]
+    out_grad = torch.chunk(out_grad, DEPTH, dim=-1)[k]
+    out_grad = torch.chunk(out_grad, DEPTH, dim=0)[j]
+    logger.info('Rank {} vocab parallel cross entropy loss backward: {}'.format(rank, check_equal(out_grad, out.grad)))
 
     return fwd_end - fwd_start, bwd_end - bwd_start

tests/test_layers/test_3d/checks_3d/common.py

@@ -10,6 +10,7 @@ HIDDEN_SIZE = 8
 NUM_CLASSES = 8
 NUM_BLOCKS = 2
 IMG_SIZE = 16
+VOCAB_SIZE = 16
 
 def check_equal(A, B):
     eq = torch.allclose(A, B, rtol=1e-3, atol=1e-2)

tests/test_layers/test_3d/test_3d.py

@@ -7,9 +7,14 @@ import torch
 import torch.multiprocessing as mp
 from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
 from colossalai.utils import free_port
 
-from checks_3d.check_layer_3d import *
+from checks_3d.check_layer_3d import (check_classifier_given_embed_weight, check_classifier_no_given_weight,
+                                      check_embed, check_layernorm, check_linear, check_loss, check_patch_embed,
+                                      check_vocab_parallel_classifier_given_embed_weight,
+                                      check_vocab_parallel_classifier_no_given_weight, check_vocab_parallel_embed,
+                                      check_vocab_parallel_loss)
 
 CONFIG = dict(
     parallel=dict(
@@ -23,13 +28,23 @@ CONFIG = dict(
 def check_layer():
     check_linear()
     check_layernorm()
-    check_classifier()
-    # check_embed()
-    # check_loss()
+    check_classifier_no_given_weight()
+    check_vocab_parallel_classifier_no_given_weight()
+    check_classifier_given_embed_weight()
+    check_vocab_parallel_classifier_given_embed_weight()
+    check_embed()
+    check_patch_embed()
+    check_vocab_parallel_embed()
+    check_loss()
+    check_vocab_parallel_loss()
 
 
 def check_layer_and_operation(rank, world_size, port):
+    disable_existing_loggers()
     launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    torch.backends.cudnn.deterministic = True
     check_layer()
     gpc.destroy()
     torch.cuda.empty_cache()