[DTensor] refactor CommSpec (#3034)

colossalai/tensor/d_tensor/comm_spec.py

@@ -0,0 +1,310 @@
+from enum import Enum
+from typing import Dict
+
+import torch
+import torch.distributed as dist
+from torch.distributed import ReduceOp
+
+__all__ = [
+    'CollectiveCommPattern',
+    'CommSpec',
+]
+
+
+class CollectiveCommPattern(Enum):
+    GATHER_FWD_SPLIT_BWD = 'gather_fwd_split_bwd'
+    ALL2ALL_FWD_ALL2ALL_BWD = 'all2all_fwd_all2all_bwd'
+    SPLIT_FWD_GATHER_BWD = 'split_fwd_gather_bwd'
+    ALLREDUCE_FWD_IDENTITY_BWD = 'all_reduce_fwd_identity_bwd'
+    IDENTITY_FWD_ALLREDUCE_BWD = 'identity_fwd_all_reduce_bwd'
+    MIXGATHER_FWD_SPLIT_BWD = "mixgather_fwd_split_bwd"
+
+
+class CommSpec:
+    '''
+    Communication spec is used to record the communication action. It converts the communication spec
+    to real action which will be used in runtime. It contains comm_pattern to determine the
+    communication method, process_groups_dict to determine the process groups, gather_dim and shard_dim
+    to determine the buffer shape, and logical_process_axis
+
+    Argument:
+        comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec.
+        process_groups_dict(Dict): A dict which contains the process groups used to apply this CommSpec.
+        gather_dim(int, Optional): The gather_dim of the tensor will be gathered.
+        shard_dim(int, Optional): The shard_dim of the tensor will be sharded.
+        logical_process_axis(Union(int, List[int]), Optional): The mesh_dim to implement the communication action.
+    '''
+
+    def __init__(self,
+                 comm_pattern: CollectiveCommPattern,
+                 process_groups_dict: Dict,
+                 gather_dim: int = None,
+                 shard_dim: int = None,
+                 logical_process_axis: int = None):
+        self.comm_pattern = comm_pattern
+        self.gather_dim = gather_dim
+        self.shard_dim = shard_dim
+        self.logical_process_axis = logical_process_axis
+        self.process_groups_dict = process_groups_dict
+
+    def __repr__(self):
+        res_list = ["CommSpec:("]
+        if self.comm_pattern == CollectiveCommPattern.GATHER_FWD_SPLIT_BWD:
+            res_list.append(f"comm_pattern:GATHER_FWD_SPLIT_BWD, ")
+            res_list.append(f"gather_dim:{self.gather_dim}, ")
+            res_list.append(f"shard_dim:{self.gather_dim}, ")
+            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
+        elif self.comm_pattern == CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD:
+            res_list.append(f"comm_pattern:ALL2ALL_FWD_ALL2ALL_BWD, ")
+            res_list.append(f"gather_dim:{self.gather_dim}, ")
+            res_list.append(f"shard_dim:{self.shard_dim}, ")
+            res_list.append(f"logical_process_axis: {self.logical_process_axis})")
+        elif self.comm_pattern == CollectiveCommPattern.SPLIT_FWD_GATHER_BWD:
+            res_list.append(f"comm_pattern:SPLIT_FWD_GATHER_BWD, ")
+            res_list.append(f"gather_dim:{self.gather_dim}, ")
+            res_list.append(f"shard_dim:{self.shard_dim}, ")
+            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
+        elif self.comm_pattern == CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD:
+            res_list.append(f"comm_pattern:ALLREDUCE_FWD_IDENTITY_BWD, ")
+            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
+        elif self.comm_pattern == CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD:
+            res_list.append(f"comm_pattern:IDENTITY_FWD_ALLREDUCE_BWD, ")
+            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
+
+        return ''.join(res_list)
+
+    def covert_spec_to_action(self, tensor):
+        '''
+        Convert CommSpec into runtime action, implement real collection communication to target tensor.
+        The collection communication action is directed by the CommSpec.
+
+        Argument:
+            tensor(torch.Tensor): Tensor stored in each device, which could be different in different ranks.
+        '''
+        if self.comm_pattern in pattern_to_func_dict:
+            tensor = pattern_to_func_dict[self.comm_pattern](tensor, self)
+        else:
+            tensor = tensor
+        return tensor
+
+
+def _all_gather(tensor: torch.Tensor, comm_spec: CommSpec):
+    '''
+    Implement all gather operation on device mesh based on information provided by comm_spec.
+    '''
+    process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
+    for rank_list, process_group in process_groups_list:
+        if dist.get_rank() in rank_list:
+            tensor_list = [
+                torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list))
+            ]
+            # without this contiguous operation, the all gather may get some unexpected results.
+            tensor = tensor.contiguous()
+            dist.all_gather(tensor_list, tensor, group=process_group)
+            output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous()
+            return output
+
+
+def _split(tensor: torch.Tensor, comm_spec: CommSpec):
+    '''
+    Implement shard operation on device mesh based on information provided by comm_spec.
+    '''
+    process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
+    for rank_list, _ in process_groups_list:
+        if dist.get_rank() in rank_list:
+            dim = comm_spec.shard_dim
+            length = tensor.shape[comm_spec.shard_dim] // len(rank_list)
+            start = length * rank_list.index(dist.get_rank())
+            output = torch.narrow(tensor, dim, start, length).contiguous()
+            return output
+
+
+def _all_to_all(tensor: torch.Tensor, comm_spec: CommSpec):
+    '''
+    Implement all to all operation on device mesh based on information provided by comm_spec.
+    '''
+    process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
+    for rank_list, process_group in process_groups_list:
+        if dist.get_rank() in rank_list:
+            new_shape = list(tensor.shape)
+            new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list)
+            new_shape = torch.Size(new_shape)
+            output_tensor_list = [
+                torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list))
+            ]
+            dim = comm_spec.shard_dim
+            length = tensor.shape[comm_spec.shard_dim] // len(rank_list)
+            input_tensor_list = [
+                torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list))
+            ]
+            group = process_group
+            dist.all_to_all(output_tensor_list, input_tensor_list, group)
+            output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous()
+            return output
+
+
+def _all_reduce(tensor: torch.Tensor, comm_spec: CommSpec, async_op: bool = False):
+    '''
+    Implement all reduce operation on device mesh based on information provided by comm_spec.
+    '''
+    process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
+    for rank_list, process_group in process_groups_list:
+        if dist.get_rank() in rank_list:
+            if not tensor.is_contiguous():
+                tensor = tensor.contiguous()
+            dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op)
+            return tensor
+
+
+class _ReduceGrad(torch.autograd.Function):
+    """
+    A customized communication operation which forward is an identity operation,
+    backward is all_reduce operation.
+
+    Args:
+        input_: input matrix.
+        comm_spec: comm_spec will give information like process group, rank list, etc.
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return input_
+
+    @staticmethod
+    def forward(ctx, input_, comm_spec):
+        ctx.comm_spec = comm_spec
+        return input_
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _all_reduce(grad_output, ctx.comm_spec), None
+
+
+class _ReduceInput(torch.autograd.Function):
+    """
+    A customized communication operation which forward is all_reduce operation,
+    backward is an identity operation.
+
+    Args:
+        input_: input matrix.
+        comm_spec: comm_spec will give information like process group, rank list, etc.
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _all_reduce(input_)
+
+    @staticmethod
+    def forward(ctx, input_, comm_spec):
+        return _all_reduce(input_, comm_spec)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output, None
+
+
+class _SplitForwardGatherBackward(torch.autograd.Function):
+    """
+    A customized communication operation which forward is split operation,
+    backward is an all gather operation.
+
+    Args:
+        input_: input matrix.
+        comm_spec: comm_spec will give information like process group, rank list, etc.
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _split(input_)
+
+    @staticmethod
+    def forward(ctx, input_, comm_spec):
+        ctx.comm_spec = comm_spec
+        return _split(input_, comm_spec)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _all_gather(grad_output, ctx.comm_spec), None
+
+
+class _GatherForwardSplitBackward(torch.autograd.Function):
+    """
+    A customized communication operation which forward is an all gather operation,
+    backward is split operation.
+
+    Args:
+        input_: input matrix.
+        comm_spec: comm_spec will give information like process group, rank list, etc.
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _all_gather(input_)
+
+    @staticmethod
+    def forward(ctx, input_, comm_spec):
+        ctx.comm_spec = comm_spec
+        return _all_gather(input_, comm_spec)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _split(grad_output, ctx.comm_spec), None
+
+
+class _AllToAll(torch.autograd.Function):
+    """
+    A customized communication operation which forward is an all to all operation,
+    backward is an all to all operation.
+
+    Args:
+        input_: input matrix.
+        comm_spec: comm_spec will give information like process group, rank list, etc.
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _all_to_all(input_)
+
+    @staticmethod
+    def forward(ctx, input_, comm_spec):
+        output = _all_to_all(input_, comm_spec)
+        comm_spec_for_backward = CommSpec(comm_pattern=comm_spec.comm_pattern,
+                                          process_groups_dict=comm_spec.process_groups_dict,
+                                          gather_dim=comm_spec.shard_dim,
+                                          shard_dim=comm_spec.gather_dim,
+                                          logical_process_axis=comm_spec.logical_process_axis)
+        ctx.comm_spec = comm_spec_for_backward
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        return _all_to_all(grad_outputs, ctx.comm_spec), None
+
+
+def reduce_grad(input_, comm_spec):
+    return _ReduceGrad.apply(input_, comm_spec)
+
+
+def reduce_input(input_, comm_spec):
+    return _ReduceInput.apply(input_, comm_spec)
+
+
+def split_forward_gather_backward(input_, comm_spec):
+    return _SplitForwardGatherBackward.apply(input_, comm_spec)
+
+
+def gather_forward_split_backward(input_, comm_spec):
+    return _GatherForwardSplitBackward.apply(input_, comm_spec)
+
+
+def all_to_all(input_, comm_spec):
+    return _AllToAll.apply(input_, comm_spec)
+
+
+pattern_to_func_dict = {
+    CollectiveCommPattern.GATHER_FWD_SPLIT_BWD: gather_forward_split_backward,
+    CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD: all_to_all,
+    CollectiveCommPattern.SPLIT_FWD_GATHER_BWD: split_forward_gather_backward,
+    CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD: reduce_input,
+    CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD: reduce_grad,
+}

colossalai/tensor/d_tensor/sharding_spec.py

@@ -171,7 +171,7 @@ class ShardingSpec:
             raise ShardingOutOfIndexError(
                 f'sharding_sequence should have {self.dims} elements, but got index {len(self.sharding_sequence)}.')
 
-        if max(list(self.dim_partition_dict.keys())) >= self.dims:
+        if list(self.dim_partition_dict.keys()) and max(list(self.dim_partition_dict.keys())) >= self.dims:
             raise ShardingOutOfIndexError(
                 f'the key of dim_partition_dict should be less than {self.dims}, but got {max(list(self.dim_partition_dict.keys()))}.'
             )