colossalai.communication
- colossalai.communication.all_gather(tensor, dim, parallel_mode, async_op=False)[source]
Gathers all tensors from the parallel group and concatenates them in a specific dimension.
Note
The parallel_mode should be concluded in
ParallelMode
. More details aboutParallelMode
could be found in parallel_mode.- Parameters
tensor (
torch.Tensor
) – Tensor to be gathered.dim (int) – The dimension concatenating in.
parallel_mode (
colossalai.context.ParallelMode
) – Parallel group mode used in this communication.async_op (bool, optional) – Whether operations are asynchronous.
- Returns
The result of all-together only, if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
- Return type
Union[tuple(
torch.Tensor
, work handle),torch.Tensor
]
- colossalai.communication.reduce_scatter(tensor, dim, parallel_mode, op=<torch.distributed.distributed_c10d.ReduceOp object>, async_op=False)[source]
Reduces all tensors then scatters it in a specific dimension to all members in the parallel group.
Note
The parallel_mode should be concluded in
ParallelMode
. More details aboutParallelMode
could be found in parallel_mode.- Parameters
tensor (
torch.Tensor
) – Tensor to be reduce_scattered.dim (int) – The dimension concatenating in.
parallel_mode (
colossalai.context.ParallelMode
) – Parallel group mode used in this communication.op (torch.distributed.ReduceOp, optional) – The type of reduce operation, should be included in [SUM, AVG, PRODUCT, MIN, MAX, BAND, BOR, BXOR]. More details about ReduceOp please refer to ReduceOp.
async_op (bool, optional) – Whether operations are asynchronous.
- Returns
The result of reduce_scatter only, if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
- Return type
Union[tuple(
torch.Tensor
, work handle),torch.Tensor
]
- colossalai.communication.all_reduce(tensor, parallel_mode, op=<torch.distributed.distributed_c10d.ReduceOp object>, async_op=False)[source]
Reduces the tensor data across whole parallel group in such a way that all get the final result.
Note
The parallel_mode should be concluded in
ParallelMode
. More details aboutParallelMode
could be found in parallel_mode.- Parameters
tensor (
torch.Tensor
) – Tensor to be all-reduced.parallel_mode (
colossalai.context.ParallelMode
) – Parallel group mode used in this communication.op (torch.distributed.ReduceOp, optional) –
The type of reduce operation, should be included in [SUM, AVG, PRODUCT, MIN, MAX, BAND, BOR, BXOR]. More details about ReduceOp please refer to ReduceOp.
async_op (bool, optional) – Whether operations are asynchronous.
- Returns
The result of all-gather only, if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
- Return type
Union[tuple(
torch.Tensor
, work handle),torch.Tensor
]
- colossalai.communication.broadcast(tensor, src, parallel_mode, async_op=False)[source]
Broadcast tensors to whole parallel group. Tensor must have the same number of elements in all processes participating in the collective.
Note
The parallel_mode should be concluded in
ParallelMode
. More details aboutParallelMode
could be found in parallel_mode.- Parameters
tensor (
torch.Tensor
) – Tensor to be broadcast.src (int) – Source rank.
parallel_mode (
colossalai.context.ParallelMode
) – Parallel group mode used in this communication.async_op (bool, optional) – Whether operations are asynchronous.
- Returns
The tensor need to be broadcast only, if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
- Return type
Union[tuple(
torch.Tensor
, work handle),torch.Tensor
]
- colossalai.communication.reduce(tensor, dst, parallel_mode, op=<torch.distributed.distributed_c10d.ReduceOp object>, async_op=False)[source]
Reduce tensors across whole parallel group. Only the process with rank
dst
is going to receive the final result.Note
The parallel_mode should be concluded in
ParallelMode
. More details aboutParallelMode
could be found in parallel_mode.- Parameters
tensor (
torch.Tensor
) – Tensor to be reduced.dst (int) – Destination rank.
parallel_mode (
colossalai.context.ParallelMode
) – Parallel group mode used in this communication.async_op (bool, optional) – Whether operations are asynchronous.
- Returns
The result of reduce only, if async_op is set to False. A tuple of output of all-gather and Async work handle, if async_op is set to True.
- Return type
Union[tuple(
torch.Tensor
, work handle),torch.Tensor
]
- colossalai.communication.send_forward(output_tensor, next_rank=None, scatter_gather_tensors=False)[source]
Sends the input tensor to the next stage in pipeline.
- Parameters
output_tensor (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor to be sent.next_rank (int, optional) – The rank of the recipient of the tensor.
- colossalai.communication.send_forward_recv_forward(output_tensor, input_tensor_shape, recv_prev=True, prev_rank=None, next_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Batched communication operation. Sends the input tensor to the next stage in pipeline, while receives the output tensor from the previous stage in pipeline as the input of this stage.
- Parameters
output_tensor (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor to be sent.input_tensor_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor to be received.
- Returns
The input tensor.
- Return type
Union[
torch.Tensor
, List[torch.Tensor
]]
- colossalai.communication.send_forward_backward_recv_forward_backward(output_tensor, input_tensor_grad, input_tensor_shape, output_grad_shape, recv_prev=True, recv_next=True, prev_rank=None, next_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Batched communication operation. Sends the input tensor to the next stage in pipeline and the gradient tensor to the previous stage, while receives the input gradient tensor from the next stage and the input tensor from the previous stage.
- Parameters
output_tensor (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor sent to the next.input_tensor_grad (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor sent to the previous.input_tensor_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor received from the previous.output_grad_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor received from the next.
- Returns
(the input tensor, the input gradient tensor)
- Return type
Tuple(Union[
torch.Tensor
, List[torch.Tensor
]], Union[torch.Tensor
, List[torch.Tensor
]])
- colossalai.communication.send_backward(input_tensor_grad, prev_rank=None, scatter_gather_tensors=False)[source]
Sends the gradient tensor to the previous stage in pipeline.
- Parameters
input_tensor_grad (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor to be sentprev_rank (int, optional) – The rank of the recipient of the tensor
- colossalai.communication.send_backward_recv_backward(input_tensor_grad, output_grad_shape, recv_next=True, prev_rank=None, next_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Batched communication operation. Sends the gradient tensor to the previous stage in pipeline, while receives the gradient tensor from the next member in pipeline as the input of this stage.
- Parameters
input_tensor_grad (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor to be sent.output_grad_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor to be received.
- Returns
The input gradient tensor.
- Return type
Union[
torch.Tensor
, List[torch.Tensor
]]
- colossalai.communication.send_backward_recv_forward(input_tensor_grad, input_tensor_shape, recv_prev=True, prev_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Batched communication operation. Sends the gradient tensor to the previous stage in pipeline, while receives the output tensor from the previous stage in pipeline as the input of this stage.
- Parameters
input_tensor_grad (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor to be sent.input_tensor_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor to be received.
- Returns
The input tensor.
- Return type
Union[
torch.Tensor
, List[torch.Tensor
]]
- colossalai.communication.send_forward_recv_backward(output_tensor, output_grad_shape, recv_next=True, next_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Batched communication operation. Sends the input tensor to the next stage in pipeline, while receives the gradient tensor from the next stage in pipeline as the input gradient tensor of this stage.
- Parameters
output_tensor (Union[
torch.Tensor
, List[torch.Tensor
]]) – Tensor to be sent.output_grad_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor to be received.
- Returns
The input gradient tensor.
- Return type
Union[
torch.Tensor
, List[torch.Tensor
]]
- colossalai.communication.recv_backward(output_grad_shape, next_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage.
- Parameters
output_grad_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor to be received.next_rank (int, optional) – The rank of the source of the tensor.
- Returns
The input gradient tensor or gradident tensor list.
- Return type
Union[
torch.Tensor
, List[torch.Tensor
]]
- colossalai.communication.recv_forward(input_tensor_shape, prev_rank=None, dtype=torch.float32, scatter_gather_tensors=False)[source]
Copy the forward output from the previous stage in pipeline as the input tensor of this stage.
- Parameters
input_tensor_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the tensor to be received.prev_rank (int, optional) – The rank of the source of the tensor.
- Returns
The input tensor or input tensor list.
- Return type
Union[
torch.Tensor
, List[torch.Tensor
]]
- colossalai.communication.ring_forward(tensor_send_next, parallel_mode)[source]
Sends a tensor to the next member and receives a tensor from the previous member. This function returns the received tensor from the previous member.
- Parameters
tensor_send_next (
torch.Tensor
) – Tensor sent to next memberparallel_mode (ParallelMode) – Parallel group mode used in this communication
- Returns
The tensor received from the previous.
- Return type
torch.Tensor
Note
The parallel_mode should be concluded in
ParallelMode
. More details aboutParallelMode
could be found in parallel_mode.
- colossalai.communication.send_obj_meta(obj, need_meta=True, next_rank=None)[source]
Sends obj meta information before sending a specific obj. Since the recipient must know the shape of the obj in p2p communications, meta information of the obj should be sent before communications. This function synchronizes with
recv_obj_meta()
.- Parameters
obj (Union[
torch.Tensor
, List[torch.Tensor
]]) – obj to be sent.need_meta (bool, optional) – If False, meta information won’t be sent.
next_rank (int) – The rank of the next member in pipeline parallel group.
- Returns
False
- Return type
bool
- colossalai.communication.recv_obj_meta(obj_shape, prev_rank=None)[source]
Receives obj meta information before receiving a specific obj. Since the recipient must know the shape of the obj in p2p communications, meta information of the obj should be received before communications. This function synchronizes with
send_obj_meta()
.- Parameters
obj_shape (Union[
torch.Size
, List[torch.Size
]]) – The shape of the obj to be received.prev_rank (int) – The rank of the source of the obj.
- Returns
The shape of the obj to be received.
- Return type
Union[
torch.Size
, List[torch.Size
]]