[legacy] move communication and nn to legacy and refactor logger (#4671)

* [legacy] move communication to legacy (#4640)

* [legacy] refactor logger and clean up legacy codes (#4654)

* [legacy] make logger independent to gpc

* [legacy] make optim independent to registry

* [legacy] move test engine to legacy

* [legacy] move nn to legacy (#4656)

* [legacy] move nn to legacy

* [checkpointio] fix save hf config

* [test] remove useledd rpc pp test

* [legacy] fix nn init

* [example] skip tutorial hybriad parallel example

* [devops] test doc check

* [devops] test doc check

colossalai/legacy/communication/p2p_v2.py

@@ -0,0 +1,268 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+
+import io
+import pickle
+from typing import Any, List, Tuple, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroupNCCL
+from torch.distributed import distributed_c10d as c10d
+
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.core import global_context as gpc
+
+TensorShape = Union[torch.Size, List[int], Tuple[int]]
+_pg_manager = {}
+_unpickler = pickle.Unpickler
+
+
+def init_process_group():
+    """initialise process group by dist.new_group in the adjacent stages
+
+    Args:
+        None
+
+    Returns:
+        None
+    """
+    world_size = gpc.get_world_size(ParallelMode.PIPELINE)
+    for i in range(world_size - 1):
+        _pg_manager[(i, i + 1)] = dist.new_group([i, i + 1])
+
+
+def _acquire_pair_group_handle(first_rank: int, second_rank: int) -> ProcessGroupNCCL:
+    """get the group handle of two given ranks
+
+    Args:
+        first_rank (int): first rank in the pair
+        second_rank (int): second rank in the pair
+
+    Returns:
+        :class:`ProcessGroupNCCL`: the handle of the group consisting of the given two ranks
+    """
+    if len(_pg_manager) == 0:
+        init_process_group()
+    if first_rank > second_rank:
+        first_rank, second_rank = second_rank, first_rank
+    pair_key = (first_rank, second_rank)
+    return _pg_manager[pair_key]
+
+
+def _cuda_safe_tensor_to_object(tensor: torch.Tensor, tensor_size: torch.Size) -> object:
+    """transform tensor to object with unpickle.
+    Info of the device in bytes stream will be modified into current device before unpickling
+
+    Args:
+        tensor (:class:`torch.tensor`): tensor to be unpickled
+        tensor_size (:class:`torch.Size`): Size of the real info in bytes
+
+    Returns:
+        Any: object after unpickled
+    """
+    buf = tensor.numpy().tobytes()[:tensor_size]
+    if b'cuda' in buf:
+        buf_array = bytearray(buf)
+        device_index = torch.cuda.current_device()
+        buf_array[buf_array.find(b'cuda') + 5] = 48 + device_index
+        buf = bytes(buf_array)
+
+    io_bytes = io.BytesIO(buf)
+    byte_pickler = _unpickler(io_bytes)
+    unpickle = byte_pickler.load()
+
+    return unpickle
+
+
+def _broadcast_object_list(object_list: List[Any], src: int, dst: int, device=None):
+    """This is a modified version of the broadcast_object_list in torch.distribution
+    The only difference is that object will be move to correct device after unpickled.
+    If local_rank = src, then object list will be sent to rank src. Otherwise, object list will
+    be updated with data sent from rank src.
+
+    Args:
+        object_list (List[Any]): list of object to broadcast
+        src (int): source rank to broadcast
+        dst (int): dst rank to broadcast
+        device (:class:`torch.device`): device to do broadcast. current device in default
+
+    """
+    group = _acquire_pair_group_handle(src, dst)
+
+    if c10d._rank_not_in_group(group):
+        c10d._warn_not_in_group("broadcast_object_list")
+        return
+
+    local_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
+    # Serialize object_list elements to tensors on src rank.
+    if local_rank == src:
+        tensor_list, size_list = zip(*[c10d._object_to_tensor(obj) for obj in object_list])
+        object_sizes_tensor = torch.cat(size_list)
+    else:
+        object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long)
+
+    is_nccl_backend = c10d._check_for_nccl_backend(group)
+    current_device = None
+
+    if device is not None:
+        if is_nccl_backend and device.type != "cuda":
+            raise ValueError("device type must be cuda for nccl backend")
+        current_device = device
+    else:
+        current_device = torch.device("cpu")
+        if is_nccl_backend:
+            current_device = torch.device("cuda", torch.cuda.current_device())
+    if is_nccl_backend:
+        object_sizes_tensor = object_sizes_tensor.to(current_device)
+
+    # Broadcast object sizes
+    c10d.broadcast(object_sizes_tensor, src=src, group=group, async_op=False)
+
+    # Concatenate and broadcast serialized object tensors
+    if local_rank == src:
+        object_tensor = torch.cat(tensor_list)
+    else:
+        object_tensor = torch.empty(    # type: ignore[call-overload]
+            torch.sum(object_sizes_tensor).item(),    # type: ignore[arg-type]
+            dtype=torch.uint8,
+        )
+
+    if is_nccl_backend:
+        object_tensor = object_tensor.to(current_device)
+
+    c10d.broadcast(object_tensor, src=src, group=group, async_op=False)
+
+    # Deserialize objects using their stored sizes.
+    offset = 0
+
+    if local_rank != src:
+        for i, obj_size in enumerate(object_sizes_tensor):
+            obj_view = object_tensor[offset:offset + obj_size]
+            obj_view = obj_view.type(torch.uint8)
+            if obj_view.device != torch.device("cpu"):
+                obj_view = obj_view.cpu()
+            offset += obj_size
+            # unpickle
+            unpickle_object = _cuda_safe_tensor_to_object(obj_view, obj_size)
+
+            # unconsistence in device
+            if isinstance(unpickle_object,
+                          torch.Tensor) and unpickle_object.device.index != torch.cuda.current_device():
+                unpickle_object = unpickle_object.cuda()
+
+            object_list[i] = unpickle_object
+
+
+def _send_object(object: Any, dst: int) -> None:
+    """send anything to dst rank
+    Args:
+        object (Any): object needed to be sent
+        dst (int): rank of the destination
+
+    Returns:
+        None
+    """
+    local_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
+    # handler = _acquire_pair_group_handle(local_rank, dst)
+
+    # transform to list if not
+    if isinstance(object, torch.Tensor):
+        object = [object]
+
+    # broadcast length first
+    # TODO : more elegant ? P.S. reduce a _broadcast_object_list
+    _broadcast_object_list([len(object)], local_rank, dst)
+    # then broadcast safely
+    _broadcast_object_list(object, local_rank, dst)
+
+
+def _recv_object(src: int) -> Any:
+    """recv anything from src
+
+    Args:
+        src (int): source rank of data. local rank will receive data from src rank.
+
+    Returns:
+        Any: Object received from src.
+    """
+    local_rank = gpc.get_local_rank(ParallelMode.PIPELINE)
+    # handler = _acquire_pair_group_handle(local_rank, src)
+    # recv length first
+    length = [0]
+    _broadcast_object_list(length, src, local_rank)
+
+    # then create recv buff from length[0] and broadcast
+    object = [None] * length[0]
+    _broadcast_object_list(object, src, local_rank)
+
+    if length[0] == 1:
+        object = object[0]
+
+    return object
+
+
+def recv_forward(prev_rank: int = None) -> Any:
+    """Copy the forward output from the previous stage in pipeline as the input tensor of this stage.
+
+    Args:
+        input_tensor_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
+        prev_rank (int, optional): The rank of the source of the tensor.
+
+    Returns:
+        Any: The input tensor or input tensor list.
+    """
+    if gpc.is_pipeline_first_stage():
+        input_tensor = None
+    else:
+        if prev_rank is None:
+            prev_rank = gpc.get_prev_global_rank(ParallelMode.PIPELINE)
+        input_tensor = _recv_object(prev_rank)
+
+    return input_tensor
+
+
+def recv_backward(next_rank: int = None) -> Any:
+    """Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage.
+
+    Args:
+        output_grad_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the tensor to be received.
+        next_rank (int, optional): The rank of the source of the tensor.
+
+    Returns:
+        Any: The input gradient tensor or gradient tensor list.
+    """
+    if gpc.is_pipeline_last_stage():
+        output_tensor_grad = None
+    else:
+        if next_rank is None:
+            next_rank = gpc.get_next_global_rank(ParallelMode.PIPELINE)
+        output_tensor_grad = _recv_object(next_rank)
+
+    return output_tensor_grad
+
+
+def send_forward(output_object: Any, next_rank: int = None) -> None:
+    """Sends the input tensor to the next stage in pipeline.
+
+    Args:
+        output_object Any: Object to be sent.
+        next_rank (int, optional): The rank of the recipient of the tensor.
+    """
+    if not gpc.is_pipeline_last_stage():
+        if next_rank is None:
+            next_rank = gpc.get_next_global_rank(ParallelMode.PIPELINE)
+        _send_object(output_object, next_rank)
+
+
+def send_backward(input_object: Any, prev_rank: int = None) -> None:
+    """Sends the gradient tensor to the previous stage in pipeline.
+
+    Args:
+        input_tensor_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Tensor to be sent
+        prev_rank (int, optional): The rank of the recipient of the tensor
+    """
+    if not gpc.is_pipeline_first_stage():
+        if prev_rank is None:
+            prev_rank = gpc.get_prev_global_rank(ParallelMode.PIPELINE)
+        _send_object(input_object, prev_rank)