[pipeline] refactor 1f1b schedule (#4115)

* [api] update optimizer wrapper to fit pipeline

* [pipeline] add base schedule

* [pipeline] add 1f1b schedule

* [test] add pipeline schedule utils test

* [pipeline] fix import

colossalai/interface/optimizer.py

 from typing import Union
 
+import torch
 import torch.nn as nn
 from torch import Tensor
 from torch.optim import Optimizer
@@ -53,6 +54,9 @@ class OptimizerWrapper:
         """
         loss.backward(*args, **kwargs)
 
+    def backward_by_grad(self, tensor: Tensor, grad: Tensor):
+        torch.autograd.backward(tensor, grad)
+
     def state_dict(self):
         """
         Returns the optimizer state.

colossalai/pipeline/schedule/__init__.py

+from .base import PipelineSchedule
+from .one_f_one_b import OneForwardOneBackwardSchedule
+
+__all__ = [
+    'PipelineSchedule',
+    'OneForwardOneBackwardSchedule',
+]

colossalai/pipeline/schedule/_utils.py

+from typing import Any, List, Optional
+
+import torch
+import torch.cuda
+from torch.nn import Module
+from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
+
+
+def to_device(x: Any, device: Optional[torch.device] = None) -> Any:
+    """Move object to device if it is a tensor.
+
+    Args:
+        x (Any): Object to be moved.
+        device (Optional[torch.device], optional): Target device. Defaults to None.
+
+    Returns:
+        Any: Moved object.
+    """
+    if isinstance(x, torch.Tensor):
+        return x.to(device)
+    return x
+
+
+def get_batch_size(batch: Any) -> int:
+    """Get the batch size (size of dimension-0) of the first tensor in the batch.
+
+    Args:
+        batch (Any): Batch to be inspected.
+
+    Raises:
+        RuntimeError: If no tensor is found in the batch.
+
+    Returns:
+        int: Batch size.
+    """
+    data_list, _ = tree_flatten(batch)
+    for data in data_list:
+        if isinstance(data, torch.Tensor):
+            return data.size(0)
+    raise RuntimeError('No tensor found in the batch')
+
+
+def get_micro_batch(batch: Any, start: int, micro_batch_size: int) -> Any:
+    """Get a micro batch of the original batch.
+
+    Args:
+        batch (Any): Batch to be sliced.
+        start (int): Start index of the micro batch.
+        micro_batch_size (int): Size of the micro batch.
+
+    Returns:
+        Any: Target micro batch.
+    """
+
+    def _get_tensor_slice(x: Any):
+        if isinstance(x, torch.Tensor):
+            return x[start:start + micro_batch_size]
+        return x
+
+    return tree_map(_get_tensor_slice, batch)
+
+
+def model_forward(model: Module, data: Any, internal_inputs: Optional[dict]) -> Any:
+    """Call model forward function with data and internal inputs.
+
+    Args:
+        model (Module): Model to be called.
+        data (Any): Data loaded from data iterator.
+        internal_inputs (Optional[dict]): Data from previous stage. It must be a dict or None if it's the first stage.
+
+    Returns:
+        Any: Outputs of the model.
+    """
+    if internal_inputs is None:
+        internal_inputs = {}
+    if isinstance(data, (list, tuple)):
+        return model(*data, **internal_inputs)
+    elif isinstance(data, dict):
+        return model(**data, **internal_inputs)
+    return model(data, **internal_inputs)
+
+
+def retain_grad(x: Any) -> None:
+    """Call retain_grad() on a tensor.
+
+    Args:
+        x (Any): Object to be called.
+    """
+    if isinstance(x, torch.Tensor):
+        x.retain_grad()
+
+
+def detach(x: Any) -> Any:
+    """Call detach() on a tensor.
+
+    Args:
+        x (Any): Object to be called.
+
+    Returns:
+        Any: The detached object.
+    """
+    if isinstance(x, torch.Tensor):
+        return x.detach()
+    return x
+
+
+def merge_batch(data: List[Any]) -> Any:
+    """Merge micro batches into a batch.
+
+    Args:
+        data (List[Any]): A list of micro batches.
+
+    Returns:
+        Any: Merge batch.
+    """
+    if len(data) == 0:
+        return
+    flattened_data = []
+    tree_spec = None
+    for d in data:
+        elems, tree_spec = tree_flatten(d)
+        flattened_data.append(elems)
+    merged_data = []
+    for elem_batch in zip(*flattened_data):
+        if isinstance(elem_batch[0], torch.Tensor):
+            merged_data.append(torch.cat(elem_batch, dim=0))
+        else:
+            merged_data.append(list(elem_batch))
+    return tree_unflatten(merged_data, tree_spec)

colossalai/pipeline/schedule/base.py

+from typing import Any, Callable, Iterable
+
+from torch import Tensor
+from torch.nn import Module
+
+from colossalai.interface import OptimizerWrapper
+from colossalai.pipeline.stage_manager import PipelineStageManager
+
+
+class PipelineSchedule:
+
+    def __init__(self, stage_manager: PipelineStageManager) -> None:
+        self.stage_manager = stage_manager
+
+    def forward_backward_step(self,
+                              model: Module,
+                              optimizer: OptimizerWrapper,
+                              data_iter: Iterable,
+                              criterion: Callable[[Any, Any], Tensor],
+                              return_loss: bool = False,
+                              return_outputs: bool = False) -> dict:
+        """Forward and backward step for pipeline training.
+
+        Args:
+            model (Module): Model to be trained.
+            optimizer (OptimizerWrapper): Optimizer to be used.
+            data_iter (Iterable): Data iterator.
+            criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor.
+            return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss.
+            return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs.
+
+        Returns:
+            dict: A dict with keys: 'loss' and 'outputs'.
+        """
+        raise NotImplementedError

colossalai/pipeline/schedule/one_f_one_b.py

+from functools import partial
+from typing import Any, Callable, Iterable, List, Optional, Union
+
+import torch
+import torch.cuda
+from torch.nn import Module
+from torch.utils._pytree import tree_map
+
+from colossalai.interface import OptimizerWrapper
+from colossalai.pipeline.p2p import PipelineP2PCommunication
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.utils.cuda import get_current_device
+
+from ._utils import detach, get_batch_size, get_micro_batch, merge_batch, model_forward, retain_grad, to_device
+from .base import PipelineSchedule
+
+
+class OneForwardOneBackwardSchedule(PipelineSchedule):
+
+    def __init__(self, num_microbatches: int, stage_manager: PipelineStageManager) -> None:
+        super().__init__(stage_manager)
+        self.comm = PipelineP2PCommunication(stage_manager)
+        self.num_microbatches = num_microbatches
+        self.batch: Optional[Any] = None
+        self.batch_size: Optional[int] = None
+        self.microbatch_offset: Optional[int] = None
+        self.microbatch_size: Optional[int] = None
+
+    def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None:
+        """Load a batch from data iterator.
+
+        Args:
+            data_iter (Iterable): Data iterator.
+            device (Optional[torch.device], optional): Target device. Defaults to None.
+        """
+        batch = next(data_iter)
+        if device is not None:
+            batch = tree_map(partial(to_device, device=device), batch)
+        self.batch = batch
+        self.batch_size = get_batch_size(batch)
+        self.microbatch_offset = 0
+        assert self.batch_size % self.num_microbatches == 0, \
+            "Batch size should divided by the number of microbatches"
+        self.microbatch_size = self.batch_size // self.num_microbatches
+
+    def load_micro_batch(self) -> Any:
+        """Load a micro batch from the current batch.
+
+        Returns:
+            Any: Micro batch.
+        """
+        micro_batch = get_micro_batch(self.batch, self.microbatch_offset, self.microbatch_size)
+        self.microbatch_offset += self.microbatch_size
+        return tree_map(partial(to_device, device=get_current_device()), micro_batch)
+
+    def forward_step(self,
+                     model: Module,
+                     input_obj: Optional[dict],
+                     criterion: Callable,
+                     accum_loss: Optional[torch.Tensor] = None,
+                     outputs: Optional[List[Any]] = None) -> Union[torch.Tensor, dict]:
+        """Forward one step of the pipeline
+
+        Args:
+            model (Module): Model to be run
+            input_obj (Optional[dict]): The output from the previous stage. If it is the first stage, the `input_obj` is None.
+            criterion (Callable): Criterion to calculate loss.
+            accum_loss (Optional[torch.Tensor], optional): Accumulated loss. Defaults to None.
+            outputs (Optional[List[Any]], optional): List to store the output of the last stage (final output). Defaults to None.
+
+        Returns:
+            Union[torch.Tensor, dict]: The intermediate output (dict) of the current stage. If it is the last stage, the output is the loss (Tensor).
+        """
+        micro_batch = self.load_micro_batch()
+
+        # for the first stage, input_obj is None
+        # for the non-first stage, input_obj is the output of the previous stage and it's must be a dict
+        output_obj = model_forward(model, micro_batch, input_obj)
+
+        if self.stage_manager.is_last_stage():
+            loss = criterion(output_obj, micro_batch) / self.num_microbatches
+            if accum_loss is not None:
+                accum_loss.add_(loss.detach())
+            if outputs is not None:
+                outputs.append(tree_map(detach, output_obj))
+            return loss
+        else:
+            return output_obj
+
+    def backward_step(self, optimizer: OptimizerWrapper, input_obj: Optional[dict],
+                      output_obj: Union[dict, torch.Tensor], output_obj_grad: Optional[dict]) -> Optional[dict]:
+        """Backward one step of the pipeline
+
+        Args:
+            optimizer (OptimizerWrapper): Optimizer to update the model
+            input_obj (Optional[dict]): Output of the previous stage. If it is the first stage, the `input_obj` is None.
+            output_obj (Union[dict, torch.Tensor]): Output of the current stage. If it is the last stage, the output is the loss (Tensor).
+            output_obj_grad (dict): Gradient of the `output_obj`. If it is the last stage, the `output_obj_grad` is None.
+
+        Returns:
+            Optional[dict]: Gradient of the `input_obj`. If it is the first stage, the `input_obj_grad` is None.
+        """
+
+        # Retain the grad on the input_obj.
+        tree_map(retain_grad, input_obj)
+
+        # Backward pass.
+        if output_obj_grad is None:
+            optimizer.backward(output_obj)
+        else:
+            for k, grad in output_obj_grad.items():
+                optimizer.backward_by_grad(output_obj[k], grad)
+
+        # Collect the grad of the input_obj.
+        input_obj_grad = None
+        if input_obj is not None:
+            input_obj_grad = {}
+            for k, v in input_obj.items():
+                if isinstance(v, torch.Tensor) and v.grad is not None:
+                    input_obj_grad[k] = v.grad
+        return input_obj_grad
+
+    def forward_backward_step(self,
+                              model: Module,
+                              optimizer: OptimizerWrapper,
+                              data_iter: Iterable,
+                              criterion: Callable[..., Any],
+                              return_loss: bool = False,
+                              return_outputs: bool = False) -> dict:
+        """Runs non-interleaved 1F1B schedule, with communication between pipeline stages.
+
+        Args:
+            model (Module): Model to be trained.
+            optimizer (OptimizerWrapper): Optimizer to be used.
+            data_iter (Iterable): Data iterator.
+            criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor.
+            return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss.
+            return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs.
+
+        Returns:
+            dict: A dict with keys: 'loss' and 'outputs'.
+        """
+        forward_only = not torch.is_grad_enabled()
+
+        self.load_batch(data_iter)
+
+        # num_warmup_microbatches is the step when not all the processes are working
+        num_warmup_microbatches = self.stage_manager.num_stages - self.stage_manager.stage - 1
+        num_warmup_microbatches = min(num_warmup_microbatches, self.num_microbatches)
+        num_microbatches_remaining = self.num_microbatches - num_warmup_microbatches
+
+        # Input, output tensors only need to be saved when doing backward passes
+        input_objs = None
+        output_objs = None
+
+        if not forward_only:
+            input_objs = []
+            output_objs = []
+
+        outputs = [] if return_outputs and self.stage_manager.is_last_stage() else None
+        if return_loss and self.stage_manager.is_last_stage():
+            accum_loss = torch.zeros(1, device=get_current_device())
+        else:
+            accum_loss = None
+
+        # Run warmup forward passes.
+        for i in range(num_warmup_microbatches):
+            input_obj = self.comm.recv_forward()
+
+            output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs)
+
+            self.comm.send_forward(output_obj)
+
+            if not forward_only:
+                input_objs.append(input_obj)
+                output_objs.append(output_obj)
+
+        # Before running 1F1B, need to receive first forward tensor.
+        # If all microbatches are run in warmup / cooldown phase, then no need to
+        # receive this tensor here.
+        if num_microbatches_remaining > 0:
+            input_obj = self.comm.recv_forward()
+
+        # Run 1F1B in steady state.
+        for i in range(num_microbatches_remaining):
+            last_iteration = (i == (num_microbatches_remaining - 1))
+
+            output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs)
+            if forward_only:
+                self.comm.send_forward(output_obj)
+
+                if not last_iteration:
+                    input_obj = self.comm.recv_forward()
+
+            else:
+                # TODO adjust here
+                self.comm.send_forward(output_obj)
+                output_obj_grad = self.comm.recv_backward()
+
+                # Add input_obj and output_obj to end of list.
+                input_objs.append(input_obj)
+                output_objs.append(output_obj)
+
+                # Pop output_obj and output_obj from the start of the list for
+                # the backward pass.
+                input_obj = input_objs.pop(0)
+                output_obj = output_objs.pop(0)
+
+                input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
+
+                if last_iteration:
+                    input_obj = None
+                else:
+                    input_obj = self.comm.recv_forward()
+                self.comm.send_backward(input_obj_grad)
+
+        # Run cooldown backward passes.
+        if not forward_only:
+            for i in range(num_warmup_microbatches):
+                input_obj = input_objs.pop(0)
+                output_obj = output_objs.pop(0)
+
+                output_obj_grad = self.comm.recv_backward()
+                input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
+                self.comm.send_backward(input_obj_grad)
+
+        if outputs is not None:
+            outputs = merge_batch(outputs)
+        return {'loss': accum_loss, 'outputs': outputs}

tests/test_pipeline/test_schedule/test_pipeline_schedule_utils.py

+import torch
+
+from colossalai.pipeline.schedule._utils import get_batch_size, get_micro_batch, merge_batch
+
+
+def test_get_batch_size():
+    tensor = torch.rand(2, 3)
+    assert get_batch_size(tensor) == 2
+    assert get_batch_size([tensor]) == 2
+    assert get_batch_size((1, tensor)) == 2
+    assert get_batch_size({'tensor': tensor}) == 2
+    assert get_batch_size({'dummy': [1], 'tensor': tensor}) == 2
+    assert get_batch_size({'tensor': [tensor]}) == 2
+
+
+def test_get_micro_batch():
+    x = torch.rand(2, 1)
+    y = torch.rand(2, 3)
+    micro_batch = get_micro_batch(x, 0, 1)
+    assert torch.equal(micro_batch, x[0:1])
+    micro_batch = get_micro_batch(x, 1, 1)
+    assert torch.equal(micro_batch, x[1:2])
+    micro_batch = get_micro_batch([x, y], 0, 1)
+    assert torch.equal(micro_batch[0], x[0:1])
+    assert torch.equal(micro_batch[1], y[0:1])
+    micro_batch = get_micro_batch([x, y], 1, 1)
+    assert torch.equal(micro_batch[0], x[1:2])
+    assert torch.equal(micro_batch[1], y[1:2])
+    micro_batch = get_micro_batch({'x': x, 'y': y}, 0, 1)
+    assert torch.equal(micro_batch['x'], x[0:1])
+    assert torch.equal(micro_batch['y'], y[0:1])
+    micro_batch = get_micro_batch({'x': x, 'y': y}, 1, 1)
+    assert torch.equal(micro_batch['x'], x[1:2])
+    assert torch.equal(micro_batch['y'], y[1:2])
+
+
+def test_merge_batch():
+    x = torch.rand(2, 1)
+    y = torch.rand(2, 3)
+    merged = merge_batch([x[0:1], x[1:2]])
+    assert torch.equal(merged, x)
+    merged = merge_batch([[x[0:1], y[0:1]], [x[1:2], y[1:2]]])
+    assert torch.equal(merged[0], x)
+    assert torch.equal(merged[1], y)
+    merged = merge_batch([{'x': x[0:1], 'y': y[0:1]}, {'x': x[1:2], 'y': y[1:2]}])
+    assert torch.equal(merged['x'], x)
+    assert torch.equal(merged['y'], y)