[shardformer] integrated linear 1D with dtensor

colossalai/nn/layer/base_layer.py

@@ -1,14 +1,16 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
+from contextlib import contextmanager
+
 import torch.nn as nn
 
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
-from contextlib import contextmanager
 
 
 class ParallelLayer(nn.Module):
+
     global_state_dict: bool = True
 
     def __init__(self):

colossalai/shardformer/layer/_operation.py

@@ -54,10 +54,10 @@ class LinearWithAsyncCommunication(torch.autograd.Function):
     """
 
     @staticmethod
-    def forward(ctx, input_, weight, bias, parallel_mode, async_grad_allreduce):
+    def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce):
         ctx.save_for_backward(input_, weight)
         ctx.use_bias = bias is not None
-        ctx.parallel_mode = parallel_mode
+        ctx.process_group = process_group
         ctx.async_grad_allreduce = async_grad_allreduce
 
         output = torch.matmul(input_, weight.t())
@@ -74,12 +74,13 @@ def backward(ctx, grad_output):
         grad_input = grad_output.matmul(weight)
         grad_output = grad_output.contiguous()
         # Convert the tensor shapes to 2D for execution compatibility
-        grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2])
-        total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2])
+        if len(grad_output.shape) > 2:
+            grad_output = grad_output.view(-1, grad_output.shape[-1])
+            total_input = total_input.view(-1, total_input.shape[-1])
 
         if ctx.async_grad_allreduce:
             # Asynchronous all-reduce
-            handle = dist.all_reduce(grad_input, group=gpc.get_group(ctx.parallel_mode), async_op=True)
+            handle = dist.all_reduce(grad_input, group=ctx.process_group, async_op=True)
             # Delay the start of weight gradient computation shortly (3us) to have
             # all-reduce scheduled first and have GPU resources allocated
             _ = torch.empty(1, device=grad_output.device) + 1
@@ -93,5 +94,123 @@ def backward(ctx, grad_output):
         return grad_input, grad_weight, grad_bias, None, None, None
 
 
-def linear_with_async_comm(input_, weight, bias, parallel_mode, async_grad_allreduce):
-    return LinearWithAsyncCommunication.apply(input_, weight, bias, parallel_mode, async_grad_allreduce)
+class _SplitForwardGatherBackward(torch.autograd.Function):
+    """
+    Split the input and keep only the corresponding chuck to the rank.
+
+    Args:
+        input_ (`torch.Tensor`): input matrix.
+        dim (int): the dimension to perform split and gather
+        process_group (`torch.distributed.ProcessGroup`): the process group used for collective communication
+
+    """
+
+    @staticmethod
+    def forward(ctx, input_, dim, process_group):
+        ctx.process_group = process_group
+        ctx.dim = dim
+        return _split(input_, dim, process_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _gather(grad_output, ctx.dim, ctx.process_group), None, None
+
+
+class _ReduceInput(torch.autograd.Function):
+    """
+    All-reduce the input from the model parallel region.
+
+    Args:
+        input_: input matrix.
+        parallel_mode: parallel mode.
+    """
+
+    @staticmethod
+    def forward(ctx, input_, process_group):
+        return _reduce(input_, process_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output, None
+
+
+def _reduce(input_, process_group):
+    # skip if only one rank involved
+    if dist.get_world_size(process_group) == 1:
+        return input_
+    else:
+        dist.all_reduce(input_, group=process_group)
+        return input_
+
+
+def _split(input_, dim=-1, process_group=None):
+    # skip if only one rank involved
+    world_size = dist.get_world_size(process_group)
+    if world_size == 1:
+        return input_
+
+    # Split along last dimension.
+    dim_size = input_.size(dim)
+    assert dim_size % world_size == 0, \
+        f'The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), ' \
+        f'cannot split tensor evenly'
+
+    tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
+    rank = dist.get_rank(process_group)
+    output = tensor_list[rank].contiguous()
+
+    return output
+
+
+def _gather(input_, dim=-1, process_group=None):
+    # skip if only one rank involved
+    world_size = dist.get_world_size(process_group)
+    if world_size == 1:
+        return input_
+
+    # all gather
+    rank = dist.get_rank(process_group)
+    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
+    tensor_list[rank] = input_
+    torch.distributed.all_gather(tensor_list, input_, group=process_group)
+
+    # concat
+    output = torch.cat(tensor_list, dim=dim).contiguous()
+
+    return output
+
+
+class _GatherForwardSplitBackward(torch.autograd.Function):
+    """Gather the input from model parallel region and concatenate.
+
+    Args:
+        input_: input matrix.
+        parallel_mode: parallel mode.
+        dim: dimension
+    """
+
+    @staticmethod
+    def forward(ctx, input_, dim, process_group):
+        ctx.process_group = process_group
+        ctx.dim = dim
+        return _gather(input_, dim, process_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _split(grad_output, ctx.dim, ctx.process_group), None, None
+
+
+def linear_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce):
+    return LinearWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce)
+
+
+def gather_forward_split_backward(input_, dim, process_group):
+    return _GatherForwardSplitBackward.apply(input_, dim, process_group)
+
+
+def split_forward_gather_backward(input_, dim, process_group):
+    return _SplitForwardGatherBackward.apply(input_, dim, process_group)
+
+
+def reduce_input(input_, process_group):
+    return _ReduceInput.apply(input_, process_group)

colossalai/shardformer/layer/dropout.py

@@ -1,58 +1,20 @@
-import os
-from contextlib import contextmanager
-
 import torch
+import torch.distributed as dist
 import torch.nn as nn
 
-
-class SeedManager:
-    """
-    This class is a random state manager to change random state for different random seed.
-
-    """
-
-    def __init__(self):
-        original_state = torch.cuda.get_rng_state()
-        # TODO: unify this seed manager with the colossalai.context.random
-        seed = os.getpid()
-        torch.cuda.manual_seed(int(seed))
-        self.dropout_state = torch.cuda.get_rng_state()
-        torch.cuda.set_rng_state(original_state)
-
-    def set_mode(self, rng_state):
-        torch.cuda.set_rng_state(rng_state)
-
-    def get_current_mode(self):
-        current_state = torch.cuda.get_rng_state()
-        return current_state
-
-    @contextmanager
-    def dropout_mode(self):
-        """
-        This is a context manager to change the dropout state and recover the original state.
-
-        Usage:
-        ::
-            >>> with _seed_manager.dropout_mode():
-            >>>     input = super().forward(input)
-        """
-        try:
-            current_mode = self.get_current_mode()
-            yield self.set_mode(self.dropout_state)
-        finally:
-            self.dropout_state = self.get_current_mode()
-            self.set_mode(current_mode)
-
-
-_seed_manager = SeedManager()
+from .utils import create_randomizer_with_offset
 
 
 class Dropout1D(nn.Dropout):
 
-    def __init__(self, p=0.5, inplace=False):
+    def __init__(self, p=0.5, inplace=False, process_group=None):
         super().__init__(p, inplace)
 
+        # offset the seed with randomizer index and rank
+        seed = torch.random.initial_seed()
+        self.randomizer = create_randomizer_with_offset(seed, process_group=process_group)
+
     def forward(self, input):
-        with _seed_manager.dropout_mode():
+        with self.randomizer.fork_rng():
             input = super().forward(input)
         return input