[Tensor] TP Linear 1D row (#843)

colossalai/tensor/_ops/linear.py

@@ -1,9 +1,10 @@
 import torch
 from colossalai.tensor.op_wrapper import colo_op_impl
 from colossalai.tensor.colo_tensor import ColoTensor
+from colossalai.context import ParallelMode
+from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input
 from packaging import version
 
-
 @colo_op_impl(torch.nn.functional.linear)
 def colo_linear(types, args, kwargs, pg):
     """Handles ``__torch_function__`` dispatch for ``torch.nn.functional.linear``.
@@ -19,12 +20,31 @@ def colo_linear(types, args, kwargs, pg):
             bias = None
     else:
         bias = kwargs.get('bias', None)
-    
+
     if isinstance(bias, ColoTensor):
         bias = bias.torch_tensor()
 
     # Add communication logic before and after linear call.
     if isinstance(weight, ColoTensor):
-        return torch.nn.functional.linear(input_tensor, weight.torch_tensor(), bias)
+        if weight.shard_spec == None:
+            return torch.nn.functional.linear(input_tensor, weight.torch_tensor(), bias)
+        elif weight.shard_spec == '1Drow':
+            """
+            Input:S[1] x Weight:S[0] = Output:P
+            All-Reduce(Output) + bias = res
+            """
+            # Input:S[1]
+            input_per_partition = split_forward_gather_backward(input_tensor, ParallelMode.PARALLEL_1D, dim=-1)
+            # Output:P
+            partial_output = torch.nn.functional.linear(input_per_partition, weight.torch_tensor())
+            # Reduce(Output)
+            output = reduce_input(partial_output, ParallelMode.PARALLEL_1D)
+            # Bias
+            if bias is not None:
+                output = output + bias
+            return output
+            
+        else:
+            raise NotImplementedError
     else:
         return torch.nn.functional.linear(input_tensor, weight, bias)

colossalai/tensor/colo_tensor.py

@@ -4,7 +4,6 @@ from typing import Tuple
 import numpy
 from .op_wrapper import _COLOSSAL_OPS
 
-
 class ColoTensor(object):
     """ Data Structure for Tensor in Colossal-AI
     1. It contains a torch.Tensor as an attribute.
@@ -24,6 +23,7 @@ class ColoTensor(object):
             pin_memory=False,
             device=None,
             torch_tensor=torch.empty(0),
+            shard_spec: str = None,
     ):
         self._size = size
         self._dtype = dtype
@@ -31,11 +31,29 @@ class ColoTensor(object):
         self._pin_memory = pin_memory
         self._device = device
         self._torch_tensor = torch_tensor
+        self._shard_spec = shard_spec
+
+    @property
+    def shard_spec(self) -> Optional[str]:
+        return self._shard_spec
+
+    @property
+    def data(self):
+        return self._torch_tensor.data
+
+    @property
+    def grad(self):
+        return self._torch_tensor.grad
+
+    @property
+    def size(self):
+        return self._size
 
     def numel(self):
         return product(self._size)
 
     @staticmethod
+
     def init_from_torch_tensor(tensor: torch.Tensor, save_payload=True) -> 'ColoTensor':
         colo_t = ColoTensor(*tensor.size(),
                             dtype=tensor.dtype,

tests/test_tensor/test_linear_tp.py

@@ -0,0 +1,91 @@
+import torch
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.tensor import ColoTensor
+
+from functools import partial
+
+import colossalai
+import pytest
+import torch
+import torch.multiprocessing as mp
+from colossalai.testing import parameterize, rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.core import global_context as gpc
+import torch.distributed as dist
+
+from test_tensor_utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
+
+def run_linear_tp1d_row_test():
+    device = get_current_device()
+    dtype = torch.float32
+    DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
+    in_features = 4
+    out_features = 5
+
+    local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    layer_master = torch.nn.Linear(in_features, out_features)
+    layer = torch.nn.Linear(in_features, out_features)
+
+    A_shape = (2, in_features)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    A = broadcast_tensor_chunk(A_master, chunk_size=1)
+    A.requires_grad = True
+
+    W_shape = (out_features, in_features)
+    W_master = torch.randn(W_shape, dtype=dtype, device=device)
+    W = broadcast_tensor_chunk(W_master, chunk_size=DEPTH, local_rank=local_rank)
+    W.requires_grad = True
+
+    B_shape = (out_features)
+    B_master = torch.randn(B_shape, dtype=dtype, device=device)
+    B = broadcast_tensor_chunk(B_master, chunk_size=1)
+    B.requires_grad = True
+
+    # replace the torch nn.Parameters with ColoTensor
+    sharded_weight = ColoTensor.init_from_torch_tensor(W)
+    sharded_weight._shard_spec = "1Drow"
+    sharded_bias = ColoTensor.init_from_torch_tensor(B)
+    replace_parameter_add_grad(layer, sharded_weight, sharded_bias)
+    out = layer(A)
+
+    replace_parameter_add_grad(layer_master, W_master, B_master)
+    A_master.requires_grad = True
+    #C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
+    C_master = layer_master(A_master)
+    C = C_master.clone()
+
+    check_equal(out, C)
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
+    grad = broadcast_tensor_chunk(grad_master, chunk_size=1)
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = W_master.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[local_rank]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = B_master.grad
+    check_equal(B_grad, layer.bias.grad)
+
+def run_dist(rank, world_size, port):
+    config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_linear_tp1d_row_test()
+
+
+@pytest.mark.dist
+@parameterize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_linear_1d(world_size):
+    run_func = partial(run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_linear_1d()

tests/test_tensor/test_tensor_utils/__init__.py

@@ -0,0 +1 @@
+from ._util import *

tests/test_tensor/test_tensor_utils/_util.py

@@ -0,0 +1,20 @@
+import torch
+import torch.distributed as dist
+
+def check_equal(A, B):
+    assert torch.allclose(A, B, rtol=1e-3, atol=1e-1) == True
+
+def replace_parameter_add_grad(layer, weight=None, bias=None):
+    if weight is not None:
+        delattr(layer, 'weight')
+        setattr(layer, 'weight', weight)
+        layer.weight.requires_grad = True
+    if bias is not None:
+        delattr(layer, 'bias')
+        setattr(layer, 'bias', bias)
+        layer.bias.requires_grad = True
+
+def broadcast_tensor_chunk(tensor, chunk_size=1, local_rank=0):
+    dist.broadcast(tensor, src=0)
+    tensor_chunk = torch.chunk(tensor, chunk_size, dim=-1)[local_rank]
+    return tensor_chunk.clone()