[Tensor] add ColoTensor TP1Dcol Embedding (#899)

colossalai/tensor/_ops/__init__.py

@@ -2,3 +2,4 @@ from .linear import colo_linear
 from .element_wise import *
 from .layernorm import colo_layernorm
 from .loss import colo_cross_entropy
+from .embedding import colo_embedding

colossalai/tensor/_ops/embedding.py

@@ -0,0 +1,56 @@
+import torch
+from colossalai.tensor.op_wrapper import colo_op_impl
+from colossalai.context import ParallelMode
+from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, reduce_input, \
+    gather_forward_split_backward, reduce_grad
+from colossalai.nn.layer.utils import divide
+from colossalai.core import global_context as gpc
+from packaging import version
+from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
+
+def colo_embedding_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, args, kwargs) -> ColoTensor:
+    # embedding_1Dcol split the weight(lookup table)
+    # Gather splitted lookup table
+    parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol_Embedding)
+    if not input_tensor.is_gathered():
+        input_tensor.gather()
+
+    output_parallel = torch.nn.functional.embedding(input_tensor.torch_tensor(), weight.torch_tensor(), 
+        *args, **kwargs)
+    output = ColoTensor.init_from_torch_tensor(output_parallel)
+    out_parallel_action_list = [ParallelAction(priority=1, parallel_mode=parallel_action.parallel_mode)]
+    output_spec = TensorSpec(out_parallel_action_list)
+    output.set_spec(output_spec, shard=False)
+    output.set_shard_pattern(ShardPattern.Col)
+    output.gather()
+    return output
+
+@colo_op_impl(torch.nn.functional.embedding)
+def colo_embedding(types, args, kwargs, pg):
+    """Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding``.
+    This method looks up an embedding table.
+    """
+    input_tensor = args[0]
+    weight = args[1]
+    args = args[2:]
+
+    if not isinstance(input_tensor, ColoTensor):
+        input_tensor = ColoTensor.init_from_torch_tensor(input_tensor)
+
+    if not isinstance(weight, ColoTensor):
+        weight = ColoTensor.init_from_torch_tensor(weight)
+                
+    # Handle differen parallel actions.
+    if not weight.has_spec(): # No Model Parallel Applied
+        input_tensor = input_tensor.torch_tensor()
+        weight = weight.torch_tensor()
+        output = torch.nn.functional.embedding(input_tensor, weight, *args, **kwargs)
+        return ColoTensor.init_from_torch_tensor(output)
+    elif weight.shard_spec.num_action == 1: # Single Model Parallel Applied
+        compute_patterns = weight.shard_spec.compute_patterns
+        if ComputePattern.TP1DCol_Embedding in compute_patterns:
+            return colo_embedding_1Dcol(input_tensor, weight, args, kwargs)
+        else:
+            raise NotImplementedError
+    else:
+        raise NotImplementedError

colossalai/tensor/_ops/layernorm.py

@@ -27,7 +27,7 @@ def colo_layernorm(types, args=(), kwargs=None, pg=None):
         eps = kwargs['eps']
 
     if isinstance(input_tensor, ColoTensor):
-        if input_tensor.is_activation() and not input_tensor.is_gathered():
+        if not input_tensor.is_gathered():
             input_tensor.gather()
         input_tensor = input_tensor.torch_tensor()
     if isinstance(weight, ColoTensor):

colossalai/tensor/_ops/linear.py

@@ -9,8 +9,8 @@ from packaging import version
 from colossalai.tensor import ComputePattern, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ShardPattern
 
 
-def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTensor) -> ColoTensor:
-    parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
+def colo_linear_1Drow(input_tensor: ColoTensor, weight: ColoTensor, bias:ColoTensor) -> ColoTensor:
+    parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow_Linear)
     # Input:S[1] x Weight:S[0] = Output:P
     # All-Reduce(Output) + bias = res
     # Input:S[1]
@@ -47,7 +47,7 @@ def colo_linear_1Dcol(input_tensor: ColoTensor, weight: ColoTensor, bias: ColoTe
     # Input:B x Weight:S[1] + Bias:S[1] = Output:S[1]
     # All-Gather(Output)
     # Input:B
-    parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
+    parallel_action = weight.shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol_Linear)
     if input_tensor.is_gathered():
         # Not splited yet.
         assert input_tensor.shape[-1] == weight.size(-1), \
@@ -108,9 +108,9 @@ def colo_linear(types, args, kwargs, pg):
         return ColoTensor.init_from_torch_tensor(torch.nn.functional.linear(input_tensor, weight, bias))
     elif weight.shard_spec.num_action == 1:    # Single Model Parallel Applied
         compute_patterns = weight.shard_spec.compute_patterns
-        if ComputePattern.TP1DRow in compute_patterns:
+        if ComputePattern.TP1DRow_Linear in compute_patterns:
             return colo_linear_1Drow(input_tensor, weight, bias)
-        elif ComputePattern.TP1DCol in compute_patterns:
+        elif ComputePattern.TP1DCol_Linear in compute_patterns:
             return colo_linear_1Dcol(input_tensor, weight, bias)
         else:
             raise NotImplementedError

colossalai/tensor/colo_tensor.py

@@ -142,14 +142,19 @@ class ColoTensor(object):
         if self._shard_pattern is not ShardPattern.NA:    # reshard
             self.gather()
         # Model Parameters
-        if ComputePattern.TP1DRow in self._shard_spec.compute_patterns:
-            parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
-            self._shard_1d(parallel_action=parallel_action, dim=-1)
-            self._shard_pattern = ShardPattern.Col    # We bind our ComputePattern on weight, which has to be transposed when linear().
-        elif ComputePattern.TP1DCol in self._shard_spec.compute_patterns:
-            parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
-            self._shard_1d(parallel_action=parallel_action, dim=0)
-            self._shard_pattern = ShardPattern.Row
+        if self._shard_spec.num_action == 1:
+            parallel_action = self._shard_spec.get_action_by_compute_pattern(
+                    self._shard_spec.compute_patterns[0])
+            if parallel_action.compute_pattern in [ComputePattern.TP1DRow_Linear, \
+                ComputePattern.TP1DCol_Embedding]:
+                self._shard_1d(parallel_action=parallel_action, dim=-1)
+                self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
+            elif parallel_action.compute_pattern in [ComputePattern.TP1DCol_Linear, \
+                ComputePattern.TP1DRow_Embedding]:
+                self._shard_1d(parallel_action=parallel_action, dim=0)
+                self._shard_pattern = ShardPattern.Row
+            else:
+                raise NotImplementedError
 
     def gather(self):
         assert self.is_activation(), 'Currently we only support gather Activation ColoTensor.'

colossalai/tensor/spec.py

@@ -4,10 +4,12 @@ from colossalai.context.parallel_mode import ParallelMode
 
 
 class ComputePattern(Enum):
-    TP1DRow = 1
-    TP1DCol = 2
-    ZeRO = 3
-    DP = 4
+    TP1DRow_Linear = 1
+    TP1DCol_Linear = 2
+    TP1DRow_Embedding = 3
+    TP1DCol_Embedding = 4
+    ZeRO = 5
+    DP = 6
 
 
 class ShardPattern(Enum):
@@ -43,14 +45,14 @@ class TensorSpec(object):
     # using ZeRO with DP-degree = 4 and 1DRowTP with TP-degree = 2.
     # parallel_action_list = [
     # ParallelAction(10, ComputePattern.ZeRO, gpc.get_group(ParallelMode.DATA)),
-    # ParallelAction(1, ComputePattern.TP1DRow, gpc.get_group(ParallelMode.PARALLEL_1D))
+    # ParallelAction(1, ComputePattern.TP1DRow_Linear, gpc.get_group(ParallelMode.PARALLEL_1D))
     # ]
     # When the ColoTensor is initialized,
     # we first splitting tensor according to ParallelAction of ZeRO,
-    # then splitting tensor according to ParallelAction of TP1DRow.
+    # then splitting tensor according to ParallelAction of TP1DRow_Linear.
     # During Linear computation
     # Before Linear Op, we gather the tensors according to ZeRO.
-    # We perform Linear Op according to compute pattern of TP1DRow.
+    # We perform Linear Op according to compute pattern of TP1DRow_Linear.
     # After Linear Op, we split the tensors according to ZeRO.
 
     def __init__(self, parallel_action_list: List[ParallelAction] = [], shard_pattern: ShardPattern = ShardPattern.NA):

tests/test_tensor/test_embedding_tp.py

@@ -0,0 +1,82 @@
+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
+from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
+
+from _utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
+
+def run_embedding_tp1d_col_test():
+    device = get_current_device()
+    dtype = torch.float32
+    DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
+    num_embeddings = 12
+    embedding_dim = 32
+
+    local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    layer_master = torch.nn.Embedding(num_embeddings, embedding_dim)
+    layer = torch.nn.Embedding(num_embeddings, embedding_dim)
+
+    A_master = torch.tensor((0,3,6,9), device=device)
+    A = broadcast_tensor_chunk(A_master, chunk_size=1)
+
+    W_shape = (num_embeddings, embedding_dim)
+    W_master = torch.randn(W_shape, dtype=dtype, device=device)
+    W = broadcast_tensor_chunk(W_master, chunk_size=1)
+    W.requires_grad = True
+
+    # replace the torch nn.Parameters with ColoTensor
+    sharded_weight = ColoTensor.init_from_torch_tensor(W)
+    parallel_action_list = [
+        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Embedding, 
+        parallel_mode=ParallelMode.PARALLEL_1D)
+    ]
+    spec = TensorSpec(parallel_action_list)
+    sharded_weight.set_spec(spec) # reshard
+    replace_parameter_add_grad(layer, sharded_weight)
+    out = layer(A)
+
+    replace_parameter_add_grad(layer_master, W_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)
+
+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_embedding_tp1d_col_test()
+
+@pytest.mark.dist
+@parameterize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_embedding_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_embedding_1d()

tests/test_tensor/test_linear_tp.py

@@ -47,7 +47,7 @@ def run_linear_tp1d_col_test():
     sharded_weight = ColoTensor.init_from_torch_tensor(W)
     sharded_bias = ColoTensor.init_from_torch_tensor(B)
     parallel_action_list = [
-        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
+        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
     ]
     spec = TensorSpec(parallel_action_list)
     sharded_weight.set_spec(spec) # reshard
@@ -110,7 +110,7 @@ def run_linear_tp1d_row_test():
     # replace the torch nn.Parameters with ColoTensor
     sharded_weight = ColoTensor.init_from_torch_tensor(W)
     parallel_action_list = [
-        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
+        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
     ]
     spec = TensorSpec(parallel_action_list)
     sharded_weight.set_spec(spec=spec) # reshard
@@ -145,7 +145,7 @@ def run_linear_tp1d_row_test():
 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()
+    run_linear_tp1d_row_test()
     run_linear_tp1d_col_test()
 
 @pytest.mark.dist

tests/test_tensor/test_model.py

@@ -38,12 +38,12 @@ def run_1d_col_tp():
         model = model_builder(checkpoint=True)
 
     parallel_action_list_row = [
-        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
+        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
     ]
     spec_row = TensorSpec(parallel_action_list_row)
 
     parallel_action_list_col = [
-        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)
+        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
     ]
     spec_col = TensorSpec(parallel_action_list_col)
 
@@ -168,7 +168,7 @@ def run_1d_row_tp():
         model = model_builder(checkpoint=True)
 
     parallel_action_list = [
-        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
+        ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow_Linear, parallel_mode=ParallelMode.PARALLEL_1D)
     ]
     spec = TensorSpec(parallel_action_list)