[Tensor] add ColoTensor TP1Dcol Embedding (#899)

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)