mirror of
https://github.com/hpcaitech/ColossalAI.git
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add DistSpec for loss and test_model (#947)
This commit is contained in:
Ziyue Jiang
GitHub
@@ -9,7 +9,8 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.utils import ColoInitContext
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from colossalai.tensor import named_params_with_colotensor, TensorSpec, ComputePattern, ParallelAction, ColoTensor, ColoOptimizer
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from colossalai.tensor import named_params_with_colotensor, TensorSpec, ComputePattern, \
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ParallelAction, ColoTensor, ColoOptimizer, dist_spec, DistSpecManager
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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@@ -85,6 +86,34 @@ def set_seed(seed):
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torch.cuda.manual_seed(seed)
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torch.backends.cudnn.deterministic = True
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def init_1d_row_linear(weight):
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spec = TensorSpec(
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dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
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[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)])
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with DistSpecManager.no_grad():
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weight.set_spec(spec)
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def init_1d_col_linear(weight, gather_out=True):
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spec = TensorSpec(
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dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
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[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D, \
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gather_out=gather_out)])
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with DistSpecManager.no_grad():
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weight.set_spec(spec)
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def init_1d_row_embedding(weight):
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spec = TensorSpec(
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dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [0], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
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[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)])
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with DistSpecManager.no_grad():
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weight.set_spec(spec)
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def init_1d_col_embedding(weight):
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spec = TensorSpec(
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dist_spec.shard(gpc.get_group(ParallelMode.PARALLEL_1D), [-1], [gpc.get_world_size(ParallelMode.PARALLEL_1D)]),
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[ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DCol, parallel_mode=ParallelMode.PARALLEL_1D)])
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with DistSpecManager.no_grad():
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weight.set_spec(spec)
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def run_1d_hybrid_tp(model_name):
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# A simple net with two stacked nn.Linear
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@@ -106,84 +135,35 @@ def run_1d_hybrid_tp(model_name):
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p2.data.copy_(p1.data)
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if 'bert' == model_name:
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parallel_action_list_row = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DRow_Linear,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_linear_row = TensorSpec(parallel_action_list_row)
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parallel_action_list_embedding_col = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DCol_Embedding,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
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parallel_action_list_embedding_row = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DRow_Embedding,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_embedding_row = TensorSpec(parallel_action_list_embedding_row)
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for name, p in model.colo_named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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# print(name)
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# num_class = type_vocab_size = 2 | (8, 2)
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if 'classifier' in name and 'weight' in name:
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p.set_spec(spec_linear_row)
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init_1d_row_linear(p)
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# num_class = vocab_size = 30524 | (30524, 8)
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if 'word_embeddings' in name and 'weight' in name:
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p.set_spec(spec_embedding_row)
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init_1d_row_embedding(p)
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# num_class = seq_len = 512 | (512, 8)
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if 'position_embeddings' in name and 'weight' in name:
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p.set_spec(spec_embedding_row)
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init_1d_row_embedding(p)
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# num_class = type_vocab_size = 2 | (2, 8)
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if 'token_type_embeddings' in name and 'weight' in name:
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p.set_spec(spec_embedding_col)
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init_1d_col_embedding(p)
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elif "simple_net" == model_name:
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parallel_action_list_row = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DRow_Linear,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_row = TensorSpec(parallel_action_list_row)
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parallel_action_list_col = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DCol_Linear,
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parallel_mode=ParallelMode.PARALLEL_1D),
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]
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spec_col = TensorSpec(parallel_action_list_col)
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parallel_action_list_classifier_col = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DCol_Linear,
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parallel_mode=ParallelMode.PARALLEL_1D,
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gather_out=False),
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]
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spec_classifier_col = TensorSpec(parallel_action_list_classifier_col)
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parallel_action_list_embedding_col = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DCol_Embedding,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_embedding_col = TensorSpec(parallel_action_list_embedding_col)
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# A naive way to set spec for all weights in Linear
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for name, p in model.colo_named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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if 'embed' in name and 'weight' in name:
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p.set_spec(spec_embedding_col)
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init_1d_col_embedding(p)
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if 'proj1' in name and ('weight' in name or 'bias' in name):
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p.set_spec(spec_col)
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init_1d_col_linear(p)
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if 'proj2' in name and 'weight' in name:
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p.set_spec(spec_row)
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init_1d_row_linear(p)
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if 'classifier' in name and ('weight' in name or 'bias' in name):
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p.set_spec(spec_classifier_col)
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init_1d_col_linear(p, gather_out=False)
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model = model.cuda()
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colo_optimizer = ColoOptimizer(dict(model.named_parameters()), torch.optim.SGD, lr=0.1)
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@@ -251,8 +231,6 @@ def run_1d_hybrid_tp(model_name):
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break
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# FIXME (ver217): enable this test
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@pytest.mark.skip
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# Test the overrided parameters() and named_parameters() member functions
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def test_model_parameters():
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# build a module with 2 Linear, 4 parameters in total.
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@@ -285,8 +263,6 @@ def test_model_parameters():
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assert param_cnt == 2
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# FIXME (ver217): enable this test
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@pytest.mark.skip
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def test_colo_optimizer():
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get_components_func = non_distributed_component_funcs.get_callable('simple_net')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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@@ -329,29 +305,14 @@ def run_1d_row_tp(model_name: str):
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if rank == 0:
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model_torch = model_builder(checkpoint=True)
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model_torch = model_torch.cuda()
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parallel_action_list = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DRow_Linear,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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parallel_action_list_embedding_row = [
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ParallelAction(priority=1,
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compute_pattern=ComputePattern.TP1DRow_Embedding,
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parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec_embedding_row = TensorSpec(parallel_action_list_embedding_row)
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# A naive way to set spec for all weights in Linear
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for name, p in model.colo_named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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if 'weight' in name and 'LayerNorm' not in name and 'ln' not in name and 'embed' not in name:
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p.set_spec(spec)
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init_1d_row_linear(p)
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if 'embed' in name and 'weight' in name:
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p.set_spec(spec_embedding_row)
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init_1d_row_embedding(p)
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model = model.cuda()
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@@ -434,9 +395,6 @@ def run_model_dist(rank, world_size, port):
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for name in ['bert', 'simple_net']:
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run_1d_hybrid_tp(name)
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# FIXME (ver217): enable this test
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@pytest.mark.skip
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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# @parameterize('world_size', [1, 4])
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@@ -454,8 +412,6 @@ def run_pretrain_load_dist(rank, world_size, port):
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# The test case has to download huggingface pretrained models from the internet
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# So we manually trigger the test.
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# FIXME (ver217): enable this test
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@pytest.mark.skip
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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