mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-16 14:41:53 +00:00
[gemini] improve compatibility and add static placement policy (#4479)
* [gemini] remove distributed-related part from colotensor (#4379) * [gemini] remove process group dependency * [gemini] remove tp part from colo tensor * [gemini] patch inplace op * [gemini] fix param op hook and update tests * [test] remove useless tests * [test] remove useless tests * [misc] fix requirements * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [misc] update requirements * [gemini] refactor gemini optimizer and gemini ddp (#4398) * [gemini] update optimizer interface * [gemini] renaming gemini optimizer * [gemini] refactor gemini ddp class * [example] update gemini related example * [example] update gemini related example * [plugin] fix gemini plugin args * [test] update gemini ckpt tests * [gemini] fix checkpoint io * [example] fix opt example requirements * [example] fix opt example * [example] fix opt example * [example] fix opt example * [gemini] add static placement policy (#4443) * [gemini] add static placement policy * [gemini] fix param offload * [test] update gemini tests * [plugin] update gemini plugin * [plugin] update gemini plugin docstr * [misc] fix flash attn requirement * [test] fix gemini checkpoint io test * [example] update resnet example result (#4457) * [example] update bert example result (#4458) * [doc] update gemini doc (#4468) * [example] update gemini related examples (#4473) * [example] update gpt example * [example] update dreambooth example * [example] update vit * [example] update opt * [example] update palm * [example] update vit and opt benchmark * [hotfix] fix bert in model zoo (#4480) * [hotfix] fix bert in model zoo * [test] remove chatglm gemini test * [test] remove sam gemini test * [test] remove vit gemini test * [hotfix] fix opt tutorial example (#4497) * [hotfix] fix opt tutorial example * [hotfix] fix opt tutorial example
This commit is contained in:
Hongxin Liu
GitHub
@@ -1,153 +0,0 @@
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import pytest
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import torch
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from numpy import allclose
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import colossalai
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from colossalai.core import global_context as gpc
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from colossalai.tensor import ColoTensor, ColoTensorSpec, ProcessGroup, ReplicaSpec, ShardSpec, distspec
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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def _run_tensor_indexing():
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pg = ProcessGroup()
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torch_t = torch.randn(2, 3)
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colo_t = ColoTensor(torch_t, ColoTensorSpec(pg))
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assert allclose(torch_t[:, 1], colo_t[:, 1])
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def _run_wrapped_tensor_func():
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pg = ProcessGroup()
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t_ref = torch.randn(4, 5)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
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# non-func attr
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assert t.is_cuda == t_ref.is_cuda
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# return 1 torch.Tensor
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t_abs = t.abs()
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assert isinstance(t_abs, ColoTensor) and torch.equal(t_abs, t_ref.abs())
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# return 1 non-torch.Tensor
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assert t.dim() == t_ref.dim()
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# return >1 torch.Tensor
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assert isinstance(t, ColoTensor)
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t_split1, t_split2 = t.split(2)
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assert isinstance(t_split1, ColoTensor) and isinstance(t_split2, ColoTensor), f"{type(t_split1)} {type(t_split2)}"
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def _run_operand(world_size):
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pg = ProcessGroup()
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t_ref = torch.randn(4, 5)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
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t_ref_res = t_ref + t_ref
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t_res = t + t
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assert isinstance(t_res, ColoTensor)
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assert torch.allclose(t_ref_res, t_res)
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pg = ProcessGroup(tp_degree=world_size)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
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t.set_dist_spec(ShardSpec([0], [world_size]))
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t_new = torch.zeros_like(t)
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assert isinstance(t_new, ColoTensor)
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assert t_new.is_sharded()
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#### Test Distributed init a Colotensor
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def _run_view(world_size):
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t_ref = torch.randn(4, 5)
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rank = gpc.get_global_rank()
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pg = ProcessGroup(rank, list(range(world_size)), tp_degree=world_size)
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t = ColoTensor.from_torch_tensor(
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t_ref, ColoTensorSpec(pg, dist_attr=ShardSpec(dims=[0], num_partitions=[pg.tp_world_size()])))
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assert t.size_global()[0] == 4 * world_size
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assert t.size_global(1) == 5
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assert t.size_global() == torch.Size([4 * world_size, 5])
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t = t.view(4 * 5 * world_size)
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assert t.shape == torch.Size([4 * 5 * world_size])
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def _run_tensor_shard_init(world_size):
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t_ref = torch.randn(4, 5)
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pg = ProcessGroup(tp_degree=world_size)
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shard_attr = ShardSpec(dims=[0], num_partitions=[pg.tp_world_size()])
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tensor_spec = ColoTensorSpec(pg, dist_attr=shard_attr)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), tensor_spec)
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t.set_dist_spec(ReplicaSpec())
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assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape} vs ({4 * world_size, 5})"
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def _run_tensor_replicated_init(world_size):
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t_ref = torch.randn(4 * world_size, 5)
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pg = ProcessGroup()
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spec = ColoTensorSpec(pg)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), spec)
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assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape}"
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def _run_process_group(world_size):
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pg1 = ProcessGroup()
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pg2 = ProcessGroup()
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assert pg1 == pg2
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def _run_redistributed(world_size):
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if world_size != 4:
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return
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pg1 = ProcessGroup(tp_degree=2, dp_degree=2)
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pg2 = ProcessGroup(tp_degree=4, dp_degree=1)
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spec1 = ColoTensorSpec(pg1)
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t1 = ColoTensor.from_torch_tensor(torch.randn(2, 3, 4), spec1)
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t1 = t1.redistribute(ShardSpec([0], [pg1.tp_world_size()]))
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assert t1.is_sharded()
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t1 = t1.redistribute(ShardSpec([-1], [pg2.tp_world_size()]), pg2)
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assert t1.is_sharded()
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pg3 = ProcessGroup(tp_degree=1, dp_degree=4)
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t1 = t1.redistribute(ReplicaSpec(), pg3)
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assert t1.is_replicate()
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def _run_set_tensor_spec(world_size):
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if world_size != 4:
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return
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pg = ProcessGroup(tp_degree=2, dp_degree=2)
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spec1 = ColoTensorSpec(pg)
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t1 = ColoTensor.from_torch_tensor(torch.randn(2, 3, 4), spec1)
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dist_spec2 = ShardSpec([-1], [pg.tp_world_size()])
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assert t1.is_replicate()
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t1.set_dist_spec(dist_spec2)
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assert t1.is_shard_1dcol()
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def run_dist_tests(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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_run_tensor_shard_init(world_size)
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_run_tensor_replicated_init(world_size)
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_run_view(world_size)
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_run_process_group(world_size)
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_run_tensor_indexing()
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_run_operand(world_size)
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_run_wrapped_tensor_func()
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_run_redistributed(world_size)
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_run_set_tensor_spec(world_size)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2])
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@rerun_if_address_is_in_use()
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def test_dist_cases(world_size):
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spawn(run_dist_tests, world_size)
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if __name__ == '__main__':
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test_dist_cases(4)
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@@ -1,148 +0,0 @@
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import pytest
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import torch
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from torch.nn.parallel import DistributedDataParallel as DDP
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import colossalai
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from colossalai.nn.parallel.data_parallel import ColoDDP
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from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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from colossalai.utils.cuda import get_current_device
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from colossalai.zero import ColoInitContext
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_tensor.common_utils import (
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debug_print,
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set_seed,
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split_param_col_tp1d,
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split_param_row_tp1d,
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tensor_equal,
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tensor_shard_equal,
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)
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def init_1d_row_spec(model, pg: ProcessGroup):
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tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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for n, p in model.named_parameters():
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p.set_process_group(pg)
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if 'weight' in n and 'ln' not in n:
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p.set_tensor_spec(*tensor_spec)
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def init_1d_col_spec(model, pg: ProcessGroup):
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spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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for n, p in model.named_parameters():
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p.set_process_group(pg)
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if 'ln' not in n and ('weight' in n or 'bias' in n):
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p.set_tensor_spec(*spec)
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def init_megatron_spec(model, pg: ProcessGroup):
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for mn, module in model.named_modules():
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# debug_print([0], mn)
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for pn, param in module.named_parameters(recurse=False):
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# debug_print([0], '\t', pn, param.compute_spec, param.shape)
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param.set_process_group(pg)
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if 'mlp.c_fc' in mn:
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if 'weight' in pn or 'bias' in pn:
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split_param_col_tp1d(param, pg)
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param.compute_spec.set_output_replicate(False)
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else:
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raise RuntimeError
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elif 'mlp.c_proj' in mn:
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if 'weight' in pn:
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split_param_row_tp1d(param, pg)
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else:
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assert 'bias' in pn
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elif 'wte' in mn or 'wpe' in mn:
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assert 'weight' in pn
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split_param_col_tp1d(param, pg)
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elif 'c_attn' in mn or 'c_proj' in mn:
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split_param_col_tp1d(param, pg)
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# debug_print([0], '\t', param.compute_spec, param.shape)
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def check_param_equal(model, torch_model, pg: ProcessGroup):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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assert pg.tp_local_rank() is not None, f"{pg.rank()} {pg.tp_world_size()} {pg._tp_degree} {pg.tp_local_rank()}1"
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assert pg.tp_world_size() is not None
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assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
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def check_grad_equal(model, torch_model, pg: ProcessGroup):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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assert tensor_shard_equal(torch_p.grad, p.grad, pg.tp_local_rank(), pg.tp_world_size())
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def run_gpt(init_spec_func, use_ddp):
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world_size = torch.distributed.get_world_size()
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# build a PG with TP and DP hybrid
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pg = ProcessGroup(dp_degree=(2 if (use_ddp and world_size >= 2) else 1))
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# set seed make processes of the same tp group use the same seed
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# set_seed(pg.tp_local_rank())
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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# make sure torch_model and model has the same parameter values
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = model.cuda()
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torch_model = model_builder().cuda()
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if use_ddp:
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torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
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model = ColoDDP(model, process_group=pg)
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p)
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init_spec_func(model, pg)
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check_param_equal(model, torch_model, pg)
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# close the dropout in eval mode
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model.eval()
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torch_model.eval()
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set_seed(pg.dp_local_rank())
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torch.distributed.barrier()
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for i, (input_ids, label) in enumerate(train_dataloader):
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colo_input = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
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logits = model(colo_input)
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torch_logits = torch_model(input_ids)
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assert tensor_equal(torch_logits, logits), f"{torch_logits - logits}"
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loss = criterion(logits, input_ids)
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torch_loss = criterion(torch_logits, input_ids)
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if use_ddp:
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model.backward(loss)
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else:
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loss.backward()
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torch_loss.backward()
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check_grad_equal(model, torch_model, pg)
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if i > 0:
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break
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set_seed(313)
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def run_dist(rank, world_size, port, use_ddp):
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if use_ddp and world_size == 1:
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return
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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# Comments below tests for speed concern
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# run_gpt(init_1d_row_spec, use_ddp)
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# run_gpt(init_1d_col_spec, use_ddp)
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run_gpt(init_megatron_spec, use_ddp)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@pytest.mark.parametrize('use_ddp', [False, True])
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@rerun_if_address_is_in_use()
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def test_gpt(world_size, use_ddp):
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spawn(run_dist, world_size, use_ddp=use_ddp)
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if __name__ == '__main__':
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test_gpt(4, use_ddp=False)
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@@ -1,334 +0,0 @@
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import pytest
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import torch
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import colossalai
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from colossalai.nn.optimizer import ColossalaiOptimizer
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from colossalai.tensor import ColoTensor, ProcessGroup
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from colossalai.tensor.colo_parameter import ColoParameter
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from colossalai.testing import free_port, rerun_if_address_is_in_use, spawn
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from colossalai.utils.cuda import get_current_device
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from colossalai.zero import ColoInitContext
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_tensor.common_utils import (
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check_equal,
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set_seed,
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split_param_col_tp1d,
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split_param_row_tp1d,
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tensor_shard_equal,
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)
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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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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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rank = torch.distributed.get_rank()
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world_size = torch.distributed.get_world_size()
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set_seed(1)
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with ColoInitContext(device=get_current_device()):
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model = model_builder(checkpoint=True)
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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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optimizer_torch = ColossalaiOptimizer(torch.optim.SGD(model_torch.parameters(), lr=0.1))
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# Make two models have the same init params
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for p1, p2 in zip(model.parameters(), model_torch.parameters()):
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p2.data.copy_(p1.data)
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else:
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model_torch = None
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optimizer_torch = None
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pg = ProcessGroup(tp_degree=world_size)
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if 'bert' == model_name:
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for name, p in model.named_parameters():
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if not isinstance(p, ColoTensor):
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continue
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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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split_param_col_tp1d(p, pg)
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# num_class = vocab_size = 30524 | (30524, 8)
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elif 'word_embeddings' in name and 'weight' in name:
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split_param_row_tp1d(p, pg)
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# num_class = seq_len = 512 | (512, 8)
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elif 'position_embeddings' in name and 'weight' in name:
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split_param_row_tp1d(p, pg)
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# num_class = type_vocab_size = 2 | (2, 8)
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elif 'token_type_embeddings' in name and 'weight' in name:
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split_param_col_tp1d(p, pg)
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elif "simple_net" == model_name:
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# A naive way to set spec for all weights in Linear
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for name, p in model.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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split_param_col_tp1d(p, pg)
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if 'proj1' in name and ('weight' in name or 'bias' in name):
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split_param_row_tp1d(p, pg)
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if 'proj2' in name and 'weight' in name:
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split_param_col_tp1d(p, pg)
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if 'classifier' in name and ('weight' in name or 'bias' in name):
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split_param_row_tp1d(p, pg)
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model = model.cuda()
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model.eval()
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if rank == 0:
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model_torch.eval()
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colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
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for i, (data, label) in enumerate(train_dataloader):
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# Zero grad
|
||||
colo_optimizer.zero_grad()
|
||||
if rank == 0:
|
||||
optimizer_torch.zero_grad()
|
||||
torch.distributed.barrier()
|
||||
|
||||
data = data.to(get_current_device())
|
||||
label = label.to(get_current_device())
|
||||
|
||||
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
|
||||
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
|
||||
|
||||
# Bcast rank0 data to all processes
|
||||
if criterion:
|
||||
output = model(data)
|
||||
loss = criterion(output, label)
|
||||
else:
|
||||
output = model(data, label)
|
||||
loss = output
|
||||
|
||||
# Test output
|
||||
if rank == 0:
|
||||
if criterion:
|
||||
output_torch = model_torch(data)
|
||||
loss_torch = criterion(output_torch, label)
|
||||
else:
|
||||
output_torch = model_torch(data, label)
|
||||
loss_torch = output_torch
|
||||
assert torch.allclose(loss, loss_torch, rtol=1e-2), f"model_name {model_name} failed"
|
||||
torch.distributed.barrier()
|
||||
|
||||
loss.backward()
|
||||
colo_optimizer.step()
|
||||
|
||||
if rank == 0:
|
||||
loss_torch.backward()
|
||||
optimizer_torch.step()
|
||||
|
||||
with torch.no_grad():
|
||||
# check param
|
||||
for p, torch_p in zip(model.parameters(), model_torch.parameters()):
|
||||
assert tensor_shard_equal(torch_p, p, pg.tp_local_rank(), pg.tp_world_size())
|
||||
torch.distributed.barrier()
|
||||
if i > 5:
|
||||
break
|
||||
|
||||
|
||||
# Test the overrided parameters() and named_parameters() member functions
|
||||
def test_model_parameters():
|
||||
colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl')
|
||||
|
||||
# build a module with 2 Linear, 4 parameters in total.
|
||||
class Net(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.fcs = torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 2))
|
||||
self.extra_param = torch.nn.Parameter(torch.randn(2))
|
||||
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = Net()
|
||||
|
||||
param_cnt = 0
|
||||
for name, p in model.named_parameters():
|
||||
param_cnt += 1
|
||||
assert param_cnt == 5
|
||||
|
||||
for name, colo_p in model.named_parameters():
|
||||
assert colo_p.is_model_data()
|
||||
|
||||
param_cnt = 0
|
||||
for name, p in model.named_parameters(recurse=False):
|
||||
param_cnt += 1
|
||||
assert param_cnt == 1
|
||||
|
||||
param_cnt = 0
|
||||
for p in model.fcs[0].parameters(recurse=False):
|
||||
param_cnt += 1
|
||||
assert param_cnt == 2
|
||||
|
||||
|
||||
def test_colo_optimizer():
|
||||
get_components_func = non_distributed_component_funcs.get_callable('simple_net')
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
set_seed(1)
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = model_builder(checkpoint=True)
|
||||
|
||||
colo_optimizer = ColossalaiOptimizer(torch.optim.SGD(model.parameters(), lr=0.1))
|
||||
for i, (data, label) in enumerate(train_dataloader):
|
||||
colo_optimizer.zero_grad()
|
||||
data = data.to(get_current_device())
|
||||
label = label.to(get_current_device())
|
||||
|
||||
# Bcast rank0 data to all processes
|
||||
if criterion:
|
||||
output = model(data)
|
||||
loss = criterion(output, label)
|
||||
else:
|
||||
output = model(data, label)
|
||||
loss = output
|
||||
|
||||
loss.backward()
|
||||
colo_optimizer.step()
|
||||
|
||||
if i > 5:
|
||||
break
|
||||
|
||||
|
||||
def run_1d_row_tp(model_name: str):
|
||||
# A simple net with two stacked nn.Linear
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
rank = torch.distributed.get_rank()
|
||||
|
||||
set_seed(1)
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = model_builder(checkpoint=True)
|
||||
|
||||
world_size = torch.distributed.get_world_size()
|
||||
pg = ProcessGroup(tp_degree=world_size)
|
||||
|
||||
set_seed(1)
|
||||
if rank == 0:
|
||||
model_torch = model_builder(checkpoint=True)
|
||||
model_torch = model_torch.cuda()
|
||||
|
||||
# A naive way to set spec for all weights in Linear
|
||||
for mo_name, module in model.named_modules():
|
||||
# print(mo_name)
|
||||
for pa_name, param in module.named_parameters(recurse=False):
|
||||
# print('\t', pa_name, param.shape)
|
||||
if not isinstance(param, ColoTensor):
|
||||
continue
|
||||
if 'weight' in pa_name:
|
||||
if 'embed' in mo_name and 'token' not in mo_name and 'LayerNorm' not in mo_name:
|
||||
split_param_row_tp1d(param, pg)
|
||||
elif 'LayerNorm' not in mo_name and 'ln' not in mo_name:
|
||||
split_param_col_tp1d(param, pg)
|
||||
|
||||
model = model.cuda()
|
||||
|
||||
for i, (data, label) in enumerate(train_dataloader):
|
||||
data = data.to(get_current_device())
|
||||
label = label.to(get_current_device())
|
||||
|
||||
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
|
||||
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
|
||||
|
||||
# Bcast rank0 data to all processes
|
||||
if criterion:
|
||||
output = model(data)
|
||||
loss = criterion(output, label)
|
||||
else:
|
||||
output = model(data, label)
|
||||
loss = output
|
||||
|
||||
# For reference
|
||||
if rank == 0:
|
||||
if criterion:
|
||||
output_torch = model_torch(data)
|
||||
loss_torch = criterion(output_torch, label)
|
||||
else:
|
||||
output_torch = model_torch(data, label)
|
||||
loss_torch = output_torch
|
||||
assert torch.allclose(loss, loss_torch, rtol=1e-2)
|
||||
torch.distributed.barrier()
|
||||
|
||||
loss.backward()
|
||||
|
||||
if rank == 0:
|
||||
loss_torch.backward()
|
||||
torch.distributed.barrier()
|
||||
|
||||
if i > 5:
|
||||
break
|
||||
|
||||
|
||||
def _run_pretrain_load():
|
||||
from transformers import BertForMaskedLM
|
||||
set_seed(1)
|
||||
model_pretrained = BertForMaskedLM.from_pretrained('bert-base-uncased')
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
|
||||
|
||||
model_pretrained = model_pretrained.cuda()
|
||||
model = model.cuda()
|
||||
|
||||
dict_pretrained = {}
|
||||
dict_col = {}
|
||||
c_ref = 0
|
||||
for name, param in model_pretrained.named_parameters():
|
||||
dict_pretrained[name] = param
|
||||
c_ref += 1
|
||||
c1 = 0
|
||||
c2 = 0
|
||||
for name, param in model.named_parameters():
|
||||
if isinstance(param, ColoParameter):
|
||||
c1 += 1
|
||||
else:
|
||||
c2 += 1
|
||||
dict_col[name] = param
|
||||
assert c_ref == c1
|
||||
assert c2 == 0
|
||||
if model_pretrained.cls.predictions.decoder.bias is model_pretrained.cls.predictions.bias:
|
||||
assert model.cls.predictions.decoder.bias is model.cls.predictions.bias
|
||||
|
||||
for name, param in dict_pretrained.items():
|
||||
check_equal(param, dict_col[name])
|
||||
|
||||
|
||||
def run_model_dist(rank, world_size, port):
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
# Comment below test for speed consideration
|
||||
# for name in ['bert', 'simple_net']:
|
||||
# run_1d_row_tp(name)
|
||||
for name in ['bert', 'simple_net']:
|
||||
run_1d_hybrid_tp(name)
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 4])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_model(world_size):
|
||||
spawn(run_model_dist, world_size)
|
||||
|
||||
|
||||
def run_pretrain_load_dist(rank, world_size, port):
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
_run_pretrain_load()
|
||||
|
||||
|
||||
# The test case has to download huggingface pretrained models from the internet
|
||||
# So we manually trigger the test.
|
||||
@pytest.mark.skip
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 4])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_pretrain_load(world_size):
|
||||
spawn(run_pretrain_load_dist, world_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# test_model_parameters()
|
||||
# test_colo_optimizer()
|
||||
test_model(4)
|
||||
# test_pretrain_load(4)
|
||||
@@ -1,227 +0,0 @@
|
||||
from copy import deepcopy
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
import colossalai
|
||||
from colossalai.nn.parallel.layers import check_colo_module, init_colo_module
|
||||
from colossalai.tensor import (
|
||||
ColoTensor,
|
||||
ColoTensorSpec,
|
||||
ComputePattern,
|
||||
ComputeSpec,
|
||||
ProcessGroup,
|
||||
ReplicaSpec,
|
||||
ShardSpec,
|
||||
distspec,
|
||||
)
|
||||
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import ColoInitContext
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.test_tensor.common_utils import set_seed, tensor_equal, tensor_shard_equal
|
||||
|
||||
|
||||
def run_model_with_spec(mode, model_name):
|
||||
get_components_func = non_distributed_component_funcs.get_callable(model_name)
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
world_size = torch.distributed.get_world_size()
|
||||
pg = ProcessGroup(tp_degree=world_size)
|
||||
rank = pg.rank()
|
||||
|
||||
set_seed(1)
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = model_builder(checkpoint=False)
|
||||
|
||||
if rank == 0:
|
||||
model_seq = model_builder(checkpoint=False)
|
||||
model_seq = model_seq.cuda()
|
||||
|
||||
# Make two models have the same init params
|
||||
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
|
||||
p2.data.copy_(p1.data)
|
||||
|
||||
compute_spec = ComputeSpec(ComputePattern.TP1D)
|
||||
# Not all layers in Bert can be mod by 4.
|
||||
# e.g. row shard for all layers is invalid because the first dim of some layer is the classification type size 2.
|
||||
if 'bert' == model_name:
|
||||
if 'col' == mode:
|
||||
init_colo_module(model.bert.embeddings, compute_spec, pg=pg, recursive=True, mode=mode)
|
||||
init_colo_module(model.bert.encoder, compute_spec, pg=pg, recursive=True, mode=mode)
|
||||
init_colo_module(model.classifier, compute_spec, pg=pg, recursive=True, mode='row')
|
||||
elif 'row' == mode:
|
||||
init_colo_module(model.bert.embeddings, compute_spec, pg=pg, recursive=True, mode='col')
|
||||
init_colo_module(model.bert.encoder, compute_spec, pg=pg, recursive=True, mode=mode)
|
||||
init_colo_module(model.classifier, compute_spec, pg=pg, recursive=True, mode=mode)
|
||||
elif 'simple_net' == model_name:
|
||||
init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
|
||||
|
||||
model = model.cuda()
|
||||
for i, (data, label) in enumerate(train_dataloader):
|
||||
data = data.to(get_current_device())
|
||||
label = label.to(get_current_device())
|
||||
|
||||
torch.distributed.broadcast(data, 0, group=pg.tp_process_group())
|
||||
torch.distributed.broadcast(label, 0, group=pg.tp_process_group())
|
||||
|
||||
if criterion:
|
||||
output = model(data)
|
||||
loss = criterion(output, label)
|
||||
else:
|
||||
output = model(data, label)
|
||||
loss = output
|
||||
|
||||
# For reference
|
||||
if rank == 0:
|
||||
if criterion:
|
||||
output_seq = model_seq(data)
|
||||
loss_seq = criterion(output_seq, label)
|
||||
else:
|
||||
output_seq = model_seq(data, label)
|
||||
loss_seq = output_seq
|
||||
|
||||
if rank == 0:
|
||||
with torch.no_grad():
|
||||
assert torch.allclose(loss, loss_seq, rtol=1e-2)
|
||||
|
||||
loss.backward()
|
||||
|
||||
if rank == 0:
|
||||
loss_seq.backward()
|
||||
|
||||
with torch.no_grad():
|
||||
# check param
|
||||
for p1, p2 in zip(model.parameters(), model_seq.parameters()):
|
||||
if p1.size() == p2.size():
|
||||
assert torch.allclose(p1, p2)
|
||||
else:
|
||||
if p1.size(-1) < p2.size(-1): # col
|
||||
world_size = p2.size(-1) // p1.size(-1)
|
||||
split_p2 = torch.chunk(p2, world_size, dim=-1)[0]
|
||||
|
||||
elif p1.size(0) < p2.size(0): # row
|
||||
world_size = p2.size(0) // p1.size(0)
|
||||
split_p2 = torch.chunk(p2, world_size, dim=0)[0]
|
||||
|
||||
assert torch.allclose(p1, split_p2)
|
||||
|
||||
if i > 3:
|
||||
break
|
||||
|
||||
|
||||
def run_linear_with_spec(mode):
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = torch.nn.Linear(4, 8)
|
||||
|
||||
model_handy = deepcopy(model)
|
||||
world_size = torch.distributed.get_world_size()
|
||||
pg = ProcessGroup(tp_degree=world_size)
|
||||
compute_spec = ComputeSpec(ComputePattern.TP1D)
|
||||
init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
|
||||
|
||||
x = torch.rand(2, 4).cuda()
|
||||
colo_x = ColoTensor.from_torch_tensor(x, ColoTensorSpec(pg))
|
||||
|
||||
out = model(x)
|
||||
colo_out = model_handy(colo_x)
|
||||
assert tensor_equal(out, colo_out)
|
||||
|
||||
grad = torch.rand_like(out)
|
||||
out.backward(grad)
|
||||
colo_out.backward(grad)
|
||||
|
||||
assert tensor_shard_equal(model_handy.weight.grad, model.weight.grad, pg.tp_local_rank(), pg.tp_world_size())
|
||||
assert tensor_shard_equal(model_handy.bias.grad, model.bias.grad, pg.tp_local_rank(), pg.tp_world_size())
|
||||
|
||||
|
||||
def run_check_shared_param():
|
||||
from transformers import BertConfig, BertForMaskedLM
|
||||
hidden_dim = 8
|
||||
num_head = 4
|
||||
sequence_length = 12
|
||||
num_layer = 2
|
||||
vocab_size = 24
|
||||
|
||||
world_size = torch.distributed.get_world_size()
|
||||
pg = ProcessGroup(tp_degree=world_size)
|
||||
rank = pg.rank()
|
||||
|
||||
config = BertConfig(vocab_size=vocab_size,
|
||||
hidden_size=hidden_dim,
|
||||
intermediate_size=hidden_dim * 4,
|
||||
num_attention_heads=num_head,
|
||||
max_position_embeddings=sequence_length,
|
||||
num_hidden_layers=num_layer,
|
||||
hidden_dropout_prob=0.,
|
||||
attention_probs_dropout_prob=0.)
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = BertForMaskedLM(config)
|
||||
|
||||
model = model.cuda()
|
||||
compute_spec = ComputeSpec(ComputePattern.TP1D)
|
||||
# model.cls.predictions.decoder and model.cls.predictions share the bias, so they should have the same spec
|
||||
assert len(model.cls.predictions.decoder.bias.shared_param_modules) == 2
|
||||
# They are all Linear, so both row is allowed. This should pass check.
|
||||
init_colo_module(model, compute_spec, pg=pg, recursive=True, mode='row')
|
||||
# This should be detected by check because you can not set weight as row while set bias as col.
|
||||
col_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
|
||||
|
||||
# TODO(jiaruifang) optimize this line
|
||||
if not model.cls.predictions.bias.has_initialized:
|
||||
model.cls.predictions.bias.pg = pg
|
||||
model.cls.predictions.bias.dist_spec = ReplicaSpec()
|
||||
model.cls.predictions.bias.has_initialized = True
|
||||
model.cls.predictions.bias.set_tensor_spec(*col_spec)
|
||||
try:
|
||||
check_colo_module(model.cls.predictions.decoder, pg=pg, recursive=False)
|
||||
except Exception as e:
|
||||
assert 'incorrectly sharded' in str(e)
|
||||
|
||||
|
||||
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_with_spec('col')
|
||||
run_linear_with_spec('row')
|
||||
|
||||
|
||||
def run_dist_model(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')
|
||||
for model_name in ['simple_net', 'bert']:
|
||||
run_model_with_spec('col', model_name)
|
||||
run_model_with_spec('row', model_name)
|
||||
|
||||
|
||||
def run_dist_check(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_check_shared_param()
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 4])
|
||||
@pytest.mark.skip("for higher testing speed")
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_module_linear_1d(world_size):
|
||||
spawn(run_dist, world_size)
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 4])
|
||||
@pytest.mark.skip("for higher testing speed")
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_module_model(world_size):
|
||||
spawn(run_dist_model, world_size)
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 2])
|
||||
@pytest.mark.skip("for higher testing speed")
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_module_check(world_size):
|
||||
spawn(run_dist_check, world_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_module_linear_1d(4)
|
||||
@@ -1,41 +0,0 @@
|
||||
import pytest
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
import colossalai
|
||||
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
|
||||
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor
|
||||
from tests.test_tensor.common_utils import tensor_shard_equal
|
||||
|
||||
|
||||
def run_dist(rank, world_size, port, dp_degree, tp_degree):
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
pg = ProcessGroup(dp_degree=dp_degree, tp_degree=tp_degree)
|
||||
x = torch.randn(4, 4)
|
||||
param = ColoTensor(torch.nn.Parameter(x), spec=ColoTensorSpec(pg))
|
||||
spec = ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)
|
||||
param.set_tensor_spec(*spec)
|
||||
|
||||
gather_tensor(param)
|
||||
if dist.get_rank() == 0:
|
||||
assert torch.all(x == param)
|
||||
else:
|
||||
assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
|
||||
dist.barrier()
|
||||
|
||||
scatter_tensor(param, spec[0])
|
||||
assert tensor_shard_equal(x, param.data, pg.tp_local_rank(), pg.tp_world_size())
|
||||
assert param.requires_grad is True
|
||||
dist.barrier()
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [4])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_checkpoint(world_size):
|
||||
spawn(run_dist, world_size, dp_degree=2, tp_degree=world_size // 2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_checkpoint(world_size=4)
|
||||
@@ -1,64 +0,0 @@
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
import colossalai
|
||||
from colossalai.tensor import (
|
||||
ColoParameter,
|
||||
ColoTensorSpec,
|
||||
ComputePattern,
|
||||
ComputeSpec,
|
||||
ProcessGroup,
|
||||
ReplicaSpec,
|
||||
ShardSpec,
|
||||
)
|
||||
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import ColoInitContext
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.test_tensor.common_utils import set_seed
|
||||
|
||||
|
||||
def run_colo_init_context(rank: int, world_size: int, port: int):
|
||||
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
|
||||
# make sure seed of each process is the same, so the params are consistent among processes and the params are exactly replicated.
|
||||
set_seed(42)
|
||||
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
|
||||
# keep parameters replicated during init
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model1 = model_builder()
|
||||
|
||||
# shard the parameters during init
|
||||
set_seed(42)
|
||||
shard_spec = ReplicaSpec()
|
||||
|
||||
# If using ShardSpec, the assertations will failed.
|
||||
# But it is not a bug, the initialized values are not consist with the original one.
|
||||
# shard_spec = ShardSpec(dims=[0], num_partitions=[world_size])
|
||||
default_pg = ProcessGroup(tp_degree=world_size)
|
||||
with ColoInitContext(device=get_current_device(), default_pg=default_pg, default_dist_spec=shard_spec):
|
||||
model2 = model_builder()
|
||||
|
||||
# reshard both models
|
||||
new_shard = ShardSpec(dims=[-1], num_partitions=[world_size])
|
||||
for p1, p2 in zip(model1.parameters(), model2.parameters()):
|
||||
p1: ColoParameter = p1
|
||||
p1.set_process_group(ProcessGroup(tp_degree=world_size))
|
||||
p1.set_dist_spec(new_shard)
|
||||
p2.set_dist_spec(new_shard)
|
||||
|
||||
for p1, p2 in zip(model1.parameters(), model2.parameters()):
|
||||
assert (torch.allclose(p1, p2))
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 4])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_colo_init_context(world_size):
|
||||
spawn(run_colo_init_context, world_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_colo_init_context(2)
|
||||
@@ -1,232 +0,0 @@
|
||||
import pytest
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
import colossalai
|
||||
from colossalai.device.device_mesh import DeviceMesh
|
||||
from colossalai.nn._ops._utils import gather_forward_split_backward
|
||||
from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup
|
||||
from colossalai.tensor.sharding_spec import ShardingSpec
|
||||
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
||||
|
||||
|
||||
def run_dist(rank, world_size, port):
|
||||
config = {}
|
||||
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
|
||||
# create mlp vars
|
||||
x = ColoTensor.from_torch_tensor(torch.rand(4, 4, 8, requires_grad=True)).cuda()
|
||||
w = ColoParameter.from_torch_tensor(torch.rand(16, 8, requires_grad=True)).cuda()
|
||||
b = ColoParameter.from_torch_tensor(torch.rand(16, requires_grad=True)).cuda()
|
||||
|
||||
# run normal forward
|
||||
out = F.linear(x, w, b)
|
||||
|
||||
# create mesh meta
|
||||
# the mesh is in the following topo
|
||||
# [[0, 1],
|
||||
# [2, 3]]
|
||||
physical_mesh_id = torch.arange(0, 4)
|
||||
mesh_shape = (2, 2)
|
||||
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
|
||||
row_id = rank // 2
|
||||
column_id = rank % 2
|
||||
|
||||
# create pg
|
||||
row_process_group = None
|
||||
col_process_group = None
|
||||
row_to_ranks = {0: [0, 1], 1: [2, 3]}
|
||||
col_to_ranks = {0: [0, 2], 1: [1, 3]}
|
||||
|
||||
for idx in range(2):
|
||||
# row ranks
|
||||
row_ranks = row_to_ranks[idx]
|
||||
row_pg = ProcessGroup(ranks=row_ranks, tp_degree=2)
|
||||
|
||||
# col ranks
|
||||
col_ranks = col_to_ranks[idx]
|
||||
col_pg = ProcessGroup(ranks=col_ranks, tp_degree=2)
|
||||
|
||||
if rank in row_ranks:
|
||||
row_process_group = row_pg
|
||||
|
||||
if rank in col_ranks:
|
||||
col_process_group = col_pg
|
||||
|
||||
########################
|
||||
# RRR x RS0 -> RRS0 #
|
||||
########################
|
||||
# w will be transposed in F.linear
|
||||
x_replica = x.detach().clone()
|
||||
w_shard = torch.chunk(w.detach().clone(), chunks=2, dim=0)[row_id]
|
||||
b_shard = torch.chunk(b.detach().clone(), chunks=2, dim=0)[row_id]
|
||||
|
||||
# adding sharding spec
|
||||
x_replica.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={})
|
||||
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [0]})
|
||||
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [0]})
|
||||
|
||||
# check sharding spec
|
||||
assert str(x_replica.sharding_spec.sharding_sequence) == "[R, R, R]"
|
||||
assert str(w_shard.sharding_spec.sharding_sequence) == "[S0, R]"
|
||||
assert str(b_shard.sharding_spec.sharding_sequence) == "[S0]"
|
||||
|
||||
w_shard.pg_axis0 = col_process_group
|
||||
w_shard.pg_axis1 = row_process_group
|
||||
|
||||
out_shard = F.linear(x_replica, w_shard, b_shard)
|
||||
assert str(out_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
|
||||
|
||||
# each row only has a mini-batch
|
||||
expected_out_shard = torch.chunk(out, chunks=2, dim=2)[row_id]
|
||||
assert torch.allclose(out_shard, expected_out_shard)
|
||||
|
||||
########################
|
||||
# S0RR x RS1 -> S0RS1 #
|
||||
########################
|
||||
# w will be transposed in F.linear
|
||||
x_shard = torch.chunk(x.detach().clone(), chunks=2, dim=0)[row_id]
|
||||
w_shard = torch.chunk(w.detach().clone(), chunks=2, dim=0)[column_id]
|
||||
b_shard = torch.chunk(b.detach().clone(), chunks=2, dim=0)[column_id]
|
||||
|
||||
# adding sharding spec
|
||||
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={0: [0]})
|
||||
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [1]})
|
||||
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [1]})
|
||||
|
||||
# check sharding spec
|
||||
assert str(x_shard.sharding_spec.sharding_sequence) == "[S0, R, R]"
|
||||
assert str(w_shard.sharding_spec.sharding_sequence) == "[S1, R]"
|
||||
assert str(b_shard.sharding_spec.sharding_sequence) == "[S1]"
|
||||
|
||||
w_shard.pg_axis0 = col_process_group
|
||||
w_shard.pg_axis1 = row_process_group
|
||||
|
||||
out_shard = F.linear(x_shard, w_shard, b_shard)
|
||||
|
||||
# each row only has a mini-batch
|
||||
expected_out_shard = torch.chunk(out, chunks=2, dim=0)[row_id]
|
||||
expected_out_shard = torch.chunk(expected_out_shard, chunks=2, dim=2)[column_id]
|
||||
assert torch.allclose(out_shard, expected_out_shard)
|
||||
|
||||
########################
|
||||
# S0RS1 x S1R -> S0RR #
|
||||
########################
|
||||
# w will be transposed in F.linear
|
||||
x_shard = torch.chunk(x.clone(), chunks=2, dim=0)[row_id]
|
||||
x_shard = torch.chunk(x_shard, chunks=2, dim=2)[column_id]
|
||||
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[column_id]
|
||||
b_replica = b.clone()
|
||||
|
||||
# adding sharding spec
|
||||
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={0: [0], 2: [1]})
|
||||
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [1]})
|
||||
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
|
||||
|
||||
# check sharding spec
|
||||
assert str(x_shard.sharding_spec.sharding_sequence) == "[S0, R, S1]"
|
||||
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S1]"
|
||||
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
|
||||
|
||||
w_shard.pg_axis0 = col_process_group
|
||||
w_shard.pg_axis1 = row_process_group
|
||||
|
||||
out_shard = F.linear(x_shard, w_shard, b_replica)
|
||||
|
||||
# each row only has a mini-batch
|
||||
expected_out_shard = torch.chunk(out, chunks=2, dim=0)[row_id]
|
||||
assert torch.allclose(out_shard, expected_out_shard)
|
||||
|
||||
########################
|
||||
# RRS0 x S0R -> RRR #
|
||||
########################
|
||||
# w will be transposed in F.linear
|
||||
x_shard = torch.chunk(x.clone(), chunks=2, dim=2)[row_id]
|
||||
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[row_id]
|
||||
b_replica = b.clone()
|
||||
|
||||
# adding sharding spec
|
||||
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={2: [0]})
|
||||
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [0]})
|
||||
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
|
||||
|
||||
# check sharding spec
|
||||
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
|
||||
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S0]"
|
||||
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
|
||||
|
||||
w_shard.pg_axis0 = col_process_group
|
||||
w_shard.pg_axis1 = row_process_group
|
||||
|
||||
out_shard = F.linear(x_shard, w_shard, b_replica)
|
||||
|
||||
# each row only has a mini-batch
|
||||
expected_out_shard = out
|
||||
assert torch.allclose(out_shard, expected_out_shard)
|
||||
|
||||
########################
|
||||
# RS0S1 x S1R -> RS0R #
|
||||
########################
|
||||
# w will be transposed in F.linear
|
||||
x_shard = torch.chunk(x.clone(), chunks=2, dim=1)[row_id]
|
||||
x_shard = torch.chunk(x_shard, chunks=2, dim=2)[column_id]
|
||||
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[column_id]
|
||||
b_replica = b.clone()
|
||||
|
||||
# adding sharding spec
|
||||
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={1: [0], 2: [1]})
|
||||
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [1]})
|
||||
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
|
||||
|
||||
# check sharding spec
|
||||
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, S0, S1]"
|
||||
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S1]"
|
||||
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
|
||||
|
||||
w_shard.pg_axis0 = col_process_group
|
||||
w_shard.pg_axis1 = row_process_group
|
||||
|
||||
out_shard = F.linear(x_shard, w_shard, b_replica)
|
||||
|
||||
# each row only has a mini-batch
|
||||
expected_out_shard = torch.chunk(out, chunks=2, dim=1)[row_id]
|
||||
assert torch.allclose(out_shard, expected_out_shard)
|
||||
|
||||
########################
|
||||
# RRS0 x S0S1 -> RRS1 #
|
||||
########################
|
||||
# w will be transposed in F.linear
|
||||
x_shard = torch.chunk(x.clone(), chunks=2, dim=2)[row_id]
|
||||
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[row_id]
|
||||
w_shard = torch.chunk(w_shard, chunks=2, dim=0)[column_id]
|
||||
b_shard = torch.chunk(b.clone(), chunks=2, dim=0)[column_id]
|
||||
|
||||
# adding sharding spec
|
||||
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={2: [0]})
|
||||
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [1], 1: [0]})
|
||||
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [1]})
|
||||
|
||||
# check sharding spec
|
||||
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
|
||||
assert str(w_shard.sharding_spec.sharding_sequence) == "[S1, S0]"
|
||||
assert str(b_shard.sharding_spec.sharding_sequence) == "[S1]"
|
||||
|
||||
w_shard.pg_axis0 = col_process_group
|
||||
w_shard.pg_axis1 = row_process_group
|
||||
|
||||
out_shard = F.linear(x_shard, w_shard, b_shard)
|
||||
|
||||
# each row only has a mini-batch
|
||||
expected_out_shard = torch.chunk(out, chunks=2, dim=2)[column_id]
|
||||
assert torch.allclose(out_shard, expected_out_shard)
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [4])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_sharded_mlp(world_size):
|
||||
spawn(run_dist, world_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_sharded_mlp(4)
|
||||
@@ -1,143 +0,0 @@
|
||||
import pytest
|
||||
import torch
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
|
||||
import colossalai
|
||||
from colossalai.amp import convert_to_apex_amp
|
||||
from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
|
||||
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
||||
from colossalai.utils.cuda import get_current_device
|
||||
from colossalai.zero import ColoInitContext, GeminiAdamOptimizer, GeminiDDP, ZeroDDP
|
||||
from colossalai.zero.gemini import search_chunk_configuration
|
||||
from tests.components_to_test.registry import non_distributed_component_funcs
|
||||
from tests.test_tensor.common_utils import set_seed, tensor_shard_equal
|
||||
from tests.test_tensor.model.test_gpt2 import init_megatron_spec
|
||||
|
||||
|
||||
def check_param(model: ZeroDDP, torch_model: torch.nn.Module, pg: ProcessGroup):
|
||||
zero_dict = model.state_dict(only_rank_0=False)
|
||||
torch_dict = torch_model.state_dict()
|
||||
|
||||
for key, value in torch_dict.items():
|
||||
# key is 'module.model.PARAMETER', so we truncate it
|
||||
key = key[7:]
|
||||
assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
|
||||
temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
|
||||
# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
|
||||
assert tensor_shard_equal(value, temp_zero_value, pg.tp_local_rank(), pg.tp_world_size()), \
|
||||
"parameter '{}' has problem.".format(key)
|
||||
|
||||
|
||||
def run_fwd_bwd(model, criterion, optimizer, input_ids):
|
||||
optimizer.zero_grad()
|
||||
logits = model(input_ids)
|
||||
logits = logits.float()
|
||||
loss = criterion(logits, input_ids)
|
||||
optimizer.backward(loss)
|
||||
return logits
|
||||
|
||||
|
||||
def init_1d_row_spec(model, pg: ProcessGroup):
|
||||
spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
|
||||
for n, p in model.named_parameters():
|
||||
p.set_process_group(pg)
|
||||
if 'weight' in n and 'ln' not in n:
|
||||
p.set_tensor_spec(*spec)
|
||||
|
||||
|
||||
def init_1d_col_spec(model, pg: ProcessGroup):
|
||||
spec = (ShardSpec([-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
|
||||
for n, p in model.named_parameters():
|
||||
p.set_process_group(pg)
|
||||
if 'ln' not in n and ('weight' in n or 'bias' in n):
|
||||
p.set_tensor_spec(*spec)
|
||||
|
||||
|
||||
@parameterize('placement_policy', ['cuda', 'cpu'])
|
||||
def run_gpt(placement_policy, tp_init_spec_func=None):
|
||||
set_seed(42)
|
||||
get_components_func = non_distributed_component_funcs.get_callable('gpt2')
|
||||
model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
|
||||
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
model = model_builder()
|
||||
model = model.cuda()
|
||||
torch_model = model_builder().cuda()
|
||||
|
||||
for torch_p, p in zip(torch_model.parameters(), model.parameters()):
|
||||
torch_p.data.copy_(p.data)
|
||||
|
||||
world_size = torch.distributed.get_world_size()
|
||||
|
||||
# world size, dp = 2, tp =2, construct a hybrid parallelism.
|
||||
if world_size == 4:
|
||||
pg = ProcessGroup(tp_degree=2)
|
||||
else:
|
||||
pg = ProcessGroup(tp_degree=world_size)
|
||||
|
||||
if tp_init_spec_func:
|
||||
tp_init_spec_func(model, pg)
|
||||
|
||||
dp_world_size = pg.dp_world_size()
|
||||
config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
|
||||
config_dict[dp_world_size]['chunk_size'] = 5000
|
||||
config_dict[dp_world_size]['keep_gathered'] = False
|
||||
if placement_policy != 'cuda':
|
||||
init_device = torch.device('cpu')
|
||||
else:
|
||||
init_device = None
|
||||
|
||||
model = GeminiDDP(model, init_device, placement_policy, True, False)
|
||||
# The same as the following 3 lines
|
||||
# chunk_manager = ChunkManager(config_dict, init_device=init_device)
|
||||
# gemini_manager = GeminiManager(placement_policy, chunk_manager)
|
||||
# model = ZeroDDP(model, gemini_manager, pin_memory=True)
|
||||
|
||||
zero_optim = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=1)
|
||||
# The same as the following 2 lines
|
||||
# optimizer = HybridAdam(model.parameters(), lr=1e-3)
|
||||
# zero_optim = ZeroOptimizer(optimizer, model, initial_scale=1)
|
||||
|
||||
amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
|
||||
torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
|
||||
torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
|
||||
torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
|
||||
|
||||
check_param(model, torch_model, pg)
|
||||
|
||||
model.eval()
|
||||
torch_model.eval()
|
||||
|
||||
set_seed(pg.dp_local_rank())
|
||||
for i, (input_ids, label) in enumerate(train_dataloader):
|
||||
if i > 2:
|
||||
break
|
||||
input_ids_colo = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
|
||||
zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids_colo)
|
||||
torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids)
|
||||
assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
|
||||
|
||||
zero_optim.step()
|
||||
torch_optim.step()
|
||||
check_param(model, torch_model, pg)
|
||||
|
||||
|
||||
def run_dist(rank, world_size, port):
|
||||
config = {}
|
||||
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
||||
if world_size == 4:
|
||||
run_gpt(tp_init_spec_func=init_megatron_spec)
|
||||
else:
|
||||
run_gpt(tp_init_spec_func=init_1d_col_spec)
|
||||
run_gpt(tp_init_spec_func=init_1d_row_spec)
|
||||
|
||||
|
||||
@pytest.mark.dist
|
||||
@pytest.mark.parametrize('world_size', [1, 4])
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_gpt(world_size):
|
||||
spawn(run_dist, world_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_gpt(4)
|
||||
Reference in New Issue
Block a user