mirror of https://github.com/hpcaitech/ColossalAI
437 lines
16 KiB
Python
437 lines
16 KiB
Python
from colossalai.tensor.colo_parameter import ColoParameter
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from tests.components_to_test.registry import non_distributed_component_funcs
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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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, \
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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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from functools import partial
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import random
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import os
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import numpy as np
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# Hack huggingface Bert ModelOutput
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# Make it available to our ColoTensor
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from transformers.file_utils import ModelOutput
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from dataclasses import fields
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def _post_init_colotensor(self):
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class_fields = fields(self)
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# Safety and consistency checks
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if len(class_fields) == 0:
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raise ValueError(f"{self.__class__.__name__} has no fields.")
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if not all(field.default is None for field in class_fields[1:]):
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raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
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first_field = getattr(self, class_fields[0].name)
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other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
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def is_tensor_with_colo(x):
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"""
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Tests if `x` is a `ColoTensor` or `torch.Tensor`.
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"""
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if isinstance(x, torch.Tensor):
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return True
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return isinstance(x, ColoTensor)
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if other_fields_are_none and not is_tensor_with_colo(first_field):
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if isinstance(first_field, dict):
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iterator = first_field.items()
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first_field_iterator = True
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else:
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try:
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iterator = iter(first_field)
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first_field_iterator = True
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except TypeError:
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first_field_iterator = False
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# if we provided an iterator as first field and the iterator is a (key, value) iterator
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# set the associated fields
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if first_field_iterator:
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for element in iterator:
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if (not isinstance(element, (list, tuple)) or not len(element) == 2 or not isinstance(element[0], str)):
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break
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setattr(self, element[0], element[1])
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if element[1] is not None:
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self[element[0]] = element[1]
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elif first_field is not None:
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self[class_fields[0].name] = first_field
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else:
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for field in class_fields:
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v = getattr(self, field.name)
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if v is not None:
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self[field.name] = v
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ModelOutput.__post_init__ = _post_init_colotensor
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# complete the hack
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def set_seed(seed):
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random.seed(seed)
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os.environ['PYTHONHASHSEED'] = str(seed)
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np.random.seed(seed)
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torch.manual_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.TP1D, 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,
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compute_pattern=ComputePattern.TP1D,
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parallel_mode=ParallelMode.PARALLEL_1D,
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gather_out=gather_out)
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])
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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.TP1D, 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.TP1D, 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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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 = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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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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colo_optimizer_torch = ColoOptimizer(dict(model_torch.named_parameters()), torch.optim.SGD, 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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if 'bert' == model_name:
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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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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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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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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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init_1d_col_embedding(p)
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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.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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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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init_1d_col_linear(p)
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if 'proj2' in name and 'weight' in name:
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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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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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for i, (data, label) in enumerate(train_dataloader):
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model.eval()
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colo_optimizer.zero_grad()
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if rank == 0:
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model_torch.eval()
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colo_optimizer_torch.zero_grad()
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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torch.distributed.broadcast(data, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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# Bcast rank0 data to all processes
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if criterion:
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output = model(data)
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loss = criterion(output, label)
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else:
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output = model(data, label)
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loss = output
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# For reference
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if rank == 0:
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if criterion:
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output_torch = model_torch(data)
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loss_torch = criterion(output_torch, label)
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else:
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output_torch = model_torch(data, label)
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loss_torch = output_torch
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if rank == 0:
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# print(loss.torch_tensor().item())
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# print('loss torch', loss_torch.item())
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with torch.no_grad():
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assert torch.allclose(loss.torch_tensor(), loss_torch, rtol=1e-2)
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loss.backward()
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colo_optimizer.step()
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if rank == 0:
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loss_torch.backward()
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colo_optimizer_torch.step()
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with torch.no_grad():
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# check param
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for p1, p2 in zip(model.parameters(), model_torch.parameters()):
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if p1.size() == p2.size():
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assert torch.allclose(p1, p2)
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else:
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# TODO(jzy) Only check 1D spec. Need to be replaced by new DistSpec.
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if p1.size(-1) < p2.size(-1): # col
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world_size = p2.size(-1) // p1.size(-1)
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split_p2 = torch.chunk(p2, world_size, dim=-1)[0]
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elif p1.size(0) < p2.size(0): # row
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world_size = p2.size(0) // p1.size(0)
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split_p2 = torch.chunk(p2, world_size, dim=0)[0]
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assert torch.allclose(p1, split_p2)
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if i > 5:
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break
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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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class Net(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.fcs = torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 2))
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self.extra_param = torch.nn.Parameter(torch.randn(2))
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with ColoInitContext(device=get_current_device()):
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model = Net()
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param_cnt = 0
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for name, p in model.named_parameters():
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param_cnt += 1
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assert param_cnt == 5
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for name, colo_p in model.colo_named_parameters():
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assert colo_p.is_model_data()
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param_cnt = 0
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for name, p in model.named_parameters(recurse=False):
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param_cnt += 1
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assert param_cnt == 1
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param_cnt = 0
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for p in model.fcs[0].parameters(recurse=False):
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param_cnt += 1
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assert param_cnt == 2
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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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set_seed(1)
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with ColoInitContext(lazy_memory_allocate=False, device=get_current_device()):
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model = model_builder(checkpoint=True)
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colo_optimizer = ColoOptimizer(dict(model.named_parameters()), torch.optim.SGD, lr=0.1)
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for i, (data, label) in enumerate(train_dataloader):
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colo_optimizer.zero_grad()
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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# Bcast rank0 data to all processes
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if criterion:
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output = model(data)
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loss = criterion(output, label)
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else:
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output = model(data, label)
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loss = output
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loss.backward()
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colo_optimizer.step()
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if i > 5:
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break
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def run_1d_row_tp(model_name: str):
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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 = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
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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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set_seed(1)
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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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# 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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init_1d_row_linear(p)
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if 'embed' in name and 'weight' in name:
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init_1d_row_embedding(p)
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model = model.cuda()
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for i, (data, label) in enumerate(train_dataloader):
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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torch.distributed.broadcast(data, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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torch.distributed.broadcast(label, 0, group=gpc.get_group(ParallelMode.PARALLEL_1D))
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# Bcast rank0 data to all processes
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if criterion:
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output = model(data)
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loss = criterion(output, label)
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else:
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output = model(data, label)
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loss = output
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# For reference
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if rank == 0:
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if criterion:
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output_torch = model_torch(data)
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loss_torch = criterion(output_torch, label)
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else:
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output_torch = model_torch(data, label)
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loss_torch = output_torch
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if rank == 0:
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# print(loss.torch_tensor().item())
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# print('loss torch', loss_torch.item())
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assert torch.allclose(loss.torch_tensor(), loss_torch, rtol=1e-2)
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loss.backward()
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if rank == 0:
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loss_torch.backward()
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if i > 5:
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break
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def _run_pretrain_load():
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from _utils import check_equal
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from transformers import BertForMaskedLM
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set_seed(1)
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model_pretrained = BertForMaskedLM.from_pretrained('bert-base-uncased')
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with ColoInitContext(lazy_memory_allocate=False, device=get_current_device()):
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model = BertForMaskedLM.from_pretrained('bert-base-uncased')
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model_pretrained = model_pretrained.cuda()
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model = model.cuda()
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dict_pretrained = {}
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dict_col = {}
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c_ref = 0
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for name, param in model_pretrained.named_parameters():
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dict_pretrained[name] = param
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c_ref += 1
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c1 = 0
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c2 = 0
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for name, param in model.colo_named_parameters():
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if isinstance(param, ColoParameter):
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c1 += 1
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else:
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c2 += 1
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dict_col[name] = param
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assert c_ref == c1
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assert c2 == 0
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if model_pretrained.cls.predictions.decoder.bias is model_pretrained.cls.predictions.bias:
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assert model.cls.predictions.decoder.bias is model.cls.predictions.bias
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for name, param in dict_pretrained.items():
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check_equal(param, dict_col[name])
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def run_model_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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for name in ['simple_net']:
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run_1d_row_tp(name)
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for name in ['bert', 'simple_net']:
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run_1d_hybrid_tp(name)
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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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@rerun_if_address_is_in_use()
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def test_model(world_size):
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run_func = partial(run_model_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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def run_pretrain_load_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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_run_pretrain_load()
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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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@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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def _test_pretrain_load(world_size):
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run_func = partial(run_pretrain_load_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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# test_model_parameters()
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# test_colo_optimizer()
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test_model(4)
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# _test_pretrain_load(4)
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