mirror of https://github.com/hpcaitech/ColossalAI
135 lines
4.9 KiB
Python
135 lines
4.9 KiB
Python
import imp
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from functools import partial
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import torch
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import torch.distributed as dist
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from colossalai.logging import get_dist_logger
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from colossalai.nn.optimizer import CPUAdam
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from colossalai.utils import checkpoint
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from colossalai.zero.shard_utils import TensorShardStrategy
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from colossalai.zero.sharded_model import ShardedModelV2
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LOGGER = get_dist_logger('zero_test')
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MP_PARALLEL_CONFIG = dict(fp16=dict(mode=None,), parallel=dict(pipeline=dict(size=1), tensor=dict(size=2, mode=None)))
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_ZERO_MODEL_CONFIG = dict(reduce_scatter_bucket_size_mb=25,
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fp32_reduce_scatter=False,
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offload_config=None,
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gradient_predivide_factor=1.0,
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use_memory_tracer=False,
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shard_strategy=TensorShardStrategy)
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_ZERO_OPTIMIZER_CONFIG = dict(
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cpu_offload=False,
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initial_scale=2**5,
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min_scale=1,
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growth_factor=2,
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backoff_factor=0.5,
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growth_interval=1000,
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hysteresis=2,
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max_scale=2**32,
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lr=1e-3)
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ZERO_PARALLEL_CONFIG = dict(fp16=dict(mode=None,),
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zero=dict(
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model_config=_ZERO_MODEL_CONFIG,
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optimizer_config=_ZERO_OPTIMIZER_CONFIG,
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),
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parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)))
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CONFIG = dict(fp16=dict(mode=None,),
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zero=dict(level=3,
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verbose=False,
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offload_optimizer_config=dict(device='cpu', pin_memory=True, buffer_count=5, fast_init=False),
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offload_param_config=dict(device='cpu',
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pin_memory=True,
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buffer_count=5,
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buffer_size=1e8,
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max_in_cpu=1e9)),
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parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)))
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def run_fwd_bwd(model, data, label, criterion, enable_autocast=False):
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model.train()
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with torch.cuda.amp.autocast(enabled=enable_autocast):
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if criterion:
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y = model(data)
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loss = criterion(y, label)
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else:
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loss = model(data, label)
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loss = loss.float()
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if isinstance(model, ShardedModelV2):
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model.backward(loss)
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else:
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loss.backward()
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def checkpoint_wrapper(module, enable=True):
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if enable:
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module.forward = partial(checkpoint, module.forward)
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return module
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def allclose(tensor_a: torch.Tensor, tensor_b: torch.Tensor, loose=False) -> bool:
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if loose:
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return torch.allclose(tensor_a, tensor_b, atol=1e-2, rtol=1e-3)
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return torch.allclose(tensor_a, tensor_b)
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def check_grads(model, zero_model, loose=False):
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_grad = zero_p.grad.clone().to(p.device)
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grad = p.grad.float()
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assert grad.dtype == zero_grad.dtype
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assert allclose(grad, zero_grad, loose=loose)
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def check_params(model, zero_model, loose=False):
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_p = zero_p.clone().to(p.device)
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# assert p.dtype == zero_p.dtype
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assert allclose(p.float(), zero_p.float(), loose=loose), f"diff {p.float() - zero_p.float()}"
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def check_grads_padding(model, zero_model, loose=False):
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rank = dist.get_rank()
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_grad = zero_p.grad.clone().to(p.device)
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chunks = torch.flatten(p.grad).chunk(dist.get_world_size())
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if rank >= len(chunks):
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continue
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grad = chunks[rank].float()
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if zero_grad.size(0) > grad.size(0):
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zero_grad = zero_grad[:grad.size(0)]
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assert grad.dtype == zero_grad.dtype
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assert allclose(grad, zero_grad, loose=loose), f'diff: {grad - zero_grad}'
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def check_params_padding(model, zero_model, loose=False):
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rank = dist.get_rank()
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_p = zero_p.clone().to(p.device)
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chunks = torch.flatten(p).chunk(dist.get_world_size())
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if rank >= len(chunks):
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continue
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p = chunks[rank]
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if zero_p.size(0) > p.size(0):
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zero_p = zero_p[:p.size(0)]
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assert p.dtype == zero_p.dtype
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assert allclose(p, zero_p, loose=loose)
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def check_sharded_params_padding(model, zero_model, loose=False):
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rank = dist.get_rank()
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for p, zero_p in zip(model.parameters(), zero_model.parameters()):
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zero_p = zero_p.col_attr.data.payload.to(p.device).float()
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chunks = torch.flatten(p).chunk(dist.get_world_size())
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if rank >= len(chunks):
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continue
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p = chunks[rank].float()
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if zero_p.size(0) > p.size(0):
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zero_p = zero_p[:p.size(0)]
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assert p.dtype == zero_p.dtype
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assert allclose(p, zero_p, loose=loose), f'{p} vs {zero_p}'
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