mirror of https://github.com/hpcaitech/ColossalAI
fix optim
Xuanlei Zhao
parent
0a3aae509b
commit
44014faa67
Binary file not shown.
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@ -144,7 +144,7 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
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# because they have different parallel strategy
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# so we need to store them separately in param_groups
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# instead of working_groups
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moe_params = list()
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self.working_moe_params = list()
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# iterate over the param group in the optimizer
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# partition these param groups for data parallel training
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@ -156,7 +156,7 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
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if self.moe_extra_dp_pg is None:
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# skip moe param
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if is_moe_tensor(param):
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moe_params.append(param)
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self.working_moe_params.append(param)
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continue
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group_params.append(param)
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@ -172,12 +172,15 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
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param_group["params"] = master_param_current_rank
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# if there are moe params, store in addtional group in optim
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if len(moe_params) > 0:
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if len(self.working_moe_params) > 0:
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param_group = dict()
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for key, value in self.optim.param_groups[0].items():
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if key != "params":
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param_group[key] = value
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param_group["params"] = moe_params
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self.master_moe_params = []
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for param in self.working_moe_params:
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self.master_moe_params.append(param.to(torch.float32))
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param_group["params"] = self.master_moe_params
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self.optim.param_groups.append(param_group)
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# intialize communication stream for
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@ -596,24 +599,40 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
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# update the params in the optimizer
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self.optim.param_groups[group_id]["params"] = real_master_params[group_id]
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# update param for moe ep
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# move grad to master param and compute norm
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if len(self.working_moe_params) > 0:
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moe_grads = []
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for master_moe_param, working_moe_param in zip(self.master_moe_params, self.working_moe_params):
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if master_moe_param.grad is not None:
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raise RuntimeError("Moe param should not have grad here")
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grad = working_moe_param.grad
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# no need to copy fp32 grad if master_weights is False
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if self._master_weights:
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grad = grad.to(master_moe_param.dtype).to(master_moe_param.device)
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master_moe_param.grad = grad
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working_moe_param.grad = None
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moe_grads.append(grad)
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grad_partition_groups.append(grad)
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norm_group = self._compute_grad_norm(gradients=moe_grads)
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norm_groups.append(norm_group)
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self.optim.param_groups[-1]["params"] = self.master_moe_params
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del moe_grads
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# unscale and clip grads
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global_norm = calculate_global_norm_from_list(norm_list=norm_groups)
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self._unscale_and_clip_grads(grad_partition_groups, global_norm)
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# TODO: we should store master param for ep
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if len(self.param_groups) > len(self._working_param_groups):
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for param in self.param_groups[-1]["params"]:
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param.data = param.data.to(torch.float32)
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param.grad = param.grad.to(torch.float32)
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# update the parameters
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self.optim.step()
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# release the moe gradm
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if len(self.param_groups) > len(self._working_param_groups):
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for param in self.param_groups[-1]["params"]:
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param.grad = None
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param.data = param.data.to(self._dtype)
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# release moe grad
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if len(self.working_moe_params) > 0:
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for master_moe_param, working_moe_param in zip(self.master_moe_params, self.working_moe_params):
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master_moe_param.grad = None
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working_moe_param.data = (
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master_moe_param.data.to(working_moe_param.device).to(working_moe_param.dtype).detach()
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)
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# release the grad
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grad_partition_groups = []
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@ -1,6 +1,7 @@
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.testing import assert_close
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from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroModel
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from colossalai.legacy.engine.gradient_handler._base_gradient_handler import BaseGradientHandler
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@ -10,7 +11,6 @@ from colossalai.moe import SparseMLP
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from colossalai.moe.manager import MOE_MANAGER
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from colossalai.moe.utils import get_moe_epsize_param_dict
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from colossalai.tensor.moe_tensor.api import get_ep_group, get_ep_size
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from tests.test_moe.moe_utils import MoeModel
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def delete_moe_info(model):
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@ -126,7 +126,7 @@ def sync_local_from_ep(local_model: SparseMLP, ep_model: SparseMLP, assert_grad_
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for (local_name, local_param), (ep_name, ep_param) in zip(
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local_model.named_parameters(), ep_model.named_parameters()
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):
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assert local_name == ep_name, print(f"{local_name} != {ep_name}")
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assert local_name in ep_name, print(f"{local_name} != {ep_name}")
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if "experts" not in local_name:
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if assert_grad_flag:
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assert torch.allclose(local_param, ep_param), f"local_param: {local_param}, ep_param: {ep_param}"
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@ -149,3 +149,19 @@ def sync_local_from_ep(local_model: SparseMLP, ep_model: SparseMLP, assert_grad_
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assert torch.allclose(local_param.grad, all_grad)
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else:
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local_param.data.copy_(all_param.data)
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def loose_close(a, b, dtype: torch.dtype = torch.float32):
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rtol = None
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atol = None
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if dtype is torch.float16:
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rtol = 5e-2
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atol = 5e-4
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elif dtype is torch.bfloat16:
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rtol = 4e-3
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atol = 4e-3
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a = a.detach().to(dtype)
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b = b.detach().to(dtype).to(a.device)
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assert_close(a, b, rtol=rtol, atol=atol)
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@ -15,64 +15,64 @@ def run_zero_test(local_rank, stage=1):
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MOE_MANAGER.__init__()
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MOE_MANAGER.setup(parallel="EP")
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moe_model = MoeModel().bfloat16()
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moe_optimizer = torch.optim.Adam(moe_model.parameters())
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moe_plugin = LowLevelZeroPlugin(stage=stage, precision="bf16")
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moe_booster = Booster(plugin=moe_plugin)
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moe_model, moe_optimizer, _, _, _ = moe_booster.boost(moe_model, moe_optimizer)
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MOE_MANAGER.__init__()
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MOE_MANAGER.setup(parallel=None)
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zero_model = MoeModel().bfloat16()
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delete_moe_info(zero_model)
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zero_optimizer = torch.optim.Adam(zero_model.parameters())
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zero_plugin = LowLevelZeroPlugin(stage=stage, precision="bf16")
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zero_booster = Booster(plugin=zero_plugin)
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zero_model, zero_optimizer, _, _, _ = zero_booster.boost(zero_model, zero_optimizer)
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MOE_MANAGER.__init__()
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MOE_MANAGER.setup(parallel=None)
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torch_model = MoeModel().bfloat16()
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delete_moe_info(torch_model)
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torch_optimizer = torch.optim.Adam(torch_model.parameters())
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torch_plugin = LowLevelZeroPlugin(stage=stage, precision="bf16")
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torch_booster = Booster(plugin=torch_plugin)
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torch_model, torch_optimizer, _, _, _ = torch_booster.boost(torch_model, torch_optimizer)
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sync_local_from_ep(torch_model, zero_model)
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sync_local_from_ep(zero_model, moe_model)
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data = torch.randn(16, 4).bfloat16().cuda()
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label = torch.randint(0, 4, (16,)).cuda()
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torch_out = run_fwd_bwd(torch_model, data, label, criterion, torch_optimizer)
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zero_out = run_fwd_bwd(zero_model, data, label, criterion, zero_optimizer)
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assert torch.allclose(torch_out, zero_out)
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moe_out = run_fwd_bwd(moe_model, data, label, criterion, moe_optimizer)
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assert torch.allclose(zero_out, moe_out)
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for (zero_name, zero_param), (torch_name, torch_param) in zip(
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zero_model.module.named_parameters(), torch_model.module.named_parameters()
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for (moe_name, moe_param), (zero_name, zero_param) in zip(
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moe_model.module.named_parameters(), zero_model.module.named_parameters()
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):
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assert zero_name == torch_name
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assert moe_name == zero_name
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moe_grad_list = moe_optimizer._grad_store.get_partitioned_gradients_by_param_id(0, id(moe_param))
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zero_grad_list = zero_optimizer._grad_store.get_partitioned_gradients_by_param_id(0, id(zero_param))
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torch_grad_list = torch_optimizer._grad_store.get_partitioned_gradients_by_param_id(0, id(torch_param))
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if hasattr(zero_param, "moe_info"):
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assert len(zero_grad_list) == 0
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if hasattr(moe_param, "moe_info"):
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assert len(moe_grad_list) == 0
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if stage == 1:
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torch_grad = torch_grad_list[local_rank].view(zero_param.grad.shape)
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zero_grad = zero_grad_list[local_rank].view(moe_param.grad.shape)
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else:
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torch_grad = torch_grad_list[0].view(zero_param.grad.shape)
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zero_grad = zero_grad_list[0].view(moe_param.grad.shape)
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assert torch.allclose(
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zero_param.grad, torch_grad, atol=1e-5
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), f"zero grad:\n{zero_param.grad}\ntorch grad:\n{torch_grad}\nmax diff: {(zero_param.grad - torch_grad).abs().max()}, mean diff: {(zero_param.grad - torch_grad).abs().mean()}"
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moe_param.grad, zero_grad, atol=1e-5
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), f"zero grad:\n{moe_param.grad}\ntorch grad:\n{zero_grad}\nmax diff: {(moe_param.grad - zero_grad).abs().max()}, mean diff: {(moe_param.grad - zero_grad).abs().mean()}"
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else:
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assert len(zero_grad_list) > 0
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assert len(zero_grad_list) == len(torch_grad_list)
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for zero_grad, torch_grad in zip(zero_grad_list, torch_grad_list):
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assert torch.allclose(zero_grad, torch_grad)
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assert len(moe_grad_list) > 0
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assert len(moe_grad_list) == len(zero_grad_list)
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for moe_grad, zero_grad in zip(moe_grad_list, zero_grad_list):
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assert torch.allclose(moe_grad, zero_grad)
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def run_dist(rank, world_size, port):
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def run_dist(rank, world_size, port, stage):
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colossalai.launch(config=dict(), rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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seed_all(42 + rank)
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run_zero_test(rank, stage=1)
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run_zero_test(rank, stage=2)
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run_zero_test(rank, stage=stage)
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2])
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@pytest.mark.parametrize("stage", [1, 2])
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@rerun_if_address_is_in_use()
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def test_moe_zero_model(world_size):
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spawn(run_dist, world_size)
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def test_moe_zero_model(world_size, stage):
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spawn(run_dist, world_size, stage=stage)
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if __name__ == "__main__":
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test_moe_zero_model(world_size=2)
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test_moe_zero_model(world_size=2, stage=1)
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@ -4,89 +4,80 @@ import torch
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import LowLevelZeroPlugin
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from colossalai.booster.plugin.low_level_zero_plugin import LowLevelZeroModel
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from colossalai.moe.manager import MOE_MANAGER
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from colossalai.tensor.moe_tensor.api import is_moe_tensor
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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from tests.test_moe.moe_utils import MoeGradientHandler, MoeModel
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from colossalai.testing.random import seed_all
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from tests.test_moe.moe_utils import MoeModel, delete_moe_info, loose_close, run_fwd_bwd, sync_local_from_ep
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def split_ddp_grad(grad, world_size):
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with torch.no_grad():
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grad = grad.clone().detach().flatten()
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padding_size = (world_size - grad.numel() % world_size) % world_size
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if padding_size > 0:
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grad = torch.nn.functional.pad(grad, [0, padding_size])
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splited_grad = grad.split(grad.numel() // world_size)
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return splited_grad
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def run_fwd_bwd(model, data, label, criterion, optimizer, 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, LowLevelZeroModel):
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optimizer.backward(loss)
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else:
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loss.backward()
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return y
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def run_zero_optim_test(local_rank, world_size, stage=1):
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def run_zero_test(local_rank, stage=1):
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criterion = torch.nn.CrossEntropyLoss()
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zero_model = MoeModel()
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zero_optimizer = torch.optim.Adam(zero_model.parameters())
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plugin = LowLevelZeroPlugin(stage=stage, precision="fp32")
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booster = Booster(plugin=plugin)
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zero_model, zero_optimizer, _, _, _ = booster.boost(zero_model, zero_optimizer)
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MOE_MANAGER.__init__()
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MOE_MANAGER.setup(parallel="EP")
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moe_model = MoeModel().bfloat16()
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moe_optimizer = torch.optim.Adam(moe_model.parameters(), lr=1.0)
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moe_plugin = LowLevelZeroPlugin(stage=stage, precision="bf16")
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moe_booster = Booster(plugin=moe_plugin)
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moe_model, moe_optimizer, _, _, _ = moe_booster.boost(moe_model, moe_optimizer)
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torch_model = MoeModel()
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for zero_param, torch_param in zip(zero_model.parameters(), torch_model.parameters()):
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torch_param.data.copy_(zero_param.data)
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torch_optimizer = torch.optim.Adam(torch_model.parameters())
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torch_model = torch_model.cuda()
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grad_handler = MoeGradientHandler(torch_model)
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MOE_MANAGER.__init__()
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MOE_MANAGER.setup(parallel=None)
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zero_model = MoeModel().bfloat16()
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delete_moe_info(zero_model)
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sync_local_from_ep(zero_model, moe_model)
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zero_optimizer = torch.optim.Adam(zero_model.parameters(), lr=1.0)
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zero_plugin = LowLevelZeroPlugin(stage=stage, precision="bf16")
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zero_booster = Booster(plugin=zero_plugin)
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zero_model, zero_optimizer, _, _, _ = zero_booster.boost(zero_model, zero_optimizer)
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for _ in range(2):
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data = torch.randn(16, 4).cuda() / (local_rank + 1)
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label = torch.randint(0, 4, (16,)).cuda()
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run_fwd_bwd(torch_model, data, label, criterion, None)
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run_fwd_bwd(zero_model, data, label, criterion, zero_optimizer)
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grad_handler.handle_gradient()
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for (moe_name, moe_param), (zero_name, zero_param) in zip(
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moe_model.named_parameters(), zero_model.named_parameters()
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):
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if ".experts." in moe_name:
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continue
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assert moe_name == zero_name
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assert torch.allclose(
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moe_param.data, zero_param.data
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), f"{moe_name}\ntorch_param {moe_param.data}\nzero_param {zero_param.data}"
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torch_optimizer.step()
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for _ in range(1):
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data = torch.randn(2, 4).bfloat16().cuda()
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label = torch.randint(0, 4, (2,)).cuda()
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moe_out = run_fwd_bwd(moe_model, data, label, criterion, moe_optimizer)
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zero_out = run_fwd_bwd(zero_model, data, label, criterion, zero_optimizer)
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assert torch.allclose(zero_out, moe_out)
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moe_optimizer.step()
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zero_optimizer.step()
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for (torch_name, torch_param), (zero_name, zero_param) in zip(
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torch_model.named_parameters(), zero_model.named_parameters()
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for (moe_name, moe_param), (zero_name, zero_param) in zip(
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moe_model.named_parameters(), zero_model.named_parameters()
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):
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assert torch.allclose(
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torch_param.data, zero_param.data
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), f"{torch_name}\ntorch_param {torch_param.data}\nzero_param {zero_param.data}"
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assert moe_name == zero_name
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if is_moe_tensor(moe_param):
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param_size = moe_param.shape[0]
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zero_param = zero_param[local_rank * param_size : (local_rank + 1) * param_size]
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loose_close(moe_param.data, zero_param.data, dtype=moe_param.dtype)
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torch_optimizer.zero_grad()
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moe_optimizer.zero_grad()
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zero_optimizer.zero_grad()
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def run_dist(rank, world_size, port):
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def run_dist(rank, world_size, port, stage):
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colossalai.launch(config=dict(), rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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MOE_MANAGER.setup(parallel="EP")
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run_zero_optim_test(rank, world_size, stage=1)
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run_zero_optim_test(rank, world_size, stage=2)
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seed_all(42 + rank)
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run_zero_test(rank, stage=stage)
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2])
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@pytest.mark.parametrize("stage", [1, 2])
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@rerun_if_address_is_in_use()
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def test_moe_zero_optim(world_size):
|
||||
spawn(run_dist, world_size)
|
||||
def test_moe_zero_optim(world_size, stage):
|
||||
spawn(run_dist, world_size, stage=stage)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_moe_zero_optim(world_size=2)
|
||||
test_moe_zero_optim(world_size=2, stage=1)
|
||||
|
|
|
|||
Loading…
Reference in New Issue