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fix(moe): remove norm&gate force sync (#448) 

* add zero broadcast_sync

* delete old sync logic

* fix merged error

* refactor code

* remove some unused function (is norm/gate group)
pull/464/head
Wenwen Qu 2023-11-01 11:29:55 +08:00 committed by GitHub
parent f77f376fd6
commit 21624f6f81
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 23 additions and 75 deletions
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10
internlm/initialize/launch.py
View File

@ -293,10 +293,6 @@ def args_sanity_check():
model._add_item("moe_use_residual", False)
if "moe_gate_k" not in model:
model._add_item("moe_gate_k", 2)
assert not (
gpc.config.model.num_experts > 1 and gpc.config.parallel.zero1.fsdp
), "FSDP does not support num_experts > 1"
# process the parallel config
if "sequence_parallel" not in gpc.config.parallel:
gpc.config.parallel._add_item("sequence_parallel", False)
@ -345,11 +341,13 @@ def args_sanity_check():
gpc.config.loss._add_item("moe_loss_coeff", 1.0)
# moe not support overlap and zero1.5 for now
if hasattr(gpc.config.model, "num_experts"):
if gpc.config.model.get("num_experts", 1) > 1:
assert not gpc.config.parallel.zero1.fsdp, "FSDP does not support num_experts > 1"
assert (
not optim_ckpt.overlap_sync_grad & optim_ckpt.overlap_sync_param
), "not support overlap and moe at the same time"
assert gpc.config.parallel.zero1.size == -1, "moe only support zero1, set zero1=dict(size=-1,...) can fix this"
assert not gpc.config.parallel.sequence_parallel, "moe not support sequence parallel for now"
def launch(
@ -413,7 +411,7 @@ def launch(
f"data parallel size: {gpc.data_parallel_size}, pipeline parallel size: {gpc.pipeline_parallel_size}, "
f"tensor parallel size: {gpc.tensor_parallel_size}",
)
if hasattr(gpc.config.model, "num_experts") and gpc.config.model.num_experts > 1:
if gpc.config.model.get("num_experts", 1) > 1:
logger.info(
f"Creating MoE with num_experts: {gpc.config.model.num_experts} | "
f"expert parallel size: {gpc.expert_parallel_size} | "

5
internlm/model/modeling_moe.py
View File

@ -127,11 +127,6 @@ class PackedFlashBaseLayer1D(nn.Module):
else:
self.norm1 = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
self.norm2 = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
for param in self.norm1.parameters():
param.is_norm = True
for param in self.norm2.parameters():
param.is_norm = True
set_fp32_attr_to_module(self.norm1)
set_fp32_attr_to_module(self.norm2)

12
internlm/model/utils.py
View File

@ -215,18 +215,6 @@ def is_moe_param(param: torch.Tensor) -> bool:
return False
def is_gate_param(param: torch.Tensor) -> bool:
if hasattr(param, "is_gate") and param.is_gate:
return True
return False
def is_norm_param(param: torch.Tensor) -> bool:
if hasattr(param, "is_norm") and param.is_norm:
return True
return False
def Silu(w1_o, w2_o):
return F.silu(w1_o) * w2_o

3
internlm/moe/sharded_moe.py
View File

@ -352,9 +352,6 @@ class TopKGate(Module):
self.drop_tokens = drop_tokens
self.use_rts = use_rts
for param in self.wg.parameters():
param.is_gate = True
def forward(
self, inputs: torch.Tensor, used_token: torch.Tensor = None
) -> Tuple[Tensor, Tensor, Tensor]: # type: ignore

31
internlm/solver/optimizer/hybrid_zero_optim.py
View File

@ -274,12 +274,6 @@ class HybridZeroOptimizer(BaseOptimizer):
def _is_moe_group(self, param_group):
return "moe" in param_group.keys() and param_group["moe"]
def _is_norm_group(self, param_group):
return "norm" in param_group.keys() and param_group["norm"]
def _is_gate_group(self, param_group):
return "gate" in param_group.keys() and param_group["gate"]
# TODO check expert dp is correct when enable moe and overlap both
def _attach_reduction_hook(self):
# we iterate over the fp16 params
@ -566,7 +560,6 @@ class HybridZeroOptimizer(BaseOptimizer):
last_stage=last_stage,
previous_param_norms=previous_param_norms,
zero_mode=self._broadcast_parallel_mode[group_id],
is_moe_group=self._is_moe_group(self.optim.param_groups[group_id]),
)
return total_param_norms
@ -589,7 +582,6 @@ class HybridZeroOptimizer(BaseOptimizer):
last_stage=last_stage,
previous_zero_grad_count=previous_zero_grad_count,
zero_mode=self._broadcast_parallel_mode[group_id],
is_moe_group=self._is_moe_group(self.optim.param_groups[group_id]),
)
return total_zero_grad_count
@ -675,16 +667,6 @@ class HybridZeroOptimizer(BaseOptimizer):
total_param_zero_grad_count[group_name],
) = compute_layer_zero_grad_count(zero_grad_count)
# Need to allreduce(avg) the norms across different ranks because moe params will not be synced
# during allreduce
if self._is_moe_group(self.optim.param_groups[group_id]):
# model and zero have been reduced!!!
pg = gpc.get_group(ParallelMode.EXPERT)
scaled_norm = total_norms[group_name] * 1.0 / float(gpc.get_world_size(ParallelMode.EXPERT))
scaled_norm_tensor = torch.tensor(scaled_norm, device=get_current_device(), dtype=torch.float)
dist.all_reduce(scaled_norm_tensor, group=pg)
total_norms[group_name] = scaled_norm_tensor.item()
timer("sync_grad").start()
self._sync_grad()
timer("sync_grad").stop()
@ -767,19 +749,6 @@ class HybridZeroOptimizer(BaseOptimizer):
param_shape == flat_fp32_avg_grads.shape
), f"fp32 param and grad have different shape {param_shape} vs {flat_fp32_avg_grads.shape}"
# Parameters shared within a TP group, such as norm and moe gate, have precision inconsistency in gradients.
# Therefore, it is recommended to synchronize gradients within the TP group to eliminate accumulated errors.
is_tp_sync_groups = (
self._is_norm_group(self.optim.param_groups[group_id]),
self._is_gate_group(self.optim.param_groups[group_id]),
)
if any(is_tp_sync_groups):
dist.all_reduce(
flat_fp32_avg_grads,
op=dist.ReduceOp.AVG,
group=gpc.get_group(ParallelMode.TENSOR),
)
single_grad_partition_groups.append(flat_fp32_avg_grads)
device = self._fp32_flat_param_groups_of_current_rank[group_id].device
self._fp32_flat_param_groups_of_current_rank[group_id].grad = flat_fp32_avg_grads.to(device)

23
internlm/solver/optimizer/utils.py
View File

@ -262,7 +262,12 @@ def reduce_grads(gradients, parameters, fine_grained=False):
def compute_norm(
gradients, parameters, last_stage=False, previous_norm=None, norm_type=2, zero_mode=ParallelMode.ZERO1
gradients,
parameters,
last_stage=False,
previous_norm=None,
norm_type=2,
zero_mode=ParallelMode.ZERO1,
):
"""Get the norm
Arguments:
@ -335,6 +340,15 @@ def compute_norm(
if torch.is_tensor(total_norm):
total_norm = total_norm.item()
# Need to allreduce(avg) the norms across different ranks because moe params will not be synced during allreduce
# model and zero have been reduced!!!
if zero_mode == ParallelMode.EXPERT_DATA:
pg = gpc.get_group(ParallelMode.EXPERT)
scaled_norm = total_norm * 1.0 / float(gpc.get_world_size(ParallelMode.DATA))
scaled_norm_tensor = torch.tensor(scaled_norm, device=get_current_device(), dtype=torch.float)
dist.all_reduce(scaled_norm_tensor, group=pg)
total_norm = scaled_norm_tensor.item()
# Scale.
if total_norm == float("inf") or total_norm == -float("inf"):
total_norm = -1
@ -353,7 +367,6 @@ def compute_param_metric(
previous_param_metrics=None,
norm_type=2,
zero_mode=ParallelMode.ZERO1,
is_moe_group=False,
):
"""Get the metrics of params
Argumemts:
@ -424,7 +437,7 @@ def compute_param_metric(
total_metrics[param_name] += param_metric
# moe
if is_moe_group:
if zero_mode == ParallelMode.EXPERT_DATA:
pg = gpc.get_group(ParallelMode.EXPERT)
total_metric_values = list(total_metrics.values())
if isinstance(total_metric_values[0], torch.Tensor):
@ -463,7 +476,6 @@ def compute_param_norm(
previous_param_norms=None,
norm_type=2,
zero_mode=ParallelMode.ZERO1,
is_moe_group=False,
):
"""Get the norm of params
Arguments:
@ -484,7 +496,6 @@ def compute_param_norm(
previous_param_metrics=previous_param_norms,
norm_type=norm_type,
zero_mode=zero_mode,
is_moe_group=is_moe_group,
)
@ -494,7 +505,6 @@ def compute_zero_grad_count(
last_stage=False,
previous_zero_grad_count=None,
zero_mode=ParallelMode.ZERO1,
is_moe_group=False,
):
"""Get the count of zero gradient for each parameters
Arguments:
@ -512,7 +522,6 @@ def compute_zero_grad_count(
last_stage=last_stage,
previous_param_metrics=previous_zero_grad_count,
zero_mode=zero_mode,
is_moe_group=is_moe_group,
)

14
internlm/train/utils.py
View File

@ -4,7 +4,7 @@ import torch
from internlm.core.context.parallel_context import ParallelMode
from internlm.core.context.parallel_context import global_context as gpc
from internlm.model.utils import is_gate_param, is_moe_param, is_norm_param
from internlm.model.utils import is_moe_param
def split_params_into_different_groups_for_optimizer(param_groups: Tuple[Dict]) -> Tuple[Dict]:
@ -39,10 +39,7 @@ def split_params_into_different_groups_for_optimizer(param_groups: Tuple[Dict])
# create new groups for fp32, norm, moe gate and moe expert
new_groups = {}
new_groups["fp32"] = {"name": "fp32", "params": [], "dp_mode": ParallelMode.DATA}
if gpc.config.model.get("num_experts", 0) > 1:
# norm and gate are special group to force sync (when enable MoE).
for key in ["gate", "norm"]:
new_groups[key] = {"name": key, key: True, "params": [], "dp_mode": ParallelMode.DATA}
if gpc.config.model.get("num_experts", 1) > 1:
for key in gpc.expert_parallel_group_names:
new_groups[key] = {"name": key, "moe": True, "params": [], "dp_mode": ParallelMode.EXPERT_DATA}
@ -58,12 +55,7 @@ def split_params_into_different_groups_for_optimizer(param_groups: Tuple[Dict])
# first split the norm and gate groups, which are special case to force sync (when enable MoE),
# then fp32 group and the moe group.
for param in pgroup["params"]:
if gpc.config.model.get("num_experts", 0) > 1 and is_norm_param(param):
new_groups["norm"]["params"].append(param)
# gate param means MoE is enabled
elif is_gate_param(param):
new_groups["gate"]["params"].append(param)
elif param.dtype == torch.float32:
if param.dtype == torch.float32:
new_groups["fp32"]["params"].append(param)
# moe param means MoE is enabled
elif is_moe_param(param):