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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Dict, Union
import torch
import torch.nn as nn
from torch.distributed._tensor import DeviceMesh, DTensor, Replicate, Shard
from ..utils import logging
from .utils import check_fqn_match, get_module_from_path, set_module_from_path
logger = logging.get_logger(__name__)
@dataclass
class SpecInfo:
para_name: str # name of the ExtraParallel this fqn belongs to
placement: Union[Shard, Replicate]
fqn: str
para_fsdp_mesh: DeviceMesh
@property
def para_mesh(self):
if self.para_fsdp_mesh is not None:
return self.para_fsdp_mesh[self.para_name]
else:
return None
class ParallelPlan:
def __init__(self, extra_parallel_plan: Dict[str, Dict[str, Shard]]):
self.extra_parallel_plan = extra_parallel_plan
self.extra_parallel_fsdp_no_shard_module = {
para_name: {".".join(list(plan.keys())[0].split(".")[:-1])}
for para_name, plan in self.extra_parallel_plan.items()
}
def apply(self, model: nn.Module, extra_parallel_fsdp_device_mesh: Dict[str, DeviceMesh]):
"""
xxx_fsdp_mesh: [replicate, replicate, ... , shard]
"""
extra_parallel_mesh = {
para: para_fsdp_mesh[para] if para_fsdp_mesh is not None else None
for para, para_fsdp_mesh in extra_parallel_fsdp_device_mesh.items()
}
fqn2spec_info = {}
for para, para_plan in self.extra_parallel_plan.items():
para_mesh = extra_parallel_mesh[para]
para_fsdp_mesh = extra_parallel_fsdp_device_mesh[para]
if para_plan and para_mesh is not None:
para_size = para_mesh.size(-1)
para_replicate = [Replicate() for _ in range(para_mesh.ndim)]
for fqn, param in model.named_parameters():
for fqn_pattern, shard in para_plan.items():
if check_fqn_match(fqn_pattern, fqn):
assert param.size(shard.dim) % para_size == 0
para_placement = para_replicate[:-1] + [shard]
logger.info_rank0(
f"{para} sharding: slicing param {fqn} along {para}_mesh with placement {para_placement}"
)
dtensor = DTensor.from_local(
local_tensor=param.data, device_mesh=para_mesh, placements=para_replicate
)
dtensor = dtensor.redistribute(device_mesh=para_mesh, placements=para_placement)
local_chunk = torch.nn.Parameter(dtensor.to_local(), requires_grad=param.requires_grad)
local_chunk.spec_info = SpecInfo(
para_name=para, para_fsdp_mesh=para_fsdp_mesh, placement=shard, fqn=fqn
)
set_module_from_path(model, fqn, local_chunk)
fqn2spec_info[fqn] = SpecInfo(
para_name=para, para_fsdp_mesh=para_fsdp_mesh, placement=shard, fqn=fqn
)
break
if fqn not in fqn2spec_info: # not sharded
param.spec_info = SpecInfo(
para_name=para, para_fsdp_mesh=para_fsdp_mesh, placement=Replicate(), fqn=fqn
)
fqn2spec_info[fqn] = SpecInfo(
para_name=para, para_fsdp_mesh=para_fsdp_mesh, placement=Replicate(), fqn=fqn
)
for fqn, param in model.named_parameters():
assert hasattr(param, "spec_info"), f"Internal Error: {fqn=} with {param=} is omitted"
return fqn2spec_info
def get_fsdp_no_shard_info(self, model: nn.Module):
if self.extra_parallel_fsdp_no_shard_module is None:
return None
fsdp_no_shard_states_fqn_to_module = {}
fsdp_no_shard_states_fqn_to_para = {}
for fqn, _param in model.named_modules():
for para, no_shard_patterns in self.extra_parallel_fsdp_no_shard_module.items():
for no_shard_pattern in no_shard_patterns:
if check_fqn_match(no_shard_pattern, fqn):
fsdp_no_shard_states_fqn_to_module[fqn] = get_module_from_path(model, fqn)
fsdp_no_shard_states_fqn_to_para[fqn] = para
assert len(fsdp_no_shard_states_fqn_to_module) > 0, (
"no module in model match `extra_parallel_fsdp_no_shard_module`"
)
return fsdp_no_shard_states_fqn_to_module, fsdp_no_shard_states_fqn_to_para
def get_extra_parallel_fsdp_no_shard_info(self, model: nn.Module, para_name: str):
if self.extra_parallel_fsdp_no_shard_module[para_name] is None:
return None
fsdp_no_shard_states_fqn_to_module = {}
for fqn, _param in model.named_modules():
for no_shard_pattern in self.extra_parallel_fsdp_no_shard_module[para_name]:
if check_fqn_match(no_shard_pattern, fqn):
fsdp_no_shard_states_fqn_to_module[fqn] = get_module_from_path(model, fqn)
assert len(fsdp_no_shard_states_fqn_to_module) > 0, (
"no module in model match `extra_parallel_fsdp_no_shard_module`"
)
return fsdp_no_shard_states_fqn_to_module
def update_prefix(self, prefix: str):
"""
Update extra_parallel_plan when model is wrappered.
"""
self.extra_parallel_plan = {
para_name: {prefix + "." + k: v for k, v in plan.items()}
for para_name, plan in self.extra_parallel_plan.items()
}
self.extra_parallel_fsdp_no_shard_module = {
para_name: {prefix + "." + no_shard_pattern for no_shard_pattern in para_fsdp_no_shard_module}
for para_name, para_fsdp_no_shard_module in self.extra_parallel_fsdp_no_shard_module.items()
}
def shard_tensor(self, tensor: "torch.Tensor", full_param_name: str, target_shape: tuple) -> "torch.Tensor":
"""
Shard tensor for one extra_parallel parallelism if needed.
In the future, we may add other tensor slicing in this function to determine TP parameter and its sharding.
Args:
tensor: The tensor to potentially shard
full_param_name: The full parameter name (e.g., "model.layers.0.mlp.experts.gate_proj.weight")
target_shape: The expected shape of the target parameter
Returns:
The original tensor or a sliced version for one extra_parallel parallelism
"""
shard_group = self._get_shard_parameter_groupname(full_param_name)
if shard_group:
return self._slice_shard_tensor(tensor, full_param_name, target_shape, shard_group)
return tensor
def _get_shard_parameter_groupname(self, parameter_name: str) -> bool:
# note that parameter_name should be full name
for para_name, para_plan in self.extra_parallel_plan.items():
for fqn_pattern in para_plan.keys():
if check_fqn_match(fqn_pattern, parameter_name):
return para_name
return None
def _slice_shard_tensor(
self, tensor: "torch.Tensor", parameter_name: str, target_shape: tuple, shard_group: str
) -> "torch.Tensor":
"""Slice shard tensor for extra_parallel parallelism."""
try:
from .parallel_state import get_parallel_state
parallel_state = get_parallel_state()
# Check if we need to slice based on tensor vs target shape mismatch
if len(tensor.shape) >= 1 and len(target_shape) >= 1:
# If tensor has more feature than target, we need to slice
if tensor.shape[0] > target_shape[0] and tensor.shape[0] % target_shape[0] == 0:
para_size = tensor.shape[0] // target_shape[0]
para_rank = (
parallel_state.extra_parallel_rank(shard_group)
if parallel_state.extra_parallel_enabled(shard_group)
else 0
)
start_idx = para_rank * target_shape[0]
end_idx = start_idx + target_shape[0]
sliced_tensor = tensor[start_idx:end_idx]
logger.info_rank0(
f"{shard_group} parameter {parameter_name}: sliced {tensor.shape} -> {sliced_tensor.shape} "
f"for {shard_group} rank {para_rank}/{para_size}"
)
return sliced_tensor
# No slicing needed
return tensor
except Exception as e:
# Fallback: if anything fails, return original tensor
logger.warning(f"Failed to slice extra_parallel tensor {parameter_name}: {e}")
return tensor
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