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Commit 4b097dee authored by liangjing's avatar liangjing
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update to core_v0.9

parent 3aca1415
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megatron/core/models/vision/vit_layer_specs.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
from megatron.core.extensions.transformer_engine import (
TEDotProductAttention,
TELayerNormColumnParallelLinear,
TERowParallelLinear,
)
from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
from megatron.core.transformer.dot_product_attention import DotProductAttention
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.identity_op import IdentityOp
from megatron.core.transformer.mlp import MLP, MLPSubmodules
from megatron.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
try:
import apex # pylint: disable=unused-import
from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
HAVE_APEX = True
LNImpl = FusedLayerNorm
except ImportError:
import warnings
from megatron.core.transformer.torch_layer_norm import WrappedTorchLayerNorm
warnings.warn(f'Apex is not installed. Falling back to Torch LayerNorm')
LNImpl = WrappedTorchLayerNorm
# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
def get_vit_layer_with_transformer_engine_spec() -> ModuleSpec:
'''
Returns ViT layer spec with Transformer Engine layers
'''
mlp = _get_mlp_module_spec(use_te=True)
return ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
self_attention=ModuleSpec(
module=SelfAttention,
params={"attn_mask_type": AttnMaskType.no_mask},
submodules=SelfAttentionSubmodules(
linear_qkv=TELayerNormColumnParallelLinear,
core_attention=TEDotProductAttention,
linear_proj=TERowParallelLinear,
),
),
self_attn_bda=get_bias_dropout_add,
pre_mlp_layernorm=IdentityOp,
mlp=mlp,
mlp_bda=get_bias_dropout_add,
),
)
def get_vit_layer_with_local_spec() -> ModuleSpec:
'''
Returns ViT layer spec with Mcore local layers
'''
mlp = _get_mlp_module_spec(use_te=False)
return ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
input_layernorm=LNImpl,
self_attention=ModuleSpec(
module=SelfAttention,
params={"attn_mask_type": AttnMaskType.causal},
submodules=SelfAttentionSubmodules(
linear_qkv=ColumnParallelLinear,
core_attention=DotProductAttention,
linear_proj=RowParallelLinear,
),
),
self_attn_bda=get_bias_dropout_add,
pre_mlp_layernorm=LNImpl,
mlp=mlp,
mlp_bda=get_bias_dropout_add,
),
)
# Helper function to get module spec for MLP/MoE
def _get_mlp_module_spec(use_te: bool = True) -> ModuleSpec:
# Dense MLP w/ or w/o TE modules.
return ModuleSpec(
module=MLP,
submodules=MLPSubmodules(
linear_fc1=TELayerNormColumnParallelLinear if use_te else ColumnParallelLinear,
linear_fc2=TERowParallelLinear if use_te else RowParallelLinear,
),
)
megatron/core/num_microbatches_calculator.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron Core number of microbatches calculators."""
import logging
from abc import ABC, abstractmethod
from typing import List, Optional, Union
logger = logging.getLogger(__name__)
# TODO: global_var merge into mcore?
_GLOBAL_NUM_MICROBATCHES_CALCULATOR: Union[
'ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator'
] = None
def get_num_microbatches() -> int:
"""Get number of microbatches."""
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get()
def get_current_global_batch_size() -> int:
"""Get current global batch size."""
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get_current_global_batch_size()
def get_micro_batch_size() -> int:
"""Get micro batch size."""
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get_micro_batch_size()
def get_current_running_global_batch_size() -> int:
"""Get current running global batch size, taking into account number of DP replicas might be
incompatible with true global batch size if `decrease_batch_size_if_needed` is True."""
return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get_current_running_global_batch_size()
def update_num_microbatches(
consumed_samples: int, consistency_check: bool = True, verbose: bool = False
) -> None:
"""Update number of microbatches.
Args:
consumed_samples (int):
Number of samples consumed.
consistency_check (bool, optional):
Option to check current schedule's consistency. Defaults to True.
verbose (bool, optional):
Option to control logging. Defaults to False.
"""
_GLOBAL_NUM_MICROBATCHES_CALCULATOR.update(consumed_samples, consistency_check, verbose)
def init_num_microbatches_calculator(
rank: int,
rampup_batch_size: Optional[List[int]],
global_batch_size: int,
micro_batch_size: int,
data_parallel_size: int,
decrease_batch_size_if_needed: bool = False,
) -> None:
"""Initialize number of microbatches calculator. Supporting backward compatibility.
Args:
rank (int):
Rank of the GPU, only rank 0 will log the information.
rampup_batch_size (Optional[List[int]]):
Rampup batch size, should be in format of [start_global_batch_size,
batch_size_increment, ramup_samples].
global_batch_size (int):
Global batch size for the model.
micro_batch_size (int):
Micro batch size at initialization.
data_parallel_size (int):
Data parallel size.
decrease_batch_size_if_needed (bool, optional):
If true, scale down batch size to ensure divisibility by DP size * microbatch size.
Defaults to False.
"""
_configure_global_num_microbatches_calculator(
rank,
rampup_batch_size,
global_batch_size,
micro_batch_size,
data_parallel_size,
decrease_batch_size_if_needed,
init=True,
)
def destroy_num_microbatches_calculator():
"""Destroy number of microbatches calculator."""
global _GLOBAL_NUM_MICROBATCHES_CALCULATOR
_GLOBAL_NUM_MICROBATCHES_CALCULATOR = None
def reconfigure_num_microbatches_calculator(
rank: int,
rampup_batch_size: Optional[List[int]],
global_batch_size: int,
micro_batch_size: int,
data_parallel_size: int,
decrease_batch_size_if_needed: bool = False,
) -> None:
"""Reconfigure number of microbatches calculator. Supporting backward compatibility.
Args:
rank (int):
Rank of the GPU, only rank 0 will log the information.
rampup_batch_size (Optional[List[int]]):
Rampup batch size, should be in format of
[start_global_batch_size, batch_size_increment, ramup_samples].
global_batch_size (int):
Global batch size for the model.
micro_batch_size (int):
Micro batch size at initialization.
data_parallel_size (int):
Data parallel size.
decrease_batch_size_if_needed (bool, optional):
If true, scale down batch size to ensure divisibility by DP size * microbatch size.
Defaults to False.
"""
_configure_global_num_microbatches_calculator(
rank,
rampup_batch_size,
global_batch_size,
micro_batch_size,
data_parallel_size,
decrease_batch_size_if_needed,
init=False,
)
def _configure_global_num_microbatches_calculator(
rank: int,
rampup_batch_size: Optional[List[int]],
global_batch_size: int,
micro_batch_size: int,
data_parallel_size: int,
decrease_batch_size_if_needed: bool = False,
init: bool = False,
) -> None:
"""Configure number of microbatches calculator. Can be used for initialization and
reconfiguration.
Args:
rank (int):
Rank of the GPU, only rank 0 will log the information.
rampup_batch_size (Optional[List[int]]):
Rampup batch size, should be in format of
[start_global_batch_size, batch_size_increment, ramup_samples].
global_batch_size (int):
Global batch size for the model.
micro_batch_size (int):
Micro batch size at initialization.
data_parallel_size (int):
Data parallel size.
decrease_batch_size_if_needed (bool, optional):
If true, scale down batch size to ensure divisibility by DP size * microbatch size.
Defaults to False.
init (bool, optional):
If true, initialize the calculator. Defaults to False.
"""
global _GLOBAL_NUM_MICROBATCHES_CALCULATOR
if init:
assert (
_GLOBAL_NUM_MICROBATCHES_CALCULATOR is None
), 'num microbatches calculator is already initialized.'
_GLOBAL_NUM_MICROBATCHES_CALCULATOR = _build_num_microbatches_calculator(
rank,
rampup_batch_size,
global_batch_size,
micro_batch_size,
data_parallel_size,
decrease_batch_size_if_needed,
)
def _build_num_microbatches_calculator(
rank: int,
rampup_batch_size: Optional[List[int]],
global_batch_size: int,
micro_batch_size: int,
data_parallel_size: int,
decrease_batch_size_if_needed: bool,
) -> Union['ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator']:
"""Build number of microbatches calculator. Internal helper method.
Args:
rank (int):
Rank of the GPU, only rank 0 will log the information.
rampup_batch_size (Optional[List[int]]):
Rampup batch size, should be in format of
[start_global_batch_size, batch_size_increment, ramup_samples].
global_batch_size (int):
Global batch size for the model.
micro_batch_size (int):
Micro batch size at initialization.
data_parallel_size (int):
Data parallel size.
decrease_batch_size_if_needed (bool):
If true, scale down batch size to ensure divisibility by DP size * microbatch size.
"""
# Constant batch size.
if rampup_batch_size is None:
num_microbatches_calculator = ConstantNumMicroBatchesCalculator(
global_batch_size,
micro_batch_size,
data_parallel_size,
decrease_batch_size_if_needed,
rank,
)
if rank == 0:
logger.info(
f'setting number of microbatches to constant {num_microbatches_calculator.get()}'
)
# Batch size ramp up.
else:
assert len(rampup_batch_size) == 3, (
'expected the following '
'format: --rampup-batch-size <start batch size> '
'<batch size incerement> <ramp-up samples>'
)
start_global_batch_size = int(rampup_batch_size[0])
batch_size_increment = int(rampup_batch_size[1])
ramup_samples = int(rampup_batch_size[2])
if rank == 0:
logger.info(
f'will use batch size rampup starting from global batch size '
f'{start_global_batch_size} to global batch size {global_batch_size} with batch'
f'size increments {batch_size_increment} over {ramup_samples} samples.'
)
num_microbatches_calculator = RampupBatchsizeNumMicroBatchesCalculator(
global_batch_size,
micro_batch_size,
data_parallel_size,
decrease_batch_size_if_needed,
rank,
start_global_batch_size,
batch_size_increment,
ramup_samples,
)
return num_microbatches_calculator
def _round(batch_size: int, divisor: int) -> int:
"""Round `batch_size` down to nearest batch size divisible by `divisor`."""
return (batch_size // divisor) * divisor
class NumMicroBatchesCalculator(ABC):
"""Base class for number of microbatches calculator."""
def __init__(self) -> None:
self.num_micro_batches = None
self.current_global_batch_size = None
self.micro_batch_size = None
self.current_running_global_batch_size = None
def get(self) -> int:
"""Get number of microbatches."""
return self.num_micro_batches
def get_current_global_batch_size(self) -> int:
"""Get current global batch size."""
return self.current_global_batch_size
def get_micro_batch_size(self) -> int:
"""Get current global batch size."""
return self.micro_batch_size
def get_current_running_global_batch_size(self) -> int:
"""Get current running global batch size. If decrease_batch_size_if_needed is False,
this just equals global batch size."""
return self.current_running_global_batch_size
@abstractmethod
def update(self, consumed_samples, consistency_check, verbose=False) -> None:
"""Update number of microbatches depending on batch size rampup."""
pass
class ConstantNumMicroBatchesCalculator(NumMicroBatchesCalculator):
"""Calculator of number of microbatches with constant global batch size.
Args:
global_batch_size (int):
Global batch size.
micro_batch_size (int):
Micro batch size.
data_parallel_size (int):
Data parallel size.
decrease_batch_size_if_needed (bool):
If true, decrease batch size to ensure divisibility by DP size * microbatch size
(if needed).
rank (int):
Rank (to determine whether logging should be performed).
"""
def __init__(
self,
global_batch_size: int,
micro_batch_size: int,
data_parallel_size: int,
decrease_batch_size_if_needed: bool,
rank: int,
) -> None:
micro_batch_times_data_parallel_size = micro_batch_size * data_parallel_size
if decrease_batch_size_if_needed:
running_global_batch_size = _round(
global_batch_size, micro_batch_times_data_parallel_size
)
assert running_global_batch_size % micro_batch_times_data_parallel_size == 0
if rank == 0:
logger.info(
f'decreasing batch size from {global_batch_size} to {running_global_batch_size}'
)
self.num_micro_batches = (
running_global_batch_size // micro_batch_times_data_parallel_size
)
else:
assert global_batch_size % micro_batch_times_data_parallel_size == 0, (
'global batch size ({}) is not divisible by micro batch size ({})'
' times data parallel size ({})'.format(
global_batch_size, micro_batch_size, data_parallel_size
)
)
running_global_batch_size = global_batch_size
self.num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size
assert (
self.num_micro_batches >= 1
), 'number of microbatches should be at least 1, got {}.'.format(self.num_micro_batches)
self.current_global_batch_size = global_batch_size
self.current_running_global_batch_size = running_global_batch_size
self.micro_batch_size = micro_batch_size
def update(self, consumed_samples, consistency_check, verbose=False) -> None:
pass
class RampupBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator):
"""Calculator of number of microbatches with batch size rampup.
Over `steps = (global-batch-size - start-batch-size) / batch_size_increment` increment batch
size from start-batch-size to global-batch-size using rampup-samples / steps
samples.
Args:
global_batch_size (int):
Global batch size post rampup.
micro_batch_size (int):
Micro batch size.
data_parallel_size (int):
Data parallel size.
decrease_batch_size_if_needed (bool):
If true, decrease batch size to ensure divisibility by DP size * microbatch size
(if needed).
rank (int):
Rank (to determine whether logging should be performed).
start_global_batch_size (int):
Global batch size to start with.
batch_size_increment (int):
Global batch size increments.
ramup_samples (int):
Number of samples to use ramp up global
batch size from `start_global_batch_size` to `global_batch_size`.
"""
def __init__(
self,
global_batch_size: int,
micro_batch_size: int,
data_parallel_size: int,
decrease_batch_size_if_needed: bool,
rank: int,
start_global_batch_size: int,
batch_size_increment: int,
ramup_samples: int,
) -> None:
assert global_batch_size > 0, 'global batch size should be positive, got {}.'.format(
global_batch_size
)
assert start_global_batch_size > 0, 'start batch size should be positive, got {}.'.format(
start_global_batch_size
)
assert batch_size_increment > 0, 'batch size increment should be positive, got {}.'.format(
batch_size_increment
)
assert ramup_samples >= 0, 'ramp-up samples should be non-negative, got {}.'.format(
ramup_samples
)
self.global_batch_size = global_batch_size
self.micro_batch_size = micro_batch_size
self.data_parallel_size = data_parallel_size
self.decrease_batch_size_if_needed = decrease_batch_size_if_needed
self.rank = rank
self.start_global_batch_size = start_global_batch_size
self.batch_size_increment = batch_size_increment
self.ramup_samples = ramup_samples
self.micro_batch_times_data_parallel_size = self.micro_batch_size * self.data_parallel_size
assert self.micro_batch_times_data_parallel_size > 0
self.current_global_batch_size = None
diff_batch_size = self.global_batch_size - self.start_global_batch_size
assert diff_batch_size >= 0, (
'expected global batch size to be greater than or equal to start batch size, '
f'got {self.global_batch_size} and {self.start_global_batch_size}'
)
assert diff_batch_size % batch_size_increment == 0, (
'expected '
f'global batch size interval ({diff_batch_size}) to be divisible by global batch '
f'size increment ({batch_size_increment})'
)
num_increments = diff_batch_size // self.batch_size_increment
self.rampup_samples_per_increment = self.ramup_samples / num_increments
# Initialize number of microbatches.
self.update(0, False)
def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None:
"""Update number of microbatches.
Args:
consumed_samples (int): Number of samples consumed.
consistency_check (bool): Option to check current schedule's consistency.
verbose (bool, optional): Option to control logging. Defaults to False.
"""
# Update current global batch size.
global_batch_size_changed = False
old_current_global_batch_size = self.current_global_batch_size
if consumed_samples > self.ramup_samples:
self.current_global_batch_size = self.global_batch_size
else:
steps = int(consumed_samples / self.rampup_samples_per_increment)
self.current_global_batch_size = (
self.start_global_batch_size + steps * self.batch_size_increment
)
assert self.current_global_batch_size <= self.global_batch_size
if old_current_global_batch_size != self.current_global_batch_size:
global_batch_size_changed = True
if self.rank == 0 and global_batch_size_changed and verbose:
logger.info(
f'ramping up batch size from {old_current_global_batch_size} to '
f'{self.current_global_batch_size}'
)
# Check consistency of the current global batch size.
if consistency_check and not self.decrease_batch_size_if_needed:
assert (
self.current_global_batch_size % self.micro_batch_times_data_parallel_size == 0
), (
'current global '
'batch size ({}) is not divisible by micro-batch-size ({}) times'
'data parallel size ({})'.format(
self.current_global_batch_size, self.micro_batch_size, self.data_parallel_size
)
)
if (
self.decrease_batch_size_if_needed
and self.current_global_batch_size % self.micro_batch_times_data_parallel_size != 0
):
self.current_running_global_batch_size = _round(
self.current_global_batch_size, self.micro_batch_times_data_parallel_size
)
if self.rank == 0 and global_batch_size_changed and verbose:
logger.info(
f'decreasing batch size from {self.current_global_batch_size} to '
f'{self.current_running_global_batch_size}'
)
assert (
self.current_running_global_batch_size % self.micro_batch_times_data_parallel_size
== 0
)
else:
self.current_running_global_batch_size = self.current_global_batch_size
self.num_micro_batches = (
self.current_running_global_batch_size // self.micro_batch_times_data_parallel_size
)
megatron/core/optimizer/__init__.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
import logging
from typing import Callable, Dict, List, Optional, Tuple
import torch
try:
from transformer_engine.pytorch.optimizers import FusedAdam as Adam
from transformer_engine.pytorch.optimizers import FusedSGD as SGD
except ImportError:
try:
from apex.optimizers import FusedAdam as Adam
from apex.optimizers import FusedSGD as SGD
except ImportError:
import warnings
warnings.warn(
f'Transformer Engine and Apex are not installed. Falling back to Torch optimizers.'
)
# Apex's FusedAdam is a drop-in replacement for torch's AdamW.
# pylint: disable-next=line-too-long.
# See https://github.com/NVIDIA/apex/blob/7b73b12361068a10b0f44844534613f252a5ea75/apex/optimizers/fused_adam.py#L16.
from torch.optim import AdamW as Adam, SGD
from megatron.core import mpu
from ..distributed.param_and_grad_buffer import _ParamAndGradBuffer
from ..transformer.module import MegatronModule
from ..utils import log_single_rank
from .distrib_optimizer import DistributedOptimizer
from .grad_scaler import ConstantGradScaler, DynamicGradScaler
from .optimizer import (
ChainedOptimizer,
Float16OptimizerWithFloat16Params,
FP32Optimizer,
MegatronOptimizer,
)
from .optimizer_config import OptimizerConfig
logger = logging.getLogger(__name__)
def _get_param_groups(
model_chunks: List[MegatronModule],
no_weight_decay_cond: Optional[Callable],
scale_lr_cond: Optional[Callable],
lr_mult: float,
lr: float,
min_lr: float,
decoupled_lr: Optional[float],
decoupled_min_lr: Optional[float],
) -> List[Dict]:
"""Create parameter groups for optimizer.
Creates parameter groups based on weight decay condition (regularized vs
non regularized), learning rate scale condition (lr vs lr_mult * lr),
and whether it is expert parameters. scale_lr_cond is used during finetuning
where head of the network requires a scaled version of the base learning rate.
Args:
model_chunks (List[MegatronModule]): model chunks to create parameter
groups for.
no_weight_decay_cond (func, optional): function to determine whether a
parameter should not perform weight decay.
scale_lr_cond (func, optional): function to determine whether a parameter
should have a scaled learning rate.
lr_mult (float): learning rate multiplier for parameters that
satisfy scale_lr_cond.
lr (float): learning rate.
min_lr (float): minimum learning rate.
decoupled_lr (Optional[float]): optional decoupled learning rate.
decoupled_min_lr (Optional[float]): optional decoupled minimum learning rate.
Returns:
List of parameter groups.
"""
use_decoupled_learning_rate = decoupled_lr is not None
# Map (wd_mult, lr_mult, is_expert_parallel, is_decoupled_lr) to params.
params_map = {}
for model_chunk in model_chunks:
for name, param in model_chunk.named_parameters():
if not param.requires_grad:
continue
is_expert_parallel = not getattr(param, 'allreduce', True)
if no_weight_decay_cond is not None:
no_wd = no_weight_decay_cond(name, param)
else:
# Do not regularize biases and norm parameters.
no_wd = name.endswith(".bias") or len(param.shape) == 1
if scale_lr_cond is not None:
scale_lr = scale_lr_cond(name, param)
else:
scale_lr = False
if not no_wd and not scale_lr:
wd_mult, _lr_mult = 1.0, 1.0
elif not no_wd and scale_lr:
wd_mult, _lr_mult = 1.0, lr_mult
elif no_wd and not scale_lr:
wd_mult, _lr_mult = 0.0, 1.0
else:
wd_mult, _lr_mult = 0.0, lr_mult
is_decoupled_lr = False
# For input/embedding and output layer: embedding.word_embeddings.weight /
# output_layer.weight.
if use_decoupled_learning_rate and getattr(
param, 'is_embedding_or_output_parameter', False
):
is_decoupled_lr = True
key = (wd_mult, _lr_mult, is_expert_parallel, is_decoupled_lr)
if key not in params_map:
params_map[key] = []
params_map[key].append(param)
param_groups = []
for (wd_mult, _lr_mult, is_expert_parallel, is_decoupled_lr), params in params_map.items():
assert len(params) > 0
param_group = {
'params': params,
'wd_mult': wd_mult,
'lr_mult': _lr_mult,
'is_expert_parallel': is_expert_parallel,
'is_decoupled_lr': is_decoupled_lr,
}
param_groups.append(param_group)
param_groups = _update_min_and_max_lr_in_param_groups(
param_groups,
lr=lr,
min_lr=min_lr,
decoupled_lr=decoupled_lr,
decoupled_min_lr=decoupled_min_lr,
)
return param_groups
def _update_min_and_max_lr_in_param_groups(
param_groups: List[Dict],
lr: float,
min_lr: float,
decoupled_lr: Optional[float],
decoupled_min_lr: Optional[float],
) -> List[Dict]:
"""
Updates `max_lr` and `min_lr` values in each parameter group, and returns new list.
By default, each group will use `lr` / `min_lr` as `max_lr` / `min_lr`.
If `decoupled_lr` is provided, then `decoupled_lr` / `decoupled_min_lr` will be used
as `max_lr` / `min_lr` for the input and output layer.
Args:
param_groups (List): parameter groups whose 'max_lr' and `min_lr` fields need to
be adjusted.
lr (float): learning rate.
min_lr (float): minimum learning rate.
decoupled_lr (Optional[float]): optional decoupled learning rate.
decoupled_min_lr (Optional[float]): optional decoupled minimum learning rate.
Returns:
List of adjusted parameter groups.
"""
if decoupled_min_lr is None:
decoupled_min_lr = min_lr
for param_group in param_groups:
if param_group['is_decoupled_lr']:
assert decoupled_lr is not None
param_group['max_lr'] = decoupled_lr
param_group['min_lr'] = decoupled_min_lr
else:
param_group['max_lr'] = lr
param_group['min_lr'] = min_lr
return param_groups
def _get_param_groups_and_buffers(
model_chunks: List[MegatronModule],
model_chunk_offset: int,
config: OptimizerConfig,
no_weight_decay_cond: Optional[Callable],
scale_lr_cond: Optional[Callable],
lr_mult: float,
filter_fn: Callable,
buffer_name: str,
) -> Tuple[List[Dict], Dict[int, List[_ParamAndGradBuffer]]]:
"""Returns parameter groups and buffer for optimizer.
Args:
model_chunks (List[MegatronModule]): model chunks to create parameter
groups for.
model_chunk_offset (int): offset of model_chunks in global model_chunks list.
config (OptimizerConfig): optimizer configuration object.
no_weight_decay_cond (func, optional): function to determine whether a
parameter should not perform weight decay.
scale_lr_cond (func, optional): function to determine whether a parameter
should have a scaled learning rate.
lr_mult (float): learning rate multiplier for parameters that
satisfy scale_lr_cond.
lr (float): learning rate.
min_lr (float): minimum learning rate.
filter_fn (callable): filtering function for param_groups.
buffer_name (str): name of buffer.
Returns:
List of parameter groups and dictionary of model chunk IDs to buffers.
"""
param_groups = _get_param_groups(
model_chunks,
no_weight_decay_cond,
scale_lr_cond,
lr_mult,
lr=config.lr,
min_lr=config.min_lr,
decoupled_lr=config.decoupled_lr,
decoupled_min_lr=config.decoupled_min_lr,
)
param_groups = list(filter(filter_fn, param_groups))
buffers = {}
for model_chunk_idx, model_chunk in enumerate(model_chunks):
if hasattr(model_chunk, buffer_name):
buffers[model_chunk_idx + model_chunk_offset] = getattr(model_chunk, buffer_name)
return param_groups, buffers
def _get_megatron_optimizer_based_on_param_groups(
config: OptimizerConfig,
model_chunks: List[MegatronModule],
param_groups: List,
per_model_buffers: Optional[Dict[int, List[_ParamAndGradBuffer]]] = None,
model_parallel_group: Optional[torch.distributed.ProcessGroup] = None,
data_parallel_group: Optional[torch.distributed.ProcessGroup] = None,
data_parallel_group_gloo: Optional[torch.distributed.ProcessGroup] = None,
data_parallel_group_idx: Optional[int] = None,
) -> MegatronOptimizer:
"""Get Megatron optimizer based on parameter groups.
Args:
config (OptimizerConfig): optimizer configuration object.
model_chunks (list): list of model chunks.
param_groups (list): list of parameter groups.
per_model_buffers (dict, optional): buffers for distributed optimizer. Defaults to None.
data_parallel_group (torch.distributed.ProcessGroup, optional): data-parallel group for
distributed optimizer. Defaults to None.
data_parallel_group_gloo (torch.distributed.ProcessGroup, optional): gloo data-parallel
group for distributed optimizer. Defaults to None.
data_parallel_group_idx (int, optional): data-parallel group index for distributed
optimizer. Defaults to None.
Returns:
Instance of MegatronOptimizer.
"""
if config.optimizer == 'adam':
optimizer = Adam(
param_groups,
lr=config.lr,
weight_decay=config.weight_decay,
betas=(config.adam_beta1, config.adam_beta2),
eps=config.adam_eps,
)
def init_state_fn(opt):
for group in opt.param_groups:
for p in group['params']:
if len(opt.state[p]) == 0:
opt.state[p]['exp_avg'] = torch.zeros_like(p.data)
opt.state[p]['exp_avg_sq'] = torch.zeros_like(p.data)
elif config.optimizer == 'sgd':
optimizer = SGD(
param_groups,
lr=config.lr,
weight_decay=config.weight_decay,
momentum=config.sgd_momentum,
)
init_state_fn = None
else:
raise Exception('{} optimizer is not supported.'.format(config.optimizer))
# Mixed precision optimizer.
# - Note: both the Float16Optimizer and the DistributedOptimizer inherit
# from the MixedPrecisionOptimizer, which manages any optimizer where
# the model params and main params are distinct.
if config.fp16 or config.bf16 or config.use_distributed_optimizer:
# Grad scaler:
# if loss-scale is provided, instantiate the constant scaler.
# if we are using fp16 and loss-scale is not present, use a
# dynamic scaler.
# otherwise we are running in bf16 with no loss-scale so
# leave it as None.
grad_scaler = None
# Constant loss scale.
if config.loss_scale:
grad_scaler = ConstantGradScaler(config.loss_scale)
# Dynamic loss scale.
else:
if config.fp16:
grad_scaler = DynamicGradScaler(
initial_scale=config.initial_loss_scale,
min_scale=config.min_loss_scale,
growth_factor=2.0,
backoff_factor=0.5,
growth_interval=config.loss_scale_window,
hysteresis=config.hysteresis,
)
optimizer_args = [optimizer, config, grad_scaler, init_state_fn]
if config.use_distributed_optimizer:
optimizer = DistributedOptimizer(
*optimizer_args,
model_chunks=model_chunks,
per_model_buffers=per_model_buffers,
data_parallel_group=data_parallel_group,
data_parallel_group_gloo=data_parallel_group_gloo,
data_parallel_group_idx=data_parallel_group_idx,
)
else:
optimizer = Float16OptimizerWithFloat16Params(*optimizer_args)
setattr(optimizer, 'model_parallel_group', model_parallel_group)
else:
# FP32 optimizer.
optimizer = FP32Optimizer(optimizer, config, init_state_fn)
setattr(optimizer, 'model_parallel_group', model_parallel_group)
return optimizer
def get_megatron_optimizer(
config: OptimizerConfig,
model_chunks: List[MegatronModule],
no_weight_decay_cond: Optional[Callable] = None,
scale_lr_cond: Optional[Callable] = None,
lr_mult: float = 1.0,
) -> MegatronOptimizer:
"""Retrieve the Megatron optimizer for model chunks.
We use separate optimizers for expert parameters and non-expert parameters.
Args:
config (OptimizerConfig): optimizer configuration object.
model_chunks (List[MegatronModule]): model chunks to get optimizer for.
no_weight_decay_cond (func, optional): function to determine whether a parameter
should not perform weight decay. Defaults to None.
scale_lr_cond (func, optional): function to determine whether a parameter
should have a scaled learning rate. Defaults to None.
lr_mult (float, optional): learning rate multiplier for parameters that
satisfy scale_lr_cond. Defaults to 1.0.
Returns:
Instance of MegatronOptimizer.
"""
log_single_rank(logger, logging.INFO, f'Setting up optimizer with config {config}')
# Separate out first model chunk if overlapping param AG with optimizer step.
if config.overlap_param_gather_with_optimizer_step:
all_dense_model_chunks = [[model_chunks[0]], model_chunks[1:]]
overlap_param_gather_with_optimizer_step_flags = [True, False]
else:
all_dense_model_chunks = [model_chunks]
overlap_param_gather_with_optimizer_step_flags = [False]
model_parallel_rank = torch.distributed.get_rank(mpu.get_model_parallel_group())
optimizers = []
model_chunk_offset = 0
for dense_model_chunks, overlap_param_gather_with_optimizer_step in zip(
all_dense_model_chunks, overlap_param_gather_with_optimizer_step_flags
):
param_groups, buffers = _get_param_groups_and_buffers(
dense_model_chunks,
model_chunk_offset=model_chunk_offset,
config=config,
no_weight_decay_cond=no_weight_decay_cond,
scale_lr_cond=scale_lr_cond,
lr_mult=lr_mult,
filter_fn=lambda g: not g['is_expert_parallel'],
buffer_name='buffers',
)
for model_chunk in dense_model_chunks:
model_chunk.overlap_param_gather_with_optimizer_step = (
overlap_param_gather_with_optimizer_step
)
optimizers.append(
_get_megatron_optimizer_based_on_param_groups(
config,
model_chunks=dense_model_chunks,
param_groups=param_groups,
per_model_buffers=buffers,
model_parallel_group=mpu.get_model_parallel_group(),
data_parallel_group=mpu.get_data_parallel_group(with_context_parallel=True),
data_parallel_group_gloo=mpu.get_data_parallel_group_gloo(
with_context_parallel=True
),
data_parallel_group_idx=model_parallel_rank,
)
)
model_chunk_offset += 1
moe_param_groups, moe_buffers = _get_param_groups_and_buffers(
model_chunks,
model_chunk_offset=0,
config=config,
no_weight_decay_cond=no_weight_decay_cond,
scale_lr_cond=scale_lr_cond,
lr_mult=lr_mult,
filter_fn=lambda g: g['is_expert_parallel'],
buffer_name='expert_parallel_buffers',
)
if len(moe_param_groups) > 0:
model_parallel_world_size = torch.distributed.get_world_size(mpu.get_model_parallel_group())
expert_parallel_rank = mpu.get_expert_model_parallel_rank()
optimizers.append(
_get_megatron_optimizer_based_on_param_groups(
config,
model_chunks=model_chunks,
param_groups=moe_param_groups,
per_model_buffers=moe_buffers,
model_parallel_group=mpu.get_model_parallel_group(with_expert_parallel=True),
data_parallel_group=mpu.get_data_modulo_expert_parallel_group(
with_context_parallel=True
),
data_parallel_group_gloo=mpu.get_data_modulo_expert_parallel_group_gloo(
with_context_parallel=True
),
data_parallel_group_idx=expert_parallel_rank * model_parallel_world_size
+ model_parallel_rank,
)
)
if len(optimizers) == 1:
return optimizers[0]
return ChainedOptimizer(optimizers)
megatron/optimizer/clip_grads.py → megatron/core/optimizer/clip_grads.py
View file @ 4b097dee
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
"""Gradient clipping."""
import os
from typing import List, Optional, Union
import torch
from torch import inf
from apex.multi_tensor_apply import multi_tensor_applier
import amp_C
from megatron.model.module import param_is_not_shared
from megatron.core.tensor_parallel import param_is_not_tensor_parallel_duplicate
def clip_grad_norm_fp32(parameters, grads_for_norm,
max_norm, norm_type=2,
model_parallel_group=None):
"""Clips gradient norm of an iterable of parameters whose gradients
are in fp32.
try:
from transformer_engine.pytorch.optimizers import (
multi_tensor_applier,
multi_tensor_l2norm,
multi_tensor_scale,
)
l2_norm_impl = multi_tensor_l2norm
multi_tensor_scale_impl = multi_tensor_scale
except ImportError:
try:
import amp_C
from apex.multi_tensor_apply import multi_tensor_applier
l2_norm_impl = amp_C.multi_tensor_l2norm
multi_tensor_scale_impl = amp_C.multi_tensor_scale
except ImportError:
import warnings
warnings.warn(
f'Transformer Engine and Apex are not installed. '
'Falling back to local implementations of multi_tensor_applier, '
'multi_tensor_l2norm, and multi_tensor_scale'
)
from megatron.core.utils import (
local_multi_tensor_applier,
local_multi_tensor_l2_norm,
local_multi_tensor_scale,
)
multi_tensor_applier = local_multi_tensor_applier
l2_norm_impl = local_multi_tensor_l2_norm
multi_tensor_scale_impl = local_multi_tensor_scale
from ..tensor_parallel import param_is_not_tensor_parallel_duplicate
from ..transformer.module import param_is_not_shared
def get_grad_norm_fp32(
grads_for_norm: Union[List[torch.Tensor], torch.Tensor],
norm_type: Union[int, float] = 2,
model_parallel_group: Optional[torch.distributed.ProcessGroup] = None,
) -> float:
"""Calculate the norm of gradients in fp32.
This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
added functionality to handle model parallel parameters. Note that
the gradients are modified in place.
added functionality to handle model parallel parameters.
Arguments:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
grads_for_norm (Iterable[Tensor]): an iterable of Tensors or a single
grads_for_norm (Iterable[Tensor] or Tensor): an iterable of Tensors or a single
Tensor that will be used for calculating the grad norm.
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
model_parallel_group (group): given the nature of the distributed
......@@ -37,76 +70,101 @@ def clip_grad_norm_fp32(parameters, grads_for_norm,
Total norm of the parameters (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
if isinstance(grads_for_norm, torch.Tensor):
grads_for_norm = [grads_for_norm]
# Grads.
grads = []
for param in parameters:
if param.grad is not None:
assert param.grad.type() == 'torch.cuda.FloatTensor'
grads.append(param.grad.detach())
# Norm parameters.
max_norm = float(max_norm)
norm_type = float(norm_type)
total_norm = 0.0
# Calculate norm.
if norm_type == inf:
total_norm = max(grad.abs().max() for grad in grads_for_norm)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
total_norm_cuda = torch.tensor([float(total_norm)], dtype=torch.float, device='cuda')
# Take max across all model-parallel GPUs.
torch.distributed.all_reduce(total_norm_cuda,
op=torch.distributed.ReduceOp.MAX,
group=model_parallel_group)
torch.distributed.all_reduce(
total_norm_cuda, op=torch.distributed.ReduceOp.MAX, group=model_parallel_group
)
total_norm = total_norm_cuda[0].item()
else:
if norm_type == 2.0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda')
# Use apex's multi-tensor applier for efficiency reasons.
# Multi-tensor applier takes a function and a list of list
# and performs the operation on that list all in one kernel.
if grads_for_norm:
grad_norm, _ = multi_tensor_applier(
amp_C.multi_tensor_l2norm,
l2_norm_impl,
dummy_overflow_buf,
[grads_for_norm],
False # no per-parameter norm
False, # no per-parameter norm
)
else:
grad_norm = torch.cuda.FloatTensor([0])
grad_norm = torch.tensor([0], dtype=torch.float, device='cuda')
# Since we will be summing across data parallel groups,
# we need the pow(norm-type).
total_norm = grad_norm ** norm_type
total_norm = grad_norm**norm_type
else:
for grad in grads_for_norm:
grad_norm = torch.norm(grad, norm_type)
total_norm += grad_norm ** norm_type
total_norm += grad_norm**norm_type
# Sum across all model-parallel GPUs.
torch.distributed.all_reduce(total_norm,
op=torch.distributed.ReduceOp.SUM,
group=model_parallel_group)
torch.distributed.all_reduce(
total_norm, op=torch.distributed.ReduceOp.SUM, group=model_parallel_group
)
total_norm = total_norm.item() ** (1.0 / norm_type)
return total_norm
def clip_grad_by_total_norm_fp32(
parameters: Union[List[torch.Tensor], torch.Tensor],
max_norm: Union[int, float],
total_norm: float,
):
"""Clips gradient of an iterable of parameters in fp32 by total norm.
Note that the gradients are modified in place.
Args:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized.
max_norm (float or int): max norm of the gradients.
total_norm (float): total norm of the gradients.
"""
# Grads.
grads = []
for param in parameters:
if param.grad is not None:
assert param.grad.type() == 'torch.cuda.FloatTensor'
grads.append(param.grad.detach())
# Scale.
clip_coeff = max_norm / (total_norm + 1.0e-6)
if clip_coeff < 1.0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
multi_tensor_applier(amp_C.multi_tensor_scale,
dummy_overflow_buf,
[grads, grads],
clip_coeff)
dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda')
multi_tensor_applier(
multi_tensor_scale_impl, dummy_overflow_buf, [grads, grads], clip_coeff
)
return total_norm
def count_zeros_fp32(
parameters: Union[List[torch.Tensor], torch.Tensor],
model_parallel_group: torch.distributed.ProcessGroup,
) -> float:
"""Counts the number of zeros in gradients associated with the passed-in list of
parameters.
def count_zeros_fp32(parameters, model_parallel_group):
Args:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have the number of zeros in its corresponding
gradient counted.
model_parallel_group (torch.distributed.ProcessGroup, optional): model-parallel
group over which grad norm needs to be aggregated.
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
......@@ -115,7 +173,7 @@ def count_zeros_fp32(parameters, model_parallel_group):
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
total_num_zeros = torch.cuda.FloatTensor([0.0])
total_num_zeros = torch.tensor([0.0], dtype=torch.float, device='cuda')
for param in parameters:
grad_not_none = param.grad is not None
is_not_shared = param_is_not_shared(param)
......@@ -126,9 +184,9 @@ def count_zeros_fp32(parameters, model_parallel_group):
total_num_zeros = num_zeros + total_num_zeros
# Sum across all model-parallel GPUs.
torch.distributed.all_reduce(total_num_zeros,
op=torch.distributed.ReduceOp.SUM,
group=model_parallel_group)
torch.distributed.all_reduce(
total_num_zeros, op=torch.distributed.ReduceOp.SUM, group=model_parallel_group
)
total_num_zeros = total_num_zeros.item()
......
megatron/core/optimizer/distrib_optimizer.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
"""Megatron distributed optimizer."""
import itertools
import warnings
from dataclasses import replace
from logging import getLogger
from typing import Callable, Dict, List, Optional, Tuple
import torch
HAVE_APEX_OR_TE = True
try:
from transformer_engine.pytorch.optimizers import FusedAdam as Adam
except ImportError:
try:
from apex.optimizers import FusedAdam as Adam
except ImportError:
from torch.optim import Adam
HAVE_APEX_OR_TE = False
from .. import tensor_parallel
from ..config_logger import has_config_logger_enabled, log_config_to_disk
from ..dist_checkpointing import ShardedTensor
from ..dist_checkpointing.dict_utils import nested_values
from ..dist_checkpointing.mapping import (
LocalNonpersistentObject,
ShardedObject,
ShardedStateDict,
ShardedTensorFactory,
)
from ..dist_checkpointing.utils import extract_sharded_tensors_and_factories
from ..distributed.param_and_grad_buffer import _ParamAndGradBuffer, partition_buckets
from ..transformer.module import MegatronModule
from ..utils import is_float8tensor
from .grad_scaler import MegatronGradScaler
from .optimizer import (
MixedPrecisionOptimizer,
_multi_tensor_copy_this_to_that,
_zero_grad_group_helper,
)
from .optimizer_config import OptimizerConfig
try:
# This will be used when "--fp8-param-gather" is enabled.
# When BF16/FP16 parameters don't exist, we need to cast the FP32 main parameters to
# FP8 directly in the optimizer.
from transformer_engine.pytorch.cpp_extensions import cast_to_fp8
except:
pass
logger = getLogger(__name__)
class Range:
"""
A range represents a start and end points for indexing a shard
from a full tensor.
Args:
start (int): Start index.
end (int): End index.
"""
def __init__(self, start: int, end: int):
self.start = start
self.end = end
self.size = end - start
def normalize(self, start: int = 0):
"""Shift start/end indexes to start at new start index.
Both start and end indexes will be shifted by [new start] - [old start].
Args:
start (int): New start index.
"""
return Range(start, start + self.size)
def __str__(self):
return "%d,%d [%d]" % (self.start, self.end, self.size)
def __len__(self):
return self.end - self.start
class DistributedOptimizer(MixedPrecisionOptimizer):
"""Distributed optimizer, for all data types (fp16, bf16, and fp32).
See __init__() below for argument details.
"""
@classmethod
def _build_model_gbuf_param_range_map(
cls,
param_world_index_map: Dict[torch.nn.Parameter, Tuple],
gbuf_world_range: Range,
bucket_offset: int,
):
"""
Build mapping from param reference to grad buffer shard ranges.
This method builds a mapping from parameter references to grad
buffer shard ranges, specific to each data-parallel (DP) rank's
set of 'owned' parameters. Each grad buffer (padded to be an even
multiple of DP-world-size) is conceptually divided into DP-world-size
contiguous regions, where each DP rank 'owns' a contiguous region.
Ownership in this sense means DP rank is responsible for reducing
the relevant subset of grads, and updating the relevant subset of
params.
This conceptual partitioning of the grad buffer does NOT respect
parameter boundaries, and as such it is assumed that each created
range references a shard (or subset) of the full parameter. It is
easiest to think of each DP rank as operating (i.e., reducing,
gathering) purely on views into the grad buffer, for all model-to-
main & main-to-model operations.
This method creates four ranges:
- The param's range within the entire grad buffer (i.e., world index).
- The param's range within the relevant grad bucket's buffer.
- The param's range within the DP rank's local view of the grad buffer.
- The param's range within itself (i.e., its shard).
"""
# Param range map.
param_range_map = {}
for param, param_world_indexes in param_world_index_map.items():
# Param range.
param_world_start, param_world_end, _ = param_world_indexes
param_local_start = max(0, param_world_start - gbuf_world_range.start)
param_local_end = min(gbuf_world_range.size, param_world_end - gbuf_world_range.start)
# Add param, if within local gbuf range.
if param_local_end > param_local_start:
param_local_range = Range(param_local_start, param_local_end)
param_world_range = param_local_range.normalize(
param_local_start + gbuf_world_range.start
)
param_world_range_in_bucket = Range(
param_world_range.start - bucket_offset, param_world_range.end - bucket_offset
)
sub_param_start = max(0, gbuf_world_range.start - param_world_start)
sub_param_range = param_local_range.normalize(sub_param_start)
param_range_map[param] = {
"gbuf_world": param_world_range,
"gbuf_world_in_bucket": param_world_range_in_bucket,
"gbuf_local": param_local_range,
"param": sub_param_range,
}
return param_range_map
@classmethod
def _build_model_gbuf_range(cls, param_and_grad_buffer: _ParamAndGradBuffer, bucket_index: int):
"""
Build mapping between params and their grad buffers.
This method does the initial setup for the method above. This setup
includes determining the shard ranges into the param_and_grad_buffer
for each data-parallel (DP) rank. Each DP rank keeps range info for
all other DP ranks, for the purpose of creating args for
reduce-scatter and all-gather.
"""
data_parallel_rank = torch.distributed.get_rank(param_and_grad_buffer.data_parallel_group)
data_parallel_world_size = param_and_grad_buffer.data_parallel_group.size()
bucket = param_and_grad_buffer.buckets[bucket_index]
gbuf_size = bucket.grad_data.numel()
assert (
gbuf_size % data_parallel_world_size == 0
), f"Each bucket's buffer size should be divisible by {data_parallel_world_size}"
max_gbuf_range_size = gbuf_size // data_parallel_world_size
# All world ranges (i.e., across all data parallel ranks).
gbuf_world_all_ranges = []
for r in range(data_parallel_world_size):
# Compute start of chunk in this bucket.
gbuf_world_start = r * max_gbuf_range_size
gbuf_world_end = min(gbuf_size, gbuf_world_start + max_gbuf_range_size)
# Add bucket's offset in grad buffer.
gbuf_world_range = Range(
gbuf_world_start + bucket.offset, gbuf_world_end + bucket.offset
)
gbuf_world_all_ranges.append(gbuf_world_range)
# Local DP's ranges.
gbuf_world_range = gbuf_world_all_ranges[data_parallel_rank]
# Get each param's ranges.
param_range_map = cls._build_model_gbuf_param_range_map(
param_and_grad_buffer.param_index_map, gbuf_world_range, bucket.offset
)
# Group into dict.
data = {"param_map": param_range_map}
return data
@classmethod
def _build_gbuf_range_map(cls, param_and_grad_buffer: _ParamAndGradBuffer):
"""
Build mapping between params and their grad buffers. These mappings are
partitioned according to data type.
Iterate through all buckets of grad buffer to construct param ranges
that this rank "owns" (the dp_rank'th shard of each bucket, where each
shard is 1/dp_world_size of the bucket).
Args:
param_and_grad_buffer (_ParamAndGradBuffer): buffer to build mapping for.
"""
return {
(param_and_grad_buffer.param_dtype, param_and_grad_buffer.grad_dtype): [
cls._build_model_gbuf_range(param_and_grad_buffer, bucket_index)
for bucket_index in range(len(param_and_grad_buffer.buckets))
]
}
@classmethod
def _build_model_param_gbuf_map(
cls, gbuf_ranges: List[Dict]
) -> Dict[torch.nn.Parameter, Tuple]:
"""
Create a reverse of the gbuf_ranges, for referencing in opposite direction.
"""
param_gbuf_map = {}
for gbuf_index, gbuf_range_map in enumerate(gbuf_ranges):
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_map.items():
for bucket_index, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets):
for param, _ in gbuf_range_map["param_map"].items():
assert param not in param_gbuf_map, (
"Param should not be in param_gbuf_map; each param only belongs "
"to a single bucket."
)
param_gbuf_map[param] = (gbuf_index, dtype, bucket_index)
return param_gbuf_map
@classmethod
def _build_optimizer_group_ranges(cls, param_groups: List[Dict], gbuf_ranges: List[Dict]):
"""
Create optimizer groups.
Given the set of parameter shard ranges that are owned by the current
data-parallel (DP) rank, gather the set of parameters that will be
used (in the method below) to create the current DP's optimizer
groups.
"""
# Param group map.
# World param group map.
# - Store a mapping of <model_parameter:group_index> for all parameters
# across all DP ranks. This is necessary because it is our first
# cross reference between the DDP mappings and the optimizer group
# parameters. This mapping only for use in the next step of building
# the local mapping over this DP rank's parameters.
world_param_group_map = {}
for group_index, group in enumerate(param_groups):
for param in group["params"]:
assert param.requires_grad
world_param_group_map[param] = group_index
# Optimizer group ranges & param-group mapping.
# - Build a mapping from groups to their contained parameters, and also
# from parameters to their containing group index and order within
# the group. The group index and order are particularly important for
# saving and loading checkpoints.
local_param_group_map = {}
group_ranges = [{"params": []} for _ in param_groups]
for gbuf_range_map in gbuf_ranges:
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_map.items():
for gbuf_range_map in gbuf_range_map_for_all_buckets:
for param in gbuf_range_map["param_map"]:
group_index = world_param_group_map[param]
group_range = group_ranges[group_index]
group_range["params"].append(param)
local_param_group_map[param] = (group_index, len(group_range["params"]) - 1)
# Squeeze zero-size group ranges.
for group_index, group_range in enumerate(group_ranges):
group_range["orig_group"] = param_groups[group_index]
group_range["orig_group_idx"] = param_groups[group_index]
return local_param_group_map, group_ranges
@classmethod
def _build_model_and_main_param_groups(
cls,
gbuf_ranges: List[Dict],
param_gbuf_map: Dict[torch.nn.Parameter, Tuple],
opt_group_ranges: List,
):
"""
Create main parameter groups needed for the optimizer step.
These groups encompass both: 1) groups used by this class, for
reducing/gather, and 2) groups used by the inner optimizer for the
parameter update. Given that the conceptual grad buffer partitioning
(created in earlier method) doesn't respect parameter boundaries,
the optimizer operates on shards of the model parameters, rather than
the full parameters.
"""
# Parameter groups:
# model_float16_groups: original float16 parameters
# model_fp32_groups: original fp32 parameters
# shard_float16_groups: shards of original float16 parameters
# shard_fp32_groups: shards of original fp32 parameters
# shard_fp32_from_float16_groups: fp32 copy of float16 parameters
model_float16_groups = []
model_fp32_groups = []
shard_float16_groups = []
shard_fp32_groups = []
shard_fp32_from_float16_groups = []
# Allocate (or slice) each group's param shard.
for group_range in opt_group_ranges:
# Params of this group.
model_float16_params_this_group = []
model_fp32_params_this_group = []
shard_float16_params_this_group = []
shard_fp32_params_this_group = []
shard_fp32_from_float16_params_this_group = []
model_float16_groups.append(model_float16_params_this_group)
model_fp32_groups.append(model_fp32_params_this_group)
shard_float16_groups.append(shard_float16_params_this_group)
shard_fp32_groups.append(shard_fp32_params_this_group)
shard_fp32_from_float16_groups.append(shard_fp32_from_float16_params_this_group)
for model_param in group_range["params"]:
assert model_param.requires_grad
gbuf_index, dtype, bucket_index = param_gbuf_map[model_param]
gbuf_range = gbuf_ranges[gbuf_index][dtype][bucket_index]
param_range = gbuf_range["param_map"][model_param]["param"]
# fp16, bf16 params.
if model_param.type() in ['torch.cuda.HalfTensor', 'torch.cuda.BFloat16Tensor']:
# Clone model -> main.
shard_model_param = model_param.detach().view(-1)[
param_range.start : param_range.end
]
# If we use FP8 params to initialize FP32 main params (compared to using the
# bf16/fp16 params to initialize the main params), there will be a loss of
# precision at the beginning of training (this problem will not occur if the
# training is long enough or if the main params are loaded from a checkpoint).
if is_float8tensor(model_param) and hasattr(
model_param, 'get_high_precision_init_val'
):
shard_main_param = (
model_param.get_high_precision_init_val()
.view(-1)[param_range.start : param_range.end]
.clone()
.to(shard_model_param.device)
.float()
)
model_param.clear_high_precision_init_val()
else:
shard_main_param = shard_model_param.clone().float()
tensor_parallel.copy_tensor_model_parallel_attributes(
shard_model_param, model_param
)
tensor_parallel.copy_tensor_model_parallel_attributes(
shard_main_param, model_param
)
if hasattr(model_param, 'shared'):
shard_model_param.shared = model_param.shared
shard_main_param.shared = model_param.shared
# Add to group.
model_float16_params_this_group.append(model_param)
shard_float16_params_this_group.append(shard_model_param)
shard_fp32_from_float16_params_this_group.append(shard_main_param)
# fp32 params.
elif model_param.type() == 'torch.cuda.FloatTensor':
shard_model_param = model_param.view(-1)[param_range.start : param_range.end]
model_fp32_params_this_group.append(model_param)
shard_fp32_params_this_group.append(shard_model_param)
tensor_parallel.copy_tensor_model_parallel_attributes(
shard_model_param, model_param
)
if hasattr(model_param, 'shared'):
shard_model_param.shared = model_param.shared
else:
raise TypeError(
'Wrapped parameters must be one of '
'torch.cuda.FloatTensor, '
'torch.cuda.HalfTensor, or '
'torch.cuda.BFloat16Tensor. '
'Received {}'.format(model_param.type())
)
# Update optimizer's params.
group_range["orig_group"]["params"] = [
*shard_fp32_params_this_group,
*shard_fp32_from_float16_params_this_group,
]
return (
model_float16_groups,
model_fp32_groups,
shard_float16_groups,
shard_fp32_groups,
shard_fp32_from_float16_groups,
)
def __init__(
self,
optimizer: torch.optim.Optimizer,
config: OptimizerConfig,
grad_scaler: MegatronGradScaler,
init_state_fn: Optional[Callable],
model_chunks: List[MegatronModule],
per_model_buffers: Dict[int, List[_ParamAndGradBuffer]],
data_parallel_group: torch.distributed.ProcessGroup,
data_parallel_group_gloo: torch.distributed.ProcessGroup,
data_parallel_group_idx: int,
):
"""
Distributed optimizer, for all data types (fp16, bf16, and fp32).
The steps in this method create the core mapping between param and grad buffers,
parameters, and parameter shard ranges, that is needed for converting between model
param indexes and main parameter shard indexes. This method also updates the optimizer
parameter groups with the newly created shards.
Args:
optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD.
config (OptimizerConfig): configuration object for optimizer.
grad_scaler (MegatronGradScaler): used for scaling gradients. Note that
this can be None. This case happens when `bf16 = True` and we don't
use any loss scale. Note that for `bf16 = True`, we can have
a constant gradient scaler. Also for `bf16 = False`, we
always require a grad scaler.
init_state_fn (Callable, optional): function to initialize state in the optimizer.
model_chunks (List[MegatronModule]): list of model chunks.
per_model_buffers (Dict[int, List[ParamAndGradBuffer]]): the implementation of the
distributed optimizer is centered on using a contiguous buffer for
communicating grads & params between the model state and the optimizer state.
You can find a more detailed description in
https://github.com/NVIDIA/Megatron-LM/blob/main/docs/source/distrib_optimizer.md.
data_parallel_group (torch.distributed.ProcessGroup): data-parallel group to use to
all-gather params after optimizer.step().
data_parallel_group_gloo (torch.distributed.ProcessGroup): gloo data-parallel group
(used in checkpoint loading and saving).
data_parallel_group_idx (int): index in data-parallel group (used by
distributed checkpointing logic).
"""
if has_config_logger_enabled(config):
log_config_to_disk(config, locals(), prefix=type(self).__name__)
assert (
HAVE_APEX_OR_TE
), f'Please install Apex or Transformer Engine to use DistributedOptimizer.'
super().__init__(optimizer, config, grad_scaler, init_state_fn)
self.model_chunks = model_chunks
self.ddp_config = self.model_chunks[0].ddp_config
for model_chunk in self.model_chunks:
assert self.ddp_config == model_chunk.ddp_config
assert isinstance(
optimizer, Adam
), "Only Adam currently supported, due to checkpointing requirements."
# Model grad buffer ranges.
assert per_model_buffers is not None, "per_model_buffers must be provided"
self.buffers = list(itertools.chain(*per_model_buffers.values()))
self.per_model_buffers = per_model_buffers
self.data_parallel_group = data_parallel_group
self.data_parallel_group_gloo = data_parallel_group_gloo
self.data_parallel_group_idx = data_parallel_group_idx
self.gbuf_idx_to_model_idx_map = {}
gbuf_idx = 0
for model_idx, buffers in self.per_model_buffers.items():
for _ in buffers:
self.gbuf_idx_to_model_idx_map[gbuf_idx] = model_idx
gbuf_idx += 1
self.per_model_bucket_groups = {}
for model_idx, buffers in self.per_model_buffers.items():
self.per_model_bucket_groups[model_idx] = partition_buckets(buffers)
self.gbuf_ranges = []
self.per_bucket_numel = []
self.per_bucket_numel_unpadded = []
for buffer in self.buffers:
self.per_bucket_numel.append(
{
(buffer.param_dtype, buffer.grad_dtype): [
bucket.grad_data.numel() for bucket in buffer.buckets
]
}
)
self.per_bucket_numel_unpadded.append(
{
(buffer.param_dtype, buffer.grad_dtype): [
bucket.numel_unpadded for bucket in buffer.buckets
]
}
)
self.gbuf_ranges.append(self._build_gbuf_range_map(buffer))
self.model_param_gbuf_map = self._build_model_param_gbuf_map(self.gbuf_ranges)
# Optimizer ranges.
(self.model_param_group_index_map, self.opt_group_ranges) = (
self._build_optimizer_group_ranges(self.optimizer.param_groups, self.gbuf_ranges)
)
# Allocate main param shards.
(
self.model_float16_groups,
self.model_fp32_groups,
self.shard_float16_groups,
self.shard_fp32_groups,
self.shard_fp32_from_float16_groups,
) = self._build_model_and_main_param_groups(
self.gbuf_ranges, self.model_param_gbuf_map, self.opt_group_ranges
)
# Update optimizer groups.
# - Also, leverage state_dict() and load_state_dict() to
# recast preexisting per-param state tensors.
self.optimizer.param_groups = [g["orig_group"] for g in self.opt_group_ranges]
self.optimizer.load_state_dict(self.optimizer.state_dict())
def enable_pre_hook(self):
"""
Enable forward pre-hook needed for param all-gather overlap with forward compute.
"""
warnings.warn(
"`DistributedOptimizer.enable_pre_hook` will be deprecated in a future release. "
"Use `DistributedDataParallel.enable_forward_pre_hook` directly."
)
for model_chunk in self.model_chunks:
model_chunk.enable_forward_pre_hook()
def disable_pre_hook(self):
"""
Disable forward pre-hook needed for param all-gather overlap with forward compute.
"""
warnings.warn(
"`DistributedOptimizer.disable_pre_hook` will be deprecated in a future release. "
"Use `DistributedDataParallel.disable_forward_pre_hook` directly."
)
for model_chunk in self.model_chunks:
model_chunk.disable_forward_pre_hook()
def _get_model_param_range_map(self, param: torch.nn.Parameter):
"""
Given a model param, get the index sub-range of the param that this
data-parallel rank owns.
"""
gbuf_index, dtype, bucket_index = self.model_param_gbuf_map[param]
gbuf_range_map = self.gbuf_ranges[gbuf_index][dtype][bucket_index]
param_range_map = gbuf_range_map["param_map"][param]
return param_range_map
def get_model_parallel_group(self) -> torch.distributed.ProcessGroup:
"""
With the distributed optimizer, the model parallel group is the
entire world.
"""
return None
def state_dict(self):
"""
The state dict contains all non-DP-rank-dependent (i.e., non-parameter-
related) optimizer variables. The returned state dict can be stored in
the standard model/RNG checkpoint file. The parameter and dependent
optimizer state (e.g., exp_avg, exp_avg_sq) are stored in a separate
checkpoint file by calling 'save_parameter_state()'.
"""
inner_state_dict = self.optimizer.state_dict()
state_dict = {}
# Extract 'step', for non-Apex/TE support.
if not HAVE_APEX_OR_TE:
steps = list(set([s["step"].item() for s in inner_state_dict["state"].values()]))
assert len(steps) == 1
step = steps[0]
# Optimizer state (do not store parameter state here).
state_dict['optimizer'] = {k: v for k, v in inner_state_dict.items() if k != "state"}
for param_group in state_dict["optimizer"]["param_groups"]:
del param_group["params"]
if not HAVE_APEX_OR_TE:
# Native PyTorch param group requires step (i.e., iteration).
param_group["step"] = step
# Grad scaler state.
if self.grad_scaler:
state_dict['grad_scaler'] = self.grad_scaler.state_dict()
return state_dict
def load_state_dict(self, state_dict):
"""Load the state dict.
As detailed in state_dict(), the state dict contains all non-
parameter-related variables. This method is notably longer than
state_dict(), because the Torch optimizers state has yet to be
allocated at this point, and so we must do a cross referencing between
the optimizers state (and the ordering it expects for parameter state)
and this DP rank's shards. The optimizer at this point does not contain
any tensor dimension information, so we must get these dimensions from
the DP shards mapped during DistributedOptimizer.__init__().
The tensor parameter state is loaded via load_parameter_state(), and
so this method also must populate the loaded state dict with dummy
tensor data (i.e., via torch.empty() below). This will be overwritten
during load_parameter_state().
** Note: Torch optimizer's state structure. **
The Torch optimizer stores its state in two levels. The top level is a
list of groups, where each group contains a list of integer indexes
(corresponding to parameters) that index into a master parameter list
that is shared by all groups. As such, three values are necessary for
maintaining this ordering:
- group_index : The group to which a parameter belongs.
- group_order : The index of a parameter within its group.
- state_order : The index of a parameter within the shared parameter
list.
"""
# Get the Torch optimizer's state dict.
# - This 'inner' optimizer at this point is unallocated, and only
# contains an integer ordering of parameters within each group, and
# the ordering of parameters within its flattened parameter state
# list.
inner_state_dict = self.optimizer.state_dict()
state_dict_param_groups = [
{**group, "params": list(inner_state_dict["param_groups"][idx]["params"])}
for idx, group in enumerate(state_dict["optimizer"]["param_groups"])
]
# Allocate or retrieve optimizer state (i.e., tensors).
if len(self.optimizer.state) == 0:
# Allocate empty optimizer state if not previously initialized.
# - If len(self.optimizer.state) == 0, this means that the optimizer
# state has not been previously initialized. Once it has been
# initialized, we skip this code block to avoid reallocating
# empty tensors (i.e., torch.empty), which in turn reduces memory
# fragmentation.
# - Real data is overwritten during load_parameter_state().
state_dict_state = []
for gbuf_range_maps in self.gbuf_ranges:
for gbuf_range_map_for_all_buckets in gbuf_range_maps.values():
for gbuf_range_map in gbuf_range_map_for_all_buckets:
for model_param, param_range_map in gbuf_range_map["param_map"].items():
# Get parameter ordering information (see method docstring
# for details).
group_index, group_order = self.model_param_group_index_map[model_param]
state_order = inner_state_dict["param_groups"][group_index]["params"][
group_order
]
# Allocate dummy tensors.
numel = len(param_range_map["gbuf_world"])
init_shard = lambda: torch.empty(
(numel,), dtype=torch.float32, device=torch.cuda.current_device()
)
state_dict_state.append(
(state_order, {"exp_avg": init_shard(), "exp_avg_sq": init_shard()})
)
# Sort by state order (see method docstring for details).
state_dict_state.sort(key=lambda s: s[0])
state_dict_state = {s[0]: s[1] for s in state_dict_state}
else:
# Retrieve existing optimizer state.
state_dict_state = inner_state_dict["state"]
# Extract 'step', for non-Apex/TE support.
if not HAVE_APEX_OR_TE:
steps = list(set([g["step"] for g in state_dict["optimizer"]["param_groups"]]))
assert len(steps) == 1
step = torch.tensor(steps[0], dtype=torch.float)
for s in state_dict_state.values():
# Native PyTorch state dict requires step (i.e., iteration).
s["step"] = step
# Optimizer.
self.optimizer.load_state_dict(
{"state": state_dict_state, "param_groups": state_dict_param_groups}
)
# Grad scaler.
if 'grad_scaler' not in state_dict:
if self.config.fp16:
logger.info(
'***WARNING*** found an old checkpoint, will not ' 'load grad scaler ...'
)
else:
if self.grad_scaler:
self.grad_scaler.load_state_dict(state_dict['grad_scaler'])
else:
logger.info(
'***WARNING*** fould the grad scaler in the '
'checkpoint but it is None in the class. '
'Skipping loading grad scaler ...'
)
if 'param_state' in state_dict:
assert 'param_state_sharding_type' in state_dict, state_dict.keys()
param_state = state_dict['param_state']
sharding_type = state_dict['param_state_sharding_type']
logger.info(f'Loading distributed optimizer sharded state of type {sharding_type}')
if sharding_type == 'dp_zero_gather_scatter':
self.load_parameter_state_from_dp_zero(param_state)
elif sharding_type == 'fully_sharded_bucket_space':
self.load_parameter_state_from_fs_bucket_space(param_state)
elif sharding_type == 'fully_sharded_model_space':
self.load_parameter_state_from_fs_model_space(param_state)
else:
raise NotImplementedError(f'Unknown sharding_type: {sharding_type}')
def get_parameter_state_fs_bucket_space(self):
"""Get internal representation of parameter state without any copies and modifications.
This is referred to as "fully sharded bucket space" because the optimizer state is
fully sharded (e.g. no gather involved) and bucket-centric (the state
follows the internal structure of the Distributed Optimizer buckets)
as opposed to model-centric (typical structure of PyT optimizers)
"""
state = {
"per_bucket_numel": self.per_bucket_numel,
"per_bucket_numel_unpadded": self.per_bucket_numel_unpadded,
}
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
# Iterate grad buffers (by data type).
dtype_state = {}
assert len(gbuf_range_maps) == 1, "single dtype supported, for now."
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items():
buckets_state = []
for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets):
bucket_state = []
for model_param, param_range_map in gbuf_range_map["param_map"].items():
# Main param & optimizer states.
group_index, group_order = self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
tensors = {
"param": main_param,
**optim_state,
"gbuf_local_start": param_range_map["gbuf_local"].start,
"gbuf_local_end": param_range_map["gbuf_local"].end,
}
bucket_state.append(tensors)
buckets_state.append(bucket_state)
dtype_state[dtype] = buckets_state
state[gbuf_idx] = dtype_state
return state
def get_parameter_state_dp_zero(self):
"""Get parameter state (i.e., parameter & optimizer tensors).
This method performs two steps:
- For each DP rank, copy param & optimizer shards to contiguous CPU
buffers (e.g., one buffer each for main_param, exp_avg, and
exp_avg_sq).
- Gather contiguous buffers on DP rank 0 and concatenate to world
buffers.
"""
# Data parallelism variables.
data_parallel_world_size = self.data_parallel_group_gloo.size()
data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group_gloo)
data_parallel_group_gloo = self.data_parallel_group_gloo
data_parallel_global_ranks = torch.distributed.get_process_group_ranks(
self.data_parallel_group_gloo
)
# Collect param states.
state = {"buckets_coalesced": True}
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
# Iterate grad buffers (by data type).
dtype_state = {}
assert len(gbuf_range_maps) == 1, "single dtype supported, for now."
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items():
buffer_numel_unpadded = self.buffers[gbuf_idx].numel_unpadded
# Create coalesced tensors for all state related to parameters in this buffer.
world_tensors = {}
if data_parallel_rank == 0:
world_tensors = {
key: torch.zeros(
(buffer_numel_unpadded,), dtype=torch.float32, device="cpu"
)
for key in ("param", "exp_avg", "exp_avg_sq")
}
world_tensors["numel_unpadded"] = buffer_numel_unpadded
offset_in_world_tensors = 0
for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets):
# Compute local DP contiguous shard's size.
gbuf_world_numel = self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel()
assert gbuf_world_numel % data_parallel_world_size == 0
gbuf_local_numel = gbuf_world_numel // data_parallel_world_size
gbuf_world_numel_unpadded = (
self.buffers[gbuf_idx].buckets[bucket_idx].numel_unpadded
)
assert gbuf_world_numel_unpadded <= gbuf_world_numel
local_shards = {
key: torch.zeros((gbuf_local_numel,), dtype=torch.float32, device="cpu")
for key in ("param", "exp_avg", "exp_avg_sq")
}
# Build contiguous DP rank shards (for param + optim states).
for model_param, param_range_map in gbuf_range_map["param_map"].items():
# Main param & optimizer states.
group_index, group_order = self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
tensors = {"param": main_param, **optim_state}
# Copy states into contiguous shard.
gbuf_local_start = param_range_map["gbuf_local"].start
gbuf_local_end = param_range_map["gbuf_local"].end
for key in local_shards:
local_shards[key][gbuf_local_start:gbuf_local_end].data.copy_(
tensors[key].detach().cpu()
)
# Gather contiguous shards on DP rank 0.
for key, send_tensor in local_shards.items():
# Gather tensor list.
if data_parallel_rank == 0:
recv_tensors = [
torch.zeros((gbuf_local_numel,), dtype=torch.float32, device="cpu")
for _ in range(data_parallel_world_size)
]
else:
recv_tensors = None
# Gather.
torch.distributed.gather(
send_tensor,
recv_tensors,
data_parallel_global_ranks[0],
data_parallel_group_gloo,
)
# Concatenate.
if data_parallel_rank == 0:
recv_tensors_concatenated = torch.cat(recv_tensors)
# Copy this bucket's collected all-gather tensors into the right place
# in the tensor for the buffer. The tensor for the buffer gets rid of
# the padding between buckets.
start = offset_in_world_tensors
end = offset_in_world_tensors + gbuf_world_numel_unpadded
world_tensors[key][start:end].copy_(
recv_tensors_concatenated[:gbuf_world_numel_unpadded]
)
offset_in_world_tensors += gbuf_world_numel_unpadded
# Collect world state.
dtype_state[dtype] = world_tensors
state[gbuf_idx] = dtype_state
return state
def save_parameter_state(self, filename: str):
"""Save the distributed parameter state on DP rank 0.
Args:
filename (str): path to save parameter state to.
"""
state_dict = self.get_parameter_state_dp_zero()
if torch.distributed.get_rank(self.data_parallel_group) == 0:
torch.save(state_dict, filename)
def sharded_state_dict(
self,
model_sharded_state_dict: ShardedStateDict,
is_loading: bool = False,
sharding_type: str = 'fully_sharded_model_space',
):
"""
Chooses between 3 param state sharding implementations as requested by `sharding_type`.
Regular state dict parameters are saved on DP rank 0 and loaded on all ranks.
"""
if not is_loading and sharding_type == 'fully_sharded_bucket_space':
logger.warning(
'`fully_sharded_bucket_space` sharding for DistributedOptimizer'
' checkpoint is deprecated and will be removed in the future.'
' Please switch to `full_sharded_model_space`.'
)
state_dict = self.state_dict()
if sharding_type != 'fully_sharded_model_space':
# State dict differs between different model parallel groups
state_dict = {
k: ShardedObject(
f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}.{k}',
v,
(1,),
(0,),
replica_id=torch.distributed.get_rank(self.data_parallel_group),
)
for k, v in state_dict.items()
}
if is_loading:
# Call the distributed optimizer's specialized load_state_dict(),
# which conditionally skips re-allocating the optimizer's state if
# already initialized, which in turn reduces memory fragmentation.
self.load_state_dict(self.state_dict())
if sharding_type == 'fully_sharded_bucket_space':
param_state = self.sharded_param_state_fs_bucket_space(
model_sharded_state_dict, is_loading
)
elif sharding_type == 'dp_zero_gather_scatter':
param_state = self.sharded_param_state_dp_zero(model_sharded_state_dict, is_loading)
elif sharding_type == 'fully_sharded_model_space':
param_state = self.sharded_param_state_fs_model_space(
model_sharded_state_dict, is_loading
)
else:
raise NotImplementedError(f'Unknown sharding_type: {sharding_type}')
state_dict['param_state'] = param_state
state_dict['param_state_sharding_type'] = sharding_type
return state_dict
def sharded_param_state_dp_zero(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False
):
"""Naive implementation which reuses gather/scatter from the legacy ckpt format.
During saving, gathers the parameters state on DP rank 0 and saves a ShardedObject
with fixed TPxPP structure. During loading, loads the saved data on DP rank 0
(None on other ranks). Relies on the parameters scatter done in load_state_dict.
"""
if is_loading:
param_state_data = None
else:
# Gather on rank 0
param_state_data = self.get_parameter_state_dp_zero()
if torch.distributed.get_rank(self.data_parallel_group) == 0:
# Fixed TPxPP. Save on DP rank 0 only
param_state = ShardedObject(
f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}.param_state',
param_state_data,
(1,),
(0,),
)
else:
# DP ranks > 0 don't save. During loading, the param_state needs to be None.
param_state = LocalNonpersistentObject(None)
return param_state
def sharded_param_state_fs_bucket_space(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False
):
"""Sharded state dict where each noncontiguous buffer is a separate ShardedTensor.
Results in fully parallel save and load without any inter-process
communication or intermediate buffers/copies.
"""
data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group)
data_parallel_world_size = torch.distributed.get_world_size(self.data_parallel_group)
state = self.get_parameter_state_fs_bucket_space()
# per_bucket_numel metadata is saved separately for each TPxPP domain.
for per_bucket_key in ('per_bucket_numel', 'per_bucket_numel_unpadded'):
key = (
f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}'
f'.{per_bucket_key}'
)
state[per_bucket_key] = ShardedObject(
key, state[per_bucket_key], (1,), (0,), replica_id=data_parallel_rank
)
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
for dtype, gbuf_range_map_for_all_buckets in state[gbuf_idx].items():
for bucket_idx, bucket_state in enumerate(gbuf_range_map_for_all_buckets):
# Compute local DP contiguous shard's size.
gbuf_world_numel = self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel()
assert gbuf_world_numel % data_parallel_world_size == 0
gbuf_local_numel = gbuf_world_numel // data_parallel_world_size
sharded_bucket_key = (
f'optimizer.distributed.dp_group_idx_{self.data_parallel_group_idx}'
f'.gbuf_idx_{gbuf_idx}.dtype_{dtype}.bucket_idx_{bucket_idx}'
)
# The global ckpt tensors must be fully covered.
# We add extra empty padding if necessary
assert bucket_state, 'empty bucket encountered'
# Insert padding between parameter tensors to ensure full coverage as needed.
all_pad_tensors = {}
for i in range(len(bucket_state) - 1):
next_param_start = bucket_state[i + 1]['gbuf_local_start']
cur_param_end = bucket_state[i]['gbuf_local_end']
if next_param_start != cur_param_end:
pad_tensors = {
k: torch.empty(
next_param_start - cur_param_end, dtype=v.dtype, device=v.device
)
for k, v in bucket_state[i].items()
if isinstance(v, torch.Tensor)
}
all_pad_tensors[i + 1] = {
**pad_tensors,
'gbuf_local_start': cur_param_end,
'gbuf_local_end': next_param_start,
'padding': True,
}
# Insert from end so that insertion positions are still correct.
indices_to_insert = sorted(list(all_pad_tensors.keys()))
for index_to_insert in reversed(indices_to_insert):
bucket_state.insert(index_to_insert, all_pad_tensors[index_to_insert])
if bucket_state[-1]['gbuf_local_end'] != gbuf_local_numel:
pad_tensors = {
k: torch.empty(
gbuf_local_numel - bucket_state[-1]['gbuf_local_end'],
dtype=v.dtype,
device=v.device,
)
for k, v in bucket_state[-1].items()
if isinstance(v, torch.Tensor)
}
bucket_state.append(
{
**pad_tensors,
'gbuf_local_start': bucket_state[-1]['gbuf_local_end'],
'gbuf_local_end': gbuf_local_numel,
'padding': True,
}
)
# Each tensor is mapped to a slice (`flattened_range`)
# of a DP-local shard of size `gbuf_local_numel`.
for bucket_params_idx in range(len(bucket_state)):
tensors = bucket_state[bucket_params_idx]
gbuf_local_start = tensors.pop('gbuf_local_start')
gbuf_local_end = tensors.pop('gbuf_local_end')
if 'padding' not in tensors:
tensors['padding'] = False
for key in tensors:
if key == 'padding':
tensors[key] = LocalNonpersistentObject(tensors[key])
continue
assert tensors[key].shape == (gbuf_local_end - gbuf_local_start,), (
tensors[key].shape,
gbuf_local_start,
gbuf_local_end,
)
tensors[key] = ShardedTensor(
f'{sharded_bucket_key}.{key}',
tensors[key],
tensors[key].dtype,
(gbuf_local_numel,),
(data_parallel_world_size * gbuf_local_numel,),
(data_parallel_rank * gbuf_local_numel,),
axis_fragmentations=(data_parallel_world_size,),
flattened_range=slice(gbuf_local_start, gbuf_local_end),
allow_shape_mismatch=True,
)
return state
def sharded_param_state_fs_model_space(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False
):
"""Sharded state dict where each buffer is mapped to corresponding model param.
In this approach the optimizer state tensors are directly related to model parameters
by linking them with metadata from `model_sharded_state_dict`.
This will allow changing TP and PP while using DistOpt (as with other optimizers).
"""
param_to_sharded_metadata = {}
model_sharded_state_dict, _ = extract_sharded_tensors_and_factories(
model_sharded_state_dict
)
for sh_base in nested_values(model_sharded_state_dict):
param_to_sharded_metadata[sh_base.data] = sh_base
prefix = 'optimizer.state'
state = {}
# Not stored in the checkpoint, used only to identify params in
# `sharded_param_state_fs_model_space`.
param_idx = 0
for gbuf_range_maps in self.gbuf_ranges:
for gbuf_range_map_for_all_buckets in gbuf_range_maps.values():
for gbuf_range_map in gbuf_range_map_for_all_buckets:
for model_param, param_range_map in gbuf_range_map["param_map"].items():
group_index, group_order = self.model_param_group_index_map[model_param]
param_range = param_range_map['param']
main_param = self.optimizer.param_groups[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
tensors = {"fp32_param": main_param, **optim_state}
# Match optimizer parameter with model ShardedTensor (or
# ShardedTensorFactory).
try:
sharded_metadata = param_to_sharded_metadata[model_param]
except KeyError as e:
raise ValueError(
f'Model param {model_param} not in model_sharded_state_dict'
) from e
# Set DP corresponding replica_id coordinate to 0.
assert (
len(sharded_metadata.replica_id) == 3
), f'Expected replica_id format (PP, TP, DP), got: {sharded_metadata}'
replica_id = (*sharded_metadata.replica_id[:2], 0)
# Instantiate ShardedTensor (or ShardedTensorFactory) for optimizer
# params.
for state_key, state_ten in tensors.items():
replace_kwargs = dict(
key=f'{prefix}.{state_key}.{sharded_metadata.key}',
data=state_ten,
dtype=state_ten.dtype,
flattened_range=slice(param_range.start, param_range.end),
replica_id=replica_id,
)
if isinstance(sharded_metadata, ShardedTensorFactory):
replace_kwargs.pop('dtype')
tensors[state_key] = replace(sharded_metadata, **replace_kwargs)
tensors[state_key].validate_metadata_integrity()
state[param_idx] = tensors
param_idx += 1
return state
def load_parameter_state_from_fs_bucket_space(self, state_dict):
"""Loads the parameter state from an internal representation.
Inverse of the `get_parameter_state_fs_bucket_space` method.
"""
logger.warning(
'`fully_sharded_bucket_space` sharding for DistributedOptimizer'
'checkpoint is deprecated. Please switch to `full_sharded_model_space`'
)
if state_dict is not None and "per_bucket_numel_unpadded" in state_dict:
per_bucket_numel_unpadded_in_checkpoint = state_dict["per_bucket_numel_unpadded"]
assert self.per_bucket_numel_unpadded == per_bucket_numel_unpadded_in_checkpoint, (
f"Number of unpadded elements in each bucket need to be the same in current run "
f"({self.per_bucket_numel_unpadded}) and checkpoint "
f"({per_bucket_numel_unpadded_in_checkpoint})"
)
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
assert len(gbuf_range_maps) == 1, "single dtype supported, for now."
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items():
for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets):
bucket_state = state_dict[gbuf_idx][dtype][bucket_idx]
bucket_state = [
bucket_state_elem
for bucket_state_elem in bucket_state
if not bucket_state_elem['padding']
]
assert len(bucket_state) == len(gbuf_range_map["param_map"]), (
len(bucket_state),
len(gbuf_range_map["param_map"]),
)
for src_tensors, (model_param, param_range_map) in zip(
bucket_state, gbuf_range_map["param_map"].items()
):
# Main param & optimizer states.
group_index, group_order = self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
dst_tensors = {"param": main_param, **optim_state}
for key in dst_tensors:
dst_tensors[key].copy_(src_tensors[key])
@torch.no_grad()
def load_parameter_state_from_fs_model_space(self, state_dict):
"""Loads the parameter state from a "model space" representation.
Inverse of the `sharded_param_state_fs_model_space` method.
"""
param_idx = 0 # matching order with `sharded_param_state_fs_model_space`
for gbuf_range_maps in self.gbuf_ranges:
for gbuf_range_map_for_all_buckets in gbuf_range_maps.values():
for gbuf_range_map in gbuf_range_map_for_all_buckets:
for model_param, param_range_map in gbuf_range_map["param_map"].items():
group_index, group_order = self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups[group_index]["params"][group_order]
optim_state = self.optimizer.state[main_param]
src_tensors = state_dict[param_idx]
dst_tensors = {"fp32_param": main_param, **optim_state}
for key in dst_tensors:
dst_tensors[key].copy_(src_tensors[key])
param_idx += 1
@classmethod
def _update_legacy_world_tensors(cls, old_tensors, new_numels):
'''Reshard buckets (where each bucket is a tensor) to new target
numels, where the total numel remains the same.'''
old_total = sum([t.numel() for t in old_tensors])
new_total = sum(new_numels)
assert old_total == new_total
unified_tensor = torch.cat(old_tensors, dim=0)
new_tensors = []
start_idx = 0
for new_numel in new_numels:
new_tensors.append(unified_tensor[start_idx : (start_idx + new_numel)])
start_idx += new_numel
return new_tensors
def load_parameter_state_from_dp_zero_legacy(self, state_dict):
"""Load parameter state (i.e., parameter & optimizer tensors) from DP 0 rank,
using the legacy checkpoint format as described below.
The difference between this method and `load_parameter_state_from_dp_zero_modern()`
is that this method is used for updating the format of checkpoints that
were saved using code from before Feb 13, 2024. Starting on this date, a
new format was used (i.e., different format for the parameter mapping and
bucket sharding).
Use arg `--ckpt-convert-update-legacy-dist-opt-format` to call this
method, along with `--ckpt-convert-format` and `--ckpt-convert-save` to
update a legacy-format checkpoint to the modern format.
"""
# Data parallelism variables.
data_parallel_world_size = self.data_parallel_group_gloo.size()
data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group_gloo)
data_parallel_group_gloo = self.data_parallel_group_gloo
data_parallel_global_ranks = torch.distributed.get_process_group_ranks(
self.data_parallel_group_gloo
)
# Scatter tensors to all DP ranks.
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items():
if data_parallel_rank == 0:
buffer_numel_unpadded = self.buffers[gbuf_idx].numel_unpadded
model_numels = [b.numel_unpadded for b in self.buffers[gbuf_idx].buckets]
checkpoint_numels = [
t.numel() for t in state_dict[gbuf_idx][torch.float32]["param"]
]
assert sum(model_numels) == sum(checkpoint_numels)
for key in ("param", "exp_avg", "exp_avg_sq"):
legacy_world_tensors = self._update_legacy_world_tensors(
state_dict[gbuf_idx][torch.float32][key],
[
self.buffers[gbuf_idx].buckets[bi].numel_unpadded
for bi in range(len(gbuf_range_map_for_all_buckets))
],
)
offset_in_world_tensors = 0
for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets):
# Compute local DP contiguous shard's size.
gbuf_world_numel = (
self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel()
)
assert gbuf_world_numel % data_parallel_world_size == 0
gbuf_local_numel = gbuf_world_numel // data_parallel_world_size
gbuf_world_numel_unpadded = (
self.buffers[gbuf_idx].buckets[bucket_idx].numel_unpadded
)
assert gbuf_world_numel_unpadded <= gbuf_world_numel
# Contiguous local shards (received from DP rank 0).
recv_tensor = torch.empty(
(gbuf_local_numel,), dtype=torch.float32, device="cpu"
)
# Scatter tensor list.
if data_parallel_rank == 0:
start = offset_in_world_tensors
end = offset_in_world_tensors + gbuf_world_numel_unpadded
world_tensor = legacy_world_tensors[bucket_idx]
assert (
world_tensor.numel() == gbuf_world_numel_unpadded
), "%d vs. %d." % (world_tensor.numel(), gbuf_world_numel_unpadded)
offset_in_world_tensors += gbuf_world_numel_unpadded
# Pad world_tensor to gbuf_world_numel. Don't pad at the front,
# pad at the back.
world_tensor = torch.nn.functional.pad(
world_tensor, (0, gbuf_world_numel - gbuf_world_numel_unpadded)
)
assert world_tensor.numel() == gbuf_world_numel
gbuf_start_idxs = list(range(0, gbuf_world_numel, gbuf_local_numel))
send_tensors = [
world_tensor[i : (i + gbuf_local_numel)] for i in gbuf_start_idxs
]
else:
send_tensors = None
# Scatter.
torch.distributed.scatter(
recv_tensor,
send_tensors,
data_parallel_global_ranks[0],
data_parallel_group_gloo,
)
# Copy local contiguous shards to param/optim shards.
for model_param, param_range_map in gbuf_range_map["param_map"].items():
# Main param & optimizer states.
group_index, group_order = self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups[group_index]["params"][
group_order
]
if key == "param":
tensor_to_copy_into = main_param
else:
optim_state = self.optimizer.state[main_param]
tensor_to_copy_into = optim_state[key]
# Copy states into contiguous shard.
gbuf_local_start = param_range_map["gbuf_local"].start
gbuf_local_end = param_range_map["gbuf_local"].end
tensor_to_copy_into.data.copy_(
recv_tensor[gbuf_local_start:gbuf_local_end]
)
def load_parameter_state_from_dp_zero(self, state_dict, *, update_legacy_format=False):
"""Load parameter state (i.e., parameter & optimizer tensors) from DP 0 rank,
using the new checkpoint format with coalesced state across buckets.
This method performs the reverse of get_parameter_state_dp_zero():
- Scatter contiguous buffers from DP rank 0 to each DP rank (each DP
rank receives its relevant subset of the world buffers).
- For each DP rank, copy param & optimizer shards from contiguous CPU
buffers. (e.g., one buffer each for main_param, exp_avg, and
exp_avg_sq).
"""
# Selectively load from a legacy checkpoint. The legacy format was used
# prior to Feb 13, 2024.
if update_legacy_format:
return self.load_parameter_state_from_dp_zero_legacy(state_dict)
# Data parallelism variables.
data_parallel_world_size = self.data_parallel_group_gloo.size()
data_parallel_rank = torch.distributed.get_rank(self.data_parallel_group_gloo)
data_parallel_group_gloo = self.data_parallel_group_gloo
data_parallel_global_ranks = torch.distributed.get_process_group_ranks(
self.data_parallel_group_gloo
)
if data_parallel_rank == 0:
# Do nothing if "--fp8-param-gather" is not used.
self.split_state_dict_if_needed(state_dict)
# Scatter tensors to all DP ranks.
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items():
if data_parallel_rank == 0:
buffer_numel_unpadded = self.buffers[gbuf_idx].numel_unpadded
checkpoint_numel_unpadded = state_dict[gbuf_idx][dtype]["numel_unpadded"]
assert buffer_numel_unpadded == checkpoint_numel_unpadded, (
f"Number of unpadded elements must be same in current run "
f"({buffer_numel_unpadded}) and checkpoint ({checkpoint_numel_unpadded})"
)
for key in ("param", "exp_avg", "exp_avg_sq"):
offset_in_world_tensors = 0
for bucket_idx, gbuf_range_map in enumerate(gbuf_range_map_for_all_buckets):
# Compute local DP contiguous shard's size.
gbuf_world_numel = (
self.buffers[gbuf_idx].buckets[bucket_idx].grad_data.numel()
)
assert gbuf_world_numel % data_parallel_world_size == 0
gbuf_local_numel = gbuf_world_numel // data_parallel_world_size
gbuf_world_numel_unpadded = (
self.buffers[gbuf_idx].buckets[bucket_idx].numel_unpadded
)
assert gbuf_world_numel_unpadded <= gbuf_world_numel
# Contiguous local shards (received from DP rank 0).
recv_tensor = torch.zeros(
(gbuf_local_numel,), dtype=torch.float32, device="cpu"
)
# Scatter tensor list.
if data_parallel_rank == 0:
world_tensors = state_dict[gbuf_idx][dtype][key]
start = offset_in_world_tensors
end = offset_in_world_tensors + gbuf_world_numel_unpadded
assert 0 <= start < end <= world_tensors.numel()
world_tensor = world_tensors[start:end]
offset_in_world_tensors += gbuf_world_numel_unpadded
# Pad world_tensor to gbuf_world_numel. Don't pad at the front,
# pad at the back.
world_tensor = torch.nn.functional.pad(
world_tensor, (0, gbuf_world_numel - gbuf_world_numel_unpadded)
)
assert world_tensor.numel() == gbuf_world_numel
gbuf_start_idxs = list(range(0, gbuf_world_numel, gbuf_local_numel))
send_tensors = [
world_tensor[i : (i + gbuf_local_numel)] for i in gbuf_start_idxs
]
else:
send_tensors = None
# Scatter.
torch.distributed.scatter(
recv_tensor,
send_tensors,
data_parallel_global_ranks[0],
data_parallel_group_gloo,
)
# Copy local contiguous shards to param/optim shards.
for model_param, param_range_map in gbuf_range_map["param_map"].items():
# Main param & optimizer states.
group_index, group_order = self.model_param_group_index_map[model_param]
main_param = self.optimizer.param_groups[group_index]["params"][
group_order
]
if key == "param":
tensor_to_copy_into = main_param
else:
optim_state = self.optimizer.state[main_param]
tensor_to_copy_into = optim_state[key]
# Copy states into contiguous shard.
gbuf_local_start = param_range_map["gbuf_local"].start
gbuf_local_end = param_range_map["gbuf_local"].end
tensor_to_copy_into.data.copy_(
recv_tensor[gbuf_local_start:gbuf_local_end]
)
def split_state_dict_if_needed(self, state_dict):
"""
When "--fp8-param-gather" is disabled, weights and biases are stored in the same
`ParamAndGradBuffer`. So, when saving a checkpoint, the optimizer's main parameters are
saved in a single continuous tensor (this also applies to "exp_avg" and "exp_avg_sq").
However, when "--fp8-param-gather" is enabled, weights(in fp8 dtype) and biases(in bf16/fp16
dtype) are stored in separate `ParamAndGradBuffer`. Therefore, when we enabled
"--fp8-param-gather", and want to load a checkpoint saved without "--fp8-param-gather", we
need to split the weights(fp8) and biases(bf16/fp16) in the static_dict into two separate
tensors.
"""
# Skip if there is no fp8 buffers.
fp8_gbuf_indices = []
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
for dtype, _ in gbuf_range_maps.items():
if is_float8tensor(self.buffers[gbuf_idx].params[0]):
fp8_gbuf_indices.append(gbuf_idx)
if len(fp8_gbuf_indices) == 0:
return
dtype_to_gbuf_idx = {}
for key in state_dict.keys():
if key != 'buckets_coalesced':
for dtype in state_dict[key].keys():
assert dtype not in dtype_to_gbuf_idx
if dtype[0] == torch.uint8:
# If the `state_dict`` already contains a torch.uint8 buffer, we assumed
# that the fp8 weights and fp16/bf16 biases in the checkpoint are already
# separated. In this case, no action is required, so we can return directly.
return
dtype_to_gbuf_idx[dtype] = key
# 1. Replace the gbuf_idx in the checkpoint with the new gbuf_idx.
# 2. Copy the non-tensor data (i.e., the "buckets_coalesced") to `new_state_dict`.
new_state_dict = {'buckets_coalesced': state_dict['buckets_coalesced']}
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
for dtype, _ in gbuf_range_maps.items():
if not is_float8tensor(self.buffers[gbuf_idx].params[0]):
new_state_dict[gbuf_idx] = state_dict[dtype_to_gbuf_idx[dtype]]
for fp8_gbuf_idx in fp8_gbuf_indices:
# Note that `self.buffers[fp8_gbuf_idx].params[0].dtype` is the dummy dtype of
# `Float8Tensor`, not torch.uint8.
non_fp8_param_and_grad_dtype = (
self.buffers[fp8_gbuf_idx].params[0].dtype,
self.buffers[fp8_gbuf_idx].grad_dtype,
)
# Iterate through all buffers to find the one that needs to be split.
non_fp8_gbuf_idx = None
for gbuf_idx, gbuf_range_maps in enumerate(self.gbuf_ranges):
for dtype, _ in gbuf_range_maps.items():
if dtype == non_fp8_param_and_grad_dtype:
non_fp8_gbuf_idx = gbuf_idx
assert non_fp8_gbuf_idx is not None
# We need the fp8_flags to determine the order of weight (fp8) and bias (fp16/bf16) in
# the buffer.
index_to_fp8_map = {}
for index in self.buffers[fp8_gbuf_idx].param_indices:
assert index not in index_to_fp8_map
index_to_fp8_map[index] = True
for index in self.buffers[non_fp8_gbuf_idx].param_indices:
assert index not in index_to_fp8_map
index_to_fp8_map[index] = False
param_indices = (
self.buffers[fp8_gbuf_idx].param_indices
+ self.buffers[non_fp8_gbuf_idx].param_indices
)
assert min(param_indices) == 0
assert max(param_indices) == len(param_indices) - 1
fp8_flags = []
for i in range(len(param_indices)):
fp8_flag.append(index_to_fp8_map[i])
fp8_buffer = self.buffers[fp8_gbuf_idx]
non_fp8_buffer = self.buffers[non_fp8_gbuf_idx]
fp8_idx = len(fp8_buffer.params) - 1
non_fp8_idx = len(non_fp8_buffer.params) - 1
offsets, fp8_offsets, non_fp8_offsets = [0], [0], [0]
# Because the parameters in `ParamAndGradBuffer` are traversed in reverse order, the
# flag here also needs to be traversed in reverse order.
for fp8_flag in fp8_flags[::-1]:
if fp8_flag:
numel = fp8_buffer.params[fp8_idx].nelement()
fp8_idx -= 1
offsets.append(offsets[-1] + numel)
fp8_offsets.append(fp8_offsets[-1] + numel)
else:
numel = non_fp8_buffer.params[non_fp8_idx].nelement()
non_fp8_idx -= 1
offsets.append(offsets[-1] + numel)
non_fp8_offsets.append(non_fp8_offsets[-1] + numel)
# Split the target buffer into two separate buffers.
fp8_state_dict, non_fp8_state_dict = {}, {}
for key in ['param', 'exp_avg', 'exp_avg_sq']:
tensor = state_dict[non_fp8_gbuf_idx][non_fp8_param_and_grad_dtype][key]
fp8_tensor = torch.empty([fp8_offsets[-1]], dtype=tensor.dtype)
non_fp8_tensor = torch.empty([non_fp8_offsets[-1]], dtype=tensor.dtype)
fp8_idx, non_fp8_idx = 0, 0
for i in range(len(offsets) - 1):
if fp8_flags[-(i + 1)]:
fp8_tensor[fp8_offsets[fp8_idx] : fp8_offsets[fp8_idx + 1]].copy_(
tensor[offsets[i] : offsets[i + 1]]
)
fp8_idx += 1
else:
non_fp8_tensor[
non_fp8_offsets[non_fp8_idx] : non_fp8_offsets[non_fp8_idx + 1]
].copy_(tensor[offsets[i] : offsets[i + 1]])
non_fp8_idx += 1
fp8_state_dict[key] = fp8_tensor
non_fp8_state_dict[key] = non_fp8_tensor
fp8_state_dict['numel_unpadded'] = fp8_offsets[-1]
non_fp8_state_dict['numel_unpadded'] = non_fp8_offsets[-1]
# Add the two separate buffers into `new_state_dict`.
new_state_dict[fp8_gbuf_idx] = {}
new_state_dict[fp8_gbuf_idx][(torch.uint8, fp8_buffer.grad_dtype)] = fp8_state_dict
new_state_dict[non_fp8_gbuf_idx][non_fp8_param_and_grad_dtype] = non_fp8_state_dict
# Inplace update state_dict
state_dict.clear()
for key, value in new_state_dict.items():
state_dict[key] = value
def load_parameter_state(self, filename: str, *, update_legacy_format=False):
"""Load the distributed parameter state from disk.
Args:
filename (str): path to load parameter state from.
"""
state_dict = None
if torch.distributed.get_rank(self.data_parallel_group) == 0:
state_dict = torch.load(filename)
self.load_parameter_state_from_dp_zero(
state_dict, update_legacy_format=update_legacy_format
)
def zero_grad(self, set_to_none: bool = True):
"""
Zeroes grads for the model related parameters, i.e., model_float16_groups
and model_fp32_groups. We additionally zero the remaining groups as a
memory optimization to reduce fragmentation; in the case of
set_to_none==True, the space used by this field can be safely deallocated.
Args:
set_to_none (bool): if true, set grads to None.
"""
for groups in (
self.model_float16_groups,
self.model_fp32_groups,
self.shard_float16_groups, # grad empty/unused here?
self.shard_fp32_groups, # throws grad-access warning
self.shard_fp32_from_float16_groups,
):
for group in groups:
_zero_grad_group_helper(group, set_to_none)
def _collect_main_grad_data_for_unscaling(self):
"""
Note: this should be equivalent to the float-16 optimizer's method,
but written differently, so the two should be combined.
"""
return [
param.grad.data for group in self.optimizer.param_groups for param in group["params"]
]
def _get_model_and_main_params_data_float16(self):
"""
Get aligned list of model and main params.
"""
model_data = []
main_data = []
for model_group, main_group in zip(
self.shard_float16_groups, self.shard_fp32_from_float16_groups
):
for model_param, main_param in zip(model_group, main_group):
model_data.append(model_param.data)
main_data.append(main_param.data)
return model_data, main_data
def _copy_model_grads_to_main_grads(self):
"""
Copy model grads to main grads.
Since this step follows a reduce-scatter through the DDP's grad
buffer, this method is responsible for copying the updated grads
from the grad buffer to the main shard's grad field.
"""
# Utility method for copying group grads.
def copy_group_grads(model_groups, shard_main_groups):
for model_group, shard_main_group in zip(model_groups, shard_main_groups):
for model_param, shard_main_param in zip(model_group, shard_main_group):
param_range_map = self._get_model_param_range_map(model_param)
param_range = param_range_map["param"]
assert param_range.size == shard_main_param.nelement()
model_grad = model_param.main_grad
shard_model_grad = model_grad.view(-1)[param_range.start : param_range.end]
shard_main_param.grad = shard_model_grad.float()
# Copy model groups to shard groups.
copy_group_grads(self.model_float16_groups, self.shard_fp32_from_float16_groups)
copy_group_grads(self.model_fp32_groups, self.shard_fp32_groups)
def _copy_main_params_to_model_params(self):
"""
Copy main params to model params.
Since this step is followed by an all-gather through the DDP's grad
buffer, this method is responsible for copying the updated params
from the main shards into the correct position in the grad buffer.
"""
# Utility method for copying group params.
def copy_group_params(shard_main_groups, model_groups):
for shard_main_group, model_group in zip(shard_main_groups, model_groups):
for shard_main_param, model_param in zip(shard_main_group, model_group):
param_range_map = self._get_model_param_range_map(model_param)
world_range = param_range_map["gbuf_world_in_bucket"]
assert world_range.size == shard_main_param.nelement()
gbuf_index, _, bucket_id = self.model_param_gbuf_map[model_param]
model_param_buffer = self.buffers[gbuf_index].buckets[bucket_id].param_data
shard_model_param = model_param_buffer.view(-1)[
world_range.start : world_range.end
]
if is_float8tensor(model_param):
# 1. When "--fp8-param-gather" is disabled, the main param is first cast to
# BF16/FP16, and then cast to FP8, so the amax_history is calculated
# using BF16/FP16 param.
# 2. When "--fp8-param-gather" is enabled, we can cast the FP32 main param
# to FP8 directly, which results in slightly different results with
# higher speed. In theory, this does not affect convergence.
# TODO: The following code maintains the logic of the point-1 above. It can
# be deleted if it is not necessary.
shard_main_param = shard_main_param.to(model_param.dtype)
cast_to_fp8(
shard_main_param.view(1, -1),
model_param._fp8_meta['scaling_fwd'],
model_param._fp8_meta_index,
model_param._fp8_dtype,
out=shard_model_param.view(1, -1),
)
else:
shard_model_param.data.copy_(shard_main_param)
# Copy shard groups to model groups.
copy_group_params(self.shard_fp32_from_float16_groups, self.model_float16_groups)
copy_group_params(self.shard_fp32_groups, self.model_fp32_groups)
def _copy_model_params_to_main_params(self):
"""
Copy model params to main params.
During finetuning, this method is used to reload the main params from
the model params. This copy does not make use of the grad buffer as
an intermediary.
"""
# Utility method for copying group params.
def copy_group_params(model_groups, shard_main_groups):
for model_group, shard_main_group in zip(model_groups, shard_main_groups):
for model_param, shard_main_param in zip(model_group, shard_main_group):
param_range_map = self._get_model_param_range_map(model_param)
param_range = param_range_map["param"]
assert param_range.size == shard_main_param.nelement()
shard_model_param = model_param.view(-1)[param_range.start : param_range.end]
shard_main_param.data.copy_(shard_model_param)
# Copy model groups to shard groups.
copy_group_params(self.model_float16_groups, self.shard_fp32_from_float16_groups)
copy_group_params(self.model_fp32_groups, self.shard_fp32_groups)
def _update_fp8_scale_inv_and_amax(self):
"""
If detect FP8 parameters, update their `_scale_inv` and do reduce-max for their
`amax_history`.
"""
amaxes = []
scales = []
scale_invs = []
# Iterate over all parameters inside this optimizer to find FP8 parameters.
for buffer in self.buffers:
for bucket in buffer.buckets:
for param in bucket.params_list:
if is_float8tensor(param):
fp8_meta = param._fp8_meta['scaling_fwd']
fp8_meta_index = param._fp8_meta_index
amaxes.append(fp8_meta.amax_history[0][fp8_meta_index].view(1))
scales.append(fp8_meta.scale[fp8_meta_index].view(1))
scale_invs.append(param._scale_inv.view(1))
# Reset transpose cache
param._reset_caches()
# If there is no FP8 parameters, skip all operations.
if len(scales) > 0:
dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda')
# Update scaling factors.
packed_scales = torch.empty(len(scales), dtype=torch.float32, device=scales[0].device)
packed_scale_views = [packed_scales[i].view(1) for i in range(len(scales))]
_multi_tensor_copy_this_to_that(scales, packed_scale_views, dummy_overflow_buf)
torch.reciprocal(packed_scales, out=packed_scales)
_multi_tensor_copy_this_to_that(packed_scale_views, scale_invs, dummy_overflow_buf)
# Reduce amaxes.
# Note: Assume each param has a separate amax.
packed_amaxes = torch.empty(len(amaxes), dtype=torch.float32, device=amaxes[0].device)
packed_amax_views = [packed_amaxes[i].view(1) for i in range(len(amaxes))]
_multi_tensor_copy_this_to_that(amaxes, packed_amax_views, dummy_overflow_buf)
torch.distributed.all_reduce(
packed_amaxes, op=torch.distributed.ReduceOp.MAX, group=self.data_parallel_group
)
_multi_tensor_copy_this_to_that(packed_amax_views, amaxes, dummy_overflow_buf)
@torch.no_grad()
def step_with_ready_grads(self) -> bool:
"""Step the optimizer with ready gradients, return successful.
Under the hood, either launch synchronous param all-gathers or get ready to launch
asynchorous all-gathers that get overlapped with the next forward pass.
"""
update_successful = super().step_with_ready_grads()
# If there is no FP8 parameters, this will do nothing.
self._update_fp8_scale_inv_and_amax()
timers = self.config.timers
if timers is not None:
timers('params-all-gather', log_level=1).start(barrier=self.config.barrier_with_L1_time)
# If not overlapping all-gather for parameters, launch synchronous all-gather
# communication calls here. If overlapping all-gather for parameters, the following
# the first all-gather is launched asynchronously in the next optimizer.zero_grad()
# call and subsequent all-gathers are launched in the forward pre-hook.
if not self.ddp_config.overlap_param_gather:
for model_chunk in self.model_chunks:
model_chunk.start_param_sync()
if timers is not None:
timers('params-all-gather').stop()
return update_successful
megatron/optimizer/grad_scaler.py → megatron/core/optimizer/grad_scaler.py
View file @ 4b097dee
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
"""Megatron grad scaler."""
from abc import ABC
from abc import abstractmethod
from abc import ABC, abstractmethod
from typing import Dict
import torch
class MegatronGradScaler(ABC):
def __init__(self, initial_scale):
def __init__(self, initial_scale: float):
"""Initialize scale value with the input initial scale."""
assert initial_scale > 0.0
self._scale = torch.cuda.FloatTensor([initial_scale])
self._scale = torch.tensor([initial_scale], dtype=torch.float, device='cuda')
@property
def scale(self):
......@@ -24,7 +23,7 @@ class MegatronGradScaler(ABC):
return self._scale.double().reciprocal().float()
@abstractmethod
def update(self, found_inf):
def update(self, found_inf: bool):
pass
@abstractmethod
......@@ -32,14 +31,16 @@ class MegatronGradScaler(ABC):
pass
@abstractmethod
def load_state_dict(self, state_dict):
def load_state_dict(self, state_dict: Dict):
pass
class ConstantGradScaler(MegatronGradScaler):
"""
Constant grad scaler (loss scale is never adjusted regardless of NaNs seen in gradients).
"""
def update(self, found_inf):
def update(self, found_inf: bool):
pass
def state_dict(self):
......@@ -49,26 +50,48 @@ class ConstantGradScaler(MegatronGradScaler):
pass
class DynamicGradScaler(MegatronGradScaler):
def __init__(self, initial_scale, min_scale,
growth_factor, backoff_factor,
growth_interval, hysteresis):
""""Grad scaler with dynamic scale that gets adjusted
during training."""
"""
Grad scaler with dynamic scale that gets adjusted during training.
Reduces loss scale by `backoff_factor` if `hysteresis` number of NaNs are seen in a row. Increases
loss scale by `growth_factor` if NaNs are not seen for `growth_interval` iterations.
"""
def __init__(
self,
initial_scale: float,
min_scale: float,
growth_factor: float,
backoff_factor: float,
growth_interval: int,
hysteresis: int,
):
"""
Grad scaler with dynamic scale that gets adjusted during training.
Args:
initial_scale (float): Initial loss scale value.
min_scale (float): Minimum loss scale value.
growth_factor (float): Factor to grow loss scale by if NaNs are not seen in `growth_interval`
training iterations. Must be greater than 1.
backoff_factor (float): Factor to decrease loss scale by if NaNs are seen in `hysteresis`
consecutive training iterations. Must be between 0 and 1.
growth_interval (int): Number of training iterations of no NaNs before loss scale is increased.
hysteresis (int): Number of training iterations of consecutive NaNs before loss scale is decreased.
"""
super(DynamicGradScaler, self).__init__(initial_scale)
# Lower bound on the scale.
assert min_scale > 0.0
assert min_scale <= initial_scale
self.min_scale = torch.cuda.FloatTensor([min_scale])
self.min_scale = torch.tensor([min_scale], dtype=torch.float, device='cuda')
# Growth and backoff factors for the scale.
assert growth_factor > 1.0
self.growth_factor = torch.cuda.FloatTensor([growth_factor])
self.growth_factor = torch.tensor([growth_factor], dtype=torch.float, device='cuda')
assert backoff_factor < 1.0
assert backoff_factor > 0.0
self.backoff_factor = torch.cuda.FloatTensor([backoff_factor])
self.backoff_factor = torch.tensor([backoff_factor], dtype=torch.float, device='cuda')
# Interval over which if we don't see any inf/nan,
# we will scale the grad scale by the growth factor.
assert growth_interval > 0
......@@ -82,8 +105,10 @@ class DynamicGradScaler(MegatronGradScaler):
self._growth_tracker = 0
self._hysteresis_tracker = self.hysteresis
def update(self, found_inf):
def update(self, found_inf: bool):
"""
Updates internal state in grad scaler based on whether NaNs are seen in grads or not.
"""
# If we have an inf/nan, growth tracker is set to 0
# and hysterisis tracker is reduced by 1.
......@@ -92,8 +117,7 @@ class DynamicGradScaler(MegatronGradScaler):
self._hysteresis_tracker -= 1
# Now if we are out of hysteresis count, scale down the loss.
if self._hysteresis_tracker <= 0:
self._scale = torch.max(self._scale * self.backoff_factor,
self.min_scale)
self._scale = torch.max(self._scale * self.backoff_factor, self.min_scale)
else:
# If there is no nan/inf, increment the growth tracker.
self._growth_tracker += 1
......@@ -105,7 +129,6 @@ class DynamicGradScaler(MegatronGradScaler):
# and scale up the loss scale.
self._scale = self._scale * self.growth_factor
def state_dict(self):
state_dict = {}
state_dict['scale'] = self._scale
......@@ -113,8 +136,7 @@ class DynamicGradScaler(MegatronGradScaler):
state_dict['hysteresis_tracker'] = self._hysteresis_tracker
return state_dict
def load_state_dict(self, state_dict):
def load_state_dict(self, state_dict: Dict):
self._scale = state_dict['scale'].cuda(torch.cuda.current_device())
self._growth_tracker = state_dict['growth_tracker']
self._hysteresis_tracker = state_dict['hysteresis_tracker']
megatron/optimizer/optimizer.py → megatron/core/optimizer/optimizer.py
View file @ 4b097dee
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
"""Megatron optimizer."""
from abc import ABC
from abc import abstractmethod
from apex.multi_tensor_apply import multi_tensor_applier
import amp_C
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from megatron import get_timers
from megatron import print_rank_0
from megatron.core import mpu, tensor_parallel
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import Float16Module
from megatron.model.module import param_is_not_shared
from megatron.utils import unwrap_model
from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32
import copy
import math
import warnings
from abc import ABC, abstractmethod
from itertools import chain
from logging import getLogger
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
def _zero_grad_group_helper(group, set_to_none):
"""Zero out the gradient for a group of parameters.
Note: copied from torch.optim.optimizer."""
try:
from transformer_engine.pytorch.optimizers import multi_tensor_applier
except ImportError:
try:
from apex.multi_tensor_apply import multi_tensor_applier
except ImportError:
from megatron.core.utils import local_multi_tensor_applier
multi_tensor_applier = local_multi_tensor_applier
try:
import amp_C
l2_norm_impl = amp_C.multi_tensor_l2norm
multi_tensor_scale_impl = amp_C.multi_tensor_scale
except ImportError:
from megatron.core.utils import local_multi_tensor_l2_norm, local_multi_tensor_scale
l2_norm_impl = local_multi_tensor_l2_norm
multi_tensor_scale_impl = local_multi_tensor_scale
from .. import parallel_state, tensor_parallel
from ..config_logger import has_config_logger_enabled, log_config_to_disk
from ..dist_checkpointing.mapping import ShardedStateDict
from ..dist_checkpointing.optimizer import (
get_param_id_to_sharded_param_map,
make_sharded_optimizer_tensor,
optim_state_to_sharding_state,
)
from ..dist_checkpointing.utils import add_prefix_for_sharding
from ..transformer.module import param_is_not_shared
from .clip_grads import clip_grad_by_total_norm_fp32, count_zeros_fp32, get_grad_norm_fp32
from .grad_scaler import MegatronGradScaler
from .optimizer_config import OptimizerConfig
logger = getLogger(__name__)
def _zero_grad_group_helper(group: List[torch.nn.Parameter], set_to_none: bool):
"""
Zero out the gradient for a group of parameters.
Note: copied from torch.optim.optimizer.
"""
for param in group:
if param.grad is not None:
if set_to_none:
......@@ -36,65 +66,65 @@ def _zero_grad_group_helper(group, set_to_none):
param.grad.zero_()
def _multi_tensor_copy_this_to_that(this, that, overflow_buf=None):
"""Use multi-tensor-applier to copy values from one list to another.
We don't have a blfoat16 implementation so for now if the overflow_buf
def _multi_tensor_copy_this_to_that(
this: List[torch.Tensor], that: List[torch.Tensor], overflow_buf: Optional[torch.Tensor] = None
):
"""
Use multi-tensor-applier to copy values from one list to another.
We don't have a bfloat16 implementation so for now if the overflow_buf
is not provided, we default back to simple loop copy to be compatible
with bfloat16."""
with bfloat16.
"""
if overflow_buf:
overflow_buf.fill_(0)
# Scaling with factor `1.0` is equivalent to copy.
multi_tensor_applier(amp_C.multi_tensor_scale,
overflow_buf,
[this, that],
1.0)
multi_tensor_applier(multi_tensor_scale_impl, overflow_buf, [this, that], 1.0)
else:
for this_, that_ in zip(this, that):
that_.copy_(this_)
class MegatronOptimizer(ABC):
"""
Base class for all Megatron optimizers.
Args:
optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD.
config (OptimizerConfig): configuration object for optimizer.
init_state_fn (Callable, optional): function to initialize state in the optimizer.
"""
def __init__(self, optimizer, clip_grad,
log_num_zeros_in_grad,
params_have_main_grad,
use_contiguous_buffers_in_local_ddp,
models):
"""Input optimizer is the base optimizer for example Adam."""
def __init__(
self,
optimizer: torch.optim.Optimizer,
config: OptimizerConfig,
init_state_fn: Callable = lambda x: None,
):
"""Input optimizer is the base optimizer (e.g., Adam)."""
self.optimizer = optimizer
assert self.optimizer, 'no optimizer is provided.'
# Set gradient clipping and logging params.
self.clip_grad = clip_grad
self.log_num_zeros_in_grad = log_num_zeros_in_grad
self.params_have_main_grad = params_have_main_grad
self.use_contiguous_buffers_in_local_ddp = use_contiguous_buffers_in_local_ddp
# 'models' are retained for access to the contiguous grad buffers.
# (see distributed optimizer)
self.models = models
if self.use_contiguous_buffers_in_local_ddp:
assert self.params_have_main_grad, \
"use of contiguous buffer requires that params have main grad"
self.config = config
self.init_state_fn = init_state_fn
def get_parameters(self):
def get_parameters(self) -> List[torch.nn.Parameter]:
"""
Get list of parameters wrapped in optimizer.
"""
params = []
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
params.append(param)
return params
def get_main_grads_for_grad_norm(self):
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
def get_main_grads_for_grad_norm(self) -> List[torch.Tensor]:
"""
Get main_grads that should be taken into account to compute the grad norm.
Filter parameters based on:
- grad should not be None.
- parameter should not be shared (i.e., grads shouldn't be double counted while
computing norms).
- should not be a replica due to tensor model parallelism.
"""
params = self.get_parameters()
grads_for_norm = []
for param in params:
......@@ -107,41 +137,69 @@ class MegatronOptimizer(ABC):
return grads_for_norm
def get_model_parallel_group(self):
def get_model_parallel_group(self) -> torch.distributed.ProcessGroup:
"""Default returned here, but the distributed optimizer overrides this."""
return mpu.get_model_parallel_group()
if hasattr(self, 'model_parallel_group'):
return self.model_parallel_group
return parallel_state.get_model_parallel_group()
@abstractmethod
def prepare_grads(self) -> bool:
"""Pre-processing gradients before the optimizer step, returns whether inf/nan is found."""
return False
def clip_grad_norm(self, clip_grad):
params = self.get_parameters()
grads_for_norm = self.get_main_grads_for_grad_norm()
return clip_grad_norm_fp32(
params, grads_for_norm, clip_grad,
model_parallel_group=self.get_model_parallel_group())
@abstractmethod
def step_with_ready_grads(self) -> bool:
"""Step the optimizer with ready gradients, return successful."""
return True
@torch.no_grad()
def get_grad_norm(self):
"""Compute and return grad norm."""
grads_for_norm = self.get_main_grads_for_grad_norm()
total_norm = get_grad_norm_fp32(
grads_for_norm, model_parallel_group=self.get_model_parallel_group()
)
return total_norm
def count_zeros(self):
def clip_grad_norm(self, clip_grad: float) -> float:
"""Compute and return grad norm, also clip grads."""
params = self.get_parameters()
return count_zeros_fp32(params,
model_parallel_group=self.get_model_parallel_group())
grads_for_norm = self.get_main_grads_for_grad_norm()
grad_norm = get_grad_norm_fp32(
grads_for_norm, model_parallel_group=self.get_model_parallel_group()
)
clip_grad_by_total_norm_fp32(params, clip_grad, grad_norm)
return grad_norm
def count_zeros(self) -> float:
"""Count number of zeros in model's gradients."""
params = self.get_parameters()
return count_zeros_fp32(params, model_parallel_group=self.get_model_parallel_group())
@abstractmethod
def zero_grad(self, set_to_none=True):
def zero_grad(self, set_to_none: bool = True):
"""Zero gradients and prepare for next forward pass."""
pass
@abstractmethod
def get_loss_scale(self):
"""The output should be a cuda tensor of size 1."""
def get_loss_scale(self) -> torch.Tensor:
"""
Get current loss scale factor.
NOTE: The output should be a CUDA tensor of size 1.
"""
pass
def scale_loss(self, loss):
def scale_loss(self, loss: torch.Tensor) -> torch.Tensor:
"""Simple scaling."""
return self.get_loss_scale() * loss
def start_param_sync(self, model_index: int, *unused):
"""
Start parameter synchronization for all optimizers.
This is a no-op for all non-distributed optimizers.
"""
pass
@abstractmethod
def reload_model_params(self):
......@@ -152,17 +210,16 @@ class MegatronOptimizer(ABC):
with main parameters, the main parameters need to also be updated."""
pass
@abstractmethod
def state_dict(self):
"""Return state_dict."""
pass
@abstractmethod
def load_state_dict(self, state_dict):
"""Load pass-in `state_dict`."""
pass
# Promote state so it can be retrieved or set via
# "optimizer_instance.state"
def _get_state(self):
......@@ -173,7 +230,6 @@ class MegatronOptimizer(ABC):
state = property(_get_state, _set_state)
# Promote param_groups so it can be retrieved or set via
# "optimizer_instance.param_groups"
# (for example, to adjust the learning rate)
......@@ -185,200 +241,104 @@ class MegatronOptimizer(ABC):
param_groups = property(_get_param_groups, _set_param_groups)
@abstractmethod
def step(self, args, timers):
pass
def gather_model_params(self, args, timers):
"""
For the case of a non-distributed-optimizer, there is nothing to
do here.
"""
def step(self):
"""Step the optimizer."""
pass
@abstractmethod
def sharded_state_dict(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False
) -> ShardedStateDict:
"""Builds sharded state dict for the optimizer, based on model's sharded state dict.
def allreduce_word_embedding_grads(self, args):
"""
All-reduce word embedding grads.
Args:
model_sharded_state_dict (ShardedStateDict): sharded state dict of the model
is_loading (bool, optional): flag indicating whether the state dict will be
used to save or load the optimizer state. Defaults to False.
Reduce grads across first and last stages to ensure that word_embeddings
parameters stay in sync. This should only run for models that support
pipelined model parallelism (BERT and GPT-2).
Returns: optimizer sharded state dict
"""
if mpu.is_rank_in_embedding_group(ignore_virtual=True) and \
mpu.get_pipeline_model_parallel_world_size() > 1:
if mpu.is_pipeline_first_stage(ignore_virtual=True):
unwrapped_model = self.models[0]
elif mpu.is_pipeline_last_stage(ignore_virtual=True):
unwrapped_model = self.models[-1]
else: # We do not support the interleaved schedule for T5 yet.
unwrapped_model = self.models[0]
unwrapped_model = unwrap_model(
unwrapped_model, (torchDDP, LocalDDP, Float16Module))
if unwrapped_model.share_embeddings_and_output_weights:
weight = unwrapped_model.shared_embedding_or_output_weight()
if args.DDP_impl == 'local':
grad = weight.main_grad
else:
grad = weight.grad
torch.distributed.all_reduce(grad, group=mpu.get_embedding_group())
def allreduce_position_embedding_grads(self, args):
"""
All-reduce position_embeddings grad across first (encoder) and
split (decoder) stages to ensure that position embeddings parameters
stay in sync. This should only run for T5 models with pipeline
parallelism.
"""
if mpu.is_rank_in_position_embedding_group() and \
mpu.get_pipeline_model_parallel_world_size() > 1 and \
args.pipeline_model_parallel_split_rank is not None:
unwrapped_model = self.models[0]
unwrapped_model = unwrap_model(
unwrapped_model, (torchDDP, LocalDDP, Float16Module))
assert args.DDP_impl == 'local', \
'T5 model is only supported with local DDP mode'
grad = unwrapped_model.language_model.embedding.position_embeddings.weight.main_grad
torch.distributed.all_reduce(grad, group=mpu.get_position_embedding_group())
def allreduce_embedding_grads(self, args):
"""All-reduce both word and position embeddings."""
self.allreduce_word_embedding_grads(args)
self.allreduce_position_embedding_grads(args)
def allreduce_layernorm_grads(self, args):
"""All-reduce layernorm grads (for sequence parallelism)."""
# All-reduce layernorm parameters across model parallel nodes
# when sequence parallelism is used
if mpu.get_tensor_model_parallel_world_size() > 1 and \
args.sequence_parallel:
grads = []
for model_module in self.models:
unwrapped_model = unwrap_model(
model_module, (torchDDP, LocalDDP, Float16Module))
for param in unwrapped_model.parameters():
if getattr(param, 'sequence_parallel', False):
grad = param.main_grad if args.DDP_impl == 'local' else param.grad
grads.append(grad.data)
coalesced = _flatten_dense_tensors(grads)
torch.distributed.all_reduce(
coalesced, group=mpu.get_tensor_model_parallel_group())
for buf, synced in zip(grads, _unflatten_dense_tensors(
coalesced, grads)):
buf.copy_(synced)
def reduce_model_grads(self, args, timers):
"""All-reduce all grads, and all-reduce embeddings."""
# All-reduce layer-norm grads (for sequence parallelism).
timers('layernorm-grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.allreduce_layernorm_grads(args)
timers('layernorm-grads-all-reduce').stop()
# All-reduce if needed.
if args.DDP_impl == 'local':
timers('grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
for model in self.models:
model.allreduce_gradients()
timers('grads-all-reduce').stop()
# All-reduce embedding grads.
timers('embedding-grads-all-reduce', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.allreduce_embedding_grads(args)
timers('embedding-grads-all-reduce').stop()
@staticmethod
def _extract_common_per_param_step(state_dict) -> Union[int, torch.Tensor]:
common_step = None
for param_idx, param_state in state_dict['state'].items():
param_step = param_state.get('step', None)
if param_step is not None:
if common_step is None:
common_step = param_step
elif common_step != param_step:
raise ValueError(
"The optimizer step differs per parameter. Mcore only supports "
"optimizers whose step is shared across all parameters."
)
return common_step
@staticmethod
def _restore_common_per_param_step(state_dict: Dict, step: Union[int, torch.Tensor]):
for param_idx, param_state in state_dict['state'].items():
param_state['step'] = copy.deepcopy(step)
class MixedPrecisionOptimizer(MegatronOptimizer):
"""Base class for both the float-16 and the distributed optimizer.
Arguments:
optimizer: base optimizer such as Adam or SGD
clip_grad: clip gradeints with this global L2 norm. Note
that clipping is ignored if clip_grad == 0
log_num_zeros_in_grad: return number of zeros in the gradients.
params_have_main_grad: flag indicating if parameters have
a `main_grad` field. If this is set, we are assuming
that the model parameters are store in the `main_grad`
field instead of the typical `grad` field. This happens
for the DDP cases where there is a continuous buffer
holding the gradients. For example for bfloat16, we want
to do gradient accumulation and all-reduces in float32
and as a result we store those gradients in the main_grad.
Note that main grad is not necessarily in float32.
use_contiguous_buffers_in_local_ddp: if true, the local DDP model
is using a contiguous buffer to hold the model grads.
fp16: if true, the model is running in fp16.
bf16: if true, the model is running in bfloat16.
params_dtype: used by distributed optimizer.
grad_scaler: used for scaling gradients. Note that this can be
None. This case happens when `bf16 = True` and we don't
Args:
optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD.
config (OptimizerConfig): configuration object for optimizer.
grad_scaler (MegatronGradScaler): used for scaling gradients. Note that
this can be None. This case happens when `bf16 = True` and we don't
use any loss scale. Note that for `bf16 = True`, we can have
a constnat gradient scaler. Also for `bf16 = False`, we
a constant gradient scaler. Also for `bf16 = False`, we
always require a grad scaler.
models: list of models (i.e., the virtual pipelining models). This
is used by the distributed optimizer for mapping parameters.
init_state_fn (Callable, optional): function to initialize state in the optimizer.
"""
def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad,
params_have_main_grad, use_contiguous_buffers_in_local_ddp,
fp16, bf16, params_dtype, grad_scaler,
models):
super().__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
params_have_main_grad, use_contiguous_buffers_in_local_ddp,
models)
self.fp16 = fp16
self.bf16 = bf16
self.params_dtype = params_dtype
def __init__(
self,
optimizer: torch.optim.Optimizer,
config: OptimizerConfig,
grad_scaler: Optional[MegatronGradScaler],
init_state_fn: Callable,
):
if has_config_logger_enabled(config):
log_config_to_disk(config, locals(), prefix=type(self).__name__)
super().__init__(optimizer, config, init_state_fn)
self.grad_scaler = grad_scaler
# None grad scaler is only supported for bf16.
if self.grad_scaler is None:
assert not self.fp16, 'fp16 expects a grad scaler.'
assert not self.config.fp16, 'fp16 expects a grad scaler.'
# Tensor used to determine if a nan/if has happend.
# Any non-zero value indicates inf/nan.
# Note that we keep this for the cases that grad scaler is none.
# We still record nan/inf if we have a bfloat16 with a grad scaler.
if self.grad_scaler:
self.found_inf = torch.cuda.FloatTensor([0.0])
self.found_inf = torch.tensor([0.0], dtype=torch.float, device='cuda')
# Dummy tensor needed for apex multi-apply tensor.
# For bfloat, we don't have multi-tensor apply and for now
# we set it to none so the multi-tensor apply gets ignored.
if bf16:
if self.config.bf16:
self._dummy_overflow_buf = None
else:
self._dummy_overflow_buf = torch.cuda.IntTensor([0])
self._dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device='cuda')
# In case grad scaler is not passed, define the unity scale.
if self.grad_scaler is None:
self._scale_one = torch.cuda.FloatTensor([1.0])
self._scale_one = torch.tensor([1.0], dtype=torch.float, device='cuda')
def get_loss_scale(self):
if self.grad_scaler is None:
return self._scale_one
return self.grad_scaler.scale
def reload_model_params(self):
self._copy_model_params_to_main_params()
def _unscale_main_grads_and_check_for_nan(self):
# Collect main grads.
......@@ -389,119 +349,137 @@ class MixedPrecisionOptimizer(MegatronOptimizer):
# Unscale and set found inf/nan
torch._amp_foreach_non_finite_check_and_unscale_(
main_grads, self.found_inf, self.grad_scaler.inv_scale)
main_grads, self.found_inf, self.grad_scaler.inv_scale
)
# Update across all model parallel instances.
torch.distributed.all_reduce(self.found_inf,
op=torch.distributed.ReduceOp.MAX,
group=self.get_model_parallel_group())
torch.distributed.all_reduce(
self.found_inf, op=torch.distributed.ReduceOp.MAX, group=self.get_model_parallel_group()
)
# Check for nan.
found_inf_flag = (self.found_inf.item() > 0)
found_inf_flag = self.found_inf.item() > 0
return found_inf_flag
@torch.no_grad()
def step(self, args, timers):
def prepare_grads(self) -> bool:
"""Pre-processing gradients before the optimizer step, returns whether inf/nan is found."""
timers = self.config.timers
# Copy gradients from model params to main params.
timers('optimizer-copy-to-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
if timers is not None:
timers('optimizer-copy-to-main-grad', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
self._copy_model_grads_to_main_grads()
timers('optimizer-copy-to-main-grad').stop()
if timers is not None:
timers('optimizer-copy-to-main-grad').stop()
# Do unscale, check for inf, and update grad scaler only for
# the case that grad scaler is provided.
if self.grad_scaler:
# Unscale and check for inf/nan.
timers('optimizer-unscale-and-check-inf', log_level=1).start(
barrier=args.barrier_with_L1_time)
if timers is not None:
timers('optimizer-unscale-and-check-inf', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
found_inf_flag = self._unscale_main_grads_and_check_for_nan()
timers('optimizer-unscale-and-check-inf').stop()
if timers is not None:
timers('optimizer-unscale-and-check-inf').stop()
# We are done with scaling gradients
# so we can update the loss scale.
self.grad_scaler.update(found_inf_flag)
# If we found inf/nan, skip the update.
if found_inf_flag:
return False, None, None
# Clip the main gradients.
timers('optimizer-clip-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
grad_norm = None
if self.clip_grad > 0.0:
grad_norm = self.clip_grad_norm(self.clip_grad)
timers('optimizer-clip-main-grad').stop()
return found_inf_flag
# Count the zeros in the grads.
timers('optimizer-count-zeros', log_level=1).start(
barrier=args.barrier_with_L1_time)
num_zeros_in_grad = self.count_zeros() if \
self.log_num_zeros_in_grad else None
timers('optimizer-count-zeros').stop()
return False
@torch.no_grad()
def step_with_ready_grads(self) -> bool:
"""Step the optimizer with ready gradients, return successful."""
timers = self.config.timers
# Step the optimizer.
timers('optimizer-inner-step', log_level=1).start(
barrier=args.barrier_with_L1_time)
if timers is not None:
timers('optimizer-inner-step', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
self.optimizer.step()
timers('optimizer-inner-step').stop()
if timers is not None:
timers('optimizer-inner-step').stop()
# Update params from main params.
timers('optimizer-copy-main-to-model-params', log_level=1).start(
barrier=args.barrier_with_L1_time)
if timers is not None:
timers('optimizer-copy-main-to-model-params', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
self._copy_main_params_to_model_params()
timers('optimizer-copy-main-to-model-params').stop()
if timers is not None:
timers('optimizer-copy-main-to-model-params').stop()
return True
@torch.no_grad()
def step(self):
timers = self.config.timers
found_inf_flag = self.prepare_grads()
if found_inf_flag:
return False, None, None
# Clip the main gradients.
if timers is not None:
timers('optimizer-clip-main-grad', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
grad_norm = None
if self.config.clip_grad > 0.0:
grad_norm = self.clip_grad_norm(self.config.clip_grad)
if timers is not None:
timers('optimizer-clip-main-grad').stop()
# Count the zeros in the grads.
if timers is not None:
timers('optimizer-count-zeros', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
num_zeros_in_grad = self.count_zeros() if self.config.log_num_zeros_in_grad else None
if timers is not None:
timers('optimizer-count-zeros').stop()
success = self.step_with_ready_grads()
# Successful update.
return True, grad_norm, num_zeros_in_grad
return success, grad_norm, num_zeros_in_grad
class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
"""Float16 optimizer for fp16 and bf16 data types.
Arguments:
optimizer: base optimizer such as Adam or SGD
clip_grad: clip gradeints with this global L2 norm. Note
that clipping is ignored if clip_grad == 0
log_num_zeros_in_grad: return number of zeros in the gradients.
params_have_main_grad: flag indicating if parameters have
a `main_grad` field. If this is set, we are assuming
that the model parameters are store in the `main_grad`
field instead of the typical `grad` field. This happens
for the DDP cases where there is a continuous buffer
holding the gradients. For example for bfloat16, we want
to do gradient accumulation and all-reduces in float32
and as a result we store those gradients in the main_grad.
Note that main grad is not necessarily in float32.
use_contiguous_buffers_in_local_ddp: if true, the local DDP model
is using a contiguous buffer to hold the model grads.
fp16: if true, the model is running in fp16.
bf16: if true, the model is running in bfloat16.
grad_scaler: used for scaling gradients. Note that this can be
None. This case happens when `bf16 = True` and we don't
Args:
optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD.
config (OptimizerConfig): configuration object for optimizer.
grad_scaler (MegatronGradScaler): used for scaling gradients. Note that
this can be None. This case happens when `bf16 = True` and we don't
use any loss scale. Note that for `bf16 = True`, we can have
a constnat gradient scaler. Also for `bf16 = False`, we
a constant gradient scaler. Also for `bf16 = False`, we
always require a grad scaler.
models: list of models (i.e., the virtual pipelining models). This
is used by the distributed optimizer for mapping parameters.
init_state_fn (Callable, optional): function to initialize state in the optimizer.
"""
def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad,
params_have_main_grad, use_contiguous_buffers_in_local_ddp,
fp16, bf16, params_dtype, grad_scaler, models):
def __init__(
self,
optimizer: torch.optim.Optimizer,
config: OptimizerConfig,
grad_scaler: MegatronGradScaler,
init_state_fn: Callable,
):
super().__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
params_have_main_grad, use_contiguous_buffers_in_local_ddp,
fp16, bf16, params_dtype, grad_scaler, models)
super().__init__(optimizer, config, grad_scaler, init_state_fn)
# ======================
# main parameter stuff
# ======================
# Handle main parameters.
# Three groups of parameters:
# float16_groups: original float16 parameters
......@@ -521,14 +499,12 @@ class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
if param.requires_grad:
# float16 params:
if param.type() in ['torch.cuda.HalfTensor',
'torch.cuda.BFloat16Tensor']:
if param.type() in ['torch.cuda.HalfTensor', 'torch.cuda.BFloat16Tensor']:
float16_params_this_group.append(param)
# Create a copy
main_param = param.detach().clone().float()
# Copy tensor model parallel attributes.
tensor_parallel.copy_tensor_model_parallel_attributes(main_param,
param)
tensor_parallel.copy_tensor_model_parallel_attributes(main_param, param)
if hasattr(param, 'shared'):
main_param.shared = param.shared
# Replace the optimizer params with the new fp32 copy.
......@@ -537,26 +513,25 @@ class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
fp32_from_float16_params_this_group.append(main_param)
# Reset existing state dict key to the new main param.
if param in self.optimizer.state:
self.optimizer.state[main_param] \
= self.optimizer.state.pop(param)
self.optimizer.state[main_param] = self.optimizer.state.pop(param)
# fp32 params.
elif param.type() == 'torch.cuda.FloatTensor':
fp32_params_this_group.append(param)
param_group['params'][i] = param
else:
raise TypeError('Wrapped parameters must be one of '
'torch.cuda.FloatTensor, '
'torch.cuda.HalfTensor, or '
'torch.cuda.BFloat16Tensor. '
'Received {}'.format(param.type()))
raise TypeError(
'Wrapped parameters must be one of '
'torch.cuda.FloatTensor, '
'torch.cuda.HalfTensor, or '
'torch.cuda.BFloat16Tensor. '
'Received {}'.format(param.type())
)
self.float16_groups.append(float16_params_this_group)
self.fp32_from_float16_groups.append(
fp32_from_float16_params_this_group)
self.fp32_from_float16_groups.append(fp32_from_float16_params_this_group)
self.fp32_from_fp32_groups.append(fp32_params_this_group)
def zero_grad(self, set_to_none=True):
"""We only need to zero the model related parameters, i.e.,
float16_groups & fp32_from_fp32_groups. We additionally zero
......@@ -570,7 +545,6 @@ class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
for group in self.fp32_from_fp32_groups:
_zero_grad_group_helper(group, set_to_none)
def _collect_main_grad_data_for_unscaling(self):
main_grads = []
......@@ -586,27 +560,23 @@ class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
for main_param in main_group:
if main_param.grad is not None:
main_grads.append(main_param.grad.data)
return main_grads
return main_grads
def _get_model_and_main_params_data_float16(self):
model_data = []
main_data = []
for model_group, main_group in zip(self.float16_groups,
self.fp32_from_float16_groups):
for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups):
for model_param, main_param in zip(model_group, main_group):
model_data.append(model_param.data)
main_data.append(main_param.data)
return model_data, main_data
def _copy_model_grads_to_main_grads(self):
# This only needs to be done for the float16 group.
for model_group, main_group in zip(self.float16_groups,
self.fp32_from_float16_groups):
for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups):
for model_param, main_param in zip(model_group, main_group):
if self.params_have_main_grad and hasattr(model_param, 'main_grad'):
if hasattr(model_param, 'main_grad'):
main_param.grad = model_param.main_grad.float()
else:
if model_param.grad is not None:
......@@ -616,36 +586,25 @@ class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
# (If using contiguous buffers, main_grad's memory should
# persist and therefore should not be deallocated.)
model_param.grad = None
if self.params_have_main_grad and \
not self.use_contiguous_buffers_in_local_ddp:
model_param.main_grad = None
# For fp32 grads, we need to reset the grads to main grad.
if self.params_have_main_grad:
for model_group in self.fp32_from_fp32_groups:
for model_param in model_group:
model_param.grad = model_param.main_grad
# Safe to de-reference model's main_grad after copying.
# (If using contiguous buffers, main_grad's memory should
# persist and therefore should not be deallocated.)
if not self.use_contiguous_buffers_in_local_ddp:
model_param.main_grad = None
for model_group in self.fp32_from_fp32_groups:
for model_param in model_group:
model_param.grad = model_param.main_grad
def _copy_main_params_to_model_params(self):
# Only needed for the float16 params.
model_data, main_data = self._get_model_and_main_params_data_float16()
_multi_tensor_copy_this_to_that(this=main_data, that=model_data,
overflow_buf=self._dummy_overflow_buf)
_multi_tensor_copy_this_to_that(
this=main_data, that=model_data, overflow_buf=self._dummy_overflow_buf
)
def _copy_model_params_to_main_params(self):
# Only needed for the float16 params.
model_data, main_data = self._get_model_and_main_params_data_float16()
_multi_tensor_copy_this_to_that(this=model_data, that=main_data,
overflow_buf=self._dummy_overflow_buf)
_multi_tensor_copy_this_to_that(
this=model_data, that=main_data, overflow_buf=self._dummy_overflow_buf
)
def state_dict(self):
state_dict = {}
......@@ -655,119 +614,454 @@ class Float16OptimizerWithFloat16Params(MixedPrecisionOptimizer):
state_dict['fp32_from_fp16_params'] = self.fp32_from_float16_groups
return state_dict
def sharded_state_dict(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False
):
if is_loading:
self.init_state_fn(self.optimizer)
state_dict = self.state_dict()
id_to_sharded_param_map = get_param_id_to_sharded_param_map(
model_sharded_state_dict, chain.from_iterable(g for g in self.float16_groups)
)
# Convert fp32_from_fp16_params
assert len(state_dict['fp32_from_fp16_params']) == len(
state_dict['optimizer']['param_groups']
)
state_dict['fp32_from_fp16_params'] = [
[
make_sharded_optimizer_tensor(
id_to_sharded_param_map[param_id],
fp32_param,
prefix=f'optimizer.state.fp32_param',
)
for param_id, fp32_param in zip(state_group['params'], fp32_group)
]
for fp32_group, state_group in zip(
state_dict['fp32_from_fp16_params'], state_dict['optimizer']['param_groups']
)
]
step = self._extract_common_per_param_step(state_dict['optimizer'])
# Convert regular optimizer state
# all optimizer parameters passed to optim_state_to_sharding_state are
# expected to have the same shape as the model parameters,
# so we save the step separately and ignore it here
optim_state_to_sharding_state(
state_dict['optimizer'], id_to_sharded_param_map, exclude_keys="step"
)
# save step as a shared step among all parameters. Separate per-parameter
# steps are not supported
state_dict['optimizer']['state']['common_step'] = step
return state_dict
def load_state_dict(self, state_dict):
pipeline_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
# Optimizer.
optimizer_key = 'optimizer'
if optimizer_key not in state_dict:
optimizer_key = 'optimizer_state_dict'
print_rank_0('***WARNING*** loading optimizer from '
'an old checkpoint ...')
logger.info('***WARNING*** loading optimizer from ' 'an old checkpoint ...')
if 'common_step' in state_dict[optimizer_key]['state']:
common_step = state_dict[optimizer_key]['state'].pop('common_step')
self._restore_common_per_param_step(state_dict[optimizer_key], common_step)
self.optimizer.load_state_dict(state_dict[optimizer_key])
# Grad scaler.
if 'grad_scaler' not in state_dict:
if self.fp16:
print_rank_0('***WARNING*** found an old checkpoint, will not '
'load grad scaler ...')
if self.config.fp16:
logger.info(
'***WARNING*** found an old checkpoint, will not ' 'load grad scaler ...'
)
else:
if self.grad_scaler:
self.grad_scaler.load_state_dict(state_dict['grad_scaler'])
else:
print_rank_0('***WARNING*** fould the grad scaler in the '
'checkpoint but it is None in the class. '
'Skipping loading grad scaler ...')
logger.info(
'***WARNING*** fould the grad scaler in the '
'checkpoint but it is None in the class. '
'Skipping loading grad scaler ...'
)
# Copy data for the main params.
fp32_from_float16_params_key = 'fp32_from_fp16_params'
if fp32_from_float16_params_key not in state_dict:
fp32_from_float16_params_key = 'fp32_from_fp16'
for current_group, saved_group in zip(
self.fp32_from_float16_groups,
state_dict[fp32_from_float16_params_key]):
self.fp32_from_float16_groups, state_dict[fp32_from_float16_params_key]
):
for current_param, saved_param in zip(current_group, saved_group):
current_param.data.copy_(saved_param.data)
class FP32Optimizer(MegatronOptimizer):
"""Float32 optimizer.
def __init__(self, optimizer, clip_grad,
log_num_zeros_in_grad,
params_have_main_grad,
use_contiguous_buffers_in_local_ddp,
models):
Args:
optimizer (torch.optim.Optimizer): base optimizer such as Adam or SGD.
config (OptimizerConfig): configuration object for optimizer.
init_state_fn (Callable, optional): function to initialize state in the optimizer.
"""
super(FP32Optimizer, self).__init__(
optimizer, clip_grad, log_num_zeros_in_grad,
params_have_main_grad, use_contiguous_buffers_in_local_ddp,
models)
def __init__(
self, optimizer: torch.optim.Optimizer, config: OptimizerConfig, init_state_fn: Callable
):
if has_config_logger_enabled(config):
log_config_to_disk(config, locals(), prefix=type(self).__name__)
self._scale = torch.cuda.FloatTensor([1.0])
super(FP32Optimizer, self).__init__(optimizer, config, init_state_fn)
self._scale = torch.tensor([1.0], dtype=torch.float, device='cuda')
def zero_grad(self, set_to_none=True):
"""Copied from torch.optim.optimizer"""
for group in self.optimizer.param_groups:
_zero_grad_group_helper(group['params'], set_to_none)
def get_loss_scale(self):
"""FP32 optimizer does not do any scaling."""
return self._scale
@torch.no_grad()
def prepare_grads(self) -> bool:
"""Pre-processing gradients before the optimizer step, returns whether inf/nan is found."""
timers = self.config.timers
# Copy main_grads to grads.
if timers is not None:
timers('optimizer-copy-to-main-grad', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
param.grad = param.main_grad
if timers is not None:
timers('optimizer-copy-to-main-grad').stop()
return False
@torch.no_grad()
def step_with_ready_grads(self) -> bool:
"""Step the optimizer with ready gradients, return successful."""
timers = self.config.timers
# Update parameters.
if timers is not None:
timers('optimizer-inner-step', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
self.optimizer.step()
if timers is not None:
timers('optimizer-inner-step').stop()
return True
@torch.no_grad()
def step(self, args, timers):
def step(self):
"""Clip gradients (if needed) and step the base optimizer.
Always return successful since there is no overflow."""
timers = self.config.timers
# Copy main_grads to grads.
timers('optimizer-copy-to-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
if self.params_have_main_grad:
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
param.grad = param.main_grad
# Safe to de-reference model's main_grad after copying.
# (If using contiguous buffers, main_grad's memory should
# persist and therefore should not be deallocated.)
if not self.use_contiguous_buffers_in_local_ddp:
param.main_grad = None
timers('optimizer-copy-to-main-grad').stop()
found_inf_flag = self.prepare_grads()
if found_inf_flag:
return False, None, None
# Clip gradients.
timers('optimizer-clip-main-grad', log_level=1).start(
barrier=args.barrier_with_L1_time)
if timers is not None:
timers('optimizer-clip-main-grad', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
grad_norm = None
if self.clip_grad > 0.0:
grad_norm = self.clip_grad_norm(self.clip_grad)
timers('optimizer-clip-main-grad').stop()
if self.config.clip_grad > 0.0:
grad_norm = self.clip_grad_norm(self.config.clip_grad)
if timers is not None:
timers('optimizer-clip-main-grad').stop()
# count the zeros in the grads
timers('optimizer-count-zeros', log_level=1).start(
barrier=args.barrier_with_L1_time)
num_zeros_in_grad = self.count_zeros() if \
self.log_num_zeros_in_grad else None
timers('optimizer-count-zeros').stop()
# Count the zeros in the grads.
if timers is not None:
timers('optimizer-count-zeros', log_level=1).start(
barrier=self.config.barrier_with_L1_time
)
num_zeros_in_grad = self.count_zeros() if self.config.log_num_zeros_in_grad else None
if timers is not None:
timers('optimizer-count-zeros').stop()
# Update parameters.
timers('optimizer-inner-step', log_level=1).start(
barrier=args.barrier_with_L1_time)
self.optimizer.step()
timers('optimizer-inner-step').stop()
success = self.step_with_ready_grads()
# No overflow for FP32 optimizer.
return True, grad_norm, num_zeros_in_grad
return success, grad_norm, num_zeros_in_grad
def reload_model_params(self):
pass
def state_dict(self):
return self.optimizer.state_dict()
def load_state_dict(self, state_dict):
pipeline_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
if 'common_step' in state_dict['state']:
common_step = state_dict['state'].pop('common_step')
self._restore_common_per_param_step(state_dict, common_step)
self.optimizer.load_state_dict(state_dict)
def sharded_state_dict(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False
):
if is_loading:
self.init_state_fn(self.optimizer)
state_dict = self.state_dict()
id_to_sharded_param_map = get_param_id_to_sharded_param_map(
model_sharded_state_dict, self.get_parameters()
)
step = self._extract_common_per_param_step(state_dict)
# all optimizer parameters passed to optim_state_to_sharding_state are
# expected to have the same shape as the model parameters,
# so we save the step separately and ignore it here
optim_state_to_sharding_state(state_dict, id_to_sharded_param_map, exclude_keys="step")
# save step as a shared step among all parameters. Separate per-parameter
# steps are not supported
state_dict['state']['common_step'] = step
return state_dict
class ProxyDict:
"""
A dictionary-like object that proxies to a list of dictionaries.
e.g., ProxyDict([{'a': 1}, {'b': 2}]) behaves like:
{
(0, 'a'): 1,
(1, 'b'): 2,
}
We use tuples as keys to avoid ambiguity with the keys of the inner dicts.
"""
def __init__(self, inner_dicts: List[dict]):
self._inner_dicts = inner_dicts
def __getitem__(self, key: Tuple[int, str]):
idx, inner_key = key
return self._inner_dicts[idx].get(inner_key)
def __setitem__(self, key: Tuple[int, str], value: Any):
idx, inner_key = key
self._inner_dicts[idx][inner_key] = value
def __len__(self) -> int:
return sum([len(inner_dict) for inner_dict in self._inner_dicts])
def __iter__(self):
for idx, inner_dict in enumerate(self._inner_dicts):
for inner_key in inner_dict:
yield (idx, inner_key)
def items(self):
"""Return generator over underlying items."""
for idx, inner_dict in enumerate(self._inner_dicts):
for inner_key, value in inner_dict.items():
yield (idx, inner_key), value
class ChainedOptimizer(MegatronOptimizer):
"""ChainedOptimizer is designed for a collection of optimizers.
These optimizers are responsible for different parts of multiple models for
a training task and will be executed one-by-one when the model is updated.
Args:
chained_optimizers: a list of optimizers.
"""
def __init__(self, chained_optimizers: List[MegatronOptimizer]):
self.model_chunks = []
self.config = getattr(chained_optimizers[0], 'config', None)
for optimizer in chained_optimizers:
if hasattr(optimizer, 'model_chunks'):
for model_chunk in optimizer.model_chunks:
if model_chunk not in self.model_chunks:
self.model_chunks.append(model_chunk)
assert self.config == getattr(optimizer, 'config', None)
self.chained_optimizers = chained_optimizers
@property
def param_groups(self) -> List[dict]:
"""Get param_groups aggregated over underlying optimizers."""
param_groups = []
for optimizer in self.chained_optimizers:
param_groups += optimizer.param_groups
return param_groups
@property
def state(self) -> ProxyDict:
"""
Return optimizer state with tuple keys, where the first element is the
index of the optimizer in the list of chained optimizers.
"""
return ProxyDict([opt.state for opt in self.chained_optimizers])
def zero_grad(self, set_to_none=True):
for optimizer in self.chained_optimizers:
optimizer.zero_grad(set_to_none)
def get_loss_scale(self):
return self.chained_optimizers[0].get_loss_scale()
def reload_model_params(self):
for optimizer in self.chained_optimizers:
optimizer.reload_model_params()
def state_dict(self):
return [optimizer.state_dict() for optimizer in self.chained_optimizers]
def sharded_state_dict(
self, model_sharded_state_dict: ShardedStateDict, is_loading: bool = False, **kwargs
):
sharded_state_dict = {}
for optimizer_idx, optimizer in enumerate(self.chained_optimizers):
optim_state_dict = optimizer.sharded_state_dict(
model_sharded_state_dict, is_loading, **kwargs
)
add_prefix_for_sharding(optim_state_dict, f'chained_{optimizer_idx}.')
sharded_state_dict[optimizer_idx] = optim_state_dict
return sharded_state_dict
def load_state_dict(self, state_dict):
if len(self.chained_optimizers) != len(state_dict):
raise RuntimeError(
f'Expected {len(self.chained_optimizers)} entries'
f' in state dict, but got {len(state_dict)}.'
)
if isinstance(state_dict, dict):
state_dict = (v for k, v in sorted(state_dict.items()))
for optimizer, state in zip(self.chained_optimizers, state_dict):
optimizer.load_state_dict(state)
@torch.no_grad()
def prepare_grads(self) -> bool:
"""Pre-processing gradients before the optimizer step, returns whether inf/nan is found."""
found_inf_flag = False
for optimizer in self.chained_optimizers:
found_inf_flag |= optimizer.prepare_grads()
return found_inf_flag
@torch.no_grad()
def step_with_ready_grads(self) -> bool:
"""Step the optimizer with ready gradients, return successful."""
success = True
for optimizer_idx, optimizer in enumerate(self.chained_optimizers):
success &= optimizer.step_with_ready_grads()
if self.config.overlap_param_gather_with_optimizer_step and optimizer_idx == 0:
assert success
assert len(optimizer.model_chunks) == 1
optimizer.model_chunks[0].start_param_sync(force_dispatch=True)
return success
def disable_pre_hook(self):
"""Disable pre-hooks for underlying distributed optimizers."""
warnings.warn(
"`ChainedOptimizer.disable_pre_hook` will be deprecated in a future release. "
"Use `DistributedDataParallel.disable_forward_pre_hook` directly."
)
for model_chunk in self.model_chunks:
model_chunk.disable_forward_pre_hook()
def enable_pre_hook(self):
"""Enable pre-hooks for underlying distributed optimizers."""
warnings.warn(
"`ChainedOptimizer.enable_pre_hook` will be deprecated in a future release. "
"Use `DistributedDataParallel.enable_forward_pre_hook` directly."
)
for model_chunk in self.model_chunks:
model_chunk.enable_forward_pre_hook()
@torch.no_grad()
def step(self):
"""ChainedOptimizer will step all optimizers one by one."""
found_inf_flag = self.prepare_grads()
if found_inf_flag:
return False, None, None
# Get grad norm.
grad_norms = []
for optimizer in self.chained_optimizers:
_grad_norm = optimizer.get_grad_norm()
grad_norms += [_grad_norm if _grad_norm else 0.0]
grad_norm = math.sqrt(sum([x**2 for x in grad_norms]))
# Clip gradients.
for optimizer in self.chained_optimizers:
if optimizer.config.clip_grad > 0.0:
clip_grad_by_total_norm_fp32(
optimizer.get_parameters(),
max_norm=optimizer.config.clip_grad,
total_norm=grad_norm,
)
# Count the zeros in the grads.
num_zeros_in_grad = 0
for optimizer in self.chained_optimizers:
num_zeros_in_grad += (
optimizer.count_zeros() if optimizer.config.log_num_zeros_in_grad else 0
)
update_successful = self.step_with_ready_grads()
return update_successful, grad_norm, num_zeros_in_grad
def save_parameter_state(self, filename: str):
"""Save the distributed parameter states of all optimizers to a file.
Args:
filename (str): path to save parameter state to.
"""
save_states = False
states = []
for optimizer in self.chained_optimizers:
if hasattr(optimizer, 'get_parameter_state_dp_zero'):
state_dict = optimizer.get_parameter_state_dp_zero()
# Save checkpoint economically, only when DP rank = 0, state dict
# needs to be saved.
if torch.distributed.get_rank(optimizer.data_parallel_group) == 0:
states.append(state_dict)
save_states = True
else:
states.append(None)
else:
states.append(None)
if save_states:
torch.save(states, filename)
def load_parameter_state(self, filename: str, *, update_legacy_format: bool = False):
"""Load the distributed parameter states of all optimizers from a file.
Args:
filename (str): path to load parameter state from.
"""
states = None
for idx, optimizer in enumerate(self.chained_optimizers):
if not hasattr(optimizer, 'load_parameter_state_from_dp_zero'):
continue
# Lazy loading checkpoint, state dict is needed only when DP rank = 0.
if torch.distributed.get_rank(optimizer.data_parallel_group) == 0 and states is None:
states = torch.load(filename)
state_dict = states[idx] if states else None
optimizer.load_parameter_state_from_dp_zero(
state_dict, update_legacy_format=update_legacy_format
)
def start_param_sync(self, model_index: int, *unused):
"""Start parameter synchronization for all optimizers."""
for optimizer in self.chained_optimizers:
optimizer.start_param_sync(model_index, *unused)
megatron/core/optimizer/optimizer_config.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
from dataclasses import dataclass
from typing import Callable, Optional
import torch
@dataclass
class OptimizerConfig:
"""Configuration for optimizer."""
##############
# General
##############
optimizer: str = 'adam'
"""Optimizer to use (one of Adam or SGD)."""
lr: Optional[float] = None
"""Initial learning rate. Depending on decay style and initial warmup, the learning rate at each
iteration would be different.
"""
min_lr: Optional[float] = None
"""Minumum value for learning rate. The scheduler clip values below this threshold."""
decoupled_lr: Optional[float] = None
"""Separate learning rate for the input and output layer."""
decoupled_min_lr: Optional[float] = None
"""Minimum value for learning rate for the input and output layer. The scheduler clip values
below this threshold.
"""
weight_decay: float = 0.01
"""Weight decay coefficient for L2 regularization."""
##############
# Precision
##############
fp16: bool = False
"""If true, train with fp16 mixed precision training. Defaults to False."""
bf16: bool = False
"""If true, train with bf16 mixed precision training. Defaults to False."""
params_dtype: torch.dtype = torch.float32
"""dtype used when intializing the weights. Defaults to torch.float32."""
###############
# Loss scaling
###############
loss_scale: Optional[float] = None
"""Static loss scaling, positive power of 2 values can improve fp16 convergence. If None,
dynamic loss scaling is used.
"""
initial_loss_scale: float = 2**32
"""Initial loss-scale for dynamic loss scaling."""
min_loss_scale: float = 1.0
"""Minimum loss scale for dynamic loss scaling."""
loss_scale_window: float = 1000
"""Window over which to raise/lower dynamic scale."""
hysteresis: int = 2
"""Hysteresis for dynamic loss scaling."""
##############
# Optimizer
##############
# Adam
adam_beta1: float = 0.9
"""First coefficient for computing running averages of gradient and its square in Adam
optimizer.
"""
adam_beta2: float = 0.999
"""Second coefficient for computing running averages of gradient and its square in Adam
optimizer.
"""
adam_eps: float = 1e-08
"""Term added to the denominator to improve numerical stability in Adam optimizer."""
# SGD.
sgd_momentum: float = 0.9
"""Momentum factor for SGD optimizer."""
#######################
# Distributed optimizer
#######################
use_distributed_optimizer: bool = False
"""Distribute optimizer state over data-parallel replicas."""
overlap_grad_reduce: bool = False
"""If true, overlap grad reduce-scatter with backward compute in distributed optimizer.
NOTE: This parameter will be deprecated in a future release. Use `overlap_grad_reduce`
in `megatron/core/distributed/distributed_data_parallel_config.py` instead."""
overlap_param_gather: bool = False
"""If true, overlap param all-gather with forward compute in distributed optimizer.
NOTE: This parameter will be deprecated in a future release. Use `overlap_param_gather`
in `megatron/core/distributed/distributed_data_parallel_config.py` instead."""
overlap_param_gather_with_optimizer_step: bool = False
"""If true, overlap param all-gather of first bucket with optimizer step."""
################
# Miscellaneous
################
clip_grad: float = 1.0
"""Gradient clipping based on global L2 norm."""
log_num_zeros_in_grad: bool = False
"""If true, calculate and log the number of zeros in gradient."""
barrier_with_L1_time: bool = False
"""If true, use barrier with level 1 time measurements."""
timers: Callable = None
"""Function to get timers."""
config_logger_dir: str = ""
"""When non-empty, dumps entry-point configs to config_logger_dir"""
megatron/optimizer_param_scheduler.py → megatron/core/optimizer_param_scheduler.py
View file @ 4b097dee
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Learning rate decay and weight decay incr functions."""
import logging
import math
from megatron import print_rank_0
class OptimizerParamScheduler(object):
"""Anneals learning rate and weight decay"""
def __init__(self, optimizer, init_lr, max_lr, min_lr,
lr_warmup_steps, lr_decay_steps, lr_decay_style,
start_wd, end_wd, wd_incr_steps, wd_incr_style,
use_checkpoint_opt_param_scheduler=True,
override_opt_param_scheduler=False):
from typing import Optional
from megatron.core.optimizer import MegatronOptimizer
from megatron.core.utils import log_single_rank
logger = logging.getLogger(__name__)
class OptimizerParamScheduler:
"""Anneals learning rate and weight decay
Args:
optimizer (MegatronOptimizer): the optimizer to be used
init_lr (float): initial learning rate
max_lr (float): maximum learning rate
min_lr (float): minimum learning rate
lr_warmup_steps (int): number of warmup steps
lr_decay_steps (int): number of decay steps
lr_decay_style (str): decay style for learning rate
start_wd (float): initial weight decay
end_wd (float): final weight decay
wd_incr_steps (int): number of weight decay increment steps
wd_incr_style (str): weight decay increment style
use_checkpoint_opt_param_scheduler (bool, optional): whether to use the checkpoint values
for the optimizer param scheduler
override_opt_param_scheduler (bool, optional): whether to override the optimizer param
scheduler values with the class values
wsd_decay_steps (int, optional): number of weight decay decay steps
lr_wsd_decay_style (str, optional): decay style for learning rate during weight decay decay
steps
"""
def __init__(
self,
optimizer: MegatronOptimizer,
init_lr: float,
max_lr: float,
min_lr: float,
lr_warmup_steps: int,
lr_decay_steps: int,
lr_decay_style: str,
start_wd: float,
end_wd: float,
wd_incr_steps: int,
wd_incr_style: str,
use_checkpoint_opt_param_scheduler: Optional[bool] = True,
override_opt_param_scheduler: Optional[bool] = False,
wsd_decay_steps: Optional[int] = None,
lr_wsd_decay_style: Optional[str] = None,
) -> None:
# Class values.
self.optimizer = optimizer
......@@ -28,10 +68,14 @@ class OptimizerParamScheduler(object):
self.lr_warmup_steps = lr_warmup_steps
self.num_steps = 0
self.lr_decay_steps = lr_decay_steps
self.wsd_decay_steps = wsd_decay_steps
self.lr_wsd_decay_style = lr_wsd_decay_style
assert self.lr_decay_steps > 0
assert self.lr_warmup_steps < self.lr_decay_steps
self.lr_decay_style = lr_decay_style
if self.lr_decay_style == "WSD":
assert self.wsd_decay_steps is not None
self.start_wd = start_wd
self.end_wd = end_wd
......@@ -43,16 +87,16 @@ class OptimizerParamScheduler(object):
self.override_opt_param_scheduler = override_opt_param_scheduler
self.use_checkpoint_opt_param_scheduler = use_checkpoint_opt_param_scheduler
if self.override_opt_param_scheduler:
assert not self.use_checkpoint_opt_param_scheduler, 'both override and '\
'use-checkpoint are set.'
assert not self.use_checkpoint_opt_param_scheduler, (
'both override and ' 'use-checkpoint are set.'
)
# Set the learning rate
self.step(0)
print_rank_0('> learning rate decay style: {}'.format(self.lr_decay_style))
log_single_rank(logger, logging.INFO, f"> learning rate decay style: {self.lr_decay_style}")
def get_wd(self):
""" Weight decay incr functions"""
def get_wd(self) -> float:
"""Weight decay incr functions"""
if self.num_steps > self.wd_incr_steps:
return self.end_wd
......@@ -70,71 +114,86 @@ class OptimizerParamScheduler(object):
elif self.wd_incr_style == 'cosine':
coeff = 0.5 * (math.cos(math.pi * (1 - incr_ratio)) + 1.0)
else:
raise Exception('{} weight decay increment style is not supported.'.format(
self.wd_incr_style))
raise Exception(f'{self.wd_incr_style} weight decay increment style is not supported.')
return self.start_wd + coeff * delta_wd
def get_lr(self):
def get_lr(self, param_group: dict) -> float:
"""Learning rate decay functions from:
https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
https://openreview.net/pdf?id=BJYwwY9ll pg. 4
Args:
param_group (dict): parameter group from the optimizer.
"""
max_lr = param_group.get('max_lr', self.max_lr)
min_lr = param_group.get('min_lr', self.min_lr)
# Use linear warmup for the initial part.
if self.lr_warmup_steps > 0 and self.num_steps <= self.lr_warmup_steps:
return (
self.init_lr
+ (
(self.max_lr - self.init_lr)
* float(self.num_steps)
/ float(self.lr_warmup_steps)
)
return self.init_lr + (
(max_lr - self.init_lr) * float(self.num_steps) / float(self.lr_warmup_steps)
)
# If the learning rate is constant, just return the initial value.
if self.lr_decay_style == 'constant':
return self.max_lr
return max_lr
# For any steps larger than `self.lr_decay_steps`, use `self.min_lr`.
# For any steps larger than `self.lr_decay_steps`, use `min_lr`.
if self.num_steps > self.lr_decay_steps:
return self.min_lr
return min_lr
# If we are done with the warmup period, use the decay style.
if self.lr_decay_style == 'inverse-square-root':
warmup_steps = max(self.lr_warmup_steps, 1)
num_steps = max(self.num_steps, 1)
lr = self.max_lr * warmup_steps ** 0.5 / (num_steps ** 0.5)
return max(self.min_lr, lr)
lr = max_lr * warmup_steps**0.5 / (num_steps**0.5)
return max(min_lr, lr)
num_steps_ = self.num_steps - self.lr_warmup_steps
decay_steps_ = self.lr_decay_steps - self.lr_warmup_steps
decay_ratio = float(num_steps_) / float(decay_steps_)
assert decay_ratio >= 0.0
assert decay_ratio <= 1.0
delta_lr = self.max_lr - self.min_lr
delta_lr = max_lr - min_lr
if self.lr_decay_style == 'linear':
coeff = (1.0 - decay_ratio)
coeff = 1.0 - decay_ratio
elif self.lr_decay_style == 'cosine':
coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
elif self.lr_decay_style == 'WSD':
wsd_anneal_start_ = self.lr_decay_steps - self.wsd_decay_steps
if self.num_steps <= wsd_anneal_start_:
coeff = 1.0
else:
wsd_steps = self.num_steps - wsd_anneal_start_
wsd_decay_ratio = float(wsd_steps) / float(self.wsd_decay_steps)
if self.lr_wsd_decay_style == "linear":
coeff = 1.0 - wsd_decay_ratio
elif self.lr_wsd_decay_style == "cosine":
coeff = 0.5 * (math.cos(math.pi * wsd_decay_ratio) + 1.0)
elif self.lr_wsd_decay_style == "exponential":
coeff = (2.0 * math.pow(0.5, wsd_decay_ratio)) - 1.0
else:
raise Exception('{} decay style is not supported.'.format(
self.lr_decay_style))
raise Exception(f'{self.lr_decay_style} decay style is not supported.')
return self.min_lr + coeff * delta_lr
return min_lr + coeff * delta_lr
def step(self, increment: int) -> None:
"""Set lr for all parameters groups.
def step(self, increment):
"""Set lr for all parameters groups."""
Args:
increment (int): number of steps to increment
"""
self.num_steps += increment
new_lr = self.get_lr()
new_wd = self.get_wd()
for group in self.optimizer.param_groups:
group['lr'] = new_lr * group.get('lr_mult', 1.0)
group['weight_decay'] = new_wd * group.get('wd_mult', 1.0)
for param_group in self.optimizer.param_groups:
new_lr = self.get_lr(param_group)
param_group['lr'] = new_lr * param_group.get('lr_mult', 1.0)
param_group['weight_decay'] = new_wd * param_group.get('wd_mult', 1.0)
def state_dict(self):
def state_dict(self) -> dict:
"""Return the state dict."""
state_dict = {
'max_lr': self.max_lr,
'lr_warmup_steps': self.lr_warmup_steps,
......@@ -145,91 +204,94 @@ class OptimizerParamScheduler(object):
'start_wd': self.start_wd,
'end_wd': self.end_wd,
'wd_incr_style': self.wd_incr_style,
'wd_incr_steps': self.wd_incr_steps
'wd_incr_steps': self.wd_incr_steps,
}
return state_dict
def _check_and_set(self, cls_value, sd_value, name):
def _check_and_set(self, cls_value: float, sd_value: float, name: str) -> float:
"""Auxiliary function for checking the values in the checkpoint and
setting them."""
setting them.
Args:
cls_value (float): class value
sd_value (float): checkpoint value
name (str): name of the parameter
"""
if self.override_opt_param_scheduler:
print_rank_0(' > overriding {} value to {}'.format(name, cls_value))
log_single_rank(logger, logging.INFO, f" > overriding {name} value to {cls_value}")
return cls_value
if not self.use_checkpoint_opt_param_scheduler:
assert cls_value == sd_value, \
f'OptimizerParamScheduler: class input value {cls_value} and checkpoint' \
assert cls_value == sd_value, (
f'OptimizerParamScheduler: class input value {cls_value} and checkpoint'
f'value {sd_value} for {name} do not match'
print_rank_0(' > using checkpoint value {} for {}'.format(sd_value,
name))
)
log_single_rank(logger, logging.INFO, f" > using checkpoint value {sd_value} for {name}")
return sd_value
def load_state_dict(self, state_dict: dict) -> None:
"""Load the state dict.
def load_state_dict(self, sd):
Args:
state_dict (dict): state dict to be load
"""
if 'start_lr' in sd:
max_lr_ = sd['start_lr']
if 'start_lr' in state_dict:
max_lr_ = state_dict['start_lr']
else:
max_lr_ = sd['max_lr']
self.max_lr = self._check_and_set(self.max_lr, max_lr_,
'learning rate')
self.min_lr = self._check_and_set(self.min_lr, sd['min_lr'],
'minimum learning rate')
if 'warmup_iter' in sd:
lr_warmup_steps_ = sd['warmup_iter']
elif 'warmup_steps' in sd:
lr_warmup_steps_ = sd['warmup_steps']
max_lr_ = state_dict['max_lr']
self.max_lr = self._check_and_set(self.max_lr, max_lr_, 'learning rate')
self.min_lr = self._check_and_set(
self.min_lr, state_dict['min_lr'], 'minimum learning rate'
)
if 'warmup_iter' in state_dict:
lr_warmup_steps_ = state_dict['warmup_iter']
elif 'warmup_steps' in state_dict:
lr_warmup_steps_ = state_dict['warmup_steps']
else:
lr_warmup_steps_ = sd['lr_warmup_steps']
self.lr_warmup_steps = self._check_and_set(self.lr_warmup_steps,
lr_warmup_steps_,
'warmup iterations')
if 'end_iter' in sd:
lr_decay_steps_ = sd['end_iter']
elif 'decay_steps' in sd:
lr_decay_steps_ = sd['decay_steps']
lr_warmup_steps_ = state_dict['lr_warmup_steps']
self.lr_warmup_steps = self._check_and_set(
self.lr_warmup_steps, lr_warmup_steps_, 'warmup iterations'
)
if 'end_iter' in state_dict:
lr_decay_steps_ = state_dict['end_iter']
elif 'decay_steps' in state_dict:
lr_decay_steps_ = state_dict['decay_steps']
else:
lr_decay_steps_ = sd['lr_decay_steps']
self.lr_decay_steps = self._check_and_set(self.lr_decay_steps, lr_decay_steps_,
'total number of iterations')
lr_decay_steps_ = state_dict['lr_decay_steps']
self.lr_decay_steps = self._check_and_set(
self.lr_decay_steps, lr_decay_steps_, 'total number of iterations'
)
if 'decay_style' in sd:
lr_decay_style_ = sd['decay_style']
if 'decay_style' in state_dict:
lr_decay_style_ = state_dict['decay_style']
else:
lr_decay_style_ = sd['lr_decay_style']
self.lr_decay_style = self._check_and_set(self.lr_decay_style,
lr_decay_style_,
'learning rate decay style')
lr_decay_style_ = state_dict['lr_decay_style']
self.lr_decay_style = self._check_and_set(
self.lr_decay_style, lr_decay_style_, 'learning rate decay style'
)
if 'num_iters' in sd:
num_steps = sd['num_iters']
if 'num_iters' in state_dict:
num_steps = state_dict['num_iters']
else:
num_steps = sd['num_steps']
num_steps = state_dict['num_steps']
self.step(increment=num_steps)
if 'start_wd' in sd:
self.start_wd = self._check_and_set(self.start_wd,
sd['start_wd'],
"start weight decay")
self.end_wd = self._check_and_set(self.end_wd,
sd['end_wd'],
"end weight decay")
self.wd_incr_steps = self._check_and_set(self.wd_incr_steps,
sd['wd_incr_steps'],
"total number of weight decay iterations")
self.wd_incr_style = self._check_and_set(self.wd_incr_style,
sd['wd_incr_style'],
"weight decay incr style")
if 'start_wd' in state_dict:
self.start_wd = self._check_and_set(
self.start_wd, state_dict['start_wd'], "start weight decay"
)
self.end_wd = self._check_and_set(self.end_wd, state_dict['end_wd'], "end weight decay")
self.wd_incr_steps = self._check_and_set(
self.wd_incr_steps,
state_dict['wd_incr_steps'],
"total number of weight decay iterations",
)
self.wd_incr_style = self._check_and_set(
self.wd_incr_style, state_dict['wd_incr_style'], "weight decay incr style"
)
megatron/core/package_info.py
View file @ 4b097dee
......@@ -2,7 +2,7 @@
MAJOR = 0
MINOR = 3
MINOR = 9
PATCH = 0
PRE_RELEASE = ''
......
megatron/core/packed_seq_params.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
from dataclasses import dataclass
from torch import Tensor
@dataclass
class PackedSeqParams:
# parameters to TEDotProductAttention and fused rope kernels for the `thd` (packed) sequence format,
qkv_format: str = None
cu_seqlens_q: Tensor = None
cu_seqlens_kv: Tensor = None
max_seqlen_q: Tensor = None
max_seqlen_kv: Tensor = None
megatron/core/parallel_state.py
View file @ 4b097dee
......@@ -3,7 +3,11 @@
"""Model and data parallel groups."""
import os
from typing import Optional
import warnings
from datetime import timedelta
from functools import partial
from itertools import cycle
from typing import Callable, List, Optional
import torch
......@@ -15,6 +19,8 @@ _TENSOR_MODEL_PARALLEL_GROUP = None
_PIPELINE_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra- and pipeline) that the current rank belongs to.
_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra-, pipeline, and expert) that the current rank belongs to.
_MODEL_AND_EXPERT_PARALLEL_GROUP = None
# Embedding group.
_EMBEDDING_GROUP = None
# Position embedding group.
......@@ -22,18 +28,33 @@ _POSITION_EMBEDDING_GROUP = None
# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None
_DATA_PARALLEL_GROUP_GLOO = None
# FP8 amax reduction group.
_AMAX_REDUCTION_GROUP = None
# tensor model parallel group and data parallel group combined
# used for fp8 and moe training
_TENSOR_AND_DATA_PARALLEL_GROUP = None
# Expert parallel group that the current rank belongs to.
_EXPERT_MODEL_PARALLEL_GROUP = None
_TENSOR_AND_EXPERT_PARALLEL_GROUP = None
_DATA_MODULO_EXPERT_PARALLEL_GROUP = None
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = None
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP = None
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = None
_PIPELINE_MODEL_PARALLEL_DECODER_START = None
# These values enable us to change the mpu sizes on the fly.
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_DATA_PARALLEL_WORLD_SIZE = None
_MPU_DATA_PARALLEL_RANK = None
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
_MPU_EXPERT_MODEL_PARALLEL_RANK = None
# A list of ranks that have a copy of the embedding.
_EMBEDDING_GLOBAL_RANKS = None
......@@ -49,21 +70,305 @@ _PIPELINE_GLOBAL_RANKS = None
# rank when broadcasting weights from src to all other data parallel ranks
_DATA_PARALLEL_GLOBAL_RANKS = None
# A list of global ranks for each tensor model parallel group to ease calculation of
# the first local rank in the tensor model parallel group
_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = None
# Context parallel group that the current rank belongs to
_CONTEXT_PARALLEL_GROUP = None
# A list of global ranks for each context parallel group to ease calculation of the
# destination rank when exchanging KV/dKV between context parallel_ranks
_CONTEXT_PARALLEL_GLOBAL_RANKS = None
# Data parallel group information with context parallel combined.
_DATA_PARALLEL_GROUP_WITH_CP = None
_DATA_PARALLEL_GROUP_WITH_CP_GLOO = None
_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = None
# combined parallel group of TP and CP
_TENSOR_AND_CONTEXT_PARALLEL_GROUP = None
# combined parallel group of TP, DP, and CP used for fp8
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None
# Memory buffers to avoid dynamic memory allocation
_GLOBAL_MEMORY_BUFFER = None
# MOE logging
_MOE_LAYER_WISE_LOGGING_TRACKER = {}
def get_nccl_options(pg_name, nccl_comm_cfgs):
"""Set the NCCL process group options.
Args:
pg_name (str): process group name
nccl_comm_cfgs (dict): nccl communicator configurations
When an option (e.g., max_ctas) is not found in the config, use the NCCL default setting.
"""
if pg_name in nccl_comm_cfgs:
nccl_options = torch.distributed.ProcessGroupNCCL.Options()
nccl_options.config.cga_cluster_size = nccl_comm_cfgs[pg_name].get('cga_cluster_size', 4)
nccl_options.config.max_ctas = nccl_comm_cfgs[pg_name].get('max_ctas', 32)
nccl_options.config.min_ctas = nccl_comm_cfgs[pg_name].get('min_ctas', 1)
return nccl_options
else:
return None
def generate_masked_orthogonal_rank_groups(
world_size: int, parallel_size: List[int], mask: List[bool]
) -> List[List[int]]:
"""Generate orthogonal parallel groups based on the parallel size and mask.
Arguments:
world_size (int): world size
parallel_size (List[int]):
The parallel size of each orthogonal parallel type. For example, if
tensor_parallel_size = 2, pipeline_model_parallel_group = 3, data_parallel_size = 4,
and the parallel mapping order is tp-pp-dp, then the parallel_size = [2, 3, 4].
mask (List[bool]):
The mask controls which parallel methods the generated groups represent. If mask[i] is
True, it means the generated group contains the i-th parallelism method. For example,
if parallel_size = [tp_size, pp_size, dp_size], and mask = [True, False , True], then
the generated group is the `tp-dp` group, if the mask = [False, True, False], then the
generated group is the `pp` group.
Algorithm:
For orthogonal parallelism, such as tp/dp/pp/cp, the global_rank and
local_rank satisfy the following equation:
global_rank = tp_rank + dp_rank * tp_size + pp_rank * tp_size * dp_size (1)
tp_rank \in [0, tp_size)
dp_rank \in [0, dp_size)
pp_rank \in [0, pp_size)
If we want to get the `dp_group` (tp_size * pp_size groups of dp_size ranks each.
For example, if the gpu size is 8 and order is 'tp-pp-dp', size is '2-2-2', and the
dp_group here is [[0, 4], [1, 5], [2, 6], [3, 7]].)
The tp_rank and pp_rank will be combined to form the `dp_group_index`.
dp_group_index = tp_rank + pp_rank * tp_size (2)
So, Given that tp_rank and pp_rank satisfy equation (2), and dp_rank in
range(0, dp_size), the ranks in dp_group[dp_group_index] satisfies the
equation (1).
This function solve this math problem.
For example, if the parallel_size = [tp_size, dp_size, pp_size] = [2, 3, 4],
and the mask = [False, True, False]. Then,
dp_group_index(0) = tp_rank(0) + pp_rank(0) * 2
dp_group_index(1) = tp_rank(1) + pp_rank(0) * 2
...
dp_group_index(7) = tp_rank(1) + pp_rank(3) * 2
dp_group[0] = 0 + range(0, 3) * 2 + 0 = [0, 2, 4]
dp_group[1] = 1 + range(0, 3) * 2 + 0 = [1, 3, 5]
...
dp_group[7] = 1 + range(0, 3) * 2 + 3 * 2 * 3 = [19, 21, 23]
"""
def prefix_product(a: List[int], init=1) -> List[int]:
r = [init]
for v in a:
init = init * v
r.append(init)
return r
def inner_product(a: List[int], b: List[int]) -> int:
return sum([x * y for x, y in zip(a, b)])
def decompose(index, shape, stride=None):
'''
This function solve the math problem below:
There is an equation:
index = sum(idx[i] * stride[i])
And given the value of index, stride.
Return the idx.
This function will used to get the pp/dp/pp_rank
from group_index and rank_in_group.
'''
if stride is None:
stride = prefix_product(shape)
idx = [(index // d) % s for s, d in zip(shape, stride)]
# stride is a prefix_product result. And the value of stride[-1]
# is not used.
assert (
sum([x * y for x, y in zip(idx, stride[:-1])]) == index
), "idx {} with shape {} mismatch the return idx {}".format(index, shape, idx)
return idx
masked_shape = [s for s, m in zip(parallel_size, mask) if m]
unmasked_shape = [s for s, m in zip(parallel_size, mask) if not m]
global_stride = prefix_product(parallel_size)
masked_stride = [d for d, m in zip(global_stride, mask) if m]
unmasked_stride = [d for d, m in zip(global_stride, mask) if not m]
group_size = prefix_product(masked_shape)[-1]
num_of_group = world_size // group_size
ranks = []
for group_index in range(num_of_group):
# get indices from unmaksed for group_index.
decomposed_group_idx = decompose(group_index, unmasked_shape)
rank = []
for rank_in_group in range(group_size):
# get indices from masked for rank_in_group.
decomposed_rank_idx = decompose(rank_in_group, masked_shape)
rank.append(
inner_product(decomposed_rank_idx, masked_stride)
+ inner_product(decomposed_group_idx, unmasked_stride)
)
ranks.append(rank)
return ranks
class RankGenerator(object):
"""A class for generating rank groups for different modes of parallelism."""
def __init__(
self, tp: int, ep: int, dp: int, pp: int, cp: int, order: str, rank_offset: int = 0
) -> None:
self.tp = tp
self.ep = ep
self.dp = dp
self.pp = pp
self.cp = cp
self.rank_offset = rank_offset
self.world_size = tp * dp * pp * cp
self.name_to_size = {
"tp": self.tp,
"pp": self.pp,
"dp": self.dp,
"ep": self.ep,
"cp": self.cp,
}
self.order = order
order = order.lower()
if 'ep' in order:
if 'ep-dp' not in order and 'dp-ep' not in order:
raise RuntimeError(f"The ep and dp must be adjacent in order ({self.order}).")
for name in self.name_to_size.keys():
if name not in order and self.name_to_size[name] != 1:
raise RuntimeError(
f"The size of ({name}) is ({self.name_to_size[name]}), but you haven't"
f"specified the order ({self.order})."
)
elif name not in order:
order = order + '-' + name
self.order_w_ep = order
self.order_wo_ep = '-'.join([token for token in order.split('-') if token != 'ep'])
self.ordered_size_wo_ep = []
self.ordered_size_w_ep = []
for token in order.split('-'):
if token == 'dp':
self.ordered_size_w_ep.append(self.dp // self.ep)
self.ordered_size_wo_ep.append(self.dp)
elif token == 'ep':
self.ordered_size_w_ep.append(self.ep)
else:
self.ordered_size_w_ep.append(self.name_to_size[token])
self.ordered_size_wo_ep.append(self.name_to_size[token])
def get_mask(self, order: str, token: str):
"""Create a mask for the specified tokens based on the given order.
Args:
order (str): The order of parallelism types (e.g., 'tp-dp-pp').
token (str): The specific parallelism types to include in the mask,
separated by hyphens (e.g., 'tp-dp').
"""
ordered_token = order.split('-')
token = token.split('-')
mask = [False] * len(ordered_token)
for t in token:
mask[ordered_token.index(t)] = True
return mask
def get_ranks(self, token, independent_ep=False):
"""Get rank group by input token.
Args:
token (str):
Specify the ranks type that want to get. If we want
to obtain multiple parallel types, we can use a hyphen
'-' to separate them. For example, if we want to obtain
the TP_DP group, the token should be 'tp-dp'.
independent_ep (bool: True):
This flag controls whether we treat EP and DP independently.
EP shares ranks with DP, if we want to get ranks related to
EP, we should set the flag. For example, get_ranks('dp', True)
will get DP modulo EP group, and get_ranks('dp', False) will
get full DP group.
"""
if independent_ep:
parallel_size = self.ordered_size_w_ep
order = self.order_w_ep
else:
parallel_size = self.ordered_size_wo_ep
order = self.order_wo_ep
mask = self.get_mask(order, token)
ranks = generate_masked_orthogonal_rank_groups(self.world_size, parallel_size, mask)
if self.rank_offset > 0:
for rank_group in ranks:
for i in range(len(rank_group)):
rank_group[i] += self.rank_offset
return ranks
def default_embedding_ranks(pp_ranks, split_rank=None):
"""Return the default ranks that constitute the stages on which the word embeddings live.
For most models, these are the first and last pipeline stages.
We also support the deprecated split rank argument for backwards compatibility."""
if len(pp_ranks) == 1:
return [pp_ranks[0]]
elif split_rank is not None and pp_ranks[split_rank] not in (pp_ranks[0], pp_ranks[-1]):
return [pp_ranks[0], pp_ranks[split_rank], pp_ranks[-1]]
else:
return [pp_ranks[0], pp_ranks[-1]]
def default_position_embedding_ranks(pp_ranks, split_rank=None):
"""Return the default ranks that constitute the stages on which the position embeddings live.
For most models, this is only the first pipeline stage.
We also support the deprecated split rank argument for backwards compatibility."""
if split_rank is not None and pp_ranks[0] != pp_ranks[split_rank]:
return [pp_ranks[0], pp_ranks[split_rank]]
else:
return [pp_ranks[0]]
def initialize_model_parallel(
tensor_model_parallel_size: int = 1,
pipeline_model_parallel_size: int = 1,
virtual_pipeline_model_parallel_size: Optional[int] = None,
pipeline_model_parallel_split_rank: Optional[int] = None,
use_fp8: bool = False,
use_sharp: bool = False,
context_parallel_size: int = 1,
expert_model_parallel_size: int = 1,
nccl_communicator_config_path: Optional[str] = None,
distributed_timeout_minutes: int = 30,
order: str = "tp-cp-ep-dp-pp",
encoder_tensor_model_parallel_size: Optional[int] = 0,
encoder_pipeline_model_parallel_size: Optional[int] = 0,
get_embedding_ranks: Optional[Callable[[List[int], Optional[int]], List[int]]] = None,
get_position_embedding_ranks: Optional[Callable[[List[int], Optional[int]], List[int]]] = None,
) -> None:
# pylint: disable=line-too-long
"""Initialize model data parallel groups.
Arguments:
Args:
tensor_model_parallel_size (int, default = 1):
The number of GPUs to split individual tensors across.
......@@ -90,7 +395,7 @@ def initialize_model_parallel(
GPU 3: [7, 8] [15, 16]
pipeline_model_parallel_split_rank (int, optional):
For models with both an encoder and decoder, the rank in
DEPRECATED. For models with both an encoder and decoder, the rank in
pipeline to switch between encoder and decoder (i.e. the
first rank of the decoder). This allows the user to set
the pipeline parallel size of the encoder and decoder
......@@ -99,17 +404,73 @@ def initialize_model_parallel(
pipeline_model_parallel_split_rank is 3, then ranks 0-2
will be the encoder and ranks 3-7 will be the decoder.
use_fp8 (bool, default = False):
Construct GPU groups needed for FP8 training, namely for
amax reduction across the product of the data-parallel and
tensor-parallel groups.
use_sharp (bool, default = False):
Set the use of SHARP for the collective communications of
data-parallel process groups. When `True`, run barrier
within each data-parallel process group, which specifies
the SHARP application target groups.
context_parallel_size (int, default = 1):
The number of tensor parallel GPU groups to split the
network input sequence length across. Compute of attention
module requires tokens of full sequence length, so GPUs
in a context parallel group need to communicate with each
other to exchange information of other sequence chunks.
Each GPU and its counterparts in other tensor parallel
groups compose a context parallel group.
For example, assume we have 8 GPUs, if tensor model parallel
size is 4 and context parallel size is 2, the network input
will be split into two sequence chunks, which are processed
by 2 different groups of 4 GPUs. One chunk is processed by
GPU0-3, the other chunk is processed by GPU4-7. Four groups
are build to do context parallel communications: [GPU0, GPU4],
[GPU1, GPU5], [GPU2, GPU6], and [GPU3, GPU7].
Context parallelism partitions sequence length, so it has no
impact on weights, which means weights are duplicated among
GPUs in a context parallel group. Hence, weight gradients
all-reduce is required in backward. For simplicity, we piggyback
GPUs of context parallelism on data parallel group for
weight gradient all-reduce.
expert_model_parallel_size (int, default = 1):
The number of Mixture of Experts parallel GPUs in each expert
parallel group.
nccl_communicator_config_path (str, default = None):
Path to the yaml file of NCCL communicator configurations.
`min_ctas`, `max_ctas`, and `cga_cluster_size` can be set
for each communicator.
distributed_timeout_minutes (int, default = 30): Timeout, in
minutes,for operations executed against distributed
process groups. See PyTorch documentation at
https://pytorch.org/docs/stable/distributed.html for
caveats.
order (str, default=tp-dp-pp):
The rank initialization order of parallelism. Now we support
tp-dp-pp and tp-pp-dp orders.
encoder_tensor_model_parallel_size (int, default = 0):
The number of GPUs to split individual tensors across in the encoder. If 0,
then we use the default, decoder's tensor model parallel size.
encoder_pipeline_model_parallel_size (int, default = 0):
The number of tensor parallel GPU groups to allocate to the encoder. As an example,
if pipeline_model_parallel_size is 4 and encoder_pipeline_model_parallel_size is 2,
then the encoder will use the first two pipeline stages for its layers, and the total
amount of pipelineing is 6.
get_embedding_ranks (Callable[[List[int], Optional[int]], List[int]], optional, default=None):
A function that takes in a list of ranks for a pipeline group and returns
those ranks that should have embeddings.
get_position_embedding_ranks (Callable[[List[int], Optional[int]], List[int]], optional, default=None):
A function that takes in a list of ranks for a pipeline group, and returns
those ranks that should have position embeddings.
Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
the model pipeline. The present function will
......@@ -127,28 +488,68 @@ def initialize_model_parallel(
ranks 8 to 15 belong to the second box.
"""
if encoder_pipeline_model_parallel_size is None:
encoder_pipeline_model_parallel_size = 0
if encoder_tensor_model_parallel_size == 0 and encoder_pipeline_model_parallel_size > 0:
encoder_tensor_model_parallel_size = tensor_model_parallel_size
if get_embedding_ranks is None:
get_embedding_ranks = partial(
default_embedding_ranks, split_rank=pipeline_model_parallel_split_rank
)
if get_position_embedding_ranks is None:
get_position_embedding_ranks = partial(
default_position_embedding_ranks, split_rank=pipeline_model_parallel_split_rank
)
if encoder_pipeline_model_parallel_size > 0:
global _PIPELINE_MODEL_PARALLEL_DECODER_START
_PIPELINE_MODEL_PARALLEL_DECODER_START = encoder_pipeline_model_parallel_size
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()
if world_size % (tensor_model_parallel_size * pipeline_model_parallel_size) != 0:
if encoder_tensor_model_parallel_size > 0:
assert encoder_pipeline_model_parallel_size > 0
assert (
encoder_tensor_model_parallel_size <= tensor_model_parallel_size
), "We do not support encoders with more TP than the decoder."
encoder_model_size = (
encoder_tensor_model_parallel_size
* encoder_pipeline_model_parallel_size
* context_parallel_size
)
decoder_model_size = (
tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size
)
total_model_size = encoder_model_size + decoder_model_size
if world_size % total_model_size != 0:
raise RuntimeError(f"world_size ({world_size}) is not divisible by {total_model_size}")
data_parallel_size: int = world_size // total_model_size
if data_parallel_size % expert_model_parallel_size != 0:
raise RuntimeError(
f"world_size ({world_size}) is not divisible by tensor_model_parallel_size "
f"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size})"
f"data_parallel_size ({data_parallel_size}) is not divisible by "
"expert_model_parallel_size "
)
data_parallel_size: int = world_size // (
tensor_model_parallel_size * pipeline_model_parallel_size
)
encoder_world_size = encoder_model_size * data_parallel_size
decoder_world_size = decoder_model_size * data_parallel_size
num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
num_data_parallel_groups: int = world_size // data_parallel_size
assert (
encoder_world_size + decoder_world_size == world_size
), f"{encoder_world_size=} + {decoder_world_size=} != {world_size=}"
if virtual_pipeline_model_parallel_size is not None:
if not pipeline_model_parallel_size > 2:
if not pipeline_model_parallel_size > 1:
raise RuntimeError(
"pipeline-model-parallel size should be greater than 2 with interleaved schedule"
"pipeline-model-parallel size should be greater than 1 with interleaved schedule"
)
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
......@@ -161,24 +562,103 @@ def initialize_model_parallel(
rank = torch.distributed.get_rank()
nccl_comm_cfgs = {}
if nccl_communicator_config_path is not None:
try:
import yaml
except ImportError:
raise RuntimeError(
"Cannot import `yaml`. Setting custom nccl communicator configs "
"requires the yaml package."
)
with open(nccl_communicator_config_path, "r") as stream:
nccl_comm_cfgs = yaml.safe_load(stream)
if encoder_world_size > 0:
encoder_rank_generator = RankGenerator(
tp=encoder_tensor_model_parallel_size,
ep=1,
dp=data_parallel_size,
pp=encoder_pipeline_model_parallel_size,
cp=context_parallel_size,
order=order,
rank_offset=0,
)
else:
encoder_rank_generator = None
decoder_rank_generator = RankGenerator(
tp=tensor_model_parallel_size,
ep=expert_model_parallel_size,
dp=data_parallel_size,
pp=pipeline_model_parallel_size,
cp=context_parallel_size,
order=order,
rank_offset=encoder_world_size,
)
def generator_wrapper(group_type, **kwargs):
"""The `RankGenerator` class produces a hyper-rectangle for a given set of
tensor, pipeline, data, expert, and context parallelism. If we have an encoder,
in addition to the default decoder, we essentially instantiate two `RankGenerator`
classes to construct the parallelism for each module separately, and we then have
to stitch them together for the right groups. For now, this means pp and tp-pp."""
d_ranks = decoder_rank_generator.get_ranks(group_type, **kwargs)
if encoder_rank_generator is None:
for x in d_ranks:
yield x
return
e_ranks = encoder_rank_generator.get_ranks(group_type, **kwargs)
if group_type == 'pp':
# Map 1 encoder tp rank to several decoder tp ranks, because
# these won't be the same size.
for x, y in zip(cycle(e_ranks), d_ranks):
yield x + y
elif group_type == 'tp-pp':
# For this group, we can just return the concatenated
# groups together, because their sizes are the same.
assert len(e_ranks) == len(d_ranks)
for x, y in zip(e_ranks, d_ranks):
yield x + y
else:
for x in e_ranks:
yield x
for x in d_ranks:
yield x
timeout = timedelta(minutes=distributed_timeout_minutes)
# Build the data-parallel groups.
global _DATA_PARALLEL_GROUP
global _DATA_PARALLEL_GROUP_GLOO
global _DATA_PARALLEL_GLOBAL_RANKS
global _DATA_PARALLEL_GROUP_WITH_CP
global _DATA_PARALLEL_GROUP_WITH_CP_GLOO
global _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP
assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized'
all_data_parallel_group_ranks = []
for i in range(pipeline_model_parallel_size):
start_rank = i * num_pipeline_model_parallel_groups
end_rank = (i + 1) * num_pipeline_model_parallel_groups
for j in range(tensor_model_parallel_size):
ranks = range(start_rank + j, end_rank, tensor_model_parallel_size)
all_data_parallel_group_ranks.append(list(ranks))
group = torch.distributed.new_group(ranks)
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
if rank in ranks:
_DATA_PARALLEL_GROUP = group
_DATA_PARALLEL_GROUP_GLOO = group_gloo
_DATA_PARALLEL_GLOBAL_RANKS = ranks
for ranks in generator_wrapper('dp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('dp', nccl_comm_cfgs)
)
group_gloo = torch.distributed.new_group(ranks, timeout=timeout, backend="gloo")
if rank in ranks:
_DATA_PARALLEL_GROUP = group
_DATA_PARALLEL_GROUP_GLOO = group_gloo
_DATA_PARALLEL_GLOBAL_RANKS = ranks
for ranks_with_cp in generator_wrapper('dp-cp'):
group_with_cp = torch.distributed.new_group(
ranks_with_cp, timeout=timeout, pg_options=get_nccl_options('dp_cp', nccl_comm_cfgs)
)
group_with_cp_gloo = torch.distributed.new_group(
ranks_with_cp, timeout=timeout, backend="gloo"
)
if rank in ranks_with_cp:
_DATA_PARALLEL_GROUP_WITH_CP = group_with_cp
_DATA_PARALLEL_GROUP_WITH_CP_GLOO = group_with_cp_gloo
_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP = ranks_with_cp
# Apply SHARP to DP process groups
if use_sharp:
......@@ -194,33 +674,59 @@ def initialize_model_parallel(
"`#SBATCH_NETWORK=sharp` should be set in the sbatch script."
)
torch.distributed.barrier(
group=get_data_parallel_group(), device_ids=[torch.cuda.current_device()]
group=get_data_parallel_group(with_context_parallel=True),
device_ids=[torch.cuda.current_device()],
)
# Set `NCCL_SHARP_DISABLE=1` to restrict SHARP application to DP process groups
os.environ["NCCL_SHARP_DISABLE"] = "1"
# Set `NCCL_COLLNET_ENABLE=0` to restrict SHARP application to DP process groups
os.environ["NCCL_COLLNET_ENABLE"] = "0"
# Build the context-parallel groups.
global _CONTEXT_PARALLEL_GROUP
global _CONTEXT_PARALLEL_GLOBAL_RANKS
assert _CONTEXT_PARALLEL_GROUP is None, 'context parallel group is already initialized'
for ranks in generator_wrapper('cp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('cp', nccl_comm_cfgs)
)
if rank in ranks:
_CONTEXT_PARALLEL_GROUP = group
_CONTEXT_PARALLEL_GLOBAL_RANKS = ranks
# Build the model-parallel groups.
global _MODEL_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized'
for i in range(data_parallel_size):
ranks = [
data_parallel_group_ranks[i]
for data_parallel_group_ranks in all_data_parallel_group_ranks
]
group = torch.distributed.new_group(ranks)
for ranks in generator_wrapper('tp-pp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('mp', nccl_comm_cfgs)
)
if rank in ranks:
_MODEL_PARALLEL_GROUP = group
# Build the model-parallel groups with expert parallel
global _MODEL_AND_EXPERT_PARALLEL_GROUP
assert (
_MODEL_AND_EXPERT_PARALLEL_GROUP is None
), 'model and expert parallel group is already initialized'
for ranks in generator_wrapper('tp-ep-pp', independent_ep=True):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('mp_exp', nccl_comm_cfgs)
)
if rank in ranks:
_MODEL_AND_EXPERT_PARALLEL_GROUP = group
# Build the tensor model-parallel groups.
global _TENSOR_MODEL_PARALLEL_GROUP
global _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS
assert (
_TENSOR_MODEL_PARALLEL_GROUP is None
), 'tensor model parallel group is already initialized'
for i in range(num_tensor_model_parallel_groups):
ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
group = torch.distributed.new_group(ranks)
for ranks in generator_wrapper('tp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_MODEL_PARALLEL_GROUP = group
_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS = ranks
# Build the pipeline model-parallel groups and embedding groups
# (first and last rank in each pipeline model-parallel group).
......@@ -235,55 +741,125 @@ def initialize_model_parallel(
global _POSITION_EMBEDDING_GROUP
global _POSITION_EMBEDDING_GLOBAL_RANKS
assert _POSITION_EMBEDDING_GROUP is None, 'position embedding group is already initialized'
for i in range(num_pipeline_model_parallel_groups):
ranks = range(i, world_size, num_pipeline_model_parallel_groups)
group = torch.distributed.new_group(ranks)
for ranks in generator_wrapper('pp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('pp', nccl_comm_cfgs)
)
if rank in ranks:
_PIPELINE_MODEL_PARALLEL_GROUP = group
_PIPELINE_GLOBAL_RANKS = ranks
# Setup embedding group (to exchange gradients between
# first and last stages).
if len(ranks) > 1:
embedding_ranks = [ranks[0], ranks[-1]]
position_embedding_ranks = [ranks[0]]
if pipeline_model_parallel_split_rank is not None:
if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:
embedding_ranks = [
ranks[0],
ranks[pipeline_model_parallel_split_rank],
ranks[-1],
]
if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:
position_embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]
else:
embedding_ranks = ranks
position_embedding_ranks = ranks
group = torch.distributed.new_group(embedding_ranks)
if _PIPELINE_MODEL_PARALLEL_GROUP is None:
_PIPELINE_MODEL_PARALLEL_GROUP = group
_PIPELINE_GLOBAL_RANKS = ranks
elif isinstance(_PIPELINE_GLOBAL_RANKS[0], list):
_PIPELINE_MODEL_PARALLEL_GROUP.append(group)
_PIPELINE_GLOBAL_RANKS.append(ranks)
else:
_PIPELINE_MODEL_PARALLEL_GROUP = [_PIPELINE_MODEL_PARALLEL_GROUP, group]
_PIPELINE_GLOBAL_RANKS = [_PIPELINE_GLOBAL_RANKS, ranks]
embedding_ranks = get_embedding_ranks(ranks)
group = torch.distributed.new_group(
embedding_ranks, timeout=timeout, pg_options=get_nccl_options('embd', nccl_comm_cfgs)
)
if rank in embedding_ranks:
_EMBEDDING_GROUP = group
if rank in ranks:
_EMBEDDING_GLOBAL_RANKS = embedding_ranks
group = torch.distributed.new_group(position_embedding_ranks)
position_embedding_ranks = get_position_embedding_ranks(ranks)
group = torch.distributed.new_group(
position_embedding_ranks,
timeout=timeout,
pg_options=get_nccl_options('embd', nccl_comm_cfgs),
)
if rank in position_embedding_ranks:
_POSITION_EMBEDDING_GROUP = group
if rank in ranks:
_POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
# Build the FP8 groups.
global _AMAX_REDUCTION_GROUP
assert _AMAX_REDUCTION_GROUP is None, 'FP8 amax reduction group is already initialized'
if use_fp8:
amax_group_size: int = tensor_model_parallel_size * data_parallel_size
num_amax_groups: int = world_size // amax_group_size
for i in range(num_amax_groups):
start_rank = i * amax_group_size
end_rank = (i + 1) * amax_group_size
ranks = range(start_rank, end_rank)
group = torch.distributed.new_group(ranks)
if rank in ranks:
_AMAX_REDUCTION_GROUP = group
# Build the tensor + data parallel groups.
global _TENSOR_AND_DATA_PARALLEL_GROUP
global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP is None
), 'Tensor + data parallel group is already initialized'
for ranks in generator_wrapper('tp-dp-cp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp_cp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = group
for ranks in generator_wrapper('tp-dp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_dp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_DATA_PARALLEL_GROUP = group
global _TENSOR_AND_CONTEXT_PARALLEL_GROUP
assert (
_TENSOR_AND_CONTEXT_PARALLEL_GROUP is None
), 'Tensor + context parallel group is already initialized'
for ranks in generator_wrapper('tp-cp'):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_cp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_CONTEXT_PARALLEL_GROUP = group
# Build the tensor + expert parallel groups
global _EXPERT_MODEL_PARALLEL_GROUP
assert _EXPERT_MODEL_PARALLEL_GROUP is None, 'Expert parallel group is already initialized'
global _TENSOR_AND_EXPERT_PARALLEL_GROUP
assert (
_TENSOR_AND_EXPERT_PARALLEL_GROUP is None
), 'Tensor + expert parallel group is already initialized'
global _DATA_MODULO_EXPERT_PARALLEL_GROUP
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP is None
), 'Data modulo expert group is already initialized'
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP is None
), 'Data modulo expert group with context parallel is already initialized'
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO
for ranks in generator_wrapper('tp-ep', independent_ep=True):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('tp_exp', nccl_comm_cfgs)
)
if rank in ranks:
_TENSOR_AND_EXPERT_PARALLEL_GROUP = group
for ranks in generator_wrapper('ep', independent_ep=True):
group = torch.distributed.new_group(
ranks, pg_options=get_nccl_options('exp', nccl_comm_cfgs)
)
if rank in ranks:
_EXPERT_MODEL_PARALLEL_GROUP = group
for ranks in generator_wrapper('dp', independent_ep=True):
group = torch.distributed.new_group(
ranks, timeout=timeout, pg_options=get_nccl_options('dp_modulo_exp', nccl_comm_cfgs)
)
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
if rank in ranks:
_DATA_MODULO_EXPERT_PARALLEL_GROUP = group
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = group_gloo
for ranks in generator_wrapper('dp-cp', independent_ep=True):
# Lazy initialization of the group
if get_context_parallel_world_size() > 1:
group = torch.distributed.new_group(
ranks,
timeout=timeout,
pg_options=get_nccl_options('dp_modulo_exp_cp', nccl_comm_cfgs),
)
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
else:
group = _DATA_MODULO_EXPERT_PARALLEL_GROUP
group_gloo = _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
if rank in ranks:
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP = group
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO = group_gloo
# Initialize global memory buffer
# This isn't really "parallel state" but there isn't another good place to
......@@ -292,13 +868,23 @@ def initialize_model_parallel(
_set_global_memory_buffer()
def is_unitialized():
def is_initialized():
"""Useful for code segments that may be accessed with or without mpu initialization"""
return _DATA_PARALLEL_GROUP is None
return _DATA_PARALLEL_GROUP is not None
def is_unitialized() -> bool:
"""Check if parallel state has been initialized
Deprecated. Use is_initialized instead.
"""
warnings.warn("is_unitialized is deprecated, use is_initialized instead", DeprecationWarning)
return not is_initialized()
def model_parallel_is_initialized():
"""Check if model and data parallel groups are initialized."""
"""Check if model- and data-parallel groups are initialized."""
if (
_TENSOR_MODEL_PARALLEL_GROUP is None
or _PIPELINE_MODEL_PARALLEL_GROUP is None
......@@ -308,14 +894,19 @@ def model_parallel_is_initialized():
return True
def get_model_parallel_group():
"""Get the model parallel group the caller rank belongs to."""
def get_model_parallel_group(with_expert_parallel=False):
"""Get the model-parallel group the caller rank belongs to."""
if with_expert_parallel:
assert (
_MODEL_AND_EXPERT_PARALLEL_GROUP is not None
), 'model parallel group is not initialized'
return _MODEL_AND_EXPERT_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is not None, 'model parallel group is not initialized'
return _MODEL_PARALLEL_GROUP
def get_tensor_model_parallel_group(check_initialized=True):
"""Get the tensor model parallel group the caller rank belongs to."""
"""Get the tensor-model-parallel group the caller rank belongs to."""
if check_initialized:
assert (
_TENSOR_MODEL_PARALLEL_GROUP is not None
......@@ -324,23 +915,51 @@ def get_tensor_model_parallel_group(check_initialized=True):
def get_pipeline_model_parallel_group():
"""Get the pipeline model parallel group the caller rank belongs to."""
"""Get the pipeline-model-parallel group the caller rank belongs to."""
assert (
_PIPELINE_MODEL_PARALLEL_GROUP is not None
), 'pipeline_model parallel group is not initialized'
return _PIPELINE_MODEL_PARALLEL_GROUP
def get_data_parallel_group():
"""Get the data parallel group the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP is not None, 'data parallel group is not initialized'
return _DATA_PARALLEL_GROUP
def get_data_parallel_group(with_context_parallel=False):
"""Get the data-parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_DATA_PARALLEL_GROUP_WITH_CP is not None
), 'data parallel group with context parallel combined is not initialized'
return _DATA_PARALLEL_GROUP_WITH_CP
else:
assert _DATA_PARALLEL_GROUP is not None, 'data parallel group is not initialized'
return _DATA_PARALLEL_GROUP
def get_data_parallel_group_gloo(with_context_parallel=False):
"""Get the Gloo data-parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_DATA_PARALLEL_GROUP_WITH_CP_GLOO is not None
), 'data parallel group-gloo with context parallel combined is not initialized'
return _DATA_PARALLEL_GROUP_WITH_CP_GLOO
else:
assert _DATA_PARALLEL_GROUP_GLOO is not None, 'data parallel group-gloo is not initialized'
return _DATA_PARALLEL_GROUP_GLOO
def get_data_parallel_group_gloo():
"""Get the data parallel group-gloo the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP_GLOO is not None, 'data parallel group-gloo is not initialized'
return _DATA_PARALLEL_GROUP_GLOO
def get_context_parallel_group(check_initialized=True):
"""Get the context-parallel group the caller rank belongs to."""
if check_initialized:
assert _CONTEXT_PARALLEL_GROUP is not None, 'context parallel group is not initialized'
return _CONTEXT_PARALLEL_GROUP
def get_context_parallel_global_ranks(check_initialized=True):
"""Get all global ranks of the context-parallel group that the caller rank belongs to."""
if check_initialized:
assert (
_CONTEXT_PARALLEL_GLOBAL_RANKS is not None
), 'context parallel group is not initialized'
return _CONTEXT_PARALLEL_GLOBAL_RANKS
def get_embedding_group():
......@@ -355,32 +974,124 @@ def get_position_embedding_group():
return _POSITION_EMBEDDING_GROUP
def get_amax_reduction_group():
def get_amax_reduction_group(with_context_parallel=False, tp_only_amax_red=False):
"""Get the FP8 amax reduction group the caller rank belongs to."""
assert _AMAX_REDUCTION_GROUP is not None, 'FP8 amax reduction group is not initialized'
return _AMAX_REDUCTION_GROUP
if with_context_parallel:
if not tp_only_amax_red:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None
), 'FP8 amax reduction group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
else:
assert (
_TENSOR_AND_CONTEXT_PARALLEL_GROUP is not None
), 'FP8 amax reduction group is not initialized'
return _TENSOR_AND_CONTEXT_PARALLEL_GROUP
else:
if not tp_only_amax_red:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP is not None
), 'FP8 amax reduction group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP
else:
assert (
_TENSOR_MODEL_PARALLEL_GROUP is not None
), 'FP8 amax reduction group is not initialized'
return _TENSOR_MODEL_PARALLEL_GROUP
def get_tensor_and_data_parallel_group(with_context_parallel=False):
"""Get the tensor- and data-parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP is not None
), 'tensor and data parallel group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
else:
assert (
_TENSOR_AND_DATA_PARALLEL_GROUP is not None
), 'tensor and data parallel group is not initialized'
return _TENSOR_AND_DATA_PARALLEL_GROUP
def get_tensor_and_context_parallel_group():
"""Get the tensor- and context-parallel group the caller rank belongs to."""
assert (
_TENSOR_AND_CONTEXT_PARALLEL_GROUP is not None
), 'tensor and context parallel group is not initialized'
return _TENSOR_AND_CONTEXT_PARALLEL_GROUP
def get_expert_model_parallel_group():
"""Get the expert-model-parallel group the caller rank belongs to."""
assert (
_EXPERT_MODEL_PARALLEL_GROUP is not None
), 'expert model parallel group is not initialized'
return _EXPERT_MODEL_PARALLEL_GROUP
def get_tensor_and_expert_parallel_group():
"""Get the tensor- and expert-parallel group the caller rank belongs to."""
assert (
_TENSOR_AND_EXPERT_PARALLEL_GROUP is not None
), 'tensor and expert parallel group is not initialized'
return _TENSOR_AND_EXPERT_PARALLEL_GROUP
def get_data_modulo_expert_parallel_group(with_context_parallel=False):
"""Get the data-modulo-expert-parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP is not None
), 'data modulo expert parallel group with context parallel is not initialized'
return _DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP
else:
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP is not None
), 'data modulo expert parallel group is not initialized'
return _DATA_MODULO_EXPERT_PARALLEL_GROUP
def get_data_modulo_expert_parallel_group_gloo(with_context_parallel=False):
"""Get the Gloo data-modulo-expert-parallel group the caller rank belongs to."""
if with_context_parallel:
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO is not None
), 'data modulo expert parallel group-gloo with context parallel is not initialized'
return _DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO
else:
assert (
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO is not None
), 'data modulo expert parallel group-gloo is not initialized'
return _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
def set_expert_model_parallel_world_size(world_size):
"""Sets the expert-model-parallel world size."""
global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_tensor_model_parallel_world_size(world_size):
"""Set the tensor model parallel size"""
"""Set the tensor-model-parallel size"""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
"""Set the pipeline-model-parallel size"""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_virtual_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
"""Set the pipeline-model-parallel size"""
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def get_tensor_model_parallel_world_size():
"""Return world size for the tensor model parallel group."""
"""Return world size for the tensor-model-parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
......@@ -388,33 +1099,49 @@ def get_tensor_model_parallel_world_size():
def get_pipeline_model_parallel_world_size():
"""Return world size for the pipeline model parallel group."""
"""Return world size for the pipeline-model-parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group())
pp_group = get_pipeline_model_parallel_group()
if isinstance(pp_group, list):
# Implicit assumption that each PP group is the same size.
sizes = []
for group in _PIPELINE_GLOBAL_RANKS:
sizes.append(len(group))
assert all(x == sizes[0] for x in sizes)
return torch.distributed.get_world_size(group=pp_group[0])
else:
return torch.distributed.get_world_size(group=pp_group)
def set_expert_model_parallel_rank(rank):
"""Set expert-model-parallel rank."""
global _MPU_EXPERT_MODEL_PARALLEL_RANK
_MPU_EXPERT_MODEL_PARALLEL_RANK = rank
def set_tensor_model_parallel_rank(rank):
"""Set tensor model parallel rank."""
"""Set tensor-model-parallel rank."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
_MPU_TENSOR_MODEL_PARALLEL_RANK = rank
def set_pipeline_model_parallel_rank(rank):
"""Set pipeline model parallel rank."""
"""Set pipeline-model-parallel rank."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
_MPU_PIPELINE_MODEL_PARALLEL_RANK = rank
def set_pipeline_model_parallel_split_rank(rank):
"""Set pipeline model parallel split rank."""
"""Set pipeline-model-parallel split rank. DEPRECATED."""
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
_PIPELINE_MODEL_PARALLEL_SPLIT_RANK = rank
def get_tensor_model_parallel_rank():
"""Return my rank for the tensor model parallel group."""
"""Return caller's rank for the tensor-model-parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None:
return _MPU_TENSOR_MODEL_PARALLEL_RANK
......@@ -422,15 +1149,27 @@ def get_tensor_model_parallel_rank():
def get_pipeline_model_parallel_rank():
"""Return my rank for the pipeline model parallel group."""
"""Return caller's rank for the pipeline-model-parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_pipeline_model_parallel_group())
rank = torch.distributed.get_rank()
pp_group = get_pipeline_model_parallel_group()
if isinstance(pp_group, list):
# Assume that if the caller exist in multiple PP groups, then it has the same index.
indices = []
for group in _PIPELINE_GLOBAL_RANKS:
for i, r in enumerate(group):
if r == rank:
indices.append(i)
assert all(x == indices[0] for x in indices)
return torch.distributed.get_rank(group=pp_group[0])
else:
return torch.distributed.get_rank(group=pp_group)
def get_pipeline_model_parallel_split_rank():
"""Return pipeline model parallel split rank."""
"""Return pipeline-model-parallel split rank."""
global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
return _PIPELINE_MODEL_PARALLEL_SPLIT_RANK
......@@ -447,13 +1186,15 @@ def is_pipeline_first_stage(ignore_virtual=False):
def is_pipeline_last_stage(ignore_virtual=False):
"""Return True if in the last pipeline model-parallel stage, False otherwise."""
"""Return True if in the last pipeline-model-parallel stage, False otherwise."""
if not ignore_virtual:
virtual_pipeline_model_parallel_world_size = (
get_virtual_pipeline_model_parallel_world_size()
)
if virtual_pipeline_model_parallel_world_size is not None and get_virtual_pipeline_model_parallel_rank() != (
virtual_pipeline_model_parallel_world_size - 1
if (
virtual_pipeline_model_parallel_world_size is not None
and get_virtual_pipeline_model_parallel_rank()
!= (virtual_pipeline_model_parallel_world_size - 1)
):
return False
return get_pipeline_model_parallel_rank() == (get_pipeline_model_parallel_world_size() - 1)
......@@ -463,6 +1204,8 @@ def is_rank_in_embedding_group(ignore_virtual=False):
"""Return true if current rank is in embedding group, False otherwise."""
rank = torch.distributed.get_rank()
global _EMBEDDING_GLOBAL_RANKS
if _EMBEDDING_GLOBAL_RANKS is None:
return False
if ignore_virtual:
return rank in _EMBEDDING_GLOBAL_RANKS
if rank in _EMBEDDING_GLOBAL_RANKS:
......@@ -479,7 +1222,7 @@ def is_rank_in_position_embedding_group():
"""Return true if current rank is in position embedding group, False otherwise."""
rank = torch.distributed.get_rank()
global _POSITION_EMBEDDING_GLOBAL_RANKS
return rank in _POSITION_EMBEDDING_GLOBAL_RANKS
return _POSITION_EMBEDDING_GLOBAL_RANKS is not None and rank in _POSITION_EMBEDDING_GLOBAL_RANKS
def is_pipeline_stage_before_split(rank=None):
......@@ -512,6 +1255,36 @@ def is_pipeline_stage_after_split(rank=None):
return False
def is_inside_encoder(rank=None):
"""Return True if pipeline stage executes encoder block for a model
with both encoder and decoder."""
if get_pipeline_model_parallel_world_size() == 1:
return True
if rank is None:
rank = get_pipeline_model_parallel_rank()
global _PIPELINE_MODEL_PARALLEL_DECODER_START
if _PIPELINE_MODEL_PARALLEL_DECODER_START is None:
return True
if rank < _PIPELINE_MODEL_PARALLEL_DECODER_START:
return True
return False
def is_inside_decoder(rank=None):
"""Return True if pipeline stage executes decoder block for a model
with both encoder and decoder."""
if get_pipeline_model_parallel_world_size() == 1:
return True
if rank is None:
rank = get_pipeline_model_parallel_rank()
global _PIPELINE_MODEL_PARALLEL_DECODER_START
if _PIPELINE_MODEL_PARALLEL_DECODER_START is None:
return True
if rank >= _PIPELINE_MODEL_PARALLEL_DECODER_START:
return True
return False
def is_pipeline_stage_at_split():
"""Return true if pipeline stage executes decoder block and next
stage executes encoder block for a model with both encoder and
......@@ -541,67 +1314,203 @@ def get_virtual_pipeline_model_parallel_world_size():
def get_tensor_model_parallel_src_rank():
"""Calculate the global rank corresponding to the first local rank
in the tensor model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_tensor_model_parallel_world_size()
return (global_rank // local_world_size) * local_world_size
assert (
_TENSOR_MODEL_PARALLEL_GLOBAL_RANKS is not None
), "Tensor model parallel group is not initialized"
return _TENSOR_MODEL_PARALLEL_GLOBAL_RANKS[0]
def get_data_parallel_src_rank():
def get_data_parallel_src_rank(with_context_parallel=False):
"""Calculate the global rank corresponding to the first local rank
in the data parallel group."""
assert _DATA_PARALLEL_GLOBAL_RANKS is not None, "Data parallel group is not initialized"
return _DATA_PARALLEL_GLOBAL_RANKS[0]
if with_context_parallel:
assert (
_DATA_PARALLEL_GLOBAL_RANKS_WITH_CP is not None
), "Data parallel group with context parallel combined is not initialized"
return _DATA_PARALLEL_GLOBAL_RANKS_WITH_CP[0]
else:
assert _DATA_PARALLEL_GLOBAL_RANKS is not None, "Data parallel group is not initialized"
return _DATA_PARALLEL_GLOBAL_RANKS[0]
def get_pipeline_model_parallel_first_rank():
"""Return the global rank of the first process in the pipeline for the
current tensor parallel group"""
"""Return the global rank of the first stage in the current rank's pipeline."""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
return _PIPELINE_GLOBAL_RANKS[0]
if isinstance(_PIPELINE_GLOBAL_RANKS[0], list):
# I assume the first rank is the same for all pp groups right now.
for rank_group in _PIPELINE_GLOBAL_RANKS:
assert rank_group[0] == _PIPELINE_GLOBAL_RANKS[0][0]
return _PIPELINE_GLOBAL_RANKS[0][0]
else:
return _PIPELINE_GLOBAL_RANKS[0]
def get_pipeline_model_parallel_last_rank():
"""Return the global rank of the last process in the pipeline for the
current tensor parallel group"""
"""Return the global rank of the last stage in the current rank's pipeline."""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
last_rank_local = get_pipeline_model_parallel_world_size() - 1
return _PIPELINE_GLOBAL_RANKS[last_rank_local]
def get_pipeline_model_parallel_next_rank():
"""Return the global rank that follows the caller in the pipeline"""
"""Return the global rank that follows the caller in the pipeline, for each
pipeline-parallel group that the rank is part of.
If it is just part of one group, an int is returned, otherwise a list of ints.
"""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
rank_in_pipeline = get_pipeline_model_parallel_rank()
world_size = get_pipeline_model_parallel_world_size()
return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size]
if isinstance(_PIPELINE_GLOBAL_RANKS[0], list):
to_return = []
for group in _PIPELINE_GLOBAL_RANKS:
to_return.append(group[(rank_in_pipeline + 1) % world_size])
return to_return
else:
return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size]
def get_pipeline_model_parallel_prev_rank():
"""Return the global rank that preceeds the caller in the pipeline"""
"""Return the global rank that precedes the caller in the pipeline, for each
pipeline-parallel group that the rank is part of.
If it is just part of one group, an int is returned, otherwise a list of ints.
"""
assert _PIPELINE_GLOBAL_RANKS is not None, "Pipeline parallel group is not initialized"
rank_in_pipeline = get_pipeline_model_parallel_rank()
world_size = get_pipeline_model_parallel_world_size()
return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size]
if isinstance(_PIPELINE_GLOBAL_RANKS[0], list):
to_return = []
for group in _PIPELINE_GLOBAL_RANKS:
to_return.append(group[(rank_in_pipeline - 1) % world_size])
return to_return
else:
return _PIPELINE_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size]
def get_data_parallel_world_size():
def get_data_parallel_world_size(with_context_parallel=False):
"""Return world size for the data parallel group."""
global _MPU_DATA_PARALLEL_WORLD_SIZE
if _MPU_DATA_PARALLEL_WORLD_SIZE is not None:
return _MPU_DATA_PARALLEL_WORLD_SIZE
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_world_size(
group=get_data_parallel_group(with_context_parallel=with_context_parallel)
)
else:
return 0
def set_data_parallel_rank(rank):
"""Return world size for the data parallel group."""
global _MPU_DATA_PARALLEL_RANK
_MPU_DATA_PARALLEL_RANK = rank
def get_data_parallel_rank(with_context_parallel=False):
"""Return caller's rank in the data-parallel group."""
global _MPU_DATA_PARALLEL_RANK
if _MPU_DATA_PARALLEL_RANK is not None:
return _MPU_DATA_PARALLEL_RANK
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_rank(
group=get_data_parallel_group(with_context_parallel=with_context_parallel)
)
else:
return 0
def get_context_parallel_world_size():
"""Return world size for the context parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_world_size(group=get_context_parallel_group())
else:
return 0
def get_context_parallel_rank():
"""Return caller's rank in the context-parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_world_size(group=get_data_parallel_group())
return torch.distributed.get_rank(group=get_context_parallel_group())
else:
return 0
def get_data_parallel_rank():
"""Return my rank for the data parallel group."""
def get_tensor_and_context_parallel_world_size():
"""Return world size for the tensor and context-parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_rank(group=get_data_parallel_group())
return torch.distributed.get_world_size(group=get_tensor_and_context_parallel_group())
else:
return 0
def get_tensor_and_context_parallel_rank():
"""Return caller's rank in the joint tensor-model-parallel and context-parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_rank(group=get_tensor_and_context_parallel_group())
else:
return 0
def get_expert_model_parallel_world_size():
"""Return world size for the expert-model-parallel group."""
if _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
if torch.distributed.is_available() and torch.distributed.is_initialized():
tensor_and_expert_parallel_world_size = torch.distributed.get_world_size(
group=get_tensor_and_expert_parallel_group()
)
return tensor_and_expert_parallel_world_size // get_tensor_model_parallel_world_size()
else:
return 0
def get_tensor_and_expert_parallel_world_size():
"""Return world size for the expert model parallel group times model parallel group.
Currently, each expert will also be distributed across TP group by default.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
tensor_and_expert_parallel_world_size = torch.distributed.get_world_size(
group=get_tensor_and_expert_parallel_group()
)
return tensor_and_expert_parallel_world_size
else:
return 0
def get_expert_model_parallel_rank():
"""Return caller's rank in the expert-model-parallel group."""
if _MPU_EXPERT_MODEL_PARALLEL_RANK is not None:
return _MPU_EXPERT_MODEL_PARALLEL_RANK
if torch.distributed.is_available() and torch.distributed.is_initialized():
tensor_and_expert_parallel_rank = torch.distributed.get_rank(
group=get_tensor_and_expert_parallel_group()
)
return tensor_and_expert_parallel_rank // get_tensor_model_parallel_world_size()
else:
return 0
def get_data_modulo_expert_parallel_rank(with_context_parallel=False):
"""Return caller's rank in the context-parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_rank(
group=get_data_modulo_expert_parallel_group(with_context_parallel=with_context_parallel)
)
else:
return 0
def get_tensor_and_expert_parallel_rank():
"""Return caller's rank in the joint tensor- and expert-model-parallel group."""
if torch.distributed.is_available() and torch.distributed.is_initialized():
return torch.distributed.get_rank(group=get_tensor_and_expert_parallel_group())
else:
return 0
def _set_global_memory_buffer():
"""Initialize global buffer"""
"""Initialize global buffer."""
global _GLOBAL_MEMORY_BUFFER
assert _GLOBAL_MEMORY_BUFFER is None, 'global memory buffer is already initialized'
_GLOBAL_MEMORY_BUFFER = GlobalMemoryBuffer()
......@@ -619,33 +1528,120 @@ def destroy_global_memory_buffer():
_GLOBAL_MEMORY_BUFFER = None
def get_all_ranks():
"""Get caller's rank in tensor-model-parallel, data-parallel, context-parallel,
pipeline-model-parallel and expert-model-parallel groups."""
ranks = [
get_tensor_model_parallel_rank(),
get_data_parallel_rank(),
get_context_parallel_rank(),
get_pipeline_model_parallel_rank(),
get_expert_model_parallel_rank(),
]
return '_'.join(map(lambda x: str(x or 0), ranks))
def get_moe_layer_wise_logging_tracker():
"""Return the moe layer wise tracker."""
global _MOE_LAYER_WISE_LOGGING_TRACKER
return _MOE_LAYER_WISE_LOGGING_TRACKER
def destroy_model_parallel():
"""Set the groups to none."""
global _MODEL_PARALLEL_GROUP
_MODEL_PARALLEL_GROUP = None
global _MODEL_AND_EXPERT_PARALLEL_GROUP
_MODEL_AND_EXPERT_PARALLEL_GROUP = None
global _TENSOR_MODEL_PARALLEL_GROUP
_TENSOR_MODEL_PARALLEL_GROUP = None
global _PIPELINE_MODEL_PARALLEL_GROUP
_PIPELINE_MODEL_PARALLEL_GROUP = None
global _DATA_PARALLEL_GROUP
_DATA_PARALLEL_GROUP = None
global _DATA_PARALLEL_GROUP_WITH_CP
_DATA_PARALLEL_GROUP_WITH_CP = None
global _CONTEXT_PARALLEL_GROUP
_CONTEXT_PARALLEL_GROUP = None
global _CONTEXT_PARALLEL_GLOBAL_RANKS
_CONTEXT_PARALLEL_GLOBAL_RANKS = None
global _EMBEDDING_GROUP
_EMBEDDING_GROUP = None
global _POSITION_EMBEDDING_GROUP
_POSITION_EMBEDDING_GROUP = None
global _AMAX_REDUCTION_GROUP
_AMAX_REDUCTION_GROUP = None
global _TENSOR_AND_DATA_PARALLEL_GROUP
_TENSOR_AND_DATA_PARALLEL_GROUP = None
global _TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP
_TENSOR_AND_DATA_PARALLEL_GROUP_WITH_CP = None
global _TENSOR_AND_CONTEXT_PARALLEL_GROUP
_TENSOR_AND_CONTEXT_PARALLEL_GROUP = None
global _EXPERT_MODEL_PARALLEL_GROUP
_EXPERT_MODEL_PARALLEL_GROUP = None
global _TENSOR_AND_EXPERT_PARALLEL_GROUP
_TENSOR_AND_EXPERT_PARALLEL_GROUP = None
global _DATA_MODULO_EXPERT_PARALLEL_GROUP
_DATA_MODULO_EXPERT_PARALLEL_GROUP = None
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP = None
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_TENSOR_MODEL_PARALLEL_RANK
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
global _GLOBAL_MEMORY_BUFFER
_GLOBAL_MEMORY_BUFFER = None
global _MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE
_MPU_EXPERT_MODEL_PARALLEL_WORLD_SIZE = None
global _MPU_EXPERT_MODEL_PARALLEL_RANK
_MPU_EXPERT_MODEL_PARALLEL_RANK = None
global _DATA_PARALLEL_GROUP_GLOO
if _DATA_PARALLEL_GROUP_GLOO is not None:
torch.distributed.destroy_process_group(_DATA_PARALLEL_GROUP_GLOO)
_DATA_PARALLEL_GROUP_GLOO = None
global _DATA_PARALLEL_GROUP_WITH_CP_GLOO
_DATA_PARALLEL_GROUP_WITH_CP_GLOO = None
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO
if _DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO is not None:
torch.distributed.destroy_process_group(_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO)
_DATA_MODULO_EXPERT_PARALLEL_GROUP_GLOO = None
global _DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO
_DATA_MODULO_EXPERT_PARALLEL_GROUP_WITH_CP_GLOO = None
global _MOE_LAYER_WISE_LOGGING_TRACKER
_MOE_LAYER_WISE_LOGGING_TRACKER = {}
megatron/core/pipeline_parallel/__init__.py
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
from .schedules import get_forward_backward_func
megatron/core/pipeline_parallel/p2p_communication.py
View file @ 4b097dee
......@@ -13,6 +13,7 @@ from megatron.core.parallel_state import (
get_pipeline_model_parallel_next_rank,
get_pipeline_model_parallel_prev_rank,
get_pipeline_model_parallel_rank,
get_pipeline_model_parallel_world_size,
)
# Types
......@@ -25,7 +26,7 @@ def _communicate_shapes(tensor_send_next, tensor_send_prev, recv_prev, recv_next
This is required when the sequence lengths across micro batches
are not uniform.
Takes the following arguments:
Args:
tensor_send_next: tensor to send to next rank (no tensor sent if
set to None).
tensor_send_prev: tensor to send to prev rank (no tensor sent if
......@@ -123,39 +124,29 @@ def _batched_p2p_ops(
tensor_recv_prev: Optional[torch.Tensor],
tensor_send_next: Optional[torch.Tensor],
tensor_recv_next: Optional[torch.Tensor],
group: torch.distributed.ProcessGroup
group: torch.distributed.ProcessGroup,
prev_pipeline_rank: int,
next_pipeline_rank: int,
):
ops = []
if tensor_send_prev is not None:
send_prev_op = torch.distributed.P2POp(
torch.distributed.isend,
tensor_send_prev,
get_pipeline_model_parallel_prev_rank(),
group,
torch.distributed.isend, tensor_send_prev, prev_pipeline_rank, group
)
ops.append(send_prev_op)
if tensor_recv_prev is not None:
recv_prev_op = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_recv_prev,
get_pipeline_model_parallel_prev_rank(),
group,
torch.distributed.irecv, tensor_recv_prev, prev_pipeline_rank, group
)
ops.append(recv_prev_op)
if tensor_send_next is not None:
send_next_op = torch.distributed.P2POp(
torch.distributed.isend,
tensor_send_next,
get_pipeline_model_parallel_next_rank(),
group,
torch.distributed.isend, tensor_send_next, next_pipeline_rank, group
)
ops.append(send_next_op)
if tensor_recv_next is not None:
recv_next_op = torch.distributed.P2POp(
torch.distributed.irecv,
tensor_recv_next,
get_pipeline_model_parallel_next_rank(),
group,
torch.distributed.irecv, tensor_recv_next, next_pipeline_rank, group
)
ops.append(recv_next_op)
if len(ops) > 0:
......@@ -171,57 +162,69 @@ def _p2p_ops(
tensor_recv_prev: Optional[torch.Tensor],
tensor_send_next: Optional[torch.Tensor],
tensor_recv_next: Optional[torch.Tensor],
group: torch.distributed.ProcessGroup
group: torch.distributed.ProcessGroup,
prev_pipeline_rank: int,
next_pipeline_rank: int,
):
reqs = []
rank = get_pipeline_model_parallel_rank()
even_send_odd_recv_group = group
if get_pipeline_model_parallel_world_size() == 2:
# Use the global process group for one of the two p2p communications
# to allow the overlap of the independent communications.
# Using the global process group is compatible because the pipeline-parallel
# communications set the source and destination by global rank.
even_recv_odd_send_group = torch.distributed.group.WORLD
else:
even_recv_odd_send_group = group
if get_pipeline_model_parallel_rank() % 2 == 0:
if tensor_send_next is not None:
send_next_req = torch.distributed.isend(
tensor=tensor_send_next, dst=get_pipeline_model_parallel_next_rank(), group=group,
tensor=tensor_send_next, dst=next_pipeline_rank, group=even_send_odd_recv_group
)
reqs.append(send_next_req)
if tensor_recv_prev is not None:
recv_prev_req = torch.distributed.irecv(
tensor=tensor_recv_prev, src=get_pipeline_model_parallel_prev_rank(), group=group,
tensor=tensor_recv_prev, src=prev_pipeline_rank, group=even_recv_odd_send_group
)
reqs.append(recv_prev_req)
if tensor_send_prev is not None:
send_prev_req = torch.distributed.isend(
tensor=tensor_send_prev, dst=get_pipeline_model_parallel_prev_rank(), group=group,
tensor=tensor_send_prev, dst=prev_pipeline_rank, group=even_send_odd_recv_group
)
reqs.append(send_prev_req)
if tensor_recv_next is not None:
recv_next_req = torch.distributed.irecv(
tensor=tensor_recv_next, src=get_pipeline_model_parallel_next_rank(), group=group,
tensor=tensor_recv_next, src=next_pipeline_rank, group=even_recv_odd_send_group
)
reqs.append(recv_next_req)
else:
if tensor_recv_prev is not None:
recv_prev_req = torch.distributed.irecv(
tensor=tensor_recv_prev, src=get_pipeline_model_parallel_prev_rank(), group=group,
tensor=tensor_recv_prev, src=prev_pipeline_rank, group=even_send_odd_recv_group
)
reqs.append(recv_prev_req)
if tensor_send_next is not None:
send_next_req = torch.distributed.isend(
tensor=tensor_send_next, dst=get_pipeline_model_parallel_next_rank(), group=group,
tensor=tensor_send_next, dst=next_pipeline_rank, group=even_recv_odd_send_group
)
reqs.append(send_next_req)
if tensor_recv_next is not None:
recv_next_req = torch.distributed.irecv(
tensor=tensor_recv_next, src=get_pipeline_model_parallel_next_rank(), group=group,
tensor=tensor_recv_next, src=next_pipeline_rank, group=even_send_odd_recv_group
)
reqs.append(recv_next_req)
if tensor_send_prev is not None:
send_prev_req = torch.distributed.isend(
tensor=tensor_send_prev, dst=get_pipeline_model_parallel_prev_rank(), group=group,
tensor=tensor_send_prev, dst=prev_pipeline_rank, group=even_recv_odd_send_group
)
reqs.append(send_prev_req)
return reqs
......@@ -235,12 +238,12 @@ def _communicate(
recv_next: bool,
tensor_shape: Shape,
config: ModelParallelConfig,
wait_on_reqs: bool = True
wait_on_reqs: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Communicate tensors between stages. Used as helper method in other
communication methods that are used in megatron/schedules.py.
Arguments:
Args:
tensor_send_next (torch.Tensor, optional):
Tensor to send to next rank (no tensor sent if None)
......@@ -270,10 +273,8 @@ def _communicate(
"""
# Create placeholder tensors for receive in forward and backward directions
# if needed.
tensor_recv_prev = None
tensor_recv_next = None
tensor_recv_prev_func = None
tensor_recv_next_func = None
if not config.variable_seq_lengths:
recv_prev_shape = tensor_shape
......@@ -283,6 +284,22 @@ def _communicate(
tensor_send_next, tensor_send_prev, recv_prev, recv_next, config
)
def create_tensor_recv_prev():
return torch.empty(
recv_prev_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=config.pipeline_dtype,
)
def create_tensor_recv_next():
return torch.empty(
recv_next_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=config.pipeline_dtype,
)
if recv_prev:
if config.pipeline_dtype is None:
raise RuntimeError("pipeline_dtype must be provided if recv_prev is True")
......@@ -291,12 +308,8 @@ def _communicate(
"tensor_shape must be specified if recv_prev is True. "
"Common tensor_shape is (seq_length, micro_batch_size, hidden_size)"
)
tensor_recv_prev = torch.empty(
recv_prev_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=config.pipeline_dtype,
)
tensor_recv_prev_func = create_tensor_recv_prev
if recv_next:
if config.pipeline_dtype is None:
raise RuntimeError("dtype must be provided if recv_next is True")
......@@ -305,12 +318,7 @@ def _communicate(
"tensor_shape must be specified if recv_next is True. "
"Common tensor_shape is (seq_length, micro_batch_size, hidden_size)"
)
tensor_recv_next = torch.empty(
recv_next_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=config.pipeline_dtype,
)
tensor_recv_next_func = create_tensor_recv_next
# Send tensors in both the forward and backward directions as appropriate.
if config.use_ring_exchange_p2p:
......@@ -326,13 +334,49 @@ def _communicate(
else:
p2p_func = _p2p_ops
reqs = p2p_func(
tensor_send_prev=tensor_send_prev,
tensor_recv_prev=tensor_recv_prev,
tensor_send_next=tensor_send_next,
tensor_recv_next=tensor_recv_next,
group=get_pipeline_model_parallel_group(),
)
# Each rank can now be part of several different pipeline parallel groups
# (specifically, this can occur when encoder tensor parallelism != decoder
# tensor parallelism, and hence a rank in the encoder is going to feed
# several different decoder ranks. We therefore have to receive or send tensors
# from several groups. For convenience, I wrap everything into lists.
pp_group = get_pipeline_model_parallel_group()
next_rank = get_pipeline_model_parallel_next_rank()
prev_rank = get_pipeline_model_parallel_prev_rank()
if not isinstance(pp_group, list):
pp_group = [pp_group]
assert not isinstance(next_rank, list)
next_rank = [next_rank]
assert not isinstance(prev_rank, list)
prev_rank = [prev_rank]
reqs = []
tensor_recv_prev_list = []
tensor_recv_next_list = []
for group, nr, pr in zip(pp_group, next_rank, prev_rank):
if tensor_recv_prev_func is not None:
tensor_recv_prev = tensor_recv_prev_func()
tensor_recv_prev_list.append(tensor_recv_prev)
else:
tensor_recv_prev = None
if tensor_recv_next_func is not None:
tensor_recv_next = tensor_recv_next_func()
tensor_recv_next_list.append(tensor_recv_next)
else:
tensor_recv_next = None
reqs.extend(
p2p_func(
tensor_send_prev=tensor_send_prev,
tensor_recv_prev=tensor_recv_prev,
tensor_send_next=tensor_send_next,
tensor_recv_next=tensor_recv_next,
group=group,
prev_pipeline_rank=pr,
next_pipeline_rank=nr,
)
)
if wait_on_reqs and len(reqs) > 0:
for req in reqs:
......@@ -344,12 +388,27 @@ def _communicate(
# User should assert that we have a modern enough PyTorch to not need this
torch.cuda.synchronize()
def _handle_tensor_list(x):
"""This basically handles all the cases that we expect to see. Either the list None,
or it's a singleton (the usual cases, since most ranks only belong to one pipeline group),
or everything returned is None, or everything returned is not None, and it has to be summed
together."""
if len(x) == 0:
return None
if len(x) == 1:
return x[0]
if all(xx is None for xx in x):
return None
return torch.stack(x, dim=0).sum(dim=0, dtype=torch.float32).to(x[0].dtype)
tensor_recv_prev = _handle_tensor_list(tensor_recv_prev_list)
tensor_recv_next = _handle_tensor_list(tensor_recv_next_list)
return tensor_recv_prev, tensor_recv_next, reqs
def recv_forward(tensor_shape: Shape, config: ModelParallelConfig) -> torch.Tensor:
""" Receive tensor from previous rank in pipeline (forward receive).
"""Receive tensor from previous rank in pipeline (forward receive).
See _communicate for argument details.
"""
......
megatron/core/pipeline_parallel/schedules.py
View file @ 4b097dee
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import contextlib
from typing import Callable, Iterator, List, Optional, Union
from typing import Iterator, List, Union
import torch
from torch.autograd.variable import Variable
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from megatron import core
from megatron.core import parallel_state
from megatron.core.enums import ModelType
from megatron.core.pipeline_parallel import p2p_communication
from megatron.core.utils import get_attr_wrapped_model, get_model_config, get_model_type
from megatron.core.transformer.moe.router import MoEAuxLossAutoScaler
from megatron.core.utils import (
drain_embedding_wgrad_compute,
get_attr_wrapped_model,
get_model_config,
get_model_type,
get_model_xattn,
)
# Types
Shape = Union[List[int], torch.Size]
......@@ -90,6 +95,10 @@ def get_forward_backward_func():
collect_non_loss_data (optional, bool, default=False): TODO
first_val_step (bool, optional): Is the first step of the validation phase. Used by
Transformer Engine modules to only update their fp8 weights only on the first validation
step.
"""
pipeline_model_parallel_size = parallel_state.get_pipeline_model_parallel_world_size()
if pipeline_model_parallel_size > 1:
......@@ -113,7 +122,7 @@ def deallocate_output_tensor(out, deallocate_pipeline_outputs=False):
return
assert isinstance(out, torch.Tensor), "expected Tensor, found %s." % type(out).__name__
assert out._base is None, "counter-productive to free a view of another tensor."
out.data = torch.empty((1,), device=out.device, dtype=out.dtype,)
out.data = torch.empty((1,), device=out.device, dtype=out.dtype)
def custom_backward(output, grad_output):
......@@ -134,7 +143,7 @@ def custom_backward(output, grad_output):
# Handle scalar output
if grad_output is None:
assert output.numel() == 1, "implicit grad requires scalar output."
grad_output = torch.ones_like(output, memory_format=torch.preserve_format,)
grad_output = torch.ones_like(output, memory_format=torch.preserve_format)
# Call c++ engine [ see torch/csrc/autograd/python_engine.cpp ]
Variable._execution_engine.run_backward(
......@@ -148,6 +157,17 @@ def custom_backward(output, grad_output):
)
def set_current_microbatch(model, microbatch_id):
decoder_exists = True
decoder = None
try:
decoder = get_attr_wrapped_model(model, "decoder")
except RuntimeError:
decoder_exists = False
if decoder_exists and decoder is not None:
decoder.current_microbatch = microbatch_id
def forward_step(
forward_step_func,
data_iterator,
......@@ -158,16 +178,84 @@ def forward_step(
config,
collect_non_loss_data=False,
checkpoint_activations_microbatch=None,
is_first_microbatch=False,
current_microbatch=None,
encoder_decoder_xattn=False,
):
"""Forward step for passed-in model.
If first stage, input tensor is obtained from data_iterator, otherwise
passed-in input_tensor is used.
Returns output tensor."""
If it is the first stage, the input tensor is obtained from the data_iterator.
Otherwise, the passed-in input_tensor is used.
Args:
forward_step_func (callable):
The forward step function for the model that takes the
data iterator as the first argument, and model as the second.
This user's forward step is expected to output a tuple of two elements:
1. The output object from the forward step. This output object needs to be a
tensor or some kind of collection of tensors. The only hard requirement
for this object is that it needs to be acceptible as input into the second
function.
2. A function to reduce (optionally) the output from the forward step. This
could be a reduction over the loss from the model, it could be a function that
grabs the output from the model and reformats, it could be a function that just
passes through the model output. This function must have one of the following
patterns, and depending on the pattern different things happen internally:
a. A tuple of reduced loss and some other data. Note that in this case
the first argument is divided by the number of global microbatches,
assuming it is a loss, so that the loss is stable as a function of
the number of devices the step is split across.
b. A triple of reduced loss, number of tokens, and some other data. This
is similar to case (a), but the loss is further averaged across the
number of tokens in the batch. If the user is not already averaging
across the number of tokens, this pattern is useful to use.
c. Any arbitrary data the user wants (eg a dictionary of tensors, a list
of tensors, etc in the case of inference). To trigger case 3 you need
to specify `collect_non_loss_data=True` and you may also want to
specify `forward_only=True` in the call to the parent forward_backward
function.
data_iterator (iterator):
The data iterator.
model (nn.Module):
The model to perform the forward step on.
num_microbatches (int):
The number of microbatches.
input_tensor (Tensor or list[Tensor]):
The input tensor(s) for the forward step.
forward_data_store (list):
The list to store the forward data. If you go down path 2.a or
2.b for the return of your forward reduction function then this will store only the
final dimension of the output, for example the metadata output by the loss function.
If you go down the path of 2.c then this will store the entire output of the forward
reduction function applied to the model output.
config (object):
The configuration object.
collect_non_loss_data (bool, optional):
Whether to collect non-loss data. Defaults to False.
This is the path to use if you want to collect arbitrary output from the model forward,
such as with inference use cases. Defaults to False.
checkpoint_activations_microbatch (int, optional):
The microbatch to checkpoint activations.
Defaults to None.
is_first_microbatch (bool, optional):
Whether it is the first microbatch. Defaults to False.
current_microbatch (int, optional):
The current microbatch. Defaults to None.
Returns:
Tensor or list[Tensor]: The output object(s) from the forward step.
Tensor: The number of tokens.
"""
if config.timers is not None:
config.timers('forward-compute', log_level=2).start()
if is_first_microbatch and hasattr(model, 'set_is_first_microbatch'):
model.set_is_first_microbatch()
if current_microbatch is not None:
set_current_microbatch(model, current_microbatch)
unwrap_output_tensor = False
if not isinstance(input_tensor, list):
input_tensor = [input_tensor]
......@@ -188,11 +276,20 @@ def forward_step(
data_iterator, model, checkpoint_activations_microbatch
)
num_tokens = torch.tensor(0, dtype=torch.int)
if parallel_state.is_pipeline_last_stage():
if not collect_non_loss_data:
output_tensor = loss_func(output_tensor)
loss, loss_reduced = output_tensor
output_tensor = loss / num_microbatches
outputs = loss_func(output_tensor)
if len(outputs) == 3:
output_tensor, num_tokens, loss_reduced = outputs
if not config.calculate_per_token_loss:
output_tensor /= num_tokens
output_tensor /= num_microbatches
else:
# preserve legacy loss averaging behavior (ie, over the number of microbatches)
assert len(outputs) == 2
output_tensor, loss_reduced = outputs
output_tensor /= num_microbatches
forward_data_store.append(loss_reduced)
else:
data = loss_func(output_tensor, non_loss_data=True)
......@@ -201,18 +298,32 @@ def forward_step(
if config.timers is not None:
config.timers('forward-compute').stop()
# If T5 model (or other model with encoder and decoder)
# and in decoder stack, then send encoder_hidden_state
# Set the loss scale for the auxiliary loss of the MoE layer.
# Since we use a trick to do backward on the auxiliary loss, we need to set the scale
# explicitly.
if hasattr(config, 'num_moe_experts') and config.num_moe_experts is not None:
# Calculate the loss scale based on the grad_scale_func if available, else default to 1.
loss_scale = (
config.grad_scale_func(torch.ones(1, device=output_tensor.device))
if config.grad_scale_func is not None
else torch.tensor(1.0)
)
# Set the loss scale
MoEAuxLossAutoScaler.set_loss_scale(loss_scale / num_microbatches)
# If T5 model and in decoder stack, then send encoder_hidden_state
# downstream as well.
model_type = get_model_type(model)
if (
parallel_state.is_pipeline_stage_after_split()
and model_type == ModelType.encoder_and_decoder
model_type == ModelType.encoder_and_decoder
and encoder_decoder_xattn
and parallel_state.is_inside_decoder()
):
return [output_tensor, input_tensor[-1]]
return [output_tensor, input_tensor[-1]], num_tokens
if unwrap_output_tensor:
return output_tensor
return [output_tensor]
return output_tensor, num_tokens
return [output_tensor], num_tokens
def backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config):
......@@ -268,10 +379,11 @@ def backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, c
# model with encoder and decoder).
if (
parallel_state.get_pipeline_model_parallel_world_size() > 1
and parallel_state.is_pipeline_stage_after_split()
and model_type == ModelType.encoder_and_decoder
and len(output_tensor_grad) > 1 # excludes models that lack a skip connection.
):
if output_tensor_grad[1] is not None:
assert input_tensor_grad[-1] is not None
input_tensor_grad[-1].add_(output_tensor_grad[1])
if unwrap_input_tensor_grad:
input_tensor_grad = input_tensor_grad[0]
......@@ -282,6 +394,13 @@ def backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, c
return input_tensor_grad
def check_first_val_step(first_val_step, forward_only, cond):
if (first_val_step is not None) and forward_only:
return first_val_step and cond
else:
return cond
def forward_backward_no_pipelining(
*,
forward_step_func,
......@@ -293,6 +412,7 @@ def forward_backward_no_pipelining(
decoder_seq_length: int = None, # unused
forward_only: bool = False,
collect_non_loss_data: bool = False,
first_val_step: bool = None,
):
"""Run forward and backward passes with no pipeline parallelism
(no inter-stage communication).
......@@ -313,10 +433,10 @@ def forward_backward_no_pipelining(
data_iterator = data_iterator[0]
config = get_model_config(model)
if config.timers is not None:
config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time)
no_sync_func = config.no_sync_func
if no_sync_func is None and isinstance(model, torchDDP):
no_sync_func = model.no_sync
if no_sync_func is None:
no_sync_func = contextlib.nullcontext
......@@ -324,9 +444,10 @@ def forward_backward_no_pipelining(
forward_data_store = []
input_tensor, output_tensor_grad = None, None
total_num_tokens = torch.zeros([], dtype=torch.int, device="cuda")
with no_sync_func():
for i in range(num_microbatches - 1):
output_tensor = forward_step(
output_tensor, num_tokens = forward_step(
forward_step_func,
data_iterator,
model,
......@@ -335,13 +456,16 @@ def forward_backward_no_pipelining(
forward_data_store,
config,
collect_non_loss_data,
is_first_microbatch=check_first_val_step(first_val_step, forward_only, i == 0),
current_microbatch=i,
)
total_num_tokens += num_tokens.item()
if not forward_only:
backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config)
# Run computation for last microbatch out of context handler (want to
# synchronize gradients).
output_tensor = forward_step(
output_tensor, num_tokens = forward_step(
forward_step_func,
data_iterator,
model,
......@@ -350,14 +474,68 @@ def forward_backward_no_pipelining(
forward_data_store,
config,
collect_non_loss_data,
is_first_microbatch=check_first_val_step(
first_val_step, forward_only, num_microbatches == 1
),
current_microbatch=num_microbatches - 1,
)
total_num_tokens += num_tokens.item()
if not forward_only:
backward_step(input_tensor, output_tensor, output_tensor_grad, model_type, config)
if config.finalize_model_grads_func is not None and not forward_only:
# Finalize model grads (perform full grad all-reduce / reduce-scatter for
# data parallelism and layernorm all-reduce for sequence parallelism).
config.finalize_model_grads_func(
[model], total_num_tokens if config.calculate_per_token_loss else None
)
if config.timers is not None:
config.timers('forward-backward').stop()
return forward_data_store
def clear_embedding_activation_buffer(config, model):
if (
parallel_state.is_pipeline_last_stage(ignore_virtual=True)
and config.defer_embedding_wgrad_compute
):
if isinstance(model, list):
embedding_module = get_attr_wrapped_model(
model[-1], 'post_process', return_model_obj=True
)
else:
embedding_module = get_attr_wrapped_model(model, 'post_process', return_model_obj=True)
# Need to ensure no stray activations exists in this buffer
embedding_module.embedding_activation_buffer.clear()
return embedding_module
else:
return None
def finish_embedding_wgrad_compute(config, embedding_module):
if (
parallel_state.is_pipeline_last_stage(ignore_virtual=True)
and config.defer_embedding_wgrad_compute
):
embedding_activation_buffer = embedding_module.embedding_activation_buffer
grad_output_buffer = embedding_module.grad_output_buffer
weight = (
embedding_module.output_layer.weight
if embedding_module.share_embeddings_and_output_weights
else embedding_module.shared_embedding_or_output_weight()
)
drain_embedding_wgrad_compute(
config, embedding_activation_buffer, grad_output_buffer, weight
)
def forward_backward_pipelining_with_interleaving(
*,
forward_step_func,
......@@ -369,6 +547,7 @@ def forward_backward_pipelining_with_interleaving(
decoder_seq_length: int = None,
forward_only: bool = False,
collect_non_loss_data: bool = False,
first_val_step: bool = None,
):
"""Run interleaved 1F1B schedule (model split into model chunks), with
communication between pipeline stages as needed.
......@@ -384,14 +563,21 @@ def forward_backward_pipelining_with_interleaving(
if config.overlap_p2p_comm and config.batch_p2p_comm:
raise ValueError("Can not use both overlap_p2p_comm and batch_p2p_comm")
# Needed only when gradients are finalized in M-Core
if config.finalize_model_grads_func is not None and not forward_only:
embedding_module = clear_embedding_activation_buffer(config, model)
if config.timers is not None:
config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time)
# Disable async grad reductions
no_sync_func = config.no_sync_func
if no_sync_func is None and all(isinstance(chunk, torchDDP) for chunk in model):
if isinstance(no_sync_func, list):
def multi_no_sync():
stack = contextlib.ExitStack()
for chunk in model:
stack.enter_context(chunk.no_sync())
for model_chunk_no_sync_func in config.no_sync_func:
stack.enter_context(model_chunk_no_sync_func())
return stack
no_sync_func = multi_no_sync
......@@ -399,6 +585,19 @@ def forward_backward_pipelining_with_interleaving(
no_sync_func = contextlib.nullcontext
no_sync_context = None
if config.grad_sync_func is not None and not isinstance(config.grad_sync_func, list):
config.grad_sync_func = [config.grad_sync_func for _ in model]
if config.param_sync_func is not None and not isinstance(config.param_sync_func, list):
config.param_sync_func = [config.param_sync_func for _ in model]
# Disable config.grad_sync_func and config.param_sync_func if only running forward passes.
# They will be re-enabled at the end of this function.
grad_sync_func, param_sync_func = None, None
if forward_only:
grad_sync_func, param_sync_func = config.grad_sync_func, config.param_sync_func
config.grad_sync_func, config.param_sync_func = None, None
def disable_grad_sync():
"""Disable asynchronous grad reductions"""
nonlocal no_sync_context
......@@ -420,6 +619,8 @@ def forward_backward_pipelining_with_interleaving(
input_tensors = [[] for _ in range(len(model))]
output_tensors = [[] for _ in range(len(model))]
total_num_tokens = torch.tensor(0, dtype=torch.int).cuda()
forward_data_store = []
if not forward_only:
output_tensor_grads = [[] for _ in range(len(model))]
......@@ -443,6 +644,7 @@ def forward_backward_pipelining_with_interleaving(
)
tensor_shape = [seq_length, micro_batch_size, config.hidden_size]
tensor_shape[0] = tensor_shape[0] // parallel_state.get_context_parallel_world_size()
if config.sequence_parallel:
tensor_shape[0] = tensor_shape[0] // parallel_state.get_tensor_model_parallel_world_size()
......@@ -482,8 +684,8 @@ def forward_backward_pipelining_with_interleaving(
# Synchronize params for first two model chunks
if config.param_sync_func is not None:
config.param_sync_func(model[0].parameters())
config.param_sync_func(model[1].parameters())
config.param_sync_func[0](model[0].parameters())
config.param_sync_func[1](model[1].parameters())
def get_model_chunk_id(microbatch_id, forward):
"""Helper method to get the model chunk ID given the iteration number."""
......@@ -493,10 +695,18 @@ def forward_backward_pipelining_with_interleaving(
model_chunk_id = num_model_chunks - model_chunk_id - 1
return model_chunk_id
def get_microbatch_id_in_model_chunk(iteration_id, forward):
"""Helper method to get the microbatch_id within model chunk given the iteration number."""
assert forward
iteration_group_id = iteration_id // (pipeline_parallel_size * num_model_chunks)
microbatch_id_in_model_chunk = (iteration_group_id * pipeline_parallel_size) + (
iteration_id % pipeline_parallel_size
)
return microbatch_id_in_model_chunk
def is_first_microbatch_for_model_chunk(microbatch_id: int) -> bool:
"""Check if an iteration is the first for a model chunk."""
microbatch_group_size = pipeline_parallel_size * num_model_chunks
num_microbatch_groups = total_num_microbatches // microbatch_group_size
microbatch_group_id = microbatch_id // microbatch_group_size
microbatch_id_in_group = microbatch_id % microbatch_group_size
if microbatch_group_id == 0:
......@@ -515,7 +725,7 @@ def forward_backward_pipelining_with_interleaving(
else:
return False
def forward_step_helper(microbatch_id, checkpoint_activations_microbatch):
def forward_step_helper(microbatch_id, current_microbatch, checkpoint_activations_microbatch):
"""Helper method to run forward step with model split into chunks
(run set_virtual_pipeline_model_parallel_rank() before calling
forward_step())."""
......@@ -535,14 +745,17 @@ def forward_backward_pipelining_with_interleaving(
):
param_sync_chunk_id = get_model_chunk_id(param_sync_microbatch_id, forward=True) + 1
if 1 < param_sync_chunk_id < num_model_chunks:
config.param_sync_func(model[param_sync_chunk_id].parameters())
config.param_sync_func[param_sync_chunk_id](
model[param_sync_chunk_id].parameters()
)
# forward step
if parallel_state.is_pipeline_first_stage():
if len(input_tensors[model_chunk_id]) == len(output_tensors[model_chunk_id]):
input_tensors[model_chunk_id].append(None)
input_tensor = input_tensors[model_chunk_id][-1]
output_tensor = forward_step(
output_tensor, num_tokens = forward_step(
forward_step_func,
data_iterator[model_chunk_id],
model[model_chunk_id],
......@@ -552,9 +765,16 @@ def forward_backward_pipelining_with_interleaving(
config,
collect_non_loss_data,
checkpoint_activations_microbatch,
check_first_val_step(
first_val_step, forward_only, is_first_microbatch_for_model_chunk(microbatch_id)
),
current_microbatch=current_microbatch,
)
output_tensors[model_chunk_id].append(output_tensor)
nonlocal total_num_tokens
total_num_tokens += num_tokens.item()
# if forward-only, no need to save tensors for a backward pass
if forward_only:
input_tensors[model_chunk_id].pop()
......@@ -596,7 +816,7 @@ def forward_backward_pipelining_with_interleaving(
):
grad_sync_chunk_id = get_model_chunk_id(grad_sync_microbatch_id, forward=False)
enable_grad_sync()
config.grad_sync_func(model[grad_sync_chunk_id].parameters())
config.grad_sync_func[grad_sync_chunk_id](model[grad_sync_chunk_id].parameters())
synchronized_model_chunks.add(grad_sync_chunk_id)
disable_grad_sync()
......@@ -624,7 +844,10 @@ def forward_backward_pipelining_with_interleaving(
else:
checkpoint_activations_microbatch = None
output_tensor = forward_step_helper(k, checkpoint_activations_microbatch)
current_microbatch = get_microbatch_id_in_model_chunk(k, forward=True)
output_tensor = forward_step_helper(
k, current_microbatch, checkpoint_activations_microbatch
)
# Determine if tensor should be received from previous stage.
next_forward_model_chunk_id = get_model_chunk_id(k + 1, forward=True)
......@@ -651,16 +874,15 @@ def forward_backward_pipelining_with_interleaving(
recv_next = True
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
recv_next = False
(
input_tensor,
output_tensor_grad,
) = p2p_communication.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
(input_tensor, output_tensor_grad) = (
p2p_communication.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
)
)
output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
else:
......@@ -687,15 +909,14 @@ def forward_backward_pipelining_with_interleaving(
if parallel_state.is_pipeline_last_stage(ignore_virtual=True):
recv_next = False
(
output_tensor_grad,
bwd_wait_handles,
) = p2p_communication.send_backward_recv_backward(
input_tensor_grad,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
overlap_p2p_comm=True,
(output_tensor_grad, bwd_wait_handles) = (
p2p_communication.send_backward_recv_backward(
input_tensor_grad,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
overlap_p2p_comm=True,
)
)
output_tensor_grads[num_model_chunks - 1].append(output_tensor_grad)
......@@ -717,6 +938,7 @@ def forward_backward_pipelining_with_interleaving(
else:
checkpoint_activations_microbatch = None
current_microbatch = get_microbatch_id_in_model_chunk(forward_k, forward=True)
if config.overlap_p2p_comm:
if fwd_wait_handles is not None:
for req in fwd_wait_handles:
......@@ -724,7 +946,9 @@ def forward_backward_pipelining_with_interleaving(
deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
output_tensor = forward_step_helper(forward_k, checkpoint_activations_microbatch)
output_tensor = forward_step_helper(
forward_k, current_microbatch, checkpoint_activations_microbatch
)
# Determine if current stage has anything to send in either direction,
# otherwise set tensor to None.
......@@ -802,7 +1026,9 @@ def forward_backward_pipelining_with_interleaving(
)
else: # no p2p overlap
output_tensor = forward_step_helper(forward_k, checkpoint_activations_microbatch)
output_tensor = forward_step_helper(
forward_k, current_microbatch, checkpoint_activations_microbatch
)
# Backward pass.
backward_k = k
......@@ -855,16 +1081,15 @@ def forward_backward_pipelining_with_interleaving(
recv_prev = False
# Communicate tensors.
(
input_tensor,
output_tensor_grad,
) = p2p_communication.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
(input_tensor, output_tensor_grad) = (
p2p_communication.send_forward_backward_recv_forward_backward(
output_tensor,
input_tensor_grad,
recv_prev=recv_prev,
recv_next=recv_next,
tensor_shape=tensor_shape,
config=config,
)
)
deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs)
......@@ -902,16 +1127,33 @@ def forward_backward_pipelining_with_interleaving(
)
)
# Launch any remaining grad reductions
enable_grad_sync()
if config.grad_sync_func is not None:
params = []
for model_chunk_id in range(num_model_chunks):
if model_chunk_id not in synchronized_model_chunks:
params.extend(model[model_chunk_id].parameters())
synchronized_model_chunks.add(model_chunk_id)
if params:
config.grad_sync_func(params)
# Launch any remaining grad reductions.
enable_grad_sync()
if config.grad_sync_func is not None:
for model_chunk_id in range(num_model_chunks):
if model_chunk_id not in synchronized_model_chunks:
config.grad_sync_func[model_chunk_id](model[model_chunk_id].parameters())
synchronized_model_chunks.add(model_chunk_id)
if config.finalize_model_grads_func is not None and not forward_only:
# If defer_embedding_wgrad_compute is enabled we need to do the
# weight gradient GEMM's here.
finish_embedding_wgrad_compute(config, embedding_module)
# Finalize model grads (perform full grad all-reduce / reduce-scatter for
# data parallelism, layernorm all-reduce for sequence parallelism, and
# embedding all-reduce for pipeline parallelism).
config.finalize_model_grads_func(
model, total_num_tokens if config.calculate_per_token_loss else None
)
# Restore config.grad_sync_func and config.param_sync_func.
if forward_only:
config.grad_sync_func, config.param_sync_func = grad_sync_func, param_sync_func
if config.timers is not None:
config.timers('forward-backward').stop()
return forward_data_store
......@@ -924,17 +1166,23 @@ def get_tensor_shapes(
micro_batch_size: int,
decoder_seq_length: int,
config,
encoder_decoder_xattn: bool,
):
# Determine right tensor sizes (based on position of rank with respect to split
# rank) and model size.
# Send two tensors if model is T5 and rank is in decoder stage:
# first tensor is decoder (pre-transpose),
# second tensor is encoder (post-transpose).
# If model is T5 and rank is at the boundary:
# send one tensor (post-transpose from encoder).
# Otherwise, send one tensor (pre-transpose).
# Determine right tensor sizes (based on position of rank with
# respect to split rank) and model size.
# Send two tensors if model decoder requires the encoder's output
# (via cross-attention) and rank is in decoder stage.
# first tensor is decoder.
# second tensor is encoder.
# If model has an encoder & decoder and rank is at the boundary:
# send one tensor.
# Otherwise, send one tensor.
tensor_shapes = []
seq_length = seq_length // parallel_state.get_context_parallel_world_size()
if model_type == ModelType.encoder_and_decoder:
decoder_seq_length = decoder_seq_length // parallel_state.get_context_parallel_world_size()
if config.sequence_parallel:
seq_length = seq_length // parallel_state.get_tensor_model_parallel_world_size()
if model_type == ModelType.encoder_and_decoder:
......@@ -943,12 +1191,14 @@ def get_tensor_shapes(
)
if model_type == ModelType.encoder_and_decoder:
if parallel_state.is_pipeline_stage_before_split(rank):
if parallel_state.is_inside_encoder(rank):
tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
else:
elif encoder_decoder_xattn:
tensor_shapes.append((decoder_seq_length, micro_batch_size, config.hidden_size))
tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
else:
else:
tensor_shapes.append((decoder_seq_length, micro_batch_size, config.hidden_size))
else: # model_type == ModelType.encoder_or_decoder
tensor_shapes.append((seq_length, micro_batch_size, config.hidden_size))
return tensor_shapes
......@@ -976,7 +1226,7 @@ def recv_backward(tensor_shapes, config):
def send_forward(output_tensors, tensor_shapes, config):
if not isinstance(output_tensors, list):
output_tensors = [output_tensors]
for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
for output_tensor, tensor_shape in zip(output_tensors, tensor_shapes):
if tensor_shape is None:
continue
p2p_communication.send_forward(output_tensor, config)
......@@ -985,7 +1235,7 @@ def send_forward(output_tensors, tensor_shapes, config):
def send_backward(input_tensor_grads, tensor_shapes, config):
if not isinstance(input_tensor_grads, list):
input_tensor_grads = [input_tensor_grads]
for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
for input_tensor_grad, tensor_shape in zip(input_tensor_grads, tensor_shapes):
if tensor_shape is None:
continue
p2p_communication.send_backward(input_tensor_grad, config)
......@@ -995,7 +1245,7 @@ def send_forward_recv_backward(output_tensors, tensor_shapes, config):
if not isinstance(output_tensors, list):
output_tensors = [output_tensors]
output_tensor_grads = []
for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
for output_tensor, tensor_shape in zip(output_tensors, tensor_shapes):
if tensor_shape is None:
output_tensor_grads.append(None)
continue
......@@ -1010,7 +1260,7 @@ def send_backward_recv_forward(input_tensor_grads, tensor_shapes, config):
if not isinstance(input_tensor_grads, list):
input_tensor_grads = [input_tensor_grads]
input_tensors = []
for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
for input_tensor_grad, tensor_shape in zip(input_tensor_grads, tensor_shapes):
if tensor_shape is None:
input_tensors.append(None)
continue
......@@ -1032,11 +1282,10 @@ def forward_backward_pipelining_without_interleaving(
decoder_seq_length: int = None,
forward_only: bool = False,
collect_non_loss_data: bool = False,
first_val_step: bool = None,
):
"""Run non-interleaved 1F1B schedule, with communication between pipeline
stages.
Returns dictionary with losses if the last stage, empty dict otherwise."""
stages. Returns dictionary with losses if the last stage, empty dict otherwise."""
if isinstance(model, list):
assert (
......@@ -1055,10 +1304,15 @@ def forward_backward_pipelining_without_interleaving(
"Non-interleaved pipeline parallelism does not support overlapping p2p communication"
)
# Needed only when gradients are finalized in M-Core
if config.finalize_model_grads_func is not None and not forward_only:
embedding_module = clear_embedding_activation_buffer(config, model)
if config.timers is not None:
config.timers('forward-backward', log_level=1).start(barrier=config.barrier_with_L1_time)
# Disable async grad reductions
no_sync_func = config.no_sync_func
if no_sync_func is None and isinstance(model, torchDDP):
no_sync_func = model.no_sync
if no_sync_func is None:
no_sync_func = contextlib.nullcontext
no_sync_context = None
......@@ -1101,6 +1355,7 @@ def forward_backward_pipelining_without_interleaving(
max_outstanding_backprops = num_warmup_microbatches + 1
model_type = get_model_type(model)
encoder_decoder_xattn = get_model_xattn(model)
rank = parallel_state.get_pipeline_model_parallel_rank()
recv_tensor_shapes = get_tensor_shapes(
......@@ -1110,6 +1365,7 @@ def forward_backward_pipelining_without_interleaving(
micro_batch_size=micro_batch_size,
decoder_seq_length=decoder_seq_length,
config=config,
encoder_decoder_xattn=encoder_decoder_xattn,
)
send_tensor_shapes = get_tensor_shapes(
rank=rank,
......@@ -1118,11 +1374,14 @@ def forward_backward_pipelining_without_interleaving(
micro_batch_size=micro_batch_size,
decoder_seq_length=decoder_seq_length,
config=config,
encoder_decoder_xattn=encoder_decoder_xattn,
)
# Input, output tensors only need to be saved when doing backward passes
input_tensors = None
output_tensors = None
total_num_tokens = torch.tensor(0, dtype=torch.int).cuda()
if not forward_only:
input_tensors = []
output_tensors = []
......@@ -1140,7 +1399,7 @@ def forward_backward_pipelining_without_interleaving(
checkpoint_activations_microbatch = None
input_tensor = recv_forward(recv_tensor_shapes, config)
output_tensor = forward_step(
output_tensor, num_tokens = forward_step(
forward_step_func,
data_iterator,
model,
......@@ -1150,8 +1409,12 @@ def forward_backward_pipelining_without_interleaving(
config,
collect_non_loss_data,
checkpoint_activations_microbatch,
check_first_val_step(first_val_step, forward_only, i == 0),
current_microbatch=i,
encoder_decoder_xattn=encoder_decoder_xattn,
)
send_forward(output_tensor, send_tensor_shapes, config)
total_num_tokens += num_tokens.item()
if not forward_only:
input_tensors.append(input_tensor)
......@@ -1176,7 +1439,7 @@ def forward_backward_pipelining_without_interleaving(
else:
checkpoint_activations_microbatch = None
output_tensor = forward_step(
output_tensor, num_tokens = forward_step(
forward_step_func,
data_iterator,
model,
......@@ -1186,7 +1449,13 @@ def forward_backward_pipelining_without_interleaving(
config,
collect_non_loss_data,
checkpoint_activations_microbatch,
check_first_val_step(
first_val_step, forward_only, (i == 0) and (num_warmup_microbatches == 0)
),
current_microbatch=i + num_warmup_microbatches,
encoder_decoder_xattn=encoder_decoder_xattn,
)
total_num_tokens += num_tokens.item()
if forward_only:
send_forward(output_tensor, send_tensor_shapes, config)
......@@ -1209,6 +1478,12 @@ def forward_backward_pipelining_without_interleaving(
input_tensor = input_tensors.pop(0)
output_tensor = output_tensors.pop(0)
# Enable grad sync for the last microbatch in the batch if the full
# backward pass completes in the 1F1B stage.
if num_warmup_microbatches == 0 and last_iteration:
if config.grad_sync_func is None or rank == 0:
enable_grad_sync()
input_tensor_grad = backward_step(
input_tensor, output_tensor, output_tensor_grad, model_type, config
)
......@@ -1245,10 +1520,26 @@ def forward_backward_pipelining_without_interleaving(
send_backward(input_tensor_grad, recv_tensor_shapes, config)
# Launch any remaining grad reductions
if no_sync_context is not None:
enable_grad_sync()
if config.grad_sync_func is not None:
config.grad_sync_func(model.parameters())
# Launch any remaining grad reductions.
if no_sync_context is not None:
enable_grad_sync()
if config.grad_sync_func is not None:
config.grad_sync_func(model.parameters())
if config.finalize_model_grads_func is not None and not forward_only:
# If defer_embedding_wgrad_compute is enabled we need to do the
# weight gradient GEMM's here.
finish_embedding_wgrad_compute(config, embedding_module)
# Finalize model grads (perform full grad all-reduce / reduce-scatter for
# data parallelism, layernorm all-reduce for sequence parallelism, and
# embedding all-reduce for pipeline parallelism).
config.finalize_model_grads_func(
[model], total_num_tokens if config.calculate_per_token_loss else None
)
if config.timers is not None:
config.timers('forward-backward').stop()
return forward_data_store
megatron/core/requirements.txt
View file @ 4b097dee
torch
\ No newline at end of file
torch
packaging
megatron/core/ssm/mamba_block.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2024, Tri Dao, Albert Gu.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
# Some of this code was adopted from https://github.com/state-spaces/mamba/
# This source code is licensed under the Apache license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass
from functools import partial
from typing import Union
from torch import Tensor, nn
from megatron.core import parallel_state
from megatron.core.dist_checkpointing.mapping import ShardedStateDict
from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding
from megatron.core.extensions.transformer_engine import TENorm
from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols as LayerSymbols
from megatron.core.ssm.mamba_hybrid_layer_allocation import allocate_layers
from megatron.core.tensor_parallel import get_cuda_rng_tracker
from megatron.core.transformer.identity_op import IdentityOp
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.spec_utils import ModuleSpec, build_module
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.transformer.utils import sharded_state_dict_default
from megatron.core.utils import make_viewless_tensor
# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
def _init_weights(
module,
n_layer,
initializer_range=0.02, # Now only used for embedding layer.
rescale_prenorm_residual=True,
n_residuals_per_layer=1, # Change to 2 if we have MLP
):
with get_cuda_rng_tracker().fork():
if isinstance(module, nn.Linear):
if not getattr(module.weight, "_no_reinit", False):
nn.init.normal_(module.weight, std=initializer_range)
if module.bias is not None:
if not getattr(module.bias, "_no_reinit", False):
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=initializer_range)
for name, p in module.named_parameters():
if name in ["in_proj.weight", "x_proj.weight", "conv1d.weight", "out_proj.weight"]:
nn.init.kaiming_uniform_(p, a=math.sqrt(5))
if rescale_prenorm_residual:
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the
# > residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of
# > 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM):
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if name in ["out_proj.weight", "fc2.weight"]:
# Special Scaled Initialization
nn.init.normal_(
p,
mean=0.0,
std=initializer_range / math.sqrt(n_residuals_per_layer * n_layer),
)
@dataclass
class MambaStackSubmodules:
"""
A class for the module specs for the MambaStack.
"""
mamba_layer: Union[ModuleSpec, type] = IdentityOp
attention_layer: Union[ModuleSpec, type] = IdentityOp
mlp_layer: Union[ModuleSpec, type] = IdentityOp
class MambaStack(MegatronModule):
"""
Constructor for the MambaStack class.
Args:
config (TransformerConfig): the transformer configuration
submodules (MambaStackSubmodules): the submodules for the stack
mamba_ssm_ngroups (int, optional): the number of groups for the
MAMBA SSM. Defaults to 8.
residual_in_fp32 (bool, optional): whether to do residual connections
in fp32. Defaults to False.
pre_process (bool, optional): whether to include an embedding layer.
Defaults to True.
hybrid_attention_ratio (float, optional): the target ratio of attention layers to
total layers. Defaults to 0.0.
hybrid_mlp_ratio (float, optional): the target ratio of mlp layers to total
layers. Defaults to 0.0.
hybrid_override_pattern (str, optional): the hybrid layer pattern to override
with. Defaults to None.
post_layer_norm (bool, optional): whether to include a final layer norm.
Defaults to True.
post_process (bool, optional): whether to include an output layer.
Defaults to True.
device (optional): the device to use. Defaults to None.
dtype (optional): the data type to use. Defaults to None.
"""
def __init__(
self,
config: TransformerConfig,
submodules: MambaStackSubmodules,
mamba_ssm_ngroups: int = 8,
residual_in_fp32=False,
pre_process: bool = True,
hybrid_attention_ratio: float = 0.0,
hybrid_mlp_ratio: float = 0.0,
hybrid_override_pattern: str = None,
post_layer_norm: bool = True,
post_process: bool = True,
device=None,
dtype=None,
) -> None:
super().__init__(config=config)
self.residual_in_fp32 = residual_in_fp32
self.pre_process = pre_process
self.post_layer_norm = post_layer_norm
self.post_process = post_process
# Required for pipeline parallel schedules
self.input_tensor = None
self.hybrid_attention_ratio = hybrid_attention_ratio
self.hybrid_mlp_ratio = hybrid_mlp_ratio
self.hybrid_override_pattern = hybrid_override_pattern
layer_type_list = allocate_layers(
self.config.num_layers,
self.hybrid_attention_ratio,
self.hybrid_mlp_ratio,
self.hybrid_override_pattern,
)
pp_layer_offset = 0
if parallel_state.get_pipeline_model_parallel_world_size() > 1:
pp_layer_offset, layer_type_list = self._select_layers_for_pipeline_parallel(
layer_type_list
)
self.layers = nn.ModuleList()
for i, layer_type in enumerate(layer_type_list):
if layer_type == LayerSymbols.MAMBA:
layer = build_module(
submodules.mamba_layer,
config=self.config,
mamba_ssm_ngroups=mamba_ssm_ngroups,
residual_in_fp32=residual_in_fp32,
layer_number=i + 1 + pp_layer_offset,
)
elif layer_type == LayerSymbols.ATTENTION:
# Transformer layers apply their own pp_layer_offset
layer = build_module(
submodules.attention_layer, config=self.config, layer_number=i + 1
)
elif layer_type == LayerSymbols.MLP:
# Transformer layers apply their own pp_layer_offset
layer = build_module(submodules.mlp_layer, config=self.config, layer_number=i + 1)
else:
assert True, "unexpected layer_type"
self.layers.append(layer)
# Required for activation recomputation
self.num_layers_per_pipeline_rank = len(self.layers)
if self.post_process and self.post_layer_norm:
# Final layer norm before output.
self.final_norm = TENorm(
config=self.config,
hidden_size=self.config.hidden_size,
eps=self.config.layernorm_epsilon,
)
self.apply(partial(_init_weights, n_layer=self.config.num_layers))
def _select_layers_for_pipeline_parallel(self, layer_type_list):
pipeline_rank = parallel_state.get_pipeline_model_parallel_rank()
num_layers_per_pipeline_rank = (
self.config.num_layers // parallel_state.get_pipeline_model_parallel_world_size()
)
assert parallel_state.get_virtual_pipeline_model_parallel_world_size() is None, (
"The Mamba hybrid model does not currently support "
"virtual/interleaved pipeline parallelism"
)
offset = pipeline_rank * num_layers_per_pipeline_rank
selected_list = layer_type_list[offset : offset + num_layers_per_pipeline_rank]
return offset, selected_list
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
"""
Allocate inference cache for each layer.
Args:
batch_size (int): The batch size to use for inference.
max_seqlen (int): The maximum sequence length to use
for inference.
dtype (optional): The data type to use for allocation.
Defaults to the data type of the model.
"""
return {
i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype)
for i, layer in enumerate(self.layers)
}
def set_input_tensor(self, input_tensor: Tensor):
"""Set input tensor to be used instead of forward()'s input.
When doing pipeline parallelism the input from the previous
stage comes from communication, not from the input, so the
model's forward_step_func won't have it. This function is thus
used by internal code to bypass the input provided by the
forward_step_func"""
self.input_tensor = input_tensor
def forward(
self,
hidden_states: Tensor,
attention_mask: Tensor,
inference_params=None,
rotary_pos_emb: Tensor = None,
):
"""
Forward function of the MambaStack class.
It either returns the Loss values if labels are given or the
final hidden units
Args:
hidden_states (Tensor): the input tensor.
attention_mask (Tensor): the attention mask.
inference_params (InferenceParams): the inference parameters.
rotary_pos_emb (Tensor, optional): the rotary positional embeddings.
Defaults to None.
Returns:
Tensor: the output tensor.
"""
if not self.pre_process:
# See set_input_tensor()
hidden_states = self.input_tensor
if inference_params:
# NOTE(bnorick): match InferenceParams attributes for
# mamba_ssm.utils.generation.InferenceParams,
# this hack supports eval
inference_params.max_seqlen = inference_params.max_sequence_length
inference_params.seqlen_offset = inference_params.sequence_len_offset
for layer in self.layers:
hidden_states = layer(
hidden_states,
attention_mask,
inference_params=inference_params,
rotary_pos_emb=rotary_pos_emb,
)
# The attention layer (currently a simplified transformer layer)
# outputs a tuple of (hidden_states, context). Context is intended
# for cross-attention, and is not needed in our model.
if isinstance(hidden_states, tuple):
hidden_states = hidden_states[0]
# Final layer norm.
if self.post_process and self.post_layer_norm:
hidden_states = self.final_norm(hidden_states)
# Ensure that the tensor passed between pipeline parallel stages is
# viewless. See related notes in TransformerBlock and TransformerLayer
output = make_viewless_tensor(
inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True
)
return hidden_states
def sharded_state_dict(
self, prefix: str = '', sharded_offsets: tuple = (), metadata: dict = None
) -> ShardedStateDict:
"""
Returns a sharded state dictionary for the current object.
This function constructs a sharded state dictionary by iterating over the layers
in the current object, computing the sharded state dictionary for each layer,
and combining the results into a single dictionary.
Parameters:
prefix (str): The prefix to use for the state dictionary keys.
sharded_offsets (tuple): The sharded offsets to use for the state dictionary.
metadata (dict): Additional metadata to use when computing the sharded state dictionary.
Returns:
dict: The sharded state dictionary for the current object.
"""
sharded_state_dict = {}
layer_prefix = f'{prefix}layers.'
for local_layer_idx, layer in enumerate(self.layers):
global_layer_offset = layer.layer_number - 1 # self.layer_number starts at 1
state_dict_prefix = (
f'{layer_prefix}{local_layer_idx}.' # module list index in MambaBlock
)
sharded_prefix = f'{layer_prefix}{global_layer_offset}.'
sharded_pp_offset = []
layer_sharded_state_dict = layer.sharded_state_dict(
state_dict_prefix, sharded_pp_offset, metadata
)
replace_prefix_for_sharding(layer_sharded_state_dict, state_dict_prefix, sharded_prefix)
sharded_state_dict.update(layer_sharded_state_dict)
# Add modules other than self.layers
for name, module in self.named_children():
if not module is self.layers:
sharded_state_dict.update(
sharded_state_dict_default(
module, f'{prefix}{name}.', sharded_offsets, metadata
)
)
return sharded_state_dict
megatron/core/ssm/mamba_hybrid_layer_allocation.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
import logging
if __name__ != "__main__":
from megatron.core.utils import log_single_rank
else:
from typing import Any
def log_single_rank(logger: logging.Logger, *args: Any, rank: int = 0, **kwargs: Any):
print(*args[1:], **kwargs)
logger = logging.getLogger(__name__)
class Symbols:
MAMBA = 'M'
ATTENTION = '*'
MLP = '-'
VALID = {MAMBA, ATTENTION, MLP}
def _allocate_auto(
total_layers_count: int, target_attention_ratio: float, target_mlp_ratio: float
) -> list:
# First, allocate attention (evenly spaced, starting and ending with mamba)
attention_layers_count: int = round(total_layers_count * target_attention_ratio)
mamba_layers_count: int = total_layers_count - attention_layers_count
mamba_sections_count: int = attention_layers_count + 1
mamba_section_length: float = mamba_layers_count / mamba_sections_count
layer_type_list = [Symbols.MAMBA] * total_layers_count
x: float = mamba_section_length
for l in range(total_layers_count):
if x < 0.5:
layer_type_list[l] = Symbols.ATTENTION
x += mamba_section_length
else:
x -= 1
# Next, allocate mlp
# (evenly distributed, but right-justified, not replacing attention)
mlp_layers_count: int = round(total_layers_count * target_mlp_ratio)
if mlp_layers_count > 0:
mamba_layers_count -= mlp_layers_count
mamba_to_mlp_ratio: float = mamba_layers_count / mlp_layers_count
x: float = mamba_to_mlp_ratio
for l in range(total_layers_count):
if layer_type_list[l] == Symbols.MAMBA:
if x < 0.5:
layer_type_list[l] = Symbols.MLP
x += mamba_to_mlp_ratio
else:
x -= 1
return layer_type_list
def _allocate_override(total_layers_count: int, override_pattern: str) -> list:
layer_type_list = list(override_pattern)
override_pattern_length = len(layer_type_list)
if override_pattern_length != total_layers_count:
raise ValueError(
"The hybrid override pattern is the wrong "
f"length: got {override_pattern_length}, expected "
f"{total_layers_count}"
)
for l in layer_type_list:
if l not in Symbols.VALID:
raise ValueError(f"In hybrid override pattern, '{l}' is not " f"one of {Symbols.VALID}")
return layer_type_list
def _layer_counts_match(a: list, b: list) -> bool:
for s in Symbols.VALID:
if a.count(s) != b.count(s):
return False
return True
def allocate_layers(
total_layers_count: int,
target_attention_ratio: float,
target_mlp_ratio: float,
override_pattern: str = None,
) -> list:
assert total_layers_count > 0
assert target_attention_ratio >= 0.0 and target_attention_ratio <= 1.0
assert target_mlp_ratio >= 0.0 and target_mlp_ratio <= 1.0
assert target_attention_ratio + target_mlp_ratio <= 1.0
# Note: target_mamba_ratio = 1.0 - target_attention_ratio - target_mlp_ratio
layer_type_list = _allocate_auto(total_layers_count, target_attention_ratio, target_mlp_ratio)
if override_pattern is not None:
layer_type_list_override = _allocate_override(total_layers_count, override_pattern)
log_single_rank(logger, logging.INFO, "Using hybrid override pattern")
if (target_attention_ratio > 0.0 or target_mlp_ratio > 0.0) and not _layer_counts_match(
layer_type_list_override, layer_type_list
):
raise ValueError(
"The number of each type of layer in the override "
"pattern must match the number in the overridden "
"pattern."
)
if layer_type_list_override == layer_type_list:
log_single_rank(
logger, logging.INFO, "The override pattern matches the overridden pattern"
)
else:
log_single_rank(logger, logging.INFO, "Warning: overriding pattern A with pattern B")
log_single_rank(logger, logging.INFO, f"A: {''.join(layer_type_list)}")
log_single_rank(logger, logging.INFO, f"B: {''.join(layer_type_list_override)}")
layer_type_list = layer_type_list_override
if target_attention_ratio > 0.0 or target_mlp_ratio > 0.0 or override_pattern is not None:
actual_attention_layers_count = layer_type_list.count(Symbols.ATTENTION)
actual_attention_ratio = actual_attention_layers_count / total_layers_count
actual_mlp_layers_count = layer_type_list.count(Symbols.MLP)
actual_mlp_ratio = actual_mlp_layers_count / total_layers_count
allocation_string = ''.join(layer_type_list)
log_single_rank(
logger,
logging.INFO,
f"Hybrid allocation ({Symbols.MAMBA} is mamba, "
f"{Symbols.ATTENTION} is attention, "
f"{Symbols.MLP} is mlp):",
)
log_single_rank(logger, logging.INFO, allocation_string)
log_single_rank(
logger,
logging.INFO,
f"{actual_attention_layers_count} attention layers in "
f"{total_layers_count} total layers.",
)
log_single_rank(
logger,
logging.INFO,
f"Target attention ratio: {target_attention_ratio:.2f}. "
f"Actual attention ratio: {actual_attention_ratio:.2f}.",
)
log_single_rank(
logger,
logging.INFO,
f"{actual_mlp_layers_count} mlp layers in " f"{total_layers_count} total layers.",
)
log_single_rank(
logger,
logging.INFO,
f"Target mlp ratio: {target_mlp_ratio:.2f}. "
f"Actual mlp ratio: {actual_mlp_ratio:.2f}.",
)
return layer_type_list
if __name__ == "__main__":
test_cases = [
# (10, 0.2, 0.0),
# (48, 0.0, 0.0), # will not print anything
# (48, 0.1, 0.0),
# 48, 0.3, 0.0),
# (48, 0.5, 0.0),
# (48, 0.6, 0.0),
# (48, 0.7, 0.0),
# (10, 0.0, 0.1),
# (10, 0.0, 0.3),
# (10, 0.0, 0.5),
# (10, 0.1, 0.1),
# (10, 0.2, 0.2),
# (10, 0.3, 0.3),
# (10, 0.5, 0.5),
# (48, 0.2, 0.3),
# (48, 0.5, 0.2),
# (48, 0.5, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
# (48, 0.25, 0.25, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
# (48, 0.25, 0.25, "MM-*MM-*MM*-MM*-MM*-MM*-M*M-M*M-M*M-M*M-*MM-*MM-"),
# (48, 0.0, 0.2, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
# (48, 0.2, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
# (48, 0.0, 0.0, "MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-MM*-"),
# (48, 0.5, 0.5),
# (10, 0.3, 0.2, "MMM*-*M*M-"),
# (10, 0.3, 0.2, "MM*M-*M*M-"),
(9, 0.0, 0.0, "M*-M*-M*-"),
(9, 0.0, 0.0, "MMMMMMMMM"),
]
for t in test_cases:
print("")
allocate_layers(*t)
megatron/core/ssm/mamba_layer.py 0 → 100644
View file @ 4b097dee
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2024, Tri Dao, Albert Gu.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
# Some of this code was adopted from https://github.com/state-spaces/mamba/
# This source code is licensed under the Apache license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Union
import torch
from torch import Tensor
from megatron.core.transformer.identity_op import IdentityOp
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.spec_utils import ModuleSpec, build_module
from megatron.core.transformer.transformer_config import TransformerConfig
@dataclass
class MambaLayerSubmodules:
norm: Union[ModuleSpec, type] = IdentityOp
mixer: Union[ModuleSpec, type] = IdentityOp
mamba_bda: Union[ModuleSpec, type] = IdentityOp
class MambaLayer(MegatronModule):
def __init__(
self,
config: TransformerConfig,
submodules: MambaLayerSubmodules,
mamba_ssm_ngroups=8,
layer_number: int = 1,
residual_in_fp32=False,
):
"""
Top level Mamba Layer
"""
super().__init__(config)
self.config = config
self.layer_number = layer_number
self.residual_in_fp32 = residual_in_fp32
self.hidden_dropout = config.hidden_dropout
self.mixer = build_module(
submodules.mixer,
self.config,
d_model=self.config.hidden_size,
ngroups=mamba_ssm_ngroups,
layer_number=layer_number,
)
self.norm = build_module(submodules.norm, self.config, self.config.hidden_size)
self.mamba_bda = build_module(submodules.mamba_bda)
self.bias_dropout_add_exec_handler = torch.enable_grad
def forward(
self,
hidden_states: Tensor,
attention_mask: Tensor, # Not used in MambaLayer
inference_params=None,
rotary_pos_emb: Tensor = None, # Not used in MambaLayer
):
residual = hidden_states
if self.residual_in_fp32:
residual = residual.to(torch.float32)
hidden_states = hidden_states.to(dtype=self.config.params_dtype)
hidden_states = self.norm(hidden_states)
mixer_out_with_bias = self.mixer(hidden_states, inference_params=inference_params)
with self.bias_dropout_add_exec_handler():
hidden_states = self.mamba_bda(self.training, self.config.bias_dropout_fusion)(
mixer_out_with_bias, residual, self.hidden_dropout
)
return hidden_states
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
return self.mixer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype)
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