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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
import torch
from pytorch_lightning import Trainer
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector
from nemo.utils import logging, model_utils
from nemo.utils.app_state import AppState
"""
Usage:
python megatron_change_num_partitions.py \
--model_file=PATH_TO_SRC_FILE \
--target_file=PATH_TO_TGT_FILE \
--tensor_model_parallel_size=2 \
--target_tensor_model_parallel_size=1
"""
def merge_partition(model, partitions, write_path=None):
idx = 0
for name, param in model.named_parameters():
if param.shape == partitions[0][idx].shape:
concated = partitions[0][idx].data
elif param.shape[0] == partitions[0][idx].shape[0]:
concated = torch.cat([partitions[i][idx].data for i in range(len(partitions))], dim=-1)
else:
concated = torch.cat([partitions[i][idx].data for i in range(len(partitions))], dim=0)
if concated.shape != param.shape:
logging.info(
f"Warning: Shape mismatch for parameter {name} required shape: {param.shape}, merged shape: {concated.shape}. Narrowing to match required size."
)
if concated.shape[1:] == param.shape[1:]:
concated = torch.narrow(concated, 0, 0, param.shape[0])
elif concated.shape[:-1] == param.shape[:-1]:
concated = torch.narrow(concated, -1, 0, param.shape[-1])
else:
raise RuntimeError(
f"Can not handle parameter {name}, required shape: {param.shape}, merged shape: {concated.shape}."
)
param.data = concated
idx += 1
if write_path is not None:
model.save_to(write_path)
def split_partition(model, partitions, tp_size, write_path=None, megatron_legacy=False):
if len(partitions) != 1:
raise ValueError(
"Can only split partitions of model with TP=1. For partitions of models with TP>1, merge first."
)
if tp_size < 1:
raise ValueError("TP size must to be >= 1.")
app_state = AppState()
app_state.data_parallel_rank = 0
app_state.pipeline_model_parallel_size = 1 # not supported yet in this script
app_state.tensor_model_parallel_size = tp_size
app_state.model_parallel_size = app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size
app_state.tensor_model_parallel_rank = tp_size - 1
idx = 0
splits = []
for param_name, param in model.named_parameters():
if param.shape == partitions[0][idx].shape:
split = [partitions[0][idx].data] * tp_size
elif param.shape[0] == partitions[0][idx].shape[0]:
split = torch.split(partitions[0][idx].data, param.shape[-1], dim=-1)
else:
# For T5-converted weights, the splitting needs to be strided such that q,k,v weights are bunched together on each tensor-parallel rank.
if 'query_key_value.weight' in param_name and megatron_legacy:
split_dim = partitions[0][idx].data.shape[0]
if split_dim % (tp_size * 3) != 0:
raise ValueError(
f"Can not split Q,K,V parameter {param_name} with shape {param.shape} into tensor parallel size {tp_size}. Not divisible by {tp_size * 3}."
)
tp_qkv_splits = torch.chunk(partitions[0][idx].data, tp_size * 3, dim=0)
split = []
for i in range(tp_size):
tp_qkv = torch.cat([tp_qkv_splits[item] for item in range(i, tp_size * 3, tp_size)])
split.append(tp_qkv)
elif 'key_value.weight' in param_name and megatron_legacy:
split_dim = partitions[0][idx].data.shape[0]
if split_dim % (tp_size * 2) != 0:
raise ValueError(
f"Can not split K,V parameter {param_name} with shape {param.shape} into tensor parallel size {tp_size}. Not divisible by {tp_size * 2}."
)
tp_qkv_splits = torch.chunk(partitions[0][idx].data, tp_size * 2, dim=0)
split = []
for i in range(tp_size):
tp_qkv = torch.cat([tp_qkv_splits[item] for item in range(i, tp_size * 2, tp_size)])
split.append(tp_qkv)
# Regular split for Megatron and NeMo-Megatron models.
else:
split = torch.split(partitions[0][idx].data, param.shape[0], dim=0)
splits.append(split)
idx += 1
for i in range(tp_size - 1, -1, -1):
app_state.tensor_model_parallel_rank = i
idx = 0
for name, param in model.named_parameters():
split_val = splits[idx][i].clone()
if param.shape != split_val.shape:
logging.info(
f"Warning: Shape mismatch for parameter {name} required shape: {param.shape}, split shape: {split_val.shape}. Padding to match required size."
)
if split_val.shape[1:] == param.shape[1:]:
pad = [0, 0] * len(split_val.shape)
pad[-1] = param.shape[0] - split_val.shape[0]
split_val = torch.nn.functional.pad(split_val, pad, 'constant')
elif split_val.shape[:-1] == param.shape[:-1]:
pad = [0, param.shape[-1] - split_val.shape[-1]]
split_val = torch.nn.functional.pad(split_val, pad, 'constant')
else:
raise RuntimeError(
f"Can not handle parameter {name}, required shape: {param.shape}, split shape: {split_val.shape}."
)
param.data = split_val
idx += 1
if write_path is not None:
model.save_to(write_path)
def main():
parser = ArgumentParser()
parser.add_argument("--model_file", type=str, required=True, help="Path to source .nemo file")
parser.add_argument("--target_file", type=str, required=True, help="Path to write target .nemo file")
parser.add_argument("--tensor_model_parallel_size", type=int, required=True, help="TP size of source model")
parser.add_argument("--target_tensor_model_parallel_size", type=int, required=True, help="TP size of target model")
parser.add_argument(
"--model_class",
type=str,
default="nemo.collections.nlp.models.language_modeling.megatron_gpt_model.MegatronGPTModel",
help="NeMo model class. This script should support all NeMo megatron models that use Tensor Parallel",
)
parser.add_argument("--precision", default=16, help="PyTorch Lightning Trainer precision flag")
parser.add_argument(
"--megatron_legacy",
action="store_true",
help="Converter for legacy megatron modles that have different q,k,v weight splits",
)
parser.add_argument(
"--tokenizer_model_path",
type=str,
required=False,
default=None,
help="Path to the tokenizer model path if your model uses a tokenizer model as an artifact. This is needed if your model uses a sentencepiece tokenizer.",
)
args = parser.parse_args()
precision = args.precision
if args.precision in ["32", "16"]:
precision = int(float(args.precision))
tp_size = args.tensor_model_parallel_size
tgt_tp_size = args.target_tensor_model_parallel_size
cls = model_utils.import_class_by_path(args.model_class)
trainer = Trainer(devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision)
app_state = AppState()
app_state.data_parallel_rank = 0
app_state.pipeline_model_parallel_size = 1 # not supported yet in this script
app_state.tensor_model_parallel_size = tp_size
app_state.model_parallel_size = app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size
if tp_size > 1:
partitions = []
for i in range(tp_size):
app_state.tensor_model_parallel_rank = i
model = cls.restore_from(restore_path=args.model_file, trainer=trainer, map_location=torch.device("cpu"))
params = [p for _, p in model.named_parameters()]
partitions.append(params)
# app_state is being updated incorrectly during restore
app_state.data_parallel_rank = 0
app_state.pipeline_model_parallel_size = 1 # not supported yet in this script
app_state.tensor_model_parallel_size = tp_size
app_state.model_parallel_size = (
app_state.pipeline_model_parallel_size * app_state.tensor_model_parallel_size
)
model.cfg.tensor_model_parallel_size = 1
app_state.model_parallel_size = 1
trainer = Trainer(devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision)
if args.tokenizer_model_path is not None:
model.cfg.tokenizer.model = args.tokenizer_model_path
model = cls(model.cfg, trainer).to('cpu')
model._save_restore_connector = NLPSaveRestoreConnector()
if tgt_tp_size > 1:
merge_partition(model, partitions)
else:
merge_partition(model, partitions, args.target_file)
else:
app_state.model_parallel_size = 1
model = cls.restore_from(restore_path=args.model_file, trainer=trainer, map_location=torch.device("cpu"))
if tgt_tp_size > 1:
partitions = []
params = [p for _, p in model.named_parameters()]
partitions.append(params)
model.cfg.tensor_model_parallel_size = tgt_tp_size
app_state.model_parallel_size = tgt_tp_size
trainer = Trainer(devices=1, strategy=NLPDDPStrategy(), accelerator="cpu", precision=precision)
if args.tokenizer_model_path is not None:
model.cfg.tokenizer.model = args.tokenizer_model_path
model = cls(model.cfg, trainer).to('cpu')
model._save_restore_connector = NLPSaveRestoreConnector()
split_partition(model, partitions, tgt_tp_size, args.target_file, args.megatron_legacy)
logging.info("Successfully finished changing partitions!")
if __name__ == '__main__':
main()
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