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# coding=utf-8
# Copyright (c) 2023 Habana Labs, Ltd. an Intel Company.
# Copyright (c) 2020, 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.
"""General utilities."""
import sys
import os
import torch
from torch.nn.parallel import DistributedDataParallel as torchDDP
from megatron import get_args
if (torch.cuda.is_available()):
from apex.multi_tensor_apply import multi_tensor_applier
import amp_C
from megatron import get_args
from megatron import print_rank_0
from megatron import get_adlr_autoresume
from megatron import mpu
from megatron.model.module import param_is_not_shared
from megatron.mpu.layers import param_is_not_tensor_parallel_duplicate
from megatron import get_num_microbatches
def unwrap_model(model, module_instances=(torchDDP)):
return_list = True
if not isinstance(model, list):
model = [model]
return_list = False
unwrapped_model = []
for model_module in model:
while isinstance(model_module, module_instances):
model_module = model_module.module
unwrapped_model.append(model_module)
if not return_list:
return unwrapped_model[0]
return unwrapped_model
def calc_params_l2_norm(model):
"""Calculate l2 norm of parameters """
args = get_args()
if not isinstance(model, list):
model = [model]
# Remove duplicate params.
params_data = []
for model_ in model:
for param in model_.parameters():
is_not_shared = param_is_not_shared(param)
is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param)
if is_not_shared and is_not_tp_duplicate:
if args.bf16:
params_data.append(param.data.float())
else:
params_data.append(param.data)
# Calculate norm
# TODO SW-56092: not having multi_tensor_applier implementation outside CUDA
if args.device.type == "cuda":
# Calculate norm
dummy_overflow_buf = torch.cuda.IntTensor([0])
norm, _ = multi_tensor_applier(
amp_C.multi_tensor_l2norm,
dummy_overflow_buf,
[params_data],
False # no per-parameter norm
)
norm_2 = norm * norm
else:
# less optimized version for now
norm_2 = 0.0
for param_tmp in params_data:
norm_2 += param_tmp.square().sum()
# Sum across all model-parallel GPUs.
torch.distributed.all_reduce(norm_2,
op=torch.distributed.ReduceOp.SUM,
group=mpu.get_model_parallel_group())
return norm_2.item() ** 0.5
def average_losses_across_data_parallel_group(losses):
"""Reduce a tensor of losses across all GPUs."""
averaged_losses = torch.cat(
[loss.clone().detach().view(1) for loss in losses])
torch.distributed.all_reduce(averaged_losses,
group=mpu.get_data_parallel_group())
averaged_losses = averaged_losses / \
torch.distributed.get_world_size(group=mpu.get_data_parallel_group())
return averaged_losses
def report_memory(name):
"""Simple GPU memory report."""
mega_bytes = 1024.0 * 1024.0
string = name + ' memory (MB)'
string += ' | allocated: {}'.format(
torch.cuda.memory_allocated() / mega_bytes)
string += ' | max allocated: {}'.format(
torch.cuda.max_memory_allocated() / mega_bytes)
string += ' | reserved: {}'.format(
torch.cuda.memory_reserved() / mega_bytes)
string += ' | max reserved: {}'.format(
torch.cuda.max_memory_reserved() / mega_bytes)
if mpu.get_data_parallel_rank() == 0:
print("[Rank {}] {}".format(torch.distributed.get_rank(), string),
flush=True)
def print_params_min_max_norm(optimizer, iteration):
"""Print min, max, and norm of all parameters."""
index = 0
rank = torch.distributed.get_rank()
string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n'
optimizer_ = optimizer.optimizer
for param_group in optimizer_.param_groups:
for param in param_group['params']:
index += 1
min_ = param.data.min()
max_ = param.data.max()
norm = torch.linalg.norm(param.data)
string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
iteration, rank, index, int(param.tensor_model_parallel))
string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
print(string, flush=True)
def check_adlr_autoresume_termination(iteration, model,
optimizer, lr_scheduler):
"""Check for autoresume signal and exit if it is received."""
from megatron.checkpointing import save_checkpoint
args = get_args()
autoresume = get_adlr_autoresume()
# Add barrier to ensure consistnecy.
torch.distributed.barrier()
if autoresume.termination_requested():
if args.save:
save_checkpoint(iteration, model, optimizer, lr_scheduler)
print_rank_0(">>> autoresume termination request found!")
if torch.distributed.get_rank() == 0:
autoresume.request_resume()
print_rank_0(">>> training terminated. Returning")
sys.exit(0)
def get_ltor_masks_and_position_ids(data,
eod_token,
reset_position_ids,
reset_attention_mask,
eod_mask_loss,
dummy_sample=None,
labels=None):
"""Build masks and position id for left to right model."""
# Extract batch size and sequence length.
micro_batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = micro_batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(torch.ones(
(att_mask_batch, seq_length, seq_length), device=data.device)).view(
att_mask_batch, 1, seq_length, seq_length)
# Loss mask.
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
if eod_mask_loss:
loss_mask[data == eod_token] = 0.0
if dummy_sample is not None:
loss_mask[dummy_sample.bool()] = 0.0
if labels is not None:
loss_mask[labels == -1] = 0.0
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long,
device=data.device)
position_ids = position_ids.unsqueeze(0).expand_as(data)
# We need to clone as the ids will be modifed based on batch index.
if reset_position_ids:
position_ids = position_ids.clone()
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(micro_batch_size):
# Find indecies where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
# Detach indecies from positions if going to modify positions.
if reset_position_ids:
eod_index = eod_index.clone()
# Loop through EOD indecies:
prev_index = 0
for j in range(eod_index.size()[0]):
i = eod_index[j]
# Mask attention loss.
if reset_attention_mask:
attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
# Reset positions.
if reset_position_ids:
position_ids[b, (i + 1):] -= (i + 1 - prev_index)
prev_index = i + 1
# Convert attention mask to binary:
attention_mask = (attention_mask < 0.5)
return attention_mask, loss_mask, position_ids
def get_parameters_in_billions(model):
gpus_per_model = torch.distributed.get_world_size(group=mpu.get_model_parallel_group())
approx_parameters_in_billions = sum([sum([p.ds_numel if hasattr(p,'ds_id') else p.nelement() for p in model_module.parameters()])
for model_module in model])
return approx_parameters_in_billions*gpus_per_model/(1e9)
def throughput_calculator(model, args, iteration_time, total_iterations):
gpus_per_model = torch.distributed.get_world_size(group = mpu.get_model_parallel_group())
batch_size = args.micro_batch_size * get_num_microbatches() * args.data_parallel_size
samples_per_model = batch_size * args.seq_length
model_replica_count = torch.distributed.get_world_size() / gpus_per_model
approx_parameters_in_billions = get_parameters_in_billions(model)
elapsed_time_per_iter = iteration_time/total_iterations
samples_per_second = batch_size / elapsed_time_per_iter
#flops calculator
hidden_size = args.hidden_size
num_layers = args.num_layers
vocab_size = args.padded_vocab_size
# General TFLOPs formula (borrowed from Equation 3 in Section 5.1 of
# https://arxiv.org/pdf/2104.04473.pdf).
# The factor of 4 is when used with activation check-pointing,
# otherwise it will be 3.
checkpoint_activations_factor = 4 if args.checkpoint_activations else 3
flops_per_iteration = (24 * checkpoint_activations_factor * batch_size * args.seq_length * num_layers * (hidden_size**2)) * (1. + (args.seq_length / (6. * hidden_size)) + (vocab_size / (16. * num_layers * hidden_size)))
tflops = flops_per_iteration / (elapsed_time_per_iter * args.world_size * (10**12))
return samples_per_second, tflops, approx_parameters_in_billions
def checkpoint_throughput_calculator(model, latency_second):
approx_parameters_in_billions = get_parameters_in_billions(model)
checkpoint_multiplier = 14 # fp16 weights (2), fp32 weights (4), fp32 momentum (4), fp32 variance (4)
checkpoint_GB = approx_parameters_in_billions * checkpoint_multiplier
GB_per_second = checkpoint_GB / latency_second
print_rank_0(f"Checkpoint Save GB: {round(checkpoint_GB, 3)}, GB/Sec: {round(GB_per_second,2)}, Latency(second): {round(latency_second, 3)}")
def found_kill_switch():
args = get_args()
if args.kill_switch_path is not None and os.path.exists(args.kill_switch_path):
return True
else:
return False
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