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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# flake8: noqa
# pylint: skip-file
"""Utilities for models."""
import itertools
from typing import Dict, Iterator, List, Optional, Union
import torch
from torch import Tensor
from nemo.utils import logging, logging_mode
try:
from apex.transformer.enums import AttnMaskType
HAVE_APEX = True
except (ImportError, ModuleNotFoundError):
HAVE_APEX = False
try:
from megatron.core import parallel_state
HAVE_MEGATRON_CORE = True
except (ImportError, ModuleNotFoundError):
HAVE_MEGATRON_CORE = False
def ApproxGELUActivation(input: Tensor):
"""
Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
"""
return input * torch.sigmoid(1.702 * input)
class ApexGuardDefaults(object):
"""
This class can be used to replace missing classes when apex is missing.
"""
def __init__(self):
super().__init__()
def __getattr__(self, item):
return None
def init_method_kaiming_uniform(val):
def init_(tensor):
return torch.nn.init.kaiming_uniform_(tensor, a=val)
return init_
def init_method_const(val):
def init_(tensor):
return torch.nn.init.constant_(tensor, val)
return init_
def init_method_normal(sigma):
"""Init method based on N(0, sigma)."""
def init_(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=sigma)
return init_
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=parallel_state.get_data_parallel_group())
averaged_losses = averaged_losses / torch.distributed.get_world_size(
group=parallel_state.get_data_parallel_group()
)
return averaged_losses
def get_ltor_masks_and_position_ids(
data, eod_token, reset_position_ids, reset_attention_mask, eod_mask_loss, compute_attention_mask=True
):
"""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 = None
if compute_attention_mask:
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
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
position_ids = position_ids.unsqueeze(0).repeat(micro_batch_size, 1)
# 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 indicies:
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
if compute_attention_mask:
# Convert attention mask to binary:
attention_mask = attention_mask < 0.5
return attention_mask, loss_mask, position_ids
def build_position_ids(token_ids):
# Create position ids
seq_length = token_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=token_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(token_ids).clone()
return position_ids
def make_attention_mask_3d(source_mask, target_mask):
"""
Returns a 3-dimensional (3-D) attention mask
:param source_block: 2-D array
:param target_block: 2-D array
"""
mask = target_mask[:, None, :] * source_mask[:, :, None]
return mask
def make_inference_attention_mask_3d(source_block, target_block, pad_id):
"""
Returns a 3-dimensional (3-D) attention mask
:param source_block: 2-D array
:param target_block: 2-D array
"""
# mask = (target_block[:, None, :] != pad_id) * (source_block[:, :, None] != pad_id)
return make_attention_mask_3d(source_block != pad_id, target_block != pad_id)
def make_inference_history_mask_3d(block):
batch, length = block.shape
arange = torch.arange(length, device=block.device)
history_mask = (arange[None,] <= arange[:, None])[None,]
history_mask = history_mask.expand(batch, length, length)
return history_mask
def build_attention_mask_3d_padding(source_mask, target_mask):
"""
Returns a 3D joint attention mask for Megatron given two 2D masks
:param source_mask - True for non-masked, else masked [batch, src length]
:param target_mask - True for non-masked, else masked [batch, tgt length]
"""
mask = make_attention_mask_3d(source_mask, target_mask)
# invert mask for Megatron
return mask < 0.5
def build_attention_mask_3d_causal(source_mask, target_mask):
"""
Returns a 3D joint attention mask for Megatron given two 2D masks
:param source_mask - True for non-masked, else masked [batch, src length]
:param target_mask - True for non-masked, else masked [batch, tgt length]
"""
causal_mask = make_inference_history_mask_3d(target_mask)
mask = make_attention_mask_3d(source_mask, target_mask)
mask = mask * causal_mask
# invert mask for Megatron
return mask < 0.5
def build_attention_mask_3d(source_mask, target_mask, attn_mask_type):
"""
Returns a 3D attention mask for Megatron given two 2D masks
:param source_mask - < 0.5 for non-masked, else masked [batch, src length]
:param target_mask - < 0.5 for non-masked, else masked [batch, tgt length]
:param attn_mask_type - AttnMaskType enum
"""
if attn_mask_type == AttnMaskType.padding:
mask = build_attention_mask_3d_padding(source_mask, target_mask)
elif attn_mask_type == AttnMaskType.causal:
mask = build_attention_mask_3d_causal(source_mask, target_mask)
else:
raise ValueError(f"Unsupported attention mask attn_mask_type = {attn_mask_type}")
return mask
def split_list(inputs, num_chunks, enforce_divisible_batch: Optional[bool] = True):
"""
Split a list into equal sized chunks
"""
chunk_size = len(inputs) // num_chunks
if enforce_divisible_batch:
assert len(inputs) % chunk_size == 0, "Issue with batch size configuration!"
return [inputs[i : i + chunk_size] for i in range(0, len(inputs), chunk_size)]
def get_iterator_k_split(
batch: Union[Dict, List[torch.Tensor]], num_microbatches: int, enforce_divisible_batch: Optional[bool] = True
) -> Iterator:
"""
Split a batch into k microbatches, where the batch size is divisible by k. Batch could be
a dictionary of tensors or a list of tensors. A dictionary batch could also have items of List type,
as long as the length of that list is the same as the batch size.
"""
if isinstance(batch, dict):
discard_items = [k for k, v in batch.items() if not isinstance(v, (torch.Tensor, list))]
if len(discard_items) > 0:
logging.warning(
f"Only support splitting torch.Tensor and List[torch.Tensor]. Discarding the following keys from the batch: {discard_items}",
mode=logging_mode.ONCE,
)
batch = {k: v for k, v in batch.items() if isinstance(v, (torch.Tensor, list))}
tensor_items = {k: v for k, v in batch.items() if isinstance(v, torch.Tensor)}
list_items = {k: v for k, v in batch.items() if isinstance(v, list)}
# Split tensor items
items = list(tensor_items.items())
if enforce_divisible_batch:
if items[0][1].shape[0] % num_microbatches != 0:
raise ValueError(
f"Issue with batch size configuration: batch size {items[0][1].shape[0]} is not divisible by {num_microbatches}!"
)
split_batch = [torch.tensor_split(item[1], num_microbatches, dim=0) for item in items]
# handle the case where the batch size from dynamic bucketting is not divisible
if items[0][1].shape[0] % num_microbatches != 0:
chunk_size = split_batch[0][-1].shape[0]
split_batch = [[j[:chunk_size] for j in i] for i in split_batch]
if len(list_items) == 0:
# Only have tensor items
microbatches = [
[(items[i][0], split_batch[i][j]) for i in range(len(items))] for j in range(num_microbatches)
]
else:
# Split list items
list_items = list(list_items.items())
split_list_batch = [
split_list(item[1], num_microbatches, enforce_divisible_batch=enforce_divisible_batch)
for item in list_items
]
# Merge tensor and list items
all_keys = [item[0] for item in items] + [item[0] for item in list_items]
all_split_batch = split_batch + split_list_batch
microbatches = [
[(all_keys[i], all_split_batch[i][j]) for i in range(len(all_keys))] for j in range(num_microbatches)
]
microbatches = [dict(elem) for elem in microbatches]
else:
# Split a list of torch tensors
assert batch[0].shape[0] % num_microbatches == 0, "Issue with batch size configuration!"
split_batch = []
for item in batch:
if torch.is_tensor(item):
split_batch.append(torch.tensor_split(item, num_microbatches, dim=0))
elif isinstance(item, list):
if isinstance(item[0], torch.Tensor):
split_tensors = [torch.tensor_split(elem, num_microbatches, dim=0) for elem in item]
split_tuple = []
for mbi in range(num_microbatches):
split_tuple.append([split_tensors[i][mbi] for i in range(len(split_tensors))])
split_tuple = tuple(split_tuple)
split_batch.append(split_tuple)
else:
split_batch.append(split_list(item, num_microbatches))
elif item is None:
split_batch.append(item)
else:
raise ValueError(f"Unsupported item type: {type(item)}")
microbatches = [
[elem[i] if elem is not None else elem for elem in split_batch] for i in range(num_microbatches)
]
return itertools.chain(microbatches)
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