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import os
import torch
import numpy as np
import torch.distributed as dist
from transformers.utils import logging
from transformers import AutoTokenizer
from itertools import cycle
from typing import List
logger = logging.get_logger(__name__)
class Memory(torch.nn.Module):
def __init__(
self,
model_config,
k_seq_dim:int=2,
v_seq_dim:int=2,
):
"""Setup necessary attributes."""
super().__init__()
self.model_config = model_config
# initialize necessary parameters
self.k_seq_dim = k_seq_dim
self.v_seq_dim = v_seq_dim
self.num_layers = model_config.num_hidden_layers
self.max_position_embeddings = model_config.max_position_embeddings
self.rng = np.random.default_rng(42)
self.ultragist_window = model_config.ultragist_window
self.ultragist_stride = model_config.ultragist_stride
self.ultragist_attn = model_config.ultragist_attn
self.ultragist_ratio = model_config.ultragist_ratio
self.ultragist_ratio_mix = model_config.ultragist_ratio_mix
self.ultragist_param = model_config.ultragist_param
self.ultragist_sink_size = model_config.ultragist_sink_size
self.ultragist_attend_prev = model_config.ultragist_attend_prev
self.ultragist_tokens = torch.zeros(1, dtype=torch.long) + model_config.vocab_size
self._post_validation()
self.reset()
def _post_validation(self, verbose=True):
assert self.ultragist_window >= self.ultragist_stride, f"Make sure the ultragist_window {self.ultragist_window} >= ultragist_stride {self.ultragist_stride}!"
for ratio in self.ultragist_ratio:
assert ratio >= 0, f"Make sure all ultragist ratios are greater than or equal to 0, found {self.ultragist_ratio}!"
assert self.ultragist_attn in ["segmentation", "step-expansion", "full-coverage"], f"ultragist_attn {self.ultragist_attn} not implemented!"
assert self.ultragist_ratio_mix in ["instance-random", "step-random", "sequence", "join"] or "adapt-" in self.ultragist_ratio_mix, f"ultragist_ratio_mix {self.ultragist_ratio_mix} not implemented!"
if self.ultragist_ratio_mix == "join":
# create another stream for moving gpu tensor to cpu
# self.stream = torch.cuda.Stream()
pass
self._cpu = torch.device("cpu")
if verbose:
info = f"applying ultragist on {self.ultragist_param} (the ultragist embedding is initialized from {'bos' if self.model_config.ultragist_embed_init == 'bos' else 'eos'} embedding), with window size {self.ultragist_window}, stride {self.ultragist_stride}, {self.ultragist_attn} attention{' (attending to previous ultragists)' if self.ultragist_attend_prev else ' (no attending to previous ultragists)'}, sink size {self.ultragist_sink_size}, condensing ratio {self.ultragist_ratio} (mixed by {self.ultragist_ratio_mix})..."
logger.info(info)
def set(self, verbose=True, **kwargs):
if "ultragist_ratio_mix" in kwargs and kwargs["ultragist_ratio_mix"] == "join" and self.ultragist_ratio_mix != "join":
raise ValueError(f"You cannot switch ultragist_ratio_mix from non-join strategy to join!")
if self.ultragist_ratio_mix == "join" and "ultragist_ratio" in kwargs and sorted(kwargs["ultragist_ratio"]) != sorted(self.ultragist_ratio):
raise ValueError(f"You cannot change ultragist_ratio given ultragist_ratio_mix=join!")
for k, v in kwargs.items():
setattr(self, k, v)
self._post_validation(verbose=verbose)
def reset(self):
"""Initialize attributes for a new sequence."""
# the cursor pointing to the start of the current window
self._start_idx = 0
# the cursor pointing to the end of the current window
self._end_idx = 0
# the ultragist sizes of all strides
self._total_ultragist_sizes = []
# the ultragist ratios of all strides
self._main_ultragist_sizes = []
# the loss per batch
self._batch_loss = None
# the valid token number per batch
self._valid_token_num = None
# the step index for processing the input_ids
self._step_idx = 0
# used in set_compression_ratio
self._ratio = None
self._ultragist_ratio_iter = None
self.all_input_ids = torch.tensor([], dtype=torch.long)
self.all_attention_mask = torch.tensor([], dtype=torch.long)
if hasattr(self, "all_labels"):
del self.all_labels
# the raw activations of recent tokens
self.raw_activations = [(None, None) for _ in range(self.num_layers)]
# the attention sink activations
self.sink_activations = [(None, None) for _ in range(self.num_layers)]
# the ultragist activations
if self.ultragist_ratio_mix == "join":
self.l1_to_ln_ultragist_activations = [
[(None, None) for _ in range(self.num_layers)]
for _ in self.ultragist_ratio
]
else:
self.l1_to_ln_ultragist_activations = [
[(None, None) for _ in range(self.num_layers)]
]
def rewind(self, size=None, trim=False):
"""
Rewind raw activations that have not been condensed yet.
Args:
trim: if true, the input_ids corresponding to the raw activations are trimmed.
"""
raw_memory_size = self.get_memory_size()[1]
if size is None:
size = raw_memory_size
assert size <= raw_memory_size, f"Make sure the rewind size ({size}) is smaller or equal to the raw memory size ({raw_memory_size})!"
if size > 0:
self._end_idx -= size
for layer_idx, (key, value) in enumerate(self.raw_activations):
key = slice_tensor(key, end=-size, dim=self.k_seq_dim)
value = slice_tensor(value, end=-size, dim=self.v_seq_dim)
self.raw_activations[layer_idx] = (key, value)
if trim:
self.all_input_ids = self.all_input_ids[:, :-size]
self.all_attention_mask = self.all_attention_mask[:, :-size]
if hasattr(self, "all_labels"):
self.all_labels = self.all_labels[:, :-size]
@property
def finish(self):
is_finish = self._end_idx == self.all_sequence_length
# print(f"{dist.get_rank()} Finish: {self._end_idx}, {self.all_sequence_length}")
# if is_finish and hasattr(self, "stream"):
# self.stream.synchronize()
return is_finish
def get_memory_size(self):
ultragist_memory_size = 0
raw_memory_size = 0
sink_memory_size = 0
if self.l1_to_ln_ultragist_activations[0][0][0] is not None:
ultragist_memory_size += self.l1_to_ln_ultragist_activations[0][0][0].shape[self.k_seq_dim]
if self.raw_activations[0][0] is not None:
raw_memory_size += self.raw_activations[0][0].shape[self.k_seq_dim]
if self.sink_activations[0][0] is not None:
sink_memory_size += self.sink_activations[0][0].shape[self.k_seq_dim]
return ultragist_memory_size, raw_memory_size, sink_memory_size
def get_memory(self, ultragist_sizes=None, total_ultragist_size=None, raw_size_to_cache=None, window_size=None):
"""
Get the compressed kv cache for generating next tokens.
"""
past_key_values = []
for layer_idx in range(self.num_layers):
sink_key, sink_value = self.sink_activations[layer_idx]
ultragist_key, ultragist_value = self.l1_to_ln_ultragist_activations[0][layer_idx]
raw_key, raw_value = self.raw_activations[layer_idx]
key = cat_tensor([
sink_key, ultragist_key, raw_key,
], dim=self.k_seq_dim)
value = cat_tensor([
sink_value, ultragist_value, raw_value,
], dim=self.v_seq_dim)
if ultragist_sizes is not None:
layer_past_key_values = (key, value, ultragist_sizes, total_ultragist_size, raw_size_to_cache, window_size)
else:
layer_past_key_values = (key, value)
past_key_values.append(layer_past_key_values)
return past_key_values
def prepare(self, input_ids, attention_mask, labels):
"""
Prepare inputs for the model. These inputs belong to the same sequence.
"""
assert input_ids.shape[0] == 1, "Make sure the batch size is 1!"
assert attention_mask is None or (attention_mask == 1).all(), "Make sure there is no padding!"
if not hasattr(self, "_device"):
self._device = input_ids.device
# accumulate input_ids and attention_mask
self.all_input_ids = torch.cat([self.all_input_ids, input_ids.cpu()], dim=1)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
self.all_attention_mask = torch.cat([self.all_attention_mask, attention_mask.cpu()], dim=1)
self.all_sequence_length = self.all_input_ids.shape[1]
if labels is not None:
# rotate labels in advance so that the loss of the last token is not ignored in every window
labels = torch.cat([labels[:, 1:].cpu(), torch.tensor([-100]).expand(labels.shape[0], 1)], dim=1)
if not hasattr(self, "all_labels"):
self.all_labels = labels
else:
self.all_labels = torch.cat([self.all_labels, labels], dim=1)
assert self.all_input_ids.shape[1] == self.all_labels.shape[1], f"Found inconsistent all_input_ids {self.all_input_ids.shape} and all_labels {self.all_labels.shape}!"
def set_compression_ratio(self, start_idx, end_idx):
"""Choose a condensing ratio from self.ultragist_ratio"""
def filter_ratio(ratios, stride):
valid_ratios = []
for ratio in ratios:
# stride must be bigger than condensing ratio because we there must be at least one ultragist
if stride < ratio:
continue
# the stride must be evenly divisible by condensing ratio
if ratio > 0 and (stride % ratio) != 0:
continue
# when training, ratio=0 is valid if previous windows contain ultragist or later windows contain ultragist
if ratio == 0 and self.training:
previous_has_zero = -1 in self._main_ultragist_sizes
following_has_nonzero = (start_idx + stride + self.ultragist_window) <= self.all_sequence_length
if previous_has_zero or (not following_has_nonzero):
continue
valid_ratios.append(ratio)
assert len(valid_ratios), f"Cannot find valid condensing ratio (among {ratios}) for stride {stride}!"
return valid_ratios
def get_max_length(ratios):
max_lengths = []
for condensing_ratio in ratios:
if condensing_ratio > 0:
max_lengths.append((self.max_position_embeddings - self.ultragist_window) * condensing_ratio + self.ultragist_window)
else:
max_lengths.append(self.max_position_embeddings)
return max_lengths
if len(self.ultragist_ratio) == 1:
return [self.ultragist_ratio[0]]
ratio_mix = self.ultragist_ratio_mix
ultragist_ratio = filter_ratio(self.ultragist_ratio, self.ultragist_stride)
if ratio_mix == "instance-random":
if self._ratio is None:
ultragist_ratio = self.rng.choice(ultragist_ratio, size=1).tolist()
self._ratio = ultragist_ratio
else:
ultragist_ratio = self._ratio
elif ratio_mix == "step-random":
ultragist_ratio = self.rng.choice(ultragist_ratio, size=1).tolist()
elif ratio_mix == "sequence":
if self._ultragist_ratio_iter is None:
self._ultragist_ratio_iter = cycle(ultragist_ratio)
ultragist_ratio = [next(self._ultragist_ratio_iter)]
elif ratio_mix == "join":
ultragist_ratio = ultragist_ratio
elif "adapt" in ratio_mix:
if self._ratio is None:
future_length = int(ratio_mix.split("-")[1])
sequence_length = self.all_input_ids.shape[1] + future_length
max_lengths = get_max_length(ultragist_ratio)
# ascendingly sort the max lengths
valid_max_lengths_and_indices = [x for x in enumerate(max_lengths) if x[1] >= sequence_length]
if len(valid_max_lengths_and_indices):
minimum_length_index = min(valid_max_lengths_and_indices, key=lambda x: x[1])[0]
# use the minimal possible length for this sequence (the smallest fold ratio)
ultragist_ratio = [ultragist_ratio[minimum_length_index]]
else:
ultragist_ratio = [max(ultragist_ratio)]
# logger.warning(f"Failed to find valid fold window and size for sequence length {sequence_length}, as the maximum theoretical length is {max(max_lengths)}. Fall back to use the maximum one: {ultragist_ratio}.")
self._ratio = ultragist_ratio
else:
ultragist_ratio = self._ratio
return ultragist_ratio
def step(self):
"""
Yield one window with the following logic:
The window size is L, the stride is S.
The window moves over S tokens at a time. The raw activations passed by the window are condensed according to a condensing_ratio.
The ultragists are added if and only if the raw activations fulfill the window.
In the future, we may switch window size to decrease cache size of raw activations.
"""
# the starting position of the current window w.r.t. the start of the current input sequence
start_idx = self._start_idx
# the end position of the current window w.r.t. the start of the current input sequence
end_idx = start_idx + self.ultragist_window
# indicates if the current window is completely filled by raw activations and new tokens
# we only append ultragist tokens for full windows
if end_idx > self.all_sequence_length:
# the input is shorter than the initial window size
end_idx = self.all_sequence_length
is_full_window = False
else:
is_full_window = True
# NOTE: in training, the entire sequence is input to the model at once
# In the last window, we do not need to append ultragists because they will not be used at all
if self.training and end_idx == self.all_sequence_length:
is_full_window = False
# the real window size (remaining_size + new_token_size)
window_size = end_idx - start_idx
if is_full_window:
ultragist_stride = self.ultragist_stride
# a list of condensing ratios
compression_ratios = self.set_compression_ratio(start_idx=start_idx, end_idx=end_idx)
ultragist_sizes = []
for condensing_ratio in compression_ratios:
if condensing_ratio > 0:
# the stride must be evenly divisible by condensing_ratio
ultragist_sizes.append(ultragist_stride // condensing_ratio)
else:
# the raw activations are used as ultragist activations
ultragist_sizes.append(-1)
# forward start_idx and end_idx
next_start_idx = start_idx + ultragist_stride
# how many raw activations to save
raw_size_to_cache = end_idx - next_start_idx
else:
# no stride because the sequence has finished
next_start_idx = start_idx
# cache all recent raw activations to be used in the next window
raw_size_to_cache = window_size
ultragist_sizes = [0]
compression_ratios = [0]
total_ultragist_size = sum(s for s in ultragist_sizes if s >= 0)
past_key_values = self.get_memory(
ultragist_sizes,
total_ultragist_size,
raw_size_to_cache,
window_size
)
# streamingly add new input_ids
input_ids = self.all_input_ids[:, self._end_idx: end_idx].to(self._device)
attention_mask = self.all_attention_mask[:, self._end_idx: end_idx].to(self._device)
if hasattr(self, "all_labels"):
labels = self.all_labels[:, self._end_idx: end_idx].to(self._device)
else:
labels = None
batch_size = input_ids.shape[0]
# append ultragists if necessary
if is_full_window:
if total_ultragist_size > 0:
input_ids = torch.cat([input_ids, self.ultragist_tokens.expand(batch_size, total_ultragist_size).to(input_ids.device, dtype=input_ids.dtype)], dim=1)
# NOTE: prepend ultragist_memory_size 1 to attention_mask because we have past_key_values
attention_mask = torch.cat([attention_mask, attention_mask.new_ones(batch_size, total_ultragist_size)], dim=1)
if labels is not None:
labels = torch.cat([labels, labels.new_zeros(batch_size, total_ultragist_size) - 100], dim=1)
# prepend 1 to attention mask for previous memory
first_key = past_key_values[0][0]
memory_size = first_key.shape[self.k_seq_dim] if first_key is not None else 0
if memory_size > 0:
attention_mask = torch.cat([attention_mask.new_ones(batch_size, memory_size), attention_mask], dim=1)
# involked in self.output()
self._total_ultragist_sizes.append(total_ultragist_size)
# involked in self.set_compression_ratio
self._main_ultragist_sizes.append(ultragist_sizes[0])
# update end_idx
self._start_idx = next_start_idx
self._end_idx = end_idx
self._step_idx += 1
# print("****************************************")
# if is_full_window:
# print(f"stride: {ultragist_stride}")
# print(f"compression ratios: {compression_ratios}")
# print(f"ultragist_sizes: {ultragist_sizes}")
# print(f"input_ids: {input_ids.shape}")
# print(f"start_idx: {start_idx}")
# print(f"next_start_idx: {next_start_idx}")
# print(f"end_idx: {end_idx}")
# x = input()
# if x == "s":
# return
return input_ids, attention_mask, past_key_values, labels
def update_memory(self, past_key_values):
"""
Accumulate ultragist activations and raw activations.
"""
for layer_idx, (key, value, ultragist_sizes, total_ultragist_size, raw_size_to_cache, window_size) in enumerate(past_key_values):
# NOTE: the past_key_values are incrementally returned (only the new keys and values are returned)
# key/value: (num_layer, 2, batch_size, num_head, new_seq_len, head_dim)
# ultragist_size: how many ultragist activations are in key and value
# raw_size_to_cache: how many raw activations should be kept
previous_raw_key, previous_raw_value = self.raw_activations[layer_idx]
if self._step_idx == 1:
# save the sink activations
# NOTE: we do not slice the key/value activations, which may cause duplication when ultragist_ratio=-1 for the first window, but it's okay
self.sink_activations[layer_idx] = [
slice_tensor(key, end=self.ultragist_sink_size, dim=self.k_seq_dim),
slice_tensor(value, end=self.ultragist_sink_size, dim=self.v_seq_dim),
]
if ultragist_sizes == [0]:
# this means the current input does not fulfill a window
# thus, the key and value are all raw activations, and we accumulate them until the window is fulfilled
assert raw_size_to_cache == window_size
raw_key = cat_tensor([
previous_raw_key,
key
], dim=self.k_seq_dim)
raw_value = cat_tensor([
previous_raw_value,
value
], dim=self.v_seq_dim)
self.raw_activations[layer_idx] = (raw_key, raw_value)
else:
for ultragist_size_idx, ultragist_size in enumerate(ultragist_sizes):
# NOTE: use the correct previous_ultragist_key and value!
previous_ultragist_key, previous_ultragist_value = self.l1_to_ln_ultragist_activations[ultragist_size_idx][layer_idx]
# if ultragist_size_idx == 0:
# ctx_manager = nullcontext()
# else:
# ctx_manager = torch.cuda.stream(self.stream)
# FIXME: only the first iteration works...
# with ctx_manager:
ultragist_key, ultragist_value, raw_key, raw_value = self._extract_ultragist_and_raw_memory(key, value, previous_ultragist_key, previous_ultragist_value, previous_raw_key, previous_raw_value, raw_size_to_cache, total_ultragist_size, ultragist_sizes, ultragist_size_idx)
self.l1_to_ln_ultragist_activations[ultragist_size_idx][layer_idx] = (ultragist_key, ultragist_value)
if ultragist_size_idx == 0:
self.raw_activations[layer_idx] = (raw_key, raw_value)
# if ultragist_size_idx != 0:
# print(self.stream.query())
def update_loss(self, batch_loss, valid_token_num):
"""
Accumulate loss for later perplexity computation and backward pass; past_key_values according to cache_method.
"""
# print(f"process {dist.get_rank()}: valid_token_num: {valid_token_num}; loss {batch_loss}")
if self._batch_loss is None:
# NOTE: multiply valid_token_num because batch_loss is divided by it in advance
self._batch_loss = batch_loss * valid_token_num
self._valid_token_num = valid_token_num
else:
# NOTE: avoid in-place operations, otherwise there will be gradient errors in training
self._batch_loss = self._batch_loss + batch_loss * valid_token_num
self._valid_token_num = self._valid_token_num + valid_token_num
def output(self, model_outputs):
"""
Override loss with accumulated loss.
"""
# override loss
if self._batch_loss is not None:
# here the batch_loss is the summation of all token losses in each element
loss = self._batch_loss.sum() / self._valid_token_num.sum()
# NOTE: prevent nan
batch_loss = self._batch_loss / self._valid_token_num
if (self._valid_token_num == 0).any():
batch_loss = batch_loss.masked_fill(self._valid_token_num == 0, 0.)
# NOTE: we must use dict to override values, otherwise trainer cannot find loss
model_outputs["loss"] = loss
model_outputs["batch_loss"] = batch_loss
model_outputs["valid_token_num"] = self._valid_token_num
# override last_hidden_states (used in generation)
ultragist_size = self._total_ultragist_sizes[-1]
# remove logits corresponding to ultragist tokens
if ultragist_size > 0:
model_outputs["logits"] = model_outputs["logits"][:, :-ultragist_size]
return model_outputs
def _extract_ultragist_and_raw_memory(self, key, value, previous_ultragist_key, previous_ultragist_value, previous_raw_key, previous_raw_value, raw_size_to_cache, total_ultragist_size, ultragist_sizes, ultragist_size_idx):
"""Extract ultragist and raw memory from the returned key and value. The raw memory is computed only if the ultragist_size_idx == 0."""
ultragist_size = ultragist_sizes[ultragist_size_idx]
# NOTE: ignore -1
previous_ultragist_size = sum(x for x in ultragist_sizes[:ultragist_size_idx] if x > 0)
if previous_ultragist_key is not None:
target_device = previous_ultragist_key.device
else:
if ultragist_size_idx == 0:
target_device = self._device
else:
target_device = self._cpu
if ultragist_size == -1:
actual_ultragist_size = self.ultragist_window - raw_size_to_cache
# the raw activations are used as ultragist activations
concat_raw_key = cat_tensor([
previous_raw_key,
key
], dim=self.k_seq_dim)
concat_raw_value = cat_tensor([
previous_raw_value,
value
], dim=self.v_seq_dim)
ultragist_key = cat_tensor([
previous_ultragist_key,
slice_tensor(concat_raw_key, end=actual_ultragist_size, dim=self.k_seq_dim).to(target_device, non_blocking=True)
], dim=self.k_seq_dim)
ultragist_value = cat_tensor([
previous_ultragist_value,
slice_tensor(concat_raw_value, end=actual_ultragist_size, dim=self.v_seq_dim).to(target_device, non_blocking=True)
], dim=self.v_seq_dim)
if ultragist_size_idx == 0:
raw_key = slice_tensor(concat_raw_key, start=actual_ultragist_size, end=self.ultragist_window, dim=self.k_seq_dim)
raw_value = slice_tensor(concat_raw_value, start=actual_ultragist_size, end=self.ultragist_window, dim=self.v_seq_dim)
else:
# [-ultragist_size:] activations are from ultragists, need to be accumulated
# [-raw_cache_size-ultragist_size:-ultragist_size] raw activations will be cached; if they are shorter than raw_cache_size, part of the previous raw activations will also be kept
ultragist_start_idx = - total_ultragist_size + previous_ultragist_size
ultragist_end_idx = ultragist_start_idx + ultragist_size
# NOTE: avoid end=0 for slicing
if ultragist_end_idx == 0:
ultragist_end_idx = None
ultragist_key = cat_tensor([
previous_ultragist_key,
slice_tensor(key, start=ultragist_start_idx, end=ultragist_end_idx, dim=self.k_seq_dim).to(target_device, non_blocking=True)
], dim=self.k_seq_dim)
ultragist_value = cat_tensor([
previous_ultragist_value,
slice_tensor(value, start=ultragist_start_idx, end=ultragist_end_idx, dim=self.v_seq_dim).to(target_device, non_blocking=True)
], dim=self.v_seq_dim)
# the raw activations are only updated once
if ultragist_size_idx == 0:
if key.shape[self.k_seq_dim] < raw_size_to_cache + ultragist_size:
concat_raw_key = cat_tensor([
previous_raw_key,
key
], dim=self.k_seq_dim)
concat_raw_value = cat_tensor([
previous_raw_value,
value
], dim=self.v_seq_dim)
raw_key = slice_tensor(concat_raw_key, start=self.ultragist_window - raw_size_to_cache, end=self.ultragist_window, dim=self.k_seq_dim)
raw_value = slice_tensor(concat_raw_value, start=self.ultragist_window - raw_size_to_cache, end=self.ultragist_window, dim=self.v_seq_dim)
else:
# becomes None when raw_size_to_cache = 0
raw_key = slice_tensor(key, start=ultragist_start_idx - raw_size_to_cache, end=ultragist_start_idx, dim=self.k_seq_dim)
raw_value = slice_tensor(value, start=ultragist_start_idx - raw_size_to_cache, end=ultragist_start_idx, dim=self.v_seq_dim)
if ultragist_size_idx == 0:
return ultragist_key, ultragist_value, raw_key, raw_value
else:
# NOTE: only l1 ultragist activations are kept on GPU
return ultragist_key.detach().to(target_device, non_blocking=True), ultragist_value.detach().to(target_device, non_blocking=True), None, None
# return ultragist_key, ultragist_value, None, None
def slice_tensor(x, start=None, end=None, dim=2):
if x is None:
return None
if end == 0:
return None
if start == x.shape[dim]:
return None
if start == end:
return None
if dim == 2:
if start is None and end is not None:
return x[:, :, :end, ...]
elif start is not None and end is None:
return x[:, :, start:, ...]
elif start is not None and end is not None:
return x[:, :, start:end, ...]
elif dim == 1:
if start is None and end is not None:
return x[:, :end, ...]
elif start is not None and end is None:
return x[:, start:, ...]
elif start is not None and end is not None:
return x[:, start:end, ...]
else:
raise NotImplementedError
def cat_tensor(list_of_tensors, dim=-1):
list_of_tensors = [t for t in list_of_tensors if t is not None]
if len(list_of_tensors) > 1:
result = torch.cat(list_of_tensors, dim=dim)
elif len(list_of_tensors) == 1:
result = list_of_tensors[0]
else:
result = None
return result
def slice_activations(activations, start=None, end=None, k_seq_dim=2, v_seq_dim=2):
new_activations = []
for key, value in activations:
new_key = slice_tensor(key, start=start, end=end, dim=k_seq_dim)
new_value = slice_tensor(value, start=start, end=end, dim=v_seq_dim)
new_activations.append([new_key, new_value])
return new_activations
def cat_activations(list_of_activations, k_seq_dim=2, v_seq_dim=2):
assert all(len(x) == len(list_of_activations[0]) for x in list_of_activations), f"Make sure all activations have the same number of layers! Found {[len(x) for x in list_of_activations]}."
new_activations = []
for layer_idx in range(len(list_of_activations[0])):
keys = [x[layer_idx][0] for x in list_of_activations]
values = [x[layer_idx][1] for x in list_of_activations]
new_key = cat_tensor(keys, dim=k_seq_dim)
new_value = cat_tensor(values, dim=v_seq_dim)
new_activations.append([new_key, new_value])
return new_activations
def interleave_activations(main_activations, augment_activations, main_spans, augment_spans, k_seq_dim=2, v_seq_dim=2, device=torch.device("cuda")):
""" Interleave main_activations and augment_activations according to main_span and augment_span.
Args:
main_span: a list of tuples (start_idx, end_idx). when start_idx and end_idx is None, the augment_activations will be plugged in.
augment_span: a list of tuples (start_idx, end_idx)
"""
assert len(main_activations) == len(augment_activations) , f"Make sure main and augment activations have the same number of layers! Found {len(main_activations)} and {len(augment_activations)}!"
assert sum(x[0] is None and x[1] is None for x in main_spans) == len(augment_spans), f"Make sure the number of slots for augmentation (start_idx=None and end_idx=None in main_spans) matches the number of augmentations. Found {sum(x for x in main_spans if x[0] is None and x[1] is None)} slots but {len(augment_spans)} augmentations!"
new_activations = []
for layer_idx in range(len(main_activations)):
main_key, main_value = main_activations[layer_idx]
augment_key, augment_value = augment_activations[layer_idx]
sliced_keys = []
sliced_values = []
augment_idx = 0
for start, end in main_spans:
if start is None and end is None:
# this means the augment key/value should be plugged in
augment_start, augment_end = augment_spans[augment_idx]
sliced_key = slice_tensor(
augment_key,
start=augment_start,
end=augment_end,
dim=k_seq_dim
).to(device)
sliced_value = slice_tensor(
augment_value,
start=augment_start,
end=augment_end,
dim=v_seq_dim
).to(device)
else:
sliced_key = slice_tensor(
main_key,
start=start,
end=end,
dim=k_seq_dim
)
sliced_value = slice_tensor(
main_value,
start=start,
end=end,
dim=v_seq_dim
)
sliced_keys.append(sliced_key)
sliced_values.append(sliced_value)
new_key = cat_tensor(sliced_keys, dim=k_seq_dim)
new_value = cat_tensor(sliced_values, dim=v_seq_dim)
new_activations.append([new_key, new_value])
return new_activations
def softmax(x:np.ndarray, axis=-1, temperature=1):
if isinstance(x, list):
x = np.array(x)
x = x / temperature
x = x - x.max(axis=axis, keepdims=True)
y = np.exp(x)
return y / y.sum(axis=axis, keepdims=True)
def l1_norm(x):
sum_x = sum(x)
x = [y/sum_x for y in x]
return x |