838 lines
38 KiB
Python
838 lines
38 KiB
Python
'''
|
|
This file is AGPL-licensed.
|
|
|
|
Some of the code in this file is from Clover Edition:
|
|
https://github.com/cloveranon/Clover-Edition/blob/master/aidungeon/gpt2generator.py
|
|
|
|
The license for Clover Edition is shown below:
|
|
|
|
Copyright (c) 2019 Nick Walton
|
|
|
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
of this software and associated documentation files (the "Software"), to deal
|
|
in the Software without restriction, including without limitation the rights
|
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
copies of the Software, and to permit persons to whom the Software is
|
|
furnished to do so, subject to the following conditions:
|
|
|
|
The above copyright notice and this permission notice shall be included in all
|
|
copies or substantial portions of the Software.
|
|
|
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|
SOFTWARE.
|
|
'''
|
|
|
|
import multiprocessing
|
|
from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar
|
|
import progressbar
|
|
import time
|
|
import os
|
|
import requests
|
|
import random
|
|
import jax
|
|
from jax.config import config
|
|
from jax.experimental import maps
|
|
import jax.numpy as jnp
|
|
import numpy as np
|
|
import optax
|
|
import haiku as hk
|
|
import transformers
|
|
from mesh_transformer.checkpoint import read_ckpt_lowmem
|
|
from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard
|
|
|
|
|
|
params: Dict[str, Any] = {}
|
|
|
|
|
|
def warper_callback(logits) -> np.array:
|
|
raise NotImplementedError("`tpu_mtj_backend.warper_callback()` needs to be defined")
|
|
|
|
def stopping_callback(generated, n_generated, excluded_world_info) -> Tuple[List[set], bool, bool]:
|
|
raise NotImplementedError("`tpu_mtj_backend.stopping_callback()` needs to be defined")
|
|
|
|
def settings_callback() -> dict:
|
|
return {
|
|
"top_p": 0.9,
|
|
"temp": 0.5,
|
|
"top_k": 0,
|
|
"tfs": 1.0,
|
|
"repetition_penalty": 1.0,
|
|
"rpslope": 0.0,
|
|
"rprange": 0,
|
|
}
|
|
|
|
def started_compiling_callback() -> None:
|
|
pass
|
|
|
|
def stopped_compiling_callback() -> None:
|
|
pass
|
|
|
|
def compiling_callback() -> None:
|
|
pass
|
|
|
|
|
|
def show_spinner():
|
|
bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength, widgets=[progressbar.Timer(), ' ', progressbar.BouncingBar(left='[', right=']', marker='█')])
|
|
i = 0
|
|
while True:
|
|
bar.update(i)
|
|
time.sleep(0.1)
|
|
i += 1
|
|
|
|
|
|
__F = TypeVar("__F", bound=Callable)
|
|
__T = TypeVar("__T")
|
|
|
|
def __move_xmap(f: __F, out_axis: str) -> __F:
|
|
return maps.xmap(
|
|
f,
|
|
in_axes=(["shard", ...], ["batch", ...]),
|
|
out_axes=[out_axis, ...],
|
|
axis_resources={'shard': 'mp', 'batch': 'dp'},
|
|
)
|
|
|
|
def __shard_xmap(batch_dim=1):
|
|
xmap = __move_xmap(lambda s, b: s, "shard")
|
|
def inner(x: __T) -> __T:
|
|
return xmap(x, np.empty(batch_dim))
|
|
return inner
|
|
|
|
def __batch_xmap(shard_dim=1):
|
|
xmap = __move_xmap(lambda s, b: b, "batch")
|
|
def inner(x: __T) -> __T:
|
|
return xmap(np.empty(shard_dim), x)
|
|
return inner
|
|
|
|
|
|
def apply_repetition_penalty_dynamic(logits, tokens, repetition_penalty, generated_index, gen_length, rpslope, rprange):
|
|
'''
|
|
This gets called by generate_loop_fn to apply repetition penalty
|
|
to the 1D array logits using the provided 1D array of tokens to penalize
|
|
'''
|
|
tokens = np.minimum(tokens, params["n_vocab"]-1) # https://github.com/google/jax/issues/3774
|
|
rpslope = np.int32(rpslope)
|
|
rprange = np.int32(rprange)
|
|
clipped_rprange = rprange if rprange > 0 else tokens.shape[-1]
|
|
penalty_arange = np.roll(np.arange(tokens.shape[-1]) + (clipped_rprange - tokens.shape[-1]), generated_index, axis=-1)
|
|
# Make a new array with the same length as the tokens array but with
|
|
# each element replaced by the value at the corresponding index in the
|
|
# logits array; e.g.
|
|
# if logits is [77, 5, 3, 98] and tokens is [0, 1, 2, 3, 2, 3, 1],
|
|
# then penalty_logits will be [77, 5, 3, 98, 3, 98, 5]
|
|
penalty_logits = np.take(logits, tokens)
|
|
# Repetition penalty slope
|
|
if rpslope != 0.0 and rprange > 0:
|
|
_penalty = (penalty_arange/(rprange - 1)) * 2 - 1
|
|
_penalty = (rpslope * _penalty) / (1 + np.abs(_penalty) * (rpslope - 1))
|
|
_penalty = 1 + ((_penalty + 1) / 2) * (repetition_penalty - 1)
|
|
repetition_penalty = _penalty
|
|
# Divide positive values by repetition_penalty and multiply negative
|
|
# values by repetition_penalty (the academic publication that described
|
|
# this technique actually just only divided, but that would cause tokens
|
|
# with negative logits to become more likely, which is obviously wrong)
|
|
penalty_logits = np.where(
|
|
penalty_arange >= 0,
|
|
np.where(
|
|
penalty_logits > 0,
|
|
penalty_logits/repetition_penalty,
|
|
penalty_logits*repetition_penalty,
|
|
),
|
|
penalty_logits,
|
|
)
|
|
# Finally, put those penalized logit values back into their original
|
|
# positions in the logits array
|
|
logits[tokens] = penalty_logits
|
|
return logits
|
|
|
|
def kobold_sample_dynamic(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
|
|
'''
|
|
This gets called by generate_loop_fn to apply a series of 4 filters
|
|
to the logits (top-k, then top-p, then TFS, then temperature) before
|
|
picking one token using the modified logits
|
|
'''
|
|
# Top-k (keep only the k tokens with the highest logits and remove
|
|
# the rest, by setting their logits to negative infinity)
|
|
def top_k_filter(logits):
|
|
# After sorting the logits array in descending order,
|
|
# sorted_indices_to_remove is a 1D array that is True for tokens
|
|
# in the sorted logits array we want to remove and False for ones
|
|
# we want to keep, in this case the first top_k elements will be
|
|
# False and the rest will be True
|
|
sorted_indices_to_remove = np.arange(len(logits)) >= top_k
|
|
# Unsort the logits array back to its original configuration and
|
|
# remove tokens we need to remove
|
|
_, indices_to_remove = jax.lax.sort_key_val(
|
|
np.argsort(-logits),
|
|
sorted_indices_to_remove,
|
|
)
|
|
return np.where(indices_to_remove, -np.inf, logits)
|
|
if top_k > 0:
|
|
logits = top_k_filter(logits)
|
|
# Top-p (after sorting the remaining tokens again in descending order of
|
|
# logit, remove the ones that have cumulative softmax probability
|
|
# greater than p)
|
|
def top_p_filter(logits):
|
|
# Sort the logits array in descending order, replace every element
|
|
# with e (Euler's number) to the power of that element, and divide
|
|
# each element of the new array by the sum of the elements in the
|
|
# new array
|
|
sorted_logits = -np.sort(-logits)
|
|
probabilities = np.array(jax.nn.softmax(sorted_logits), copy=True)
|
|
# Calculate cumulative_probabilities as the prefix-sum array of
|
|
# probabilities
|
|
cumulative_probabilities = np.cumsum(probabilities, axis=-1)
|
|
# We want to remove tokens with cumulative probability higher
|
|
# than top_p
|
|
sorted_indices_to_remove = cumulative_probabilities > top_p
|
|
# Don't ever remove the token with the highest logit, even if
|
|
# the probability is higher than top_p
|
|
sorted_indices_to_remove[0] = False
|
|
# Unsort and remove
|
|
_, indices_to_remove = jax.lax.sort_key_val(
|
|
np.argsort(-logits),
|
|
sorted_indices_to_remove,
|
|
)
|
|
return np.where(indices_to_remove, -np.inf, logits)
|
|
if top_p < 1.0:
|
|
logits = top_p_filter(logits)
|
|
# Tail free sampling (basically top-p a second time on remaining tokens
|
|
# except it's the "cumulative normalized absolute second finite
|
|
# differences of the softmax probabilities" instead of just the
|
|
# cumulative softmax probabilities)
|
|
def tail_free_filter(logits):
|
|
# Sort in descending order
|
|
sorted_logits = -np.sort(-logits)
|
|
# Softmax again
|
|
probabilities = np.array(jax.nn.softmax(sorted_logits), copy=True)
|
|
# Calculate the second finite differences of that array (i.e.
|
|
# calculate the difference array and then calculate the difference
|
|
# array of the difference array)
|
|
d2 = np.diff(np.diff(probabilities))
|
|
# Get the absolute values of all those second finite differences
|
|
d2 = np.abs(d2)
|
|
# Normalize (all elements in the array are divided by the sum of the
|
|
# array's elements)
|
|
d2 = d2 / d2.sum(axis=-1, keepdims=True)
|
|
# Get the prefix-sum array
|
|
cumulative_d2 = np.cumsum(d2, axis=-1)
|
|
# We will remove the tokens with a cumulative normalized absolute
|
|
# second finite difference larger than the TFS value
|
|
sorted_indices_to_remove = cumulative_d2 > tfs
|
|
# Don't remove the token with the highest logit
|
|
sorted_indices_to_remove[0] = False
|
|
# Since the d2 array has two fewer elements than the logits array,
|
|
# we'll add two extra Trues to the end
|
|
sorted_indices_to_remove = np.pad(
|
|
sorted_indices_to_remove,
|
|
(0, 2),
|
|
constant_values=True,
|
|
)
|
|
# Unsort and remove
|
|
_, indices_to_remove = jax.lax.sort_key_val(
|
|
np.argsort(-logits),
|
|
sorted_indices_to_remove,
|
|
)
|
|
return np.where(indices_to_remove, -np.inf, logits)
|
|
if tfs < 1.0:
|
|
logits = tail_free_filter(logits)
|
|
# Temperature (just divide the logits by the temperature)
|
|
logits /= temp
|
|
# Finally, pick one token using the softmax thingy again (it gives
|
|
# an array whose elements sum to 1 so it can be used nicely as a
|
|
# probability distribution)
|
|
return jax.random.categorical(key, logits, -1).astype(np.uint32)
|
|
|
|
def apply_repetition_penalty_static(logits, tokens, repetition_penalty, generated_index, gen_length, rpslope, rprange):
|
|
'''
|
|
This gets called by generate_loop_fn to apply repetition penalty
|
|
to the 1D array logits using the provided 1D array of tokens to penalize
|
|
'''
|
|
rpslope = jnp.int32(rpslope)
|
|
rprange = jnp.int32(rprange)
|
|
clipped_rprange = jax.lax.cond(rprange > 0, lambda x: x, lambda x: tokens.shape[-1], rprange)
|
|
penalty_arange = jnp.roll(jnp.arange(tokens.shape[-1]) + (clipped_rprange - tokens.shape[-1]), generated_index, axis=-1)
|
|
# Make a new array with the same length as the tokens array but with
|
|
# each element replaced by the value at the corresponding index in the
|
|
# logits array; e.g.
|
|
# if logits is [77, 5, 3, 98] and tokens is [0, 1, 2, 3, 2, 3, 1],
|
|
# then penalty_logits will be [77, 5, 3, 98, 3, 98, 5]
|
|
penalty_logits = jnp.take(logits, tokens)
|
|
# Repetition penalty slope
|
|
def apply_slope(carry):
|
|
repetition_penalty, rprange = carry
|
|
_penalty = (penalty_arange/(rprange - 1)) * 2 - 1
|
|
_penalty = (rpslope * _penalty) / (1 + jnp.abs(_penalty) * (rpslope - 1))
|
|
_penalty = 1 + ((_penalty + 1) / 2) * (repetition_penalty - 1)
|
|
return _penalty
|
|
repetition_penalty = jax.lax.cond(
|
|
(rpslope != 0.0) & (rprange > 0), # Not a typo; do not use `and` here, it makes JAX crash
|
|
apply_slope,
|
|
lambda carry: jnp.full(tokens.shape, carry[0]),
|
|
(repetition_penalty, rprange),
|
|
)
|
|
# Divide positive values by repetition_penalty and multiply negative
|
|
# values by repetition_penalty (the academic publication that described
|
|
# this technique actually just only divided, but that would cause tokens
|
|
# with negative logits to become more likely, which is obviously wrong)
|
|
penalty_logits = jnp.where(
|
|
penalty_arange >= 0,
|
|
jnp.where(
|
|
penalty_logits > 0,
|
|
penalty_logits/repetition_penalty,
|
|
penalty_logits*repetition_penalty,
|
|
),
|
|
penalty_logits,
|
|
)
|
|
# Finally, put those penalized logit values back into their original
|
|
# positions in the logits array
|
|
return logits.at[tokens].set(penalty_logits)
|
|
|
|
def kobold_sample_static(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
|
|
'''
|
|
This gets called by generate_loop_fn to apply a series of 4 filters
|
|
to the logits (top-k, then top-p, then TFS, then temperature) before
|
|
picking one token using the modified logits
|
|
'''
|
|
# Top-k (keep only the k tokens with the highest logits and remove
|
|
# the rest, by setting their logits to negative infinity)
|
|
def top_k_filter(logits):
|
|
# After sorting the logits array in descending order,
|
|
# sorted_indices_to_remove is a 1D array that is True for tokens
|
|
# in the sorted logits array we want to remove and False for ones
|
|
# we want to keep, in this case the first top_k elements will be
|
|
# False and the rest will be True
|
|
sorted_indices_to_remove = jnp.arange(len(logits)) >= top_k
|
|
# Unsort the logits array back to its original configuration and
|
|
# remove tokens we need to remove
|
|
_, indices_to_remove = jax.lax.sort_key_val(
|
|
jnp.argsort(-logits),
|
|
sorted_indices_to_remove,
|
|
)
|
|
return jnp.where(indices_to_remove, -jnp.inf, logits)
|
|
logits = jax.lax.cond(top_k > 0, top_k_filter, lambda x: x, logits)
|
|
# Top-p (after sorting the remaining tokens again in descending order of
|
|
# logit, remove the ones that have cumulative softmax probability
|
|
# greater than p)
|
|
def top_p_filter(logits):
|
|
# Sort the logits array in descending order, replace every element
|
|
# with e (Euler's number) to the power of that element, and divide
|
|
# each element of the new array by the sum of the elements in the
|
|
# new array
|
|
sorted_logits = -jnp.sort(-logits)
|
|
probabilities = jax.nn.softmax(sorted_logits)
|
|
# Calculate cumulative_probabilities as the prefix-sum array of
|
|
# probabilities
|
|
cumulative_probabilities = jnp.cumsum(probabilities, axis=-1)
|
|
# We want to remove tokens with cumulative probability higher
|
|
# than top_p
|
|
sorted_indices_to_remove = cumulative_probabilities > top_p
|
|
# Don't ever remove the token with the highest logit, even if
|
|
# the probability is higher than top_p
|
|
sorted_indices_to_remove = sorted_indices_to_remove.at[0].set(False)
|
|
# Unsort and remove
|
|
_, indices_to_remove = jax.lax.sort_key_val(
|
|
jnp.argsort(-logits),
|
|
sorted_indices_to_remove,
|
|
)
|
|
return jnp.where(indices_to_remove, -jnp.inf, logits)
|
|
logits = jax.lax.cond(top_p < 1.0, top_p_filter, lambda x: x, logits)
|
|
# Tail free sampling (basically top-p a second time on remaining tokens
|
|
# except it's the "cumulative normalized absolute second finite
|
|
# differences of the softmax probabilities" instead of just the
|
|
# cumulative softmax probabilities)
|
|
def tail_free_filter(logits):
|
|
# Sort in descending order
|
|
sorted_logits = -jnp.sort(-logits)
|
|
# Softmax again
|
|
probabilities = jax.nn.softmax(sorted_logits)
|
|
# Calculate the second finite differences of that array (i.e.
|
|
# calculate the difference array and then calculate the difference
|
|
# array of the difference array)
|
|
d2 = jnp.diff(jnp.diff(probabilities))
|
|
# Get the absolute values of all those second finite differences
|
|
d2 = jnp.abs(d2)
|
|
# Normalize (all elements in the array are divided by the sum of the
|
|
# array's elements)
|
|
d2 = d2 / d2.sum(axis=-1, keepdims=True)
|
|
# Get the prefix-sum array
|
|
cumulative_d2 = jnp.cumsum(d2, axis=-1)
|
|
# We will remove the tokens with a cumulative normalized absolute
|
|
# second finite difference larger than the TFS value
|
|
sorted_indices_to_remove = cumulative_d2 > tfs
|
|
# Don't remove the token with the highest logit
|
|
sorted_indices_to_remove = sorted_indices_to_remove.at[0].set(False)
|
|
# Since the d2 array has two fewer elements than the logits array,
|
|
# we'll add two extra Trues to the end
|
|
sorted_indices_to_remove = jnp.pad(
|
|
sorted_indices_to_remove,
|
|
(0, 2),
|
|
constant_values=True,
|
|
)
|
|
# Unsort and remove
|
|
_, indices_to_remove = jax.lax.sort_key_val(
|
|
jnp.argsort(-logits),
|
|
sorted_indices_to_remove,
|
|
)
|
|
return jnp.where(indices_to_remove, -jnp.inf, logits)
|
|
logits = jax.lax.cond(tfs < 1.0, tail_free_filter, lambda x: x, logits)
|
|
# Temperature (just divide the logits by the temperature)
|
|
def temp_filter(logits):
|
|
return logits / temp
|
|
logits = jax.lax.cond(True, temp_filter, lambda x: x, logits)
|
|
# Finally, pick one token using the softmax thingy again (it gives
|
|
# an array whose elements sum to 1 so it can be used nicely as a
|
|
# probability distribution)
|
|
return jax.random.categorical(key, logits, -1).astype(jnp.uint32)
|
|
|
|
pad_token_id = 50256
|
|
|
|
def sample_func(data, key, numseqs_aux, badwords, repetition_penalty, generated_index, gen_length, rpslope, rprange, sampler_options):
|
|
numseqs = numseqs_aux.shape[0]
|
|
gi = data[0][1]
|
|
def sample_loop_fn(carry):
|
|
generated, generated_index, logits, _ = carry[0][0]
|
|
sample_key = carry[1]
|
|
# Get the pseudo-random number generator key that will
|
|
# be used by kobold_sample_dynamic to randomly pick a token
|
|
sample_key, new_key = jax.random.split(sample_key, num=2)
|
|
# Apply repetition penalty to all tokens that are
|
|
# currently inside the "generated" array
|
|
logits = apply_repetition_penalty_dynamic(
|
|
logits,
|
|
generated,
|
|
repetition_penalty,
|
|
generated_index,
|
|
gen_length,
|
|
rpslope,
|
|
rprange,
|
|
)
|
|
# Remove any tokens in the badwords list by setting
|
|
# their logits to negative infinity which effectively
|
|
# makes their probabilities of being chosen zero
|
|
logits[badwords] = -np.inf
|
|
# Use the sampler (kobold_sample_dynamic) to pick one token
|
|
# based on the logits array as a 0D uint32 array
|
|
# (higher logit means higher probability of being
|
|
# picked, non-linearly)
|
|
next_token = kobold_sample_dynamic(
|
|
sample_key,
|
|
logits,
|
|
**sampler_options,
|
|
)
|
|
# Remember what token was picked
|
|
generated[generated_index] = next_token
|
|
generated_index += 1
|
|
# Re-pack the current sample_loop_fn's state so we can
|
|
# get back the same variables the next time
|
|
carry[0][0] = [generated, generated_index, logits, next_token]
|
|
carry[0].append(carry[0].pop(0))
|
|
return carry[0], new_key
|
|
# return jax.lax.while_loop(
|
|
# lambda carry: carry[0][0][1] == gi,
|
|
# sample_loop_fn,
|
|
# (data, key),
|
|
# )
|
|
carry = (data, key)
|
|
while carry[0][0][1] == gi:
|
|
carry = sample_loop_fn(carry)
|
|
return carry
|
|
|
|
class PenalizingCausalTransformer(CausalTransformer):
|
|
def __init__(self, config, **kwargs):
|
|
# Initialize
|
|
super().__init__(config, **kwargs)
|
|
def generate_static(state, key, ctx, ctx_length, gen_length, numseqs_aux, sampler_options, soft_embeddings=None):
|
|
compiling_callback()
|
|
numseqs = numseqs_aux.shape[0]
|
|
# These are the tokens that we don't want the AI to ever write
|
|
self.badwords = jnp.array(vars.badwordsids).squeeze()
|
|
@hk.transform
|
|
def generate_sample(context, ctx_length):
|
|
# Give the initial context to the transformer
|
|
transformer = CausalTransformerShard(config)
|
|
def generate_initial_scan_fn(sequence_index, _):
|
|
_, initial_state = transformer.generate_initial(context, ctx_length, soft_embeddings=soft_embeddings)
|
|
# The "generated" array will contain the tokens from the
|
|
# context as well as the tokens picked by the sampler at
|
|
# each stage, padded with a bunch of 50256s, so we know
|
|
# which tokens have to be repetition penalized
|
|
generated = jnp.pad(context, (0, config["seq"]), constant_values=pad_token_id) # Let it start off with just the 2048 context tokens, plus some 50256s which will be eventually filled with sampler-chosen tokens
|
|
generated_index = config["seq"]
|
|
# Add that information to generate_loop_fn's starting state
|
|
initial_state = (generated, generated_index, sequence_index) + initial_state
|
|
return sequence_index+1, initial_state
|
|
_, initial_states = jax.lax.scan(generate_initial_scan_fn, 0, None, numseqs)
|
|
sample_key = initial_states[-1][0]
|
|
initial_states = list(jax.tree_map(lambda x: x[i], initial_states[:-1]) for i in range(numseqs))
|
|
# Get repetition penalty from the arguments
|
|
repetition_penalty = sampler_options.pop('repetition_penalty', None)
|
|
rpslope = sampler_options.pop('rpslope', None)
|
|
rprange = sampler_options.pop('rprange', None)
|
|
# This is the main generation loop
|
|
def generate_loop_fn(carry):
|
|
# Unpack current generate_loop_fn state
|
|
generated, generated_index, sequence_index, next_token, decode_state = carry[0][0]
|
|
sample_key = carry[1]
|
|
# Get the pseudo-random number generator key that will
|
|
# be used by kobold_sample_static to randomly pick a token
|
|
sample_key, new_key = jax.random.split(sample_key)
|
|
# Give the context to the model and get the logits it
|
|
# spits out
|
|
# (a 2D array with 1 row and 50400 columns representing
|
|
# how strongly it thinks each of the 50257 tokens in its
|
|
# vocabulary should be appended to the context, followed
|
|
# by 143 apparently useless columns ???)
|
|
logits, new_state = transformer.generate_once(next_token, decode_state, soft_embeddings=soft_embeddings)
|
|
# Verify that logits does indeed have that many rows and
|
|
# columns (if you get an error here, pray for mercy)
|
|
assert logits.shape == (1, config["n_vocab"])
|
|
# Flatten it into a 1D array to make it easier to use
|
|
logits = logits[0]
|
|
# Apply repetition penalty to all tokens that are
|
|
# currently inside the "generated" array
|
|
if repetition_penalty is not None:
|
|
logits = apply_repetition_penalty_static(
|
|
logits,
|
|
generated,
|
|
repetition_penalty,
|
|
generated_index,
|
|
gen_length,
|
|
rpslope,
|
|
rprange,
|
|
)
|
|
# Remove any tokens in the badwords list by setting
|
|
# their logits to negative infinity which effectively
|
|
# makes their probabilities of being chosen zero
|
|
logits = logits.at[self.badwords].set(-jnp.inf)
|
|
# Use the sampler (kobold_sample_static) to pick one token
|
|
# based on the logits array as a 0D uint32 array
|
|
# (higher logit means higher probability of being
|
|
# picked, non-linearly)
|
|
next_token = kobold_sample_static(
|
|
sample_key,
|
|
logits,
|
|
**sampler_options,
|
|
)
|
|
# Remember what token was picked
|
|
generated = generated.at[generated_index].set(next_token)
|
|
generated_index += 1
|
|
# Re-pack the current generate_loop_fn's state so we can
|
|
# get back the same variables the next time
|
|
carry[0][0] = (generated, generated_index, sequence_index, next_token[jnp.newaxis], new_state)
|
|
carry[0].append(carry[0].pop(0))
|
|
return carry[0], new_key
|
|
return jax.lax.while_loop(
|
|
lambda carry: carry[0][0][1] - config["seq"] < gen_length,
|
|
generate_loop_fn,
|
|
(initial_states, sample_key),
|
|
)
|
|
return generate_sample.apply(state["params"], key, ctx, ctx_length)
|
|
self.generate_static_xmap = jax.experimental.maps.xmap(
|
|
fun=generate_static,
|
|
in_axes=(
|
|
["shard", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["shard", ...],
|
|
),
|
|
out_axes=["shard", "batch", ...],
|
|
axis_resources={'shard': 'mp', 'batch': 'dp'},
|
|
)
|
|
def generate_initial(state, key, ctx, ctx_length, numseqs_aux, soft_embeddings=None):
|
|
compiling_callback()
|
|
numseqs = numseqs_aux.shape[0]
|
|
@hk.transform
|
|
def generate_initial_inner(context, ctx_length):
|
|
# Give the initial context to the transformer
|
|
transformer = CausalTransformerShard(config)
|
|
def generate_initial_scan_fn(sequence_index, c):
|
|
_, initial_state = transformer.generate_initial(c, ctx_length, soft_embeddings=soft_embeddings)
|
|
generated_index = config["seq"]
|
|
# Add that information to generate_loop_fn's starting state
|
|
initial_state = (jnp.empty(config["n_vocab"], dtype=jnp.float32), generated_index, sequence_index) + initial_state
|
|
return sequence_index+1, initial_state
|
|
_, initial_states = jax.lax.scan(generate_initial_scan_fn, 0, context, numseqs)
|
|
sample_key = initial_states[-1][0]
|
|
initial_states = list(list(jax.tree_map(lambda x: x[i], initial_states[:-1])) for i in range(numseqs))
|
|
return initial_states, sample_key
|
|
return generate_initial_inner.apply(state["params"], key, ctx, ctx_length)
|
|
self.generate_initial_xmap = jax.experimental.maps.xmap(
|
|
fun=generate_initial,
|
|
in_axes=(
|
|
["shard", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["batch", ...],
|
|
["shard", ...],
|
|
),
|
|
out_axes=["shard", "batch", ...],
|
|
axis_resources={'shard': 'mp', 'batch': 'dp'},
|
|
)
|
|
def generate_once(data, state, numseqs_aux, soft_embeddings=None):
|
|
numseqs = numseqs_aux.shape[0]
|
|
@hk.without_apply_rng
|
|
@hk.transform
|
|
def generate_once_inner():
|
|
gi = data[0][1]
|
|
# Give the initial context to the transformer
|
|
transformer = CausalTransformerShard(config)
|
|
# This is the main generation loop
|
|
def generate_loop_fn(carry):
|
|
# Unpack current generate_loop_fn state
|
|
_, generated_index, sequence_index, next_token, decode_state = carry[0][0]
|
|
# Give the context to the model and get the logits it
|
|
# spits out
|
|
# (a 2D array with 1 row and 50400 columns representing
|
|
# how strongly it thinks each of the 50257 tokens in its
|
|
# vocabulary should be appended to the context, followed
|
|
# by 143 apparently useless columns ???)
|
|
logits, new_state = transformer.generate_once(next_token, decode_state, soft_embeddings=soft_embeddings)
|
|
# Verify that logits does indeed have that many rows and
|
|
# columns (if you get an error here, pray for mercy)
|
|
assert logits.shape == (1, config["n_vocab"])
|
|
assert logits.dtype == jnp.float32
|
|
# Flatten it into a 1D array to make it easier to use
|
|
logits = logits[0]
|
|
# Re-pack the current generate_loop_fn's state so we can
|
|
# get back the same variables the next time
|
|
generated_index += 1
|
|
carry[0][0] = [logits, generated_index, sequence_index, next_token, new_state]
|
|
carry[0].append(carry[0].pop(0))
|
|
return carry[0],
|
|
return jax.lax.while_loop(
|
|
lambda carry: carry[0][0][1] == gi,
|
|
generate_loop_fn,
|
|
(data,),
|
|
)
|
|
return generate_once_inner.apply(state["params"])
|
|
self.generate_once_xmap = jax.experimental.maps.xmap(
|
|
fun=generate_once,
|
|
in_axes=(
|
|
["shard", "batch", ...],
|
|
["shard", ...],
|
|
["batch", ...],
|
|
["shard", ...],
|
|
),
|
|
out_axes=["shard", "batch", ...],
|
|
axis_resources={'shard': 'mp', 'batch': 'dp'},
|
|
)
|
|
def generate_dynamic(self, ctx, ctx_length, gen_length, numseqs, return_logits=False, soft_embeddings=None, excluded_world_info=None, use_callback=True):
|
|
assert excluded_world_info is not None
|
|
assert not return_logits
|
|
assert gen_length.ndim == 1
|
|
assert soft_embeddings is not None
|
|
key = hk.PRNGSequence(random.randint(0, 2 ** 60))
|
|
batch_size = ctx.shape[0]
|
|
self.batch_size = batch_size
|
|
_numseqs_aux = jnp.empty((batch_size, numseqs), dtype=np.uint32)
|
|
numseqs_aux = batch_xmap(_numseqs_aux)
|
|
sample_data = [
|
|
[
|
|
np.pad(ctx[0][i], (0, params["seq"]), constant_values=pad_token_id),
|
|
params["seq"],
|
|
None,
|
|
np.empty((), dtype=np.uint32),
|
|
]
|
|
for i in range(numseqs)
|
|
]
|
|
n_generated = 0
|
|
regeneration_required = False
|
|
halt = False
|
|
started_compiling_callback()
|
|
generate_data, sample_key = self.generate_initial_xmap(self.state, jnp.array(key.take(batch_size)), ctx, ctx_length, numseqs_aux, soft_embeddings)
|
|
sample_key = np.asarray(sample_key[0, 0])
|
|
while True:
|
|
generate_data, = self.generate_once_xmap(generate_data, self.state, numseqs_aux, soft_embeddings)
|
|
for i in range(numseqs):
|
|
sample_data[i][2] = np.array(generate_data[i][0][0, 0], copy=True)
|
|
if use_callback:
|
|
logits = np.float32(tuple(d[2] for d in sample_data))
|
|
logits = warper_callback(logits)
|
|
for i in range(numseqs):
|
|
sample_data[i][2] = logits[i]
|
|
sampler_options = settings_callback()
|
|
repetition_penalty = sampler_options.pop("repetition_penalty", 1.0)
|
|
rpslope = sampler_options.pop("rpslope", 0.0)
|
|
rprange = sampler_options.pop("rprange", 0)
|
|
sample_data, sample_key = sample_func(sample_data, sample_key, _numseqs_aux, badwords, repetition_penalty, params["seq"] + n_generated, gen_length, rpslope, rprange, sampler_options)
|
|
n_generated += 1
|
|
for i in range(numseqs):
|
|
generate_data[i][3] = np.tile(sample_data[i][0][sample_data[i][1]-1][np.newaxis, np.newaxis], (params["cores_per_replica"], 1, 1))
|
|
if use_callback:
|
|
generated = np.uint32(tuple(d[0] for d in sample_data))
|
|
excluded_world_info, regeneration_required, halt = stopping_callback(generated, n_generated, excluded_world_info)
|
|
if regeneration_required or halt:
|
|
break
|
|
else:
|
|
break
|
|
stopped_compiling_callback()
|
|
return sample_data, n_generated, regeneration_required, halt
|
|
def generate_static(self, ctx, ctx_length, gen_length, numseqs, sampler_options, return_logits=False, soft_embeddings=None):
|
|
assert not return_logits
|
|
key = hk.PRNGSequence(random.randint(0, 2 ** 60))
|
|
batch_size = ctx.shape[0]
|
|
self.batch_size = batch_size
|
|
started_compiling_callback()
|
|
result = self.generate_static_xmap(
|
|
self.state,
|
|
jnp.array(key.take(batch_size)),
|
|
ctx,
|
|
np.array(ctx_length, dtype=np.uint32),
|
|
np.array(gen_length, dtype=np.uint32),
|
|
np.empty((batch_size, numseqs), dtype=np.uint8),
|
|
sampler_options,
|
|
soft_embeddings,
|
|
)
|
|
stopped_compiling_callback()
|
|
return result
|
|
|
|
|
|
def infer_dynamic(
|
|
context: np.array,
|
|
numseqs=1,
|
|
gen_len=80,
|
|
soft_embeddings: Optional[np.array] = None,
|
|
soft_tokens: Optional[np.array] = None,
|
|
excluded_world_info = None,
|
|
use_callback=True,
|
|
) -> Tuple[List[np.array], int, bool, bool]:
|
|
assert excluded_world_info is not None
|
|
maps.thread_resources.env = thread_resources_env
|
|
total_batch = 1
|
|
tokens = context
|
|
if(soft_tokens is not None):
|
|
tokens = np.uint32(np.concatenate((np.tile(soft_tokens, (tokens.shape[0], 1)), tokens), axis=-1))
|
|
provided_ctx = tokens.shape[-1]
|
|
pad_amount = seq - provided_ctx
|
|
padded_tokens = np.pad(tokens, ((0, 0), (pad_amount, 0)), constant_values=pad_token_id)
|
|
batched_tokens = np.array([padded_tokens] * total_batch)
|
|
samples = []
|
|
output = network.generate_dynamic(
|
|
batched_tokens,
|
|
np.ones(total_batch, dtype=np.uint32) * provided_ctx,
|
|
np.ones(total_batch, dtype=np.uint32) * gen_len,
|
|
numseqs,
|
|
soft_embeddings=soft_embeddings,
|
|
excluded_world_info=excluded_world_info,
|
|
use_callback=use_callback,
|
|
)
|
|
for out in output[0]:
|
|
samples.append(out[0][params["seq"] : params["seq"] + gen_len])
|
|
return (samples,) + output[1:]
|
|
|
|
def infer_static(
|
|
context: np.array,
|
|
top_p=0.9,
|
|
temp=0.5,
|
|
top_k=0,
|
|
tfs=1.0,
|
|
repetition_penalty=1.0,
|
|
rpslope=0.0,
|
|
rprange=0,
|
|
numseqs=1,
|
|
gen_len=80,
|
|
soft_embeddings: Optional[np.array] = None,
|
|
soft_tokens: Optional[np.array] = None,
|
|
) -> List[np.array]:
|
|
maps.thread_resources.env = thread_resources_env
|
|
total_batch = 1
|
|
tokens = context
|
|
if(soft_tokens is not None):
|
|
tokens = np.uint32(np.concatenate((soft_tokens, tokens)))
|
|
provided_ctx = tokens.shape[0]
|
|
pad_amount = seq - provided_ctx
|
|
padded_tokens = np.pad(tokens, ((pad_amount, 0),), constant_values=pad_token_id)
|
|
batched_tokens = np.array([padded_tokens] * total_batch)
|
|
samples = []
|
|
batched_generator_params = {
|
|
"temp": temp * np.ones(total_batch),
|
|
"top_p": top_p * np.ones(total_batch),
|
|
"tfs": tfs * np.ones(total_batch),
|
|
"repetition_penalty": repetition_penalty * np.ones(total_batch),
|
|
"rpslope": rpslope * np.ones(total_batch),
|
|
"rprange": np.full(total_batch, rprange, dtype=np.uint32),
|
|
"top_k": np.full(total_batch, top_k, dtype=np.uint32)
|
|
}
|
|
output = network.generate_static(
|
|
batched_tokens,
|
|
np.ones(total_batch, dtype=np.uint32) * provided_ctx,
|
|
np.ones(total_batch, dtype=np.uint32) * gen_len,
|
|
numseqs,
|
|
batched_generator_params,
|
|
soft_embeddings=soft_embeddings,
|
|
)[0]
|
|
for o in output:
|
|
samples.append(o[0][0, 0, params["seq"] : params["seq"] + gen_len])
|
|
return samples
|
|
|
|
|
|
def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs) -> None:
|
|
global thread_resources_env, seq, tokenizer, network, params
|
|
|
|
default_params = {
|
|
"compat": "j",
|
|
"layers": 28,
|
|
"d_model": 4096,
|
|
"n_heads": 16,
|
|
"n_vocab": 50400,
|
|
"n_vocab_padding": 0,
|
|
"norm": "layernorm",
|
|
"pe": "rotary",
|
|
"pe_rotary_dims": 64,
|
|
"seq": 2048,
|
|
"cores_per_replica": 8,
|
|
}
|
|
params = kwargs
|
|
for param in default_params:
|
|
if param not in params:
|
|
params[param] = default_params[param]
|
|
|
|
# Disable JAX warnings about these two functions having been renamed
|
|
jax.host_count = jax.process_count
|
|
jax.host_id = jax.process_index
|
|
|
|
print("Connecting to your Colab instance's TPU", flush=True)
|
|
spinner = multiprocessing.Process(target=show_spinner, args=())
|
|
spinner.start()
|
|
colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0]
|
|
url = f'http://{colab_tpu_addr}:8475/requestversion/{driver_version}'
|
|
requests.post(url)
|
|
spinner.terminate()
|
|
print()
|
|
config.FLAGS.jax_xla_backend = "tpu_driver"
|
|
config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR']
|
|
|
|
cores_per_replica = params["cores_per_replica"]
|
|
seq = params["seq"]
|
|
params["optimizer"] = optax.scale(0)
|
|
mesh_shape = (1, cores_per_replica)
|
|
devices = np.array(jax.devices()[:cores_per_replica]).reshape(mesh_shape)
|
|
thread_resources_env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')), ())
|
|
maps.thread_resources.env = thread_resources_env
|
|
tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
|
|
|
|
global shard_xmap, batch_xmap
|
|
shard_xmap = __shard_xmap()
|
|
batch_xmap = __batch_xmap(shard_dim=cores_per_replica)
|
|
|
|
global badwords
|
|
# These are the tokens that we don't want the AI to ever write
|
|
badwords = jnp.array(vars.badwordsids).squeeze()
|
|
|
|
if not path.endswith("/"):
|
|
path += "/"
|
|
|
|
network = PenalizingCausalTransformer(params, dematerialized=True)
|
|
network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
|
|
network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|