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e76b79a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from contextlib import contextmanager
import logging
import numpy as np
import random
from typing import Any, List, Optional, Union, Dict
import torch
import torch.nn.functional as F
from .tensor import (
assert_logprobs,
is_1hot_tensor,
)
logger = logging.getLogger(__name__)
"""
Mental model:
1. This file contains everything that enables + modifies sampling from distributions.
"""
SeedDict = Dict[str, Any]
def get_rng_states() -> SeedDict:
""" Retrieves a dictionary of seeds for all rngs. """
rng_states = {
'torch': torch.get_rng_state(),
'torch.cuda': torch.cuda.get_rng_state(),
'random': random.getstate(),
'numpy': np.random.get_state(),
}
return rng_states
def set_rng_states(rng_state: SeedDict):
""" Sets seeds of all rngs based on a dictionary of rng states. """
torch.set_rng_state(rng_state['torch'])
torch.cuda.set_rng_state(rng_state['torch.cuda'])
random.setstate(rng_state['random'])
np.random.set_state(rng_state['numpy'])
def set_rng_seeds(seed: Union[SeedDict, int]):
""" Sets seeds of all rngs based on a single integer """
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
@contextmanager
def set_rng_seeds_context(seeds: Union[SeedDict, int], disable=False):
""" Context manager for temporarily setting seeds.
For example, can be used when random sampling to get
deterministic outputs for test cases. """
if disable:
yield
else:
rng_states = get_rng_states()
set_rng_seeds(seeds)
yield
set_rng_states(rng_states)
def top_p_filtering(logits, top_p=0.0, filter_value=-float('Inf')):
"""
Filter a distribution of logits using nucleus (top-p) filtering
Args:
logits: prob distribution shape (vocabulary size)
top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
Returns:
given logits with filter_value at low probabilities (not in top-p)
"""
sorted_logits, sorted_indices = torch.sort(logits, dim=-1, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
assert (cumulative_probs[..., -1].round() == 1).all(), "Probabilities don't add up to one"
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = False
# Reorder the mask to locations before sorting and apply it to logits
sorted_indices_to_remove = sorted_indices_to_remove.gather(-1, sorted_indices.argsort(-1))
logits[sorted_indices_to_remove] = filter_value
return logits
def explore_ratio(probs, x_old, exploration_ratio):
"""
Description:
Given:
1. a tensor with arbitrary leading dimensions
and valid probabilities in the final dimension
2. a tensor of same size, but 1-hot in the trailing dimension
apply "eps-force" to all of the distributions.
"eps-force" is a heuristic used in 2021 to prevent self-mutations.
It does two things:
1. It bounds the probability of positions in `probs` that
are ==1 in `x_old` to be at most == `exploration_ratio`.
2. If this caused a reduction in probability mass, redistribute
that reduction (`overage`) among the other positions via
__scaling__ the other positions multiplicatively.
This retains the proportionality of the other positions.
Args:
probs (torch.float32): [*, K]
x_old: 1 hot of previous values, to cap. [*, K]
Example:
Given:
probs: [0.8, 0.15, 0.05]
x_old: [1, 0, 0]
exploration_ratio = 0.6
Return:
[0.8-0.2, 0.15*2, 0.05*2] == [0.6, 0.3, 0.1]
"""
assert probs.shape == x_old.shape, (probs.shape, x_old.shape)
assert is_1hot_tensor(x_old)
thresh = 1 - exploration_ratio
# Amount that x_old exceeded thresh, in all batch dims.
overage = ((probs * x_old).sum(-1, keepdim=True) - thresh).clamp(0) # [*]
# Push down old positions accoring to overage.
probs = probs - x_old * overage # [*, K]
# Scale up all other positions (probs_fresh) so that it sums to overage.
probs_fresh = probs * (1-x_old) + 1e-8 # [*, K]
probs = probs + probs_fresh * overage / probs_fresh.sum(-1, keepdim=True) # [*, K]
return probs
def modify_logits(
# Input:
logits,
# Settings:
x_old = None,
mask = None,
force_propose_new_tokens = None,
nucleus_sampling_rate = None,
epsilon_force_mutation_ratio = None,
epsilon_greedy_bypass = None,
temperature = None,
):
"""
General purpose modifier of logits/logprobs.
Args:
logits: [*, K] (potentially batched) tensor of distributions in final dim, K.
x_old: [*, K] optional one-hot tensor representing current values.
Some subsequent flags use this to promote sampling of new values.
mask: [K] Boolean tensor for final dimension.
Probability mass is only allowed where mask == True.
force_propose_new_tokens (bool): If true, don't allow any probability mass on returns logprobs where x_old == True.
nucleus_sampling_rate (float in range [0, 1]): Fraction of nucleus sampling to use.
epsilon_force_mutation_ratio (float in range [0, 1]): Fraction of eps-force to use.
epsilon_greedy_bypass (float in range [0, 1]): Fraction of epsilon greedy to use.
temperature (float): The temperature used for conversion of logits -> probs in Softmax operation.
Returns:
logprobs [*, K]. This is good for numerical precision issues, and is a bit nicer than logits.
"""
# Ensure logprobs at the start
logprobs = logits.log_softmax(-1)
assert_logprobs(logprobs)
K = logprobs.size(-1)
if x_old is not None:
assert logprobs.shape == x_old.shape
# Handle optional specification of mask, which will clear some positions.
if mask is None:
mask = torch.ones(K, device=logprobs.device).bool()
logits = logprobs.masked_fill(~mask, -float('inf'))
# Do logprob-based modifications.
if force_propose_new_tokens:
logits = logits.masked_fill(x_old.bool(), -float('inf'))
if nucleus_sampling_rate is not None:
logits = top_p_filtering(logits=logits, top_p=nucleus_sampling_rate)
# Convert to probs for prob-based modifications.
probs = F.softmax(logits/temperature, dim=-1)
# Do prob-based modifications.
if epsilon_force_mutation_ratio is not None:
probs = explore_ratio(probs, x_old, epsilon_force_mutation_ratio)
if epsilon_greedy_bypass is not None and torch.rand(1) < epsilon_greedy_bypass: # do only for valid AA's?
probs = torch.ones_like(probs) / K
# 4. probs -> logits
# NOTE: This eps used to be way too high.
# Keep in mind that eps needs to be lower than 1e-32 for proper test case matching
# in some cases, when adding the global EPS.
log_probs = (probs + 1e-100).log()
logits = log_probs.masked_fill(~mask, -float('inf'))
return logits |