335 lines
17 KiB
Python
335 lines
17 KiB
Python
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import multiprocessing
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from typing import Any, Dict, List
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import progressbar
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import time
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import os
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import requests
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import random
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import jax
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from jax.config import config
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from jax.experimental import maps
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import jax.numpy as jnp
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import numpy as np
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import optax
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import haiku as hk
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import transformers
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from mesh_transformer.checkpoint import read_ckpt_lowmem
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from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard
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params: Dict[str, Any] = {}
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def show_spinner():
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bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength, widgets=[progressbar.Timer(), ' ', progressbar.BouncingBar(left='[', right=']', marker='█')])
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i = 0
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while True:
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bar.update(i)
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time.sleep(0.1)
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i += 1
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def apply_repetition_penalty(logits, tokens, repetition_penalty):
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'''
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This gets called by generate_scan_fn to apply repetition penalty
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to the 1D array logits using the provided 1D array of tokens to penalize
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'''
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# Make a new array with the same length as the tokens array but with
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# each element replaced by the value at the corresponding index in the
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# logits array; e.g.
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# if logits is [77, 5, 3, 98] and tokens is [0, 1, 2, 3, 2, 3, 1],
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# then penalty_logits will be [77, 5, 3, 98, 3, 98, 5]
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penalty_logits = jnp.take(logits, tokens)
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# Divide positive values by repetition_penalty and multiply negative
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# values by repetition_penalty (the academic publication that described
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# this technique actually just only divided, but that would cause tokens
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# with negative logits to become more likely, which is obviously wrong)
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penalty_logits = jnp.where(
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penalty_logits > 0,
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penalty_logits/repetition_penalty,
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penalty_logits*repetition_penalty,
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)
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# Finally, put those penalized logit values back into their original
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# positions in the logits array
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return logits.at[tokens].set(penalty_logits)
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def kobold_sample(key, logits, _, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
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'''
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This gets called by generate_scan_fn to apply a series of 4 filters
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to the logits (top-k, then top-p, then TFS, then temperature) before
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picking one token using the modified logits
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'''
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# Top-k (keep only the k tokens with the highest logits and remove
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# the rest, by setting their logits to negative infinity)
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def top_k_filter(logits):
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# After sorting the logits array in descending order,
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# sorted_indices_to_remove is a 1D array that is True for tokens
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# in the sorted logits array we want to remove and False for ones
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# we want to keep, in this case the first top_k elements will be
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# False and the rest will be True
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sorted_indices_to_remove = jnp.arange(len(logits)) >= top_k
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# Unsort the logits array back to its original configuration and
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# remove tokens we need to remove
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_, indices_to_remove = jax.lax.sort_key_val(
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jnp.argsort(-logits),
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sorted_indices_to_remove,
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)
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return jnp.where(indices_to_remove, -jnp.inf, logits)
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logits = jax.lax.cond(top_k > 0, top_k_filter, lambda x: x, logits)
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# Top-p (after sorting the remaining tokens again in descending order of
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# logit, remove the ones that have cumulative softmax probability
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# greater than p)
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def top_p_filter(logits):
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# Sort the logits array in descending order, replace every element
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# with e (Euler's number) to the power of that element, and divide
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# each element of the new array by the sum of the elements in the
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# new array
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sorted_logits = -jnp.sort(-logits)
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probabilities = jax.nn.softmax(sorted_logits)
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# Calculate cumulative_probabilities as the prefix-sum array of
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# probabilities
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cumulative_probabilities = jnp.cumsum(probabilities, axis=-1)
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# We want to remove tokens with cumulative probability higher
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# than top_p
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sorted_indices_to_remove = cumulative_probabilities > top_p
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# Don't ever remove the token with the highest logit, even if
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# the probability is higher than top_p
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sorted_indices_to_remove = sorted_indices_to_remove.at[0].set(False)
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# Unsort and remove
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_, indices_to_remove = jax.lax.sort_key_val(
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jnp.argsort(-logits),
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sorted_indices_to_remove,
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)
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return jnp.where(indices_to_remove, -jnp.inf, logits)
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logits = jax.lax.cond(top_p < 1.0, top_p_filter, lambda x: x, logits)
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# Tail free sampling (basically top-p a second time on remaining tokens
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# except it's the "cumulative normalized absolute second finite
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# differences of the softmax probabilities" instead of just the
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# cumulative softmax probabilities)
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def tail_free_filter(logits):
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# Sort in descending order
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sorted_logits = -jnp.sort(-logits)
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# Softmax again
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probabilities = jax.nn.softmax(sorted_logits)
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# Calculate the second finite differences of that array (i.e.
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# calculate the difference array and then calculate the difference
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# array of the difference array)
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d2 = jnp.diff(jnp.diff(probabilities))
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# Get the absolute values of all those second finite differences
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d2 = jnp.abs(d2)
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# Normalize (all elements in the array are divided by the sum of the
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# array's elements)
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d2 = d2 / d2.sum(axis=-1, keepdims=True)
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# Get the prefix-sum array
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cumulative_d2 = jnp.cumsum(d2, axis=-1)
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# We will remove the tokens with a cumulative normalized absolute
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# second finite difference larger than the TFS value
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sorted_indices_to_remove = cumulative_d2 > tfs
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# Don't remove the token with the highest logit
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sorted_indices_to_remove = sorted_indices_to_remove.at[0].set(False)
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# Since the d2 array has two fewer elements than the logits array,
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# we'll add two extra Trues to the end
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sorted_indices_to_remove = jnp.pad(
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sorted_indices_to_remove,
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(0, 2),
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constant_values=True,
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)
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# Unsort and remove
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_, indices_to_remove = jax.lax.sort_key_val(
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jnp.argsort(-logits),
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sorted_indices_to_remove,
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)
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return jnp.where(indices_to_remove, -jnp.inf, logits)
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logits = jax.lax.cond(tfs < 1.0, tail_free_filter, lambda x: x, logits)
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# Temperature (just divide the logits by the temperature)
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def temp_filter(logits):
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return logits / temp
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logits = jax.lax.cond(True, temp_filter, lambda x: x, logits)
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# Finally, pick one token using the softmax thingy again (it gives
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# an array whose elements sum to 1 so it can be used nicely as a
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# probability distribution)
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return jax.random.categorical(key, logits, -1).astype(jnp.uint32)[jnp.newaxis], None
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pad_token_id = 50256
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class PenalizingCausalTransformer(CausalTransformer):
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def __init__(self, config):
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# Initialize
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super().__init__(config)
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def generate(state, key, ctx, ctx_length, aux, sampler_options):
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gen_length = self.gen_length
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# These are the tokens that we don't want the AI to ever write
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self.badwords = jnp.array([6880, 50256, 42496, 4613, 17414, 22039, 16410, 27, 29, 38430, 37922, 15913, 24618, 28725, 58, 47175, 36937, 26700, 12878, 16471, 37981, 5218, 29795, 13412, 45160, 3693, 49778, 4211, 20598, 36475, 33409, 44167, 32406, 29847, 29342, 42669, 685, 25787, 7359, 3784, 5320, 33994, 33490, 34516, 43734, 17635, 24293, 9959, 23785, 21737, 28401, 18161, 26358, 32509, 1279, 38155, 18189, 26894, 6927, 14610, 23834, 11037, 14631, 26933, 46904, 22330, 25915, 47934, 38214, 1875, 14692, 41832, 13163, 25970, 29565, 44926, 19841, 37250, 49029, 9609, 44438, 16791, 17816, 30109, 41888, 47527, 42924, 23984, 49074, 33717, 31161, 49082, 30138, 31175, 12240, 14804, 7131, 26076, 33250, 3556, 38381, 36338, 32756, 46581, 17912, 49146])
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def generate_sample(context, ctx_length, aux):
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# Give the initial context to the transformer
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transformer = CausalTransformerShard(config)
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_, initial_state = transformer.generate_initial(context, ctx_length)
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# The "generated" array will contain the tokens from the
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# context as well as the tokens picked by the sampler at
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# each stage, padded with a bunch of 50256s, so we know
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# which tokens have to be repetition penalized
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generated = jnp.pad(context, (0, gen_length), constant_values=pad_token_id) # Let it start off with just the 2048 context tokens, plus gen_length 50256s which will be eventually filled with sampler-chosen tokens
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generated_index = config["seq"]
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# Add that information to generate_scan_fn's starting state
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initial_state = (generated, generated_index) + initial_state
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# Get repetition penalty from the arguments
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repetition_penalty = sampler_options.pop('repetition_penalty', None)
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def generate_scan_fn(carry, sampler_input):
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# Unpack current generate_scan_fn state
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generated, generated_index, next_token, decode_state, sample_key = carry
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# Get the pseudo-random number generator key that will
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# be used by kobold_sample to randomly pick a token
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sample_key, new_key = jax.random.split(sample_key)
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# Give the context to the model and get the logits it
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# spits out
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# (a 2D array with 1 row and 50400 columns representing
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# how strongly it thinks each of the 50257 tokens in its
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# vocabulary should be appended to the context, followed
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# by 143 apparently useless columns ???)
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logits, new_state = transformer.generate_once(next_token, decode_state)
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# Verify that logits does indeed have that many rows and
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# columns (if you get an error here, pray for mercy)
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assert logits.shape == (1, config["n_vocab"])
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# Flatten it into a 1D array to make it easier to use
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logits = logits[0]
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# Apply repetition penalty to all tokens that are
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# currently inside the "generated" array
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if repetition_penalty is not None:
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logits = apply_repetition_penalty(
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logits,
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generated,
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repetition_penalty
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)
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# Remove any tokens in the badwords list by setting
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# their logits to negative infinity which effectively
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# makes their probabilities of being chosen zero
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logits = logits.at[self.badwords].set(-jnp.inf)
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# Use the sampler (kobold_sample) to pick one token
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# based on the logits array as a 1D array with 1 element
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# (higher logit means higher probability of being
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# picked, non-linearly)
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next_token, sample_info = kobold_sample(
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sample_key,
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logits,
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sampler_input,
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**sampler_options,
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)
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# Remember what token was picked so we can repetition
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# penalize it next time
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generated = generated.at[generated_index].set(next_token[0])
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generated_index += 1
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# self.return_logits isn't used in this program, but
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# for the sake of compatibility...
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if self.return_logits:
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output = (next_token, sample_info, logits[jnp.newaxis])
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else:
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output = (next_token, sample_info)
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# Re-pack the current generate_scan_fn's state so we can
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# get back the same variables the next time
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new_carry = (generated, generated_index, next_token, new_state, new_key)
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return new_carry, output
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# jax.lax.scan is a function that calls generate_scan_fn
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# gen_length times, each time passing a state object from
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# its return value (new_carry) back into one of the
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# function's arguments (carry), and of course gathering the
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# token it generates each time into the "outputs" array;
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# we have to use jax.lax.scan instead of a normal loop
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# because of JAX's JIT-compilation shenanigans
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final_state, outputs = jax.lax.scan(
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generate_scan_fn,
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initial_state,
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xs=aux,
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length=gen_length,
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)
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return final_state, outputs
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generate_fn = hk.transform(generate_sample).apply
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return generate_fn(state["params"], key, ctx, ctx_length, aux)
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self.generate_xmap = jax.experimental.maps.xmap(fun=generate, in_axes=(["shard", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["batch", ...]), out_axes=["batch", ...], axis_resources={'shard': 'mp', 'batch': 'dp'})
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def generate(self, ctx, ctx_length, gen_length, sampler_options, return_logits=False):
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key = hk.PRNGSequence(random.randint(0, 2 ** 60))
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batch_size = ctx.shape[0]
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aux = jnp.zeros((batch_size, gen_length), dtype=jnp.uint32)
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self.gen_length = gen_length
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self.batch_size = batch_size
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self.return_logits = return_logits
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return self.generate_xmap(
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self.state,
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jnp.array(key.take(batch_size)),
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ctx,
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np.array(ctx_length, dtype=np.uint32),
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aux,
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sampler_options
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)
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def infer(context, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, repetition_penalty=1.0, numseqs=1, gen_len=80) -> List[str]:
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maps.thread_resources.env = thread_resources_env
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total_batch = numseqs
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tokens = tokenizer.encode(context, max_length=params["seq"], truncation=True)
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provided_ctx = len(tokens)
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pad_amount = seq - provided_ctx
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padded_tokens = np.pad(np.asarray(tokens, dtype=np.uint32), ((pad_amount, 0),), constant_values=pad_token_id)
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batched_tokens = np.array([padded_tokens] * total_batch)
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length = np.ones(total_batch, dtype=np.uint32) * len(tokens)
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samples = []
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batched_generator_params = {
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"temp": temp * np.ones(total_batch),
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"top_p": top_p * np.ones(total_batch),
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"tfs": tfs * np.ones(total_batch),
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"repetition_penalty": repetition_penalty * np.ones(total_batch),
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"top_k": np.full(total_batch, top_k, dtype=np.uint32)
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}
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output = network.generate(batched_tokens, length, gen_len, batched_generator_params)
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decoded_tokens = output[1][0]
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for o in decoded_tokens[:, :, 0]:
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samples.append(tokenizer.decode(o))
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return samples
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def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs) -> None:
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global thread_resources_env, seq, tokenizer, network, params
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default_params = {
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"compat": "j",
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"layers": 28,
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"d_model": 4096,
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"n_heads": 16,
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"n_vocab": 50400,
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"n_vocab_padding": 0,
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"norm": "layernorm",
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"pe": "rotary",
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"pe_rotary_dims": 64,
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"seq": 2048,
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"cores_per_replica": 8,
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}
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params = kwargs
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for param in default_params:
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if param not in params:
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params[param] = default_params[param]
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||
|
|
|
||
|
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print("Connecting to your Colab instance's TPU", flush=True)
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||
|
|
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')
|
||
|
|
|
||
|
|
if not path.endswith("/"):
|
||
|
|
path += "/"
|
||
|
|
|
||
|
|
network = PenalizingCausalTransformer(params)
|
||
|
|
network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
|
||
|
|
network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|