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# Copyright 2023 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Prediction functions for clippo/generative.py."""
import functools
import big_vision.pp.ops_text as pp_ops_text
import big_vision.utils as u
import jax
import jax.numpy as jnp
import numpy as np
# pylint: disable=missing-function-docstring
# We do not jit/pmap this function, because it is passed to evaluator that
# does it later. We output as many intermediate tensors as possible for
# maximal flexibility. Later `jit` will prune out things that are not needed.
def predict_fn_perplexity(
train_state, batch, *, model):
logits = model.apply(
{"params": train_state["params"]},
batch["image"],
batch["labels"],
train=False,
)
return logits, {"logits": logits}
def predict_fn_enc_rep(train_state, batch, *, model):
logits, out = model.apply(
{"params": train_state["params"]},
batch["image"],
None,
train=False,
return_enc_features=True,
)
return logits, out
def predict_fn_score(
train_state, batch, *, model, prompt="", prompt_tokenizer=""):
"""For a batch of images, return score (LL) for each image-label pair."""
encoded = model.apply(
{"params": train_state["params"]},
batch["image"],
train=False,
method=model.encode,
)
# This needs to be added by the evaluator. It is the pre-computed tokenized
# list of all available labels. For ImageNet-1k, that's (1000, 13).
all_labels = batch["_label_tokens"]
# Optionally prefix a single prompt to all labels:
if prompt and prompt_tokenizer:
prompt = make_prompt(prompt, prompt_tokenizer) # Note: this is cached.
prompts = jnp.tile(prompt, (all_labels.shape[0], 1))
all_labels = jnp.concatenate([prompts, all_labels], axis=-1)
# For ImageNet-1k and a prompt of length 2, we now have (1000, 15).
def score_label(label):
"""Score (LogLik) each minibatch example (image) with a single `label`."""
label_rep = jnp.tile(label, (encoded.shape[0], 1))
logits = model.apply(
{"params": train_state["params"]},
encoded,
label_rep,
train=False,
decode=False,
method=model.decode,
)
# The returned value is (batch,) scalars, the score each image has with
# this label. We turn the softmax_xent's NLL into LL so higher = better.
return -u.weighted_softmax_xent(
logits=logits,
labels=label_rep,
weights=(label_rep > 0).astype(jnp.float32), # Ignore <PAD> (=0).
reduction=False,
normalize=False,
)
# Use lax.map() instead of vmap() to conserve memory.
nlls = jax.lax.map(score_label, all_labels) # -> (nlabel, batch)
return nlls.T # -> (batch, nlabel) array of scores.
@functools.cache
def make_prompt(prompt, tokenizer_path, seq_len=None):
"""Tokenizes `prompt` with specified tokenizer, with optional padding."""
tokenizer = pp_ops_text.create_tokenizer(tokenizer_path, add_eos=False)
prompt = tokenizer.tokenize(prompt).numpy()
if seq_len:
prompt = np.pad(prompt, (0, seq_len - len(prompt))).astype(np.int32)
return prompt
def get_predict_fns(model):
"""Returns `predict_fns` for evaluators."""
fns = {
"perplexity": predict_fn_perplexity,
"score": predict_fn_score,
"enc_rep": predict_fn_enc_rep,
}
return {name: functools.partial(fn, model=model) for name, fn in fns.items()}
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