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2f86d9b | 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 | # Copyright 2026 DeepMind Technologies Limited.
#
# 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.
"""Detokenization metric for logging prompt-response text."""
from __future__ import annotations
import dataclasses
import flax.struct
from gemma.diffusion.hackable_diffusion_adapter.eval import base_metric
from kauldron import kd
from kauldron.ktyping import Int, typechecked # pylint: disable=g-multiple-import,g-importing-member
################################################################################
# MARK: DetokenizePromptAndResponse
################################################################################
@dataclasses.dataclass(kw_only=True, frozen=True)
class DetokenizePromptAndResponse(base_metric.BaseTokenizerMetric):
"""Detokenize prompt and response tokens, log them as combined text."""
prompt: kd.kontext.Key = kd.kontext.REQUIRED
response: kd.kontext.Key = kd.kontext.REQUIRED
num_texts: int = 5
separator: str = ""
@flax.struct.dataclass
class State(kd.metrics.AutoState["DetokenizePromptAndResponse"]):
"""Collects the first num_texts prompt+response pairs."""
prompt: Int["b p"] = kd.metrics.truncate_field(num_field="parent.num_texts")
response: Int["b r"] = kd.metrics.truncate_field(
num_field="parent.num_texts"
)
def compute(self) -> list[str]:
"""Detokenizes collected prompts and responses, pairing them together."""
results = []
for p, r in zip(self.prompt, self.response):
prompt_text = self.parent.tokenizer.decode(p)
response_text = self.parent.tokenizer.decode(r)
results.append(prompt_text + self.parent.separator + response_text)
return results
@typechecked
def get_state(
self,
*,
prompt: Int["b p ..."],
response: Int["b r ..."],
) -> DetokenizePromptAndResponse.State:
"""Collects prompt and response tokens into the metric state."""
# Squeeze trailing singleton dim if present.
prompt = self._squeeze_3d(prompt)
response = self._squeeze_3d(response)
return self.State(prompt=prompt, response=response)
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