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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 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 | # 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.
"""PubMedQA data loader pipeline and custom PyGrain DataSource."""
from __future__ import annotations
import json
from etils import epath
from gemma import gm
from gemma.diffusion.hackable_diffusion_adapter.data import data as adapter_data
from kauldron import kd
class JsonlDataSource:
"""Standalone random-access data source backed by a JSONL file.
Implements the ``grain.RandomAccessDataSource`` Protocol via structural
typing (requires only ``__len__`` and ``__getitem__``).
"""
def __init__(
self, path: str, slice_start: int = 0, slice_stop: int | None = None
):
self.path = path
text = epath.Path(path).read_text()
# Scientific abstracts often contain vertical tabs (\x0b) or form
# feeds (\x0c), which splitlines() splits on, breaking JSON structures.
# split('\n') is safe.
self._data = [json.loads(line) for line in text.strip().split("\n")]
if slice_start != 0 or slice_stop is not None:
self._data = self._data[slice_start:slice_stop]
def __len__(self) -> int:
return len(self._data)
def __getitem__(self, record_key: int):
import jax # pylint: disable=g-import-not-at-top
ex = self._data[record_key]
return jax.tree.map(lambda x: x, ex) # deep-copy for mutation safety
def make_pubmedqa_ds(
training: bool,
batch_size: int,
num_canvases: int,
canvas_size: int,
prompt_len: int,
use_long_answer: bool = False,
train_path: str = "",
test_path: str = "",
slice_start: int = 0,
slice_stop: int | None = None,
num_workers: int = 4,
):
"""Build the PubMedQA dataset pipeline.
Args:
training: Whether this is training or evaluation.
batch_size: Per-device batch size.
num_canvases: Number of canvas chunks for the response.
canvas_size: Token length of each canvas chunk.
prompt_len: Maximum prompt token length.
use_long_answer: If True, use the ``long_response`` field (detailed
explanation) as the training target. Otherwise use the short ``response``
field (yes/no/maybe).
train_path: Path to the training JSONL file.
test_path: Path to the test JSONL file.
slice_start: Start index for dataset slicing.
slice_stop: Stop index for dataset slicing.
num_workers: Number of workers for data loading.
Returns:
A ``kd.data.py.DataSource`` dataset config.
"""
tokenizer = gm.text.Gemma4Tokenizer()
path = train_path if training else test_path
response_field = "long_response" if use_long_answer else "response"
transforms = [
# Rename to canonical prompt / response keys.
kd.data.Elements(rename={response_field: "response_text"}),
# Append structured answer marker to response text so the model
# learns to produce "The answer is: {yes|no|maybe}" at the end.
adapter_data.ReformatPubMedQAAnswer(
response_key="response_text",
answer_key="short_answer",
),
# Wrap in instruction-tuning turn tokens with a system prompt.
gm.data.FormatText(
key="prompt",
template=(
"<|turn>system\nYou are a medical research assistant. Given"
" a medical research context and a question, provide a"
" brief explanation and then state your final answer.\n\n"
"When you show the final answer, use the expression"
' "The answer is: " followed by yes, no, or maybe.'
" Use the expression without any modification.\n\n"
"For example:\n"
"The study shows a statistically significant improvement"
" in outcomes for the treatment group compared to"
" the control group (p < 0.05)."
" The answer is: yes<turn|>\n"
"<|turn>user\n{text}<turn|>\n<|turn>model\n"
),
),
gm.data.FormatText(
key="response_text",
template="{text}<turn|>",
),
# Tokenize prompt (with BOS) and response (without BOS).
gm.data.Tokenize(tokenizer=tokenizer, key=["prompt"], add_bos=True),
gm.data.Tokenize(tokenizer=tokenizer, key=["response_text"]),
# Pad the prompt to a fixed maximum length.
gm.data.Pad(key="prompt", max_length=prompt_len, truncate=True),
# Chunk the response into multiple flattened canvases.
adapter_data.CanvasChunker(
in_response="response_text",
out_canvas="canvas",
out_canvas_id="canvas_id",
out_canvas_mask="canvas_mask",
num_canvases=num_canvases,
canvas_size=canvas_size,
eos_token=gm.text.Gemma4Tokenizer.special_tokens.EOS,
pad_token=gm.text.Gemma4Tokenizer.special_tokens.PAD,
),
# Shift the full sequence to create targets for AR loss.
adapter_data.SequenceTargetShift(
pad_token=gm.text.Gemma4Tokenizer.special_tokens.PAD,
),
# Add a trailing dimension because the model expects it.
kd.data.Rearrange(key="canvas", pattern="c -> c 1"),
]
keep_fields = [
"prompt",
"canvas",
"canvas_id",
"canvas_mask",
"encoder_target",
"encoder_target_mask",
]
if not training:
# (eval only) Tokenize and pad short_answer for accuracy metrics.
transforms.extend([
gm.data.Tokenize(tokenizer=tokenizer, key=["short_answer"]),
gm.data.Pad(key="short_answer", max_length=32, truncate=True),
kd.data.Elements(rename={"short_answer": "short_answer_tokens"}),
])
keep_fields.append("short_answer_tokens")
if use_long_answer:
# If long-answer config, copy and pad reference tokens for BLEU metric.
transforms.extend([
adapter_data.CopyField(
src_key="response_text", dst_key="response_text_copy"
),
gm.data.Pad(
key="response_text_copy",
max_length=num_canvases * canvas_size,
truncate=True,
),
kd.data.Elements(rename={"response_text_copy": "long_answer_tokens"}),
])
keep_fields.append("long_answer_tokens")
transforms.append(kd.data.Elements(keep=keep_fields))
return kd.data.py.DataSource(
data_source=JsonlDataSource(
path=path, slice_start=slice_start, slice_stop=slice_stop
),
shuffle=training,
num_epochs=None if training else 1,
batch_size=batch_size,
num_workers=num_workers,
transforms=transforms,
)
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