File size: 10,139 Bytes
ebfc1e6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 | # Copyright 2025 the LlamaFactory team.
#
# 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.
from collections.abc import Callable
from math import ceil
from typing import Any
import torch
from torch.utils.data import default_collate
from ...utils.constants import IGNORE_INDEX
from ...utils.helper import pad_and_truncate
from ...utils.objects import StatefulBuffer
from ...utils.plugin import BasePlugin
from ...utils.types import BatchInfo, BatchInput, DataLoader, ModelInput
class BatchingPlugin(BasePlugin):
def get_data_provider_batch_size(self, batch_info: BatchInfo) -> int:
"""Return the raw data provider batch size for this batching strategy."""
return self["get_data_provider_batch_size"](batch_info)
def compute_length(self, data_provider: DataLoader, batch_info: BatchInfo) -> int:
"""Compute the length of the batch generator.
The approximate length is used to calculate the lr schedule.
"""
return self["compute_length"](data_provider, batch_info)
def fill_buffer(
self,
buffer: StatefulBuffer,
batch_info: BatchInfo,
next_samples: Callable[[bool], list[ModelInput] | None],
) -> None:
"""Fill the buffer with data."""
return self["fill_buffer"](buffer, batch_info, next_samples)
def generate_batch(self, buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
"""Generate a batch from the buffer."""
return self["generate_batch"](buffer, batch_info)
def _get_dynamic_micro_batch_sizes(samples: list[ModelInput], batch_info: BatchInfo) -> list[int]:
"""Return sample counts for micro batches formed by one padded-token budget."""
budget = batch_info["cutoff_len"] * batch_info["micro_batch_size"]
cutoff_len = batch_info["cutoff_len"]
sizes = []
index = 0
while index < len(samples) and len(sizes) < batch_info["num_micro_batch"]:
max_sample_len = 0
used = 0
is_complete = False
while index + used < len(samples):
sample_len = min(len(samples[index + used]["input_ids"]), cutoff_len)
padded_tokens = max(max_sample_len, sample_len) * (used + 1)
if used > 0 and padded_tokens > budget:
is_complete = True
break
max_sample_len = max(max_sample_len, sample_len)
used += 1
if max_sample_len * used >= budget:
is_complete = True
break
if used == 0 or not is_complete:
break
sizes.append(used)
index += used
return sizes
def _get_dynamic_padding_free_micro_batch_sizes(samples: list[ModelInput], batch_info: BatchInfo) -> list[int]:
budget = batch_info["cutoff_len"] * batch_info["micro_batch_size"]
cutoff_len = batch_info["cutoff_len"]
sizes = []
index = 0
while index < len(samples) and len(sizes) < batch_info["num_micro_batch"]:
current_tokens = 0
used = 0
is_complete = False
while index + used < len(samples):
sample = samples[index + used]
sample_len = min(len(sample["input_ids"]), cutoff_len)
if current_tokens + sample_len > budget:
is_complete = True
break
current_tokens += sample_len
used += 1
if used <= 0 or not is_complete:
break
sizes.append(used)
index += used
return sizes
def _pack_padding_free_samples(samples: list[ModelInput], cutoff_len: int) -> BatchInput | None:
"""Pack fixed samples into one padding-free sequence without a token budget."""
packed: dict[str, list[Any]] = {}
position_ids: list[int] = []
for sample_index, sample in enumerate(samples):
# Padding-free still truncates each sample by cutoff_len before packing
# all samples into one contiguous sequence.
sample_len = min(len(sample["input_ids"]), cutoff_len)
if sample_len <= 0:
continue
for key, value in sample.items():
if key in ("attention_mask", "position_ids") or isinstance(value, str):
continue
if key not in packed:
packed[key] = []
sliced_value = list(value[:sample_len])
if sample_index > 0 and sliced_value:
if key == "labels":
sliced_value[0] = IGNORE_INDEX
elif key == "loss_weights":
sliced_value[0] = 0.0
packed[key].extend(sliced_value)
position_ids.extend(range(sample_len))
if not position_ids:
return None
packed["position_ids"] = position_ids
packed["attention_mask"] = None
return {key: None if value is None else torch.tensor(value).unsqueeze(0) for key, value in packed.items()}
@BatchingPlugin("padding_free").register("get_data_provider_batch_size")
def get_padding_free_data_provider_batch_size(batch_info: BatchInfo) -> int:
return batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
@BatchingPlugin("padding_free").register("compute_length")
def compute_padding_free_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
return len(data_provider)
@BatchingPlugin("padding_free").register("fill_buffer")
def fill_padding_free_buffer(
buffer: StatefulBuffer,
batch_info: BatchInfo,
next_samples: Callable[[bool], list[ModelInput] | None],
) -> None:
while len(buffer) < batch_info["micro_batch_size"] * batch_info["num_micro_batch"]:
samples = next_samples(False)
if samples is None:
break
buffer.put(samples)
@BatchingPlugin("padding_free").register("generate_batch")
def generate_padding_free_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
micro_batch_size = batch_info["micro_batch_size"]
num_micro_batch = batch_info["num_micro_batch"]
cutoff_len = batch_info["cutoff_len"]
batch_size = micro_batch_size * num_micro_batch
if len(buffer) < batch_size:
return None
samples = buffer.get(batch_size)
batch = []
for i in range(num_micro_batch):
micro_batch = samples[i * micro_batch_size : (i + 1) * micro_batch_size]
packed_micro_batch = _pack_padding_free_samples(micro_batch, cutoff_len)
if packed_micro_batch is None:
return None
batch.append(packed_micro_batch)
return batch
@BatchingPlugin("dynamic_batching").register("get_data_provider_batch_size")
def get_dynamic_batching_data_provider_batch_size(batch_info: BatchInfo) -> int:
return 1
@BatchingPlugin("dynamic_batching").register("compute_length")
def compute_dynamic_batching_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
batch_size = batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
return ceil(len(data_provider) / batch_size)
@BatchingPlugin("dynamic_batching").register("fill_buffer")
def fill_dynamic_batching_buffer(
buffer: StatefulBuffer,
batch_info: BatchInfo,
next_samples: Callable[[bool], list[ModelInput] | None],
) -> None:
while len(_get_dynamic_micro_batch_sizes(buffer.samples, batch_info)) < batch_info["num_micro_batch"]:
samples = next_samples(True)
if samples is None:
break
buffer.put(samples)
@BatchingPlugin("dynamic_batching").register("generate_batch")
def generate_dynamic_batching_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
micro_batch_sample_counts = _get_dynamic_micro_batch_sizes(buffer.samples, batch_info)
if len(micro_batch_sample_counts) < batch_info["num_micro_batch"]:
return None
batch = []
cutoff_len = batch_info["cutoff_len"]
for num_samples in micro_batch_sample_counts:
samples = buffer.get(num_samples)
batch.append(default_collate(pad_and_truncate(samples, cutoff_len)))
return batch
@BatchingPlugin("dynamic_padding_free").register("get_data_provider_batch_size")
def get_dynamic_padding_free_data_provider_batch_size(batch_info: BatchInfo) -> int:
return 1
@BatchingPlugin("dynamic_padding_free").register("compute_length")
def compute_dynamic_padding_free_length(data_provider: DataLoader, batch_info: BatchInfo) -> int:
batch_size = batch_info["micro_batch_size"] * batch_info["num_micro_batch"]
return ceil(len(data_provider) / batch_size)
@BatchingPlugin("dynamic_padding_free").register("fill_buffer")
def fill_dynamic_padding_free_buffer(
buffer: StatefulBuffer,
batch_info: BatchInfo,
next_samples: Callable[[bool], list[ModelInput] | None],
) -> None:
while len(_get_dynamic_padding_free_micro_batch_sizes(buffer.samples, batch_info)) < batch_info["num_micro_batch"]:
samples = next_samples(True)
if samples is None:
break
buffer.put(samples)
@BatchingPlugin("dynamic_padding_free").register("generate_batch")
def generate_dynamic_padding_free_batch(buffer: StatefulBuffer, batch_info: BatchInfo) -> list[BatchInput] | None:
micro_batch_sample_counts = _get_dynamic_padding_free_micro_batch_sizes(buffer.samples, batch_info)
if len(micro_batch_sample_counts) < batch_info["num_micro_batch"]:
return None
batch = []
cutoff_len = batch_info["cutoff_len"]
for num_samples in micro_batch_sample_counts:
samples = buffer.get(num_samples)
packed_batch = _pack_padding_free_samples(samples, cutoff_len)
if packed_batch is None:
return None
batch.append(packed_batch)
return batch
|