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8c9ba62 | 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 | """Utils for ccompatibility issues with verl."""
import os
from logging import Logger
from typing import List
import numpy as np
import torch
from verl import DataProto
from verl.trainer.ppo.metric_utils import _compute_response_info
from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path
from trinity.common.config import Config
from trinity.common.experience import (
Experience,
gather_action_masks,
gather_attention_masks,
gather_response_attrs,
gather_token_ids,
split_dpo_experience_to_single_turn,
)
def to_data_proto(
experiences: List[Experience], pad_token_id: int, logger: Logger
) -> DataProto: # noqa: C901
"""Convert List[Experience] to verl DataProto."""
assert len(experiences) > 0, "No experiences provided."
if experiences[0].experience_type == "dpo":
experiences = split_dpo_experience_to_single_turn(experiences)
max_prompt_length = max([exp.prompt_length for exp in experiences])
max_response_length = max([len(exp.tokens) - exp.prompt_length for exp in experiences]) # type: ignore
attention_mask = gather_attention_masks(
experiences, max_prompt_length, max_response_length
).long()
cumsum = torch.cumsum(attention_mask, dim=-1)
position_ids = torch.clip(cumsum - 1, 0, None).long()
tokens = gather_token_ids(
experiences, max_prompt_length, max_response_length, pad_token_id
).long()
batch_dict = {
"uid": np.array([exp.eid.tid for exp in experiences]),
"unique_ids": np.array([exp.eid.uid for exp in experiences]),
"position_ids": position_ids,
"input_ids": tokens,
"responses": tokens[:, max_prompt_length:],
"attention_mask": attention_mask,
"response_mask": gather_action_masks(experiences, max_response_length),
}
have_reward = all(exp.reward is not None for exp in experiences)
have_token_level_reward = all(exp.token_level_reward is not None for exp in experiences)
if have_reward or have_token_level_reward:
assert all(exp.logprobs is not None for exp in experiences), "No logprobs provided."
if have_token_level_reward:
if have_reward:
logger.warning(
"Both experiences.rewards and experiences.token_level_rewards are provided. "
"Using experiences.token_level_rewards."
)
token_level_rewards = gather_response_attrs(
experiences, "token_level_reward", max_response_length
)
else:
token_level_rewards = torch.zeros(attention_mask.shape, dtype=torch.float32)
eos_mask_idx = cumsum.argmax(dim=-1)
token_level_rewards[torch.arange(len(experiences)), eos_mask_idx] = torch.tensor(
[exp.reward for exp in experiences],
dtype=torch.float32,
)
token_level_rewards = token_level_rewards[:, max_prompt_length:]
batch_dict.update(
{
"token_level_scores": token_level_rewards,
"rollout_log_probs": gather_response_attrs(
experiences, "logprobs", max_response_length
),
}
)
for attr in ["advantages", "returns", "teacher_logprobs"]:
if all(getattr(exp, attr, None) is not None for exp in experiences):
batch_dict[attr] = gather_response_attrs(experiences, attr, max_response_length)
# For hint OPD: teacher_logprobs_valid_mask when all experiences have it
if all(
getattr(exp, "teacher_logprobs_valid_mask", None) is not None
for exp in experiences
):
batch_dict["teacher_logprobs_valid_mask"] = gather_response_attrs(
experiences, "teacher_logprobs_valid_mask", max_response_length, pad_value=False
)
if all(exp.multi_modal_inputs is not None for exp in experiences):
keys = experiences[0].multi_modal_inputs.keys()
batch_dict["multi_modal_inputs"] = np.array(
[{key: exp.multi_modal_inputs[key] for key in keys} for exp in experiences], # type: ignore
dtype=object,
)
custom_fields_set = set(tuple(exp.custom_fields) for exp in experiences)
if len(custom_fields_set) == 1:
custom_fields = list(custom_fields_set)[0]
for custom_field in custom_fields:
batch_dict[custom_field.destination_field] = torch.tensor(
[exp.info[custom_field.source_field] for exp in experiences],
dtype=custom_field.data_type,
)
else:
raise ValueError("Custom fields are not consistent across experiences.")
meta_info = {
"model_versions": np.array([exp.info.get("model_version", 0) for exp in experiences])
}
return DataProto.from_single_dict(batch_dict, meta_info=meta_info)
def compute_data_metrics(batch: DataProto) -> dict:
"""
Computes various metrics from a batch of data for PPO training.
Modified from verl.trainer.ppo.metric_utils.compute_data_metrics
This function calculates metrics related to scores, rewards, advantages, returns, values,
and sequence lengths from a batch of data. It provides statistical information (mean, max, min)
for each metric category.
Args:
batch: A DataProto object containing batch data with token-level scores, rewards, advantages, etc.
Returns:
A dictionary of metrics including:
- critic/score/mean, max, min: Statistics about sequence scores
- critic/rewards/mean, max, min: Statistics about sequence rewards
- critic/advantages/mean, max, min: Statistics about advantages
- critic/returns/mean, max, min: Statistics about returns
- critic/values/mean, max, min: Statistics about critic values
- critic/vf_explained_var: Explained variance of the value function
- response_length/mean, max, min, clip_ratio: Statistics about response lengths
- prompt_length/mean, max, min, clip_ratio: Statistics about prompt lengths
"""
metrics = {}
if "token_level_rewards" in batch.batch and "token_level_scores" in batch.batch:
sequence_score = batch.batch["token_level_scores"].sum(-1)
sequence_reward = batch.batch["token_level_rewards"].sum(-1)
metrics.update(
{
# score
"critic/score/mean": torch.mean(sequence_score).detach().item(),
"critic/score/max": torch.max(sequence_score).detach().item(),
"critic/score/min": torch.min(sequence_score).detach().item(),
# reward
"critic/rewards/mean": torch.mean(sequence_reward).detach().item(),
"critic/rewards/max": torch.max(sequence_reward).detach().item(),
"critic/rewards/min": torch.min(sequence_reward).detach().item(),
}
)
max_response_length = batch.batch["responses"].shape[-1]
prompt_mask = batch.batch["attention_mask"][:, :-max_response_length].bool()
response_mask = batch.batch["attention_mask"][:, -max_response_length:].bool()
max_prompt_length = prompt_mask.size(-1)
response_info = _compute_response_info(batch)
prompt_length = response_info["prompt_length"]
response_length = response_info["response_length"]
metrics.update(
{
# response length
"response_length/mean": torch.mean(response_length).detach().item(),
"response_length/max": torch.max(response_length).detach().item(),
"response_length/min": torch.min(response_length).detach().item(),
"response_length/clip_ratio": torch.mean(
torch.eq(response_length, max_response_length).float()
)
.detach()
.item(),
# prompt length
"prompt_length/mean": torch.mean(prompt_length).detach().item(),
"prompt_length/max": torch.max(prompt_length).detach().item(),
"prompt_length/min": torch.min(prompt_length).detach().item(),
"prompt_length/clip_ratio": torch.mean(
torch.eq(prompt_length, max_prompt_length).float()
)
.detach()
.item(),
}
)
if "advantages" in batch.batch:
# adv
advantages = batch.batch["advantages"]
if response_mask.numel() > 0:
valid_adv = torch.masked_select(advantages, response_mask)
else:
valid_adv = torch.zeros(1)
metrics.update(
{
# adv
"critic/advantages/mean": torch.mean(valid_adv).detach().item(),
"critic/advantages/max": torch.max(valid_adv).detach().item(),
"critic/advantages/min": torch.min(valid_adv).detach().item(),
}
)
if "returns" in batch.batch:
# returns
returns = batch.batch["returns"]
if response_mask.numel() > 0:
valid_returns = torch.masked_select(returns, response_mask)
else:
valid_returns = torch.zeros(1)
metrics.update(
{
"critic/returns/mean": torch.mean(valid_returns).detach().item(),
"critic/returns/max": torch.max(valid_returns).detach().item(),
"critic/returns/min": torch.min(valid_returns).detach().item(),
}
)
return metrics
def get_latest_hf_checkpoint_path(config: Config):
"""Get the latest huggingface checkpoint path"""
if config.trainer.trainer_type != "verl":
raise ValueError("This function is only for verl trainer.")
checkpoint_dir = find_latest_ckpt_path(config.checkpoint_job_dir)
hf_checkpoint_dir = os.path.join(checkpoint_dir, "actor", "huggingface")
if not os.path.exists(hf_checkpoint_dir):
raise ValueError(f"No huggingface checkpoint found in {hf_checkpoint_dir}")
return hf_checkpoint_dir
|