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from typing import Any, List, Tuple
import torch
from tinker import types
from trinity.common.experience import Experience, split_dpo_experience_to_single_turn
def to_tinker_input(
experiences: List[Experience], logger: Logger
) -> Tuple[List[types.Datum], List[types.ModelInput], List[dict]]:
assert len(experiences) > 0, "No experiences provided."
if experiences[0].experience_type == "dpo":
experiences = split_dpo_experience_to_single_turn(experiences)
batch = []
batch_input_tokens = []
model_inputs_list = []
for exp in experiences:
tokens = exp.tokens
input_tokens = tokens.long()
prompt_length = exp.prompt_length
total_length = len(tokens) # type: ignore
response_length = total_length - prompt_length
loss_fn_inputs = {
"weights": torch.concat(
[
torch.zeros(prompt_length - 1, dtype=torch.float32),
exp.action_mask.float(),
]
),
"target_tokens": input_tokens.tolist()[1:],
}
model_inputs = {
"total_length": total_length,
"action_mask": exp.action_mask,
}
if exp.reward is not None or exp.token_level_reward is not None:
assert exp.logprobs is not None
if exp.token_level_reward is not None:
if exp.reward is not None:
logger.warning(
"Both exp.rewards and exp.token_level_rewards are provided. "
"Using exp.token_level_rewards."
)
token_level_reward = exp.token_level_reward
else:
token_level_reward = torch.zeros(response_length, dtype=torch.float32)
token_level_reward[-1] = exp.reward
model_inputs.update(
{
"token_level_scores": token_level_reward,
"old_logprob": exp.logprobs,
}
)
for attr in ["advantages", "returns", "teacher_logprobs"]:
if getattr(exp, attr, None) is not None:
model_inputs[attr] = getattr(exp, attr)
# TODO: if tinker support multi-modal input, we can add it here
for custom_field in exp.custom_fields:
model_inputs[custom_field.destination_field] = torch.tensor(
exp.info[custom_field.source_field],
dtype=custom_field.data_type,
)
batch.append(
types.Datum(
model_input=types.ModelInput.from_ints(tokens=input_tokens.tolist()[:-1]),
loss_fn_inputs=loss_fn_inputs,
)
)
batch_input_tokens.append(types.ModelInput.from_ints(input_tokens.tolist()))
model_inputs_list.append(model_inputs)
return batch, batch_input_tokens, model_inputs_list
def compute_data_metrics(batch: List[dict[str, torch.Tensor]]) -> 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.
use_critic: Whether to include critic-specific metrics. Defaults to True.
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 = {}
assert len(batch) > 0, "Batch is empty"
if "token_level_rewards" in batch[0] and "token_level_scores" in batch[0]:
sequence_score = torch.tensor([data["token_level_scores"].sum() for data in batch])
sequence_reward = torch.tensor([data["token_level_rewards"].sum() for data in batch])
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(),
}
)
response_length = torch.tensor([len(data["action_mask"]) for data in batch]).float()
token_length = torch.tensor([data["total_length"] for data in batch]).float()
prompt_length = token_length - response_length
max_response_length = max(response_length)
max_prompt_length = max(prompt_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[0]:
valid_adv = torch.concat([data["advantages"] for data in batch])
metrics.update(
{
"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[0]:
valid_returns = torch.concat([data["returns"] for data in batch])
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 compute_timing_metrics(
batch: List[dict[str, torch.Tensor]], timing_raw: dict[str, float]
) -> dict[str, Any]:
"""
Computes timing metrics for different processing stages in PPO training.
Modified from `verl.trainer.ppo.metric_utils.compute_timing_metrics`.
This function calculates both raw timing metrics (in seconds) and per-token timing metrics
(in milliseconds) for various processing stages like generation, reference computation,
value computation, advantage computation, and model updates.
Args:
batch: A DataProto object containing batch data with responses and attention masks.
timing_raw: A dictionary mapping stage names to their execution times in seconds.
Returns:
A dictionary containing:
- timing_s/{name}: Raw timing in seconds for each stage
- timing_per_token_ms/{name}: Per-token timing in milliseconds for each stage
Note:
Different stages use different token counts for normalization:
- "gen" uses only response tokens
- Other stages ("ref", "values", "adv", "update_critic", "update_actor") use all tokens
(prompt + response)
"""
num_overall_tokens = sum(data["total_length"] for data in batch)
num_response_tokens = sum(len(data["action_mask"]) for data in batch)
num_tokens_of_section = {
"gen": num_response_tokens,
**{
name: num_overall_tokens
for name in ["ref", "values", "adv", "update_critic", "update_actor"]
},
}
return {
**{f"timing_s/{name}": value for name, value in timing_raw.items()},
**{
f"timing_per_token_ms/{name}": timing_raw[name] * 1000 / num_tokens_of_section[name]
for name in set(num_tokens_of_section.keys()) & set(timing_raw.keys())
},
}
def compute_throughout_metrics(
batch: List[dict[str, torch.Tensor]], timing_raw: dict[str, float]
) -> dict[str, Any]:
"""
Computes throughput metrics for PPO training.
Modified from `verl.trainer.ppo.metric_utils.compute_throughout_metrics`.
This function calculates performance metrics related to token processing speed,
including the total number of tokens processed and time per step.
Args:
batch: A DataProto object containing batch data with meta information about token counts.
timing_raw: A dictionary mapping stage names to their execution times in seconds.
Must contain a "step" key with the total step time.
Returns:
A dictionary containing:
- perf/total_num_tokens: Total number of tokens processed in the batch
- perf/time_per_step: Time taken for the step in seconds
"""
total_num_tokens = sum(data["total_length"] for data in batch)
time = timing_raw["step"]
return {
"perf/total_num_tokens": total_num_tokens,
"perf/time_per_step": time,
}
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