ColossalAI/applications/ColossalChat/coati/distributed/utils.py

from collections import defaultdict
from typing import Any, Dict, List

import torch

from colossalai.shardformer.layer.loss import dist_log_prob


def unbind_batch(batch: Dict[str, torch.Tensor]) -> List[Dict[str, torch.Tensor]]:
    batches = []
    for k, v in batch.items():
        if len(batches) == 0:
            unbinded_tensors = v.unbind(0)
            batches = [{k: tensor} for tensor in unbinded_tensors]
        else:
            unbinded_tensors = v.unbind(0)
            assert len(batches) == len(unbinded_tensors)
            for i, tensor in enumerate(unbinded_tensors):
                batches[i][k] = tensor
    return batches


def bind_batch(batches: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
    batch = {}
    for k in batches[0].keys():
        batch[k] = torch.stack([batch[k] for batch in batches], dim=0)
    return batch


def pad_batch(batches: List[Dict[str, torch.Tensor]], tokenizer: Any = None) -> List[Dict[str, torch.Tensor]]:
    max_len = defaultdict(int)
    for sample in batches:
        for k in sample:
            if k in ["input_ids", "attention_mask", "action_log_probs", "action_mask"]:
                max_len[k] = max(max_len[k], sample[k].size(-1))
    for idx, sample in enumerate(batches):
        for k in sample:
            if k in ["input_ids", "attention_mask", "action_log_probs", "action_mask"]:
                # right pad with 0s
                if k in ["attention_mask", "action_mask"]:
                    batches[idx][k] = torch.nn.functional.pad(
                        batches[idx][k], (0, max_len[k] - batches[idx][k].size(-1)), "constant", False
                    )
                else:
                    batches[idx][k] = torch.nn.functional.pad(
                        batches[idx][k], (0, max_len[k] - batches[idx][k].size(-1)), "constant", 0
                    )
    return batches


def pre_send(batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
    # compress mask to save bandwidth
    if "attention_mask" in batch:
        batch["attention_mask"] = batch["attention_mask"].to(torch.bool)
    if "action_mask" in batch:
        batch["action_mask"] = batch["action_mask"].to(torch.bool)
    return batch


def post_recv(batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
    # decompress mask
    if "attention_mask" in batch:
        batch["attention_mask"] = batch["attention_mask"].to(torch.int)
    if "action_mask" in batch:
        batch["action_mask"] = batch["action_mask"].to(torch.int)
    return batch


def update_by_default(data: Dict[str, Any], default: Dict[str, Any]) -> Dict[str, Any]:
    data = data.copy()
    for k, v in default.items():
        if k not in data:
            data[k] = v
    return data


def log_probs_from_logits(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
    """
    Compute the log probabilities from logits for the given labels.

    Args:
        logits (torch.Tensor): The input logits.
        labels (torch.Tensor): The target labels.

    Returns:
        torch.Tensor: The log probabilities corresponding to the labels.
    """
    log_probs = torch.log_softmax(logits, dim=-1)
    per_label_logps = log_probs.gather(dim=-1, index=labels.unsqueeze(-1))
    return per_label_logps.squeeze(-1)


def calc_action_log_probs(
    logits: torch.Tensor,
    sequences: torch.LongTensor,
    num_actions: int,
    shard_config,
    vocab_size: int = None,
) -> torch.Tensor:
    """Calculate action log probs.

    Args:
        logits (torch.Tensor): Output tensor of Actor.forward.logits.
        sequences (torch.LongTensor): Input sequences.
        num_actions (int): Number of actions.
        shard_config
        vocab_size


    Returns:
        torch.Tensor: Action log probs.
    """
    # labels: torch.Tensor,  # [B, S] or [B, S, Vocab_size]
    # logits: torch.Tensor,  # [B, S, Vocab_size]
    log_probs = dist_log_prob(sequences, logits, shard_config, vocab_size, logits.dtype)
    log_probs = log_probs.squeeze(-1)
    return log_probs[:, -num_actions:]


def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor:
    """
    Compute the masked mean of a tensor along a specified dimension.

    Args:
        tensor (torch.Tensor): The input tensor.
        mask (torch.Tensor): The mask tensor with the same shape as the input tensor.
        dim (int, optional): The dimension along which to compute the mean. Default is 1.

    Returns:
        torch.Tensor: The masked mean tensor.

    """
    tensor = tensor * mask
    tensor = tensor.sum(dim=dim)
    mask_sum = mask.sum(dim=dim)
    mean = tensor / (mask_sum + 1e-8)
    return mean


def masked_sum(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor:
    """
    Compute the masked sum of a tensor along a specified dimension.

    Args:
        tensor (torch.Tensor): The input tensor.
        mask (torch.Tensor): The mask tensor with the same shape as the input tensor.
        dim (int, optional): The dimension along which to compute the sum. Default is 1.

    Returns:
        torch.Tensor: The masked sum tensor.

    """
    tensor = tensor * mask
    return tensor.sum(dim=dim)