Datasets:
File size: 3,067 Bytes
453129d | 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 | import torch
import torch.nn.functional as F
import torch.distributed as dist
from typing import Tuple, Any
def solution(
hidden_states: torch.Tensor,
weight: torch.Tensor,
labels: torch.Tensor,
old_logprobs: torch.Tensor,
advantages: torch.Tensor,
ignore_index: int = -100,
) -> Tuple[torch.Tensor, Any, torch.Tensor, torch.Tensor, torch.Tensor]:
logits = F.linear(hidden_states, weight)
logits_flat = logits.view(-1, logits.size(-1))
labels_flat = labels.view(-1)
per_token_ce = F.cross_entropy(logits_flat, labels_flat, ignore_index=ignore_index, reduction='none')
new_logprobs_flat = -per_token_ce.detach()
old_logprobs_flat = old_logprobs.view(-1)
advantages_flat = advantages.view(-1)
valid_mask = (labels_flat != ignore_index)
n_valid_local = valid_mask.sum().float()
n_valid_global = n_valid_local.clone()
dist.all_reduce(n_valid_global, op=dist.ReduceOp.SUM)
n_valid_global_clamped = n_valid_global.clamp(min=1.0)
delta = (new_logprobs_flat - old_logprobs_flat).masked_fill(~valid_mask, 0.0).clamp(min=-20.0, max=20.0)
ratio = torch.exp(delta)
per_token_pg = -(ratio * advantages_flat).masked_fill(~valid_mask, 0.0)
local_pg_sum = per_token_pg.sum()
global_pg_sum = local_pg_sum.clone()
dist.all_reduce(global_pg_sum, op=dist.ReduceOp.SUM)
true_pg = global_pg_sum / n_valid_global_clamped
w = (ratio.detach() * advantages_flat).masked_fill(~valid_mask, 0.0)
local_surrogate_sum = (w * per_token_ce).sum()
surrogate = local_surrogate_sum / n_valid_global_clamped
loss = true_pg.detach() + surrogate - surrogate.detach()
ratio_valid = ratio.masked_fill(~valid_mask, 0.0)
sum_ratio_local = ratio_valid.sum()
dist.all_reduce(sum_ratio_local, op=dist.ReduceOp.SUM)
ratio_mean = sum_ratio_local / n_valid_global_clamped
ratio_for_min = ratio.masked_fill(~valid_mask, float('inf'))
min_ratio_local = ratio_for_min.min() if n_valid_local > 0 else torch.tensor(float('inf'), device=ratio.device)
dist.all_reduce(min_ratio_local, op=dist.ReduceOp.MIN)
ratio_for_max = ratio.masked_fill(~valid_mask, float('-inf'))
max_ratio_local = ratio_for_max.max() if n_valid_local > 0 else torch.tensor(float('-inf'), device=ratio.device)
dist.all_reduce(max_ratio_local, op=dist.ReduceOp.MAX)
k3_local = (ratio - delta - 1.0).masked_fill(~valid_mask, 0.0).sum()
dist.all_reduce(k3_local, op=dist.ReduceOp.SUM)
k3_mean = k3_local / n_valid_global_clamped
entropy_local = per_token_ce.detach().masked_fill(~valid_mask, 0.0).sum()
dist.all_reduce(entropy_local, op=dist.ReduceOp.SUM)
entropy_mean = entropy_local / n_valid_global_clamped
metrics = torch.stack([ratio_mean, min_ratio_local, max_ratio_local, k3_mean, entropy_mean])
per_token_logprobs = new_logprobs_flat.view_as(labels)
per_token_loss = per_token_pg.view_as(labels)
return loss, None, per_token_logprobs, per_token_loss, metrics
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