Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
59 lines
2.6 KiB
Python
59 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
"""Utility methods for model layers."""
|
|
from typing import Tuple
|
|
|
|
import torch
|
|
|
|
|
|
def get_token_bin_counts_and_mask(
|
|
tokens: torch.Tensor,
|
|
vocab_size: int,
|
|
num_seqs: int,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# Compute the bin counts for the tokens.
|
|
# vocab_size + 1 for padding.
|
|
bin_counts = torch.zeros((num_seqs, vocab_size + 1),
|
|
dtype=torch.long,
|
|
device=tokens.device)
|
|
bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
|
|
bin_counts = bin_counts[:, :vocab_size]
|
|
mask = bin_counts > 0
|
|
|
|
return bin_counts, mask
|
|
|
|
|
|
def apply_penalties(logits: torch.Tensor, prompt_tokens_tensor: torch.Tensor,
|
|
output_tokens_tensor: torch.Tensor,
|
|
presence_penalties: torch.Tensor,
|
|
frequency_penalties: torch.Tensor,
|
|
repetition_penalties: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Applies penalties in place to the logits tensor
|
|
logits : The input logits tensor of shape [num_seqs, vocab_size]
|
|
prompt_tokens_tensor: A tensor containing the prompt tokens. The prompts
|
|
are padded to the maximum prompt length within the batch using
|
|
`vocab_size` as the padding value. The value `vocab_size` is used
|
|
for padding because it does not correspond to any valid token ID
|
|
in the vocabulary.
|
|
output_tokens_tensor: The output tokens tensor.
|
|
presence_penalties: The presence penalties of shape (num_seqs, )
|
|
frequency_penalties: The frequency penalties of shape (num_seqs, )
|
|
repetition_penalties: The repetition penalties of shape (num_seqs, )
|
|
"""
|
|
num_seqs, vocab_size = logits.shape
|
|
_, prompt_mask = get_token_bin_counts_and_mask(prompt_tokens_tensor,
|
|
vocab_size, num_seqs)
|
|
output_bin_counts, output_mask = get_token_bin_counts_and_mask(
|
|
output_tokens_tensor, vocab_size, num_seqs)
|
|
repetition_penalties = repetition_penalties.unsqueeze_(dim=1).repeat(
|
|
1, vocab_size)
|
|
logits[logits > 0] /= torch.where(prompt_mask | output_mask,
|
|
repetition_penalties, 1.0)[logits > 0]
|
|
logits[logits <= 0] *= torch.where(prompt_mask | output_mask,
|
|
repetition_penalties, 1.0)[logits <= 0]
|
|
# We follow the definition in OpenAI API.
|
|
# Refer to https://platform.openai.com/docs/api-reference/parameter-details
|
|
logits -= frequency_penalties.unsqueeze_(dim=1) * output_bin_counts
|
|
logits -= presence_penalties.unsqueeze_(dim=1) * output_mask
|
|
return logits
|