wehub-resource-sync
231 lines
9.2 KiB
Python
231 lines
9.2 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding: utf-8
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"""
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Data helpers used by inference (`inference_lance.py`, `ValidationDataset`) and the
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Lance model core (`modeling/lance/lance.py`).
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Exported utilities:
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- Position id helpers (image / video, interpolate / extrapolate)
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- Patchify helpers (image + video-with-merge)
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- create_sparse_mask : flex-attention sparse mask builder
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- add_special_tokens : register chat / vision tokens on a tokenizer
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- len2weight : CE loss reweighting factor
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"""
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from einops import rearrange
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import torch
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from torch.nn.attention.flex_attention import or_masks, and_masks
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# ------------------------------------------------------------------
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# Position id helpers
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# ------------------------------------------------------------------
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def get_flattened_position_ids_interpolate_video(num_frames, img_h, img_w, patch_size, max_num_frames, max_num_patches_per_side):
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num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size
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# temporal
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boundaries_t = torch.arange(1 / max_num_frames, 1.0, 1 / max_num_frames)
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fractional_coords_t = torch.arange(0, 1 - 1e-6, 1 / num_frames)
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bucket_coords_t = torch.bucketize(fractional_coords_t, boundaries_t, right=True)
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# spatial
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boundaries_s = torch.arange(1 / max_num_patches_per_side, 1.0, 1 / max_num_patches_per_side)
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fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / num_patches_h)
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fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / num_patches_w)
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bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries_s, right=True)
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bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries_s, right=True)
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pos_ids = (
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bucket_coords_t[:, None, None] * max_num_patches_per_side * max_num_patches_per_side
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+ bucket_coords_h[None, :, None] * max_num_patches_per_side
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+ bucket_coords_w[None, None, :]
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).flatten()
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return pos_ids
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def get_flattened_position_ids_extrapolate_video(t, h, w, max_latent_size):
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"""
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Defaults:
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num_frames = 7 (corresponding to 25 frames)
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max_num_patches_per_side = 64
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"""
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coords_t = torch.arange(0, t)
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coords_h = torch.arange(0, h)
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coords_w = torch.arange(0, w)
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pos_ids = (
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coords_t[:, None, None] * max_latent_size * max_latent_size
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+ coords_h[None, :, None] * max_latent_size
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+ coords_w[None, None, :]
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).flatten()
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return pos_ids
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def get_flattened_position_ids_extrapolate(img_h, img_w, patch_size, max_num_patches_per_side):
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num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size
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coords_h = torch.arange(0, num_patches_h)
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coords_w = torch.arange(0, num_patches_w)
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pos_ids = (coords_h[:, None] * max_num_patches_per_side + coords_w).flatten()
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return pos_ids
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def get_flattened_position_ids_interpolate(img_h, img_w, patch_size, max_num_patches_per_side):
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num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size
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boundaries = torch.arange(1 / max_num_patches_per_side, 1.0, 1 / max_num_patches_per_side)
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fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / num_patches_h)
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fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / num_patches_w)
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bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
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bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
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pos_ids = (bucket_coords_h[:, None] * max_num_patches_per_side + bucket_coords_w).flatten()
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return pos_ids
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# ------------------------------------------------------------------
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# Patchify helpers
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# ------------------------------------------------------------------
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def patchify(image, patch_size):
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p = patch_size
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c, h, w = image.shape
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assert h % p == 0 and w % p == 0
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image = image.reshape(c, h // p, p, w // p, p)
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image = torch.einsum("chpwq->hwpqc", image)
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image = image.reshape(-1, p**2 * c)
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return image
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def patchify_video_with_merge(video, spatial_patch_size, temporal_patch_size, merge_size=2):
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"""
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Args:
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video: Tensor of shape [C, T, H, W]
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spatial_patch_size: patch size for H/W
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temporal_patch_size: patch size for T
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merge_size: merging factor for spatial grid (fixed at 2)
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Returns:
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patches: Tensor of shape [num_patches, patch_dim]
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"""
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video = rearrange(video, "C T H W -> T C H W")
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T, C, H, W = video.shape
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p, tp, ms = spatial_patch_size, temporal_patch_size, merge_size
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gt, gh, gw = T // tp, H // p, W // p
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video = video.reshape(gt, tp, C, gh // ms, ms, p, gw // ms, ms, p)
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video = video.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
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patches = video.reshape(gt * gh * gw, C * tp * p * p)
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return patches
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# ------------------------------------------------------------------
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# Sparse attention mask (flex-attention)
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# ------------------------------------------------------------------
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def create_sparse_mask(document_lens, split_lens, attn_modes, device):
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def causal_mask(b, h, q_idx, kv_idx):
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return q_idx >= kv_idx
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def full_and_noise_mask(b, h, q_idx, kv_idx):
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return (full_and_noise_seq_id[q_idx] == full_and_noise_seq_id[kv_idx]) & (full_and_noise_seq_id[q_idx] >= 0)
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def remove_noise_mask(b, h, q_idx, kv_idx):
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return ~((noise_seq_id[kv_idx] >= 0) & (noise_seq_id[q_idx] != noise_seq_id[kv_idx]))
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def sample_mask(b, h, q_idx, kv_idx):
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return document_id[q_idx] == document_id[kv_idx]
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full_and_noise_tmp = []
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noise_tmp = []
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for i, (length, mode) in enumerate(zip(split_lens, attn_modes)):
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value = i if mode in ["full", "noise"] else -1
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full_and_noise_tmp.extend([value] * length)
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value_noise = i if mode == "noise" else -1
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noise_tmp.extend([value_noise] * length)
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full_and_noise_seq_id = torch.Tensor(full_and_noise_tmp).to(device)
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noise_seq_id = torch.Tensor(noise_tmp).to(device)
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document_id = torch.cat([torch.full((l,), i) for i, l in enumerate(document_lens, start=1)]).to(device)
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return and_masks(or_masks(causal_mask, full_and_noise_mask), remove_noise_mask, sample_mask)
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def prepare_attention_mask_per_sample(split_lens, attn_modes, device="cpu"):
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"""
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nested_split_lens: A list of N lists of ints. Each int indicates the length of a split within
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a sample, where each sample contains multiple splits with different attn modes.
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nested_attn_modes: whether to use full attn in each split.
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"""
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sample_len = sum(split_lens)
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attention_mask = torch.zeros((sample_len, sample_len), dtype=torch.bool, device=device)
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csum = 0
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for s, attn_mode in zip(split_lens, attn_modes):
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assert attn_mode in ["causal", "full", "noise"]
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if attn_mode == "causal":
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attention_mask[csum : csum + s, csum : csum + s] = torch.ones((s, s), device=device).tril()
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attention_mask[csum : csum + s, :csum] = 1
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else:
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attention_mask[csum : csum + s, csum : csum + s] = torch.ones((s, s))
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attention_mask[csum : csum + s, :csum] = 1
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csum += s
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csum = 0
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for s, attn_mode in zip(split_lens, attn_modes):
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if attn_mode == "noise":
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attention_mask[:, csum : csum + s] = torch.zeros((sample_len, s))
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attention_mask[csum : csum + s, csum : csum + s] = torch.ones((s, s))
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csum += s
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attention_mask = torch.zeros_like(attention_mask, dtype=torch.float).masked_fill_(~attention_mask, float("-inf"))
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return attention_mask
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# ------------------------------------------------------------------
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# Tokenizer / loss helpers
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# ------------------------------------------------------------------
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def add_special_tokens(tokenizer):
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all_special_tokens = []
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for k, v in tokenizer.special_tokens_map.items():
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if isinstance(v, str):
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all_special_tokens.append(v)
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elif isinstance(v, list):
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all_special_tokens += v
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new_tokens = []
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for tok in ("<|im_start|>", "<|im_end|>", "<|vision_start|>", "<|vision_end|>"):
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if tok not in all_special_tokens:
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new_tokens.append(tok)
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num_new_tokens = tokenizer.add_tokens(new_tokens)
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new_token_ids = dict(
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bos_token_id=tokenizer.convert_tokens_to_ids("<|im_start|>"),
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eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>"),
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start_of_image=tokenizer.convert_tokens_to_ids("<|vision_start|>"),
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end_of_image=tokenizer.convert_tokens_to_ids("<|vision_end|>"),
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)
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return tokenizer, new_token_ids, num_new_tokens
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def len2weight(x, loss_reduction="square"):
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if x == 0:
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return x
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if loss_reduction == "token":
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return 1
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if loss_reduction == "sample":
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return 1 / x
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if loss_reduction == "square":
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return 1 / (x**0.5)
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raise NotImplementedError(loss_reduction)
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