File size: 7,738 Bytes
5f0e4a2 | 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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """Schema and validation for exact BF16 Predictor-v2 trajectories."""
from __future__ import annotations
from typing import Mapping
import torch
from .schema import (
CHUNK_LATENT_FRAMES,
HIDDEN_SIZE,
LATENT_CHANNELS,
LATENT_HEIGHT,
LATENT_WIDTH,
NUM_STEPS,
STEP_FIELDS,
TOKENS_PER_CHUNK,
)
LEGACY_SCHEMA_VERSION_V2 = "predictor_v2_exact_ar_context_kv_bf16"
SCHEMA_VERSION_V2 = "predictor_v2_padded_masked_ar_context_kv_bf16"
CONTEXT_BLOCK_IDS = (0, 1, 52, 53)
ATTENTION_HEADS = 16
HEAD_DIM = HIDDEN_SIZE // ATTENTION_HEADS
TOKENS_PER_FRAME = LATENT_HEIGHT * LATENT_WIDTH
DEFAULT_TEXT_KV_TOKENS = 903
DEFAULT_CONTEXT_KV_FRAMES = 20
def block_key(block_id: int, field: str) -> str:
if block_id not in CONTEXT_BLOCK_IDS:
raise ValueError(f"Unsupported context block: {block_id}")
return f"block_{block_id:02d}_{field}"
def expected_step_keys() -> set[str]:
keys = {
"action_labels",
"target_viewmats",
"target_Ks",
"rope_temporal_size",
"start_rope_start_idx",
}
for step in range(NUM_STEPS):
keys.update(f"step_{step}_{field}" for field in STEP_FIELDS)
return keys
def _validate_bf16(name: str, tensor: torch.Tensor) -> None:
if tensor.is_floating_point() and tensor.dtype != torch.bfloat16:
raise ValueError(f"{name} must be BF16, got {tensor.dtype}")
if tensor.is_floating_point() and not torch.isfinite(tensor).all():
raise ValueError(f"Non-finite values in {name}")
def _validate_mask(name: str, mask: torch.Tensor, expected_shape: tuple[int, int]) -> None:
if tuple(mask.shape) != expected_shape:
raise ValueError(f"Unexpected {name} shape: {tuple(mask.shape)} != {expected_shape}")
if mask.dtype != torch.bool:
raise ValueError(f"{name} must be bool, got {mask.dtype}")
if mask.shape[1] and not torch.equal(
mask, torch.arange(mask.shape[1], device=mask.device)[None] < mask.sum(dim=1)[:, None]
):
raise ValueError(f"{name} must contain a contiguous valid prefix")
def validate_case_tensors_v2(tensors: Mapping[str, torch.Tensor]) -> int:
required = {"image_condition_latent", "text_valid_mask"}
for block_id in CONTEXT_BLOCK_IDS:
required.update(
{block_key(block_id, "k_txt"), block_key(block_id, "v_txt")}
)
missing = required.difference(tensors)
if missing:
raise ValueError(f"Missing v2 case tensor keys: {sorted(missing)}")
image_condition = tensors["image_condition_latent"]
if tuple(image_condition.shape) != (1, LATENT_CHANNELS, 1, LATENT_HEIGHT, LATENT_WIDTH):
raise ValueError(f"Unexpected image condition shape: {tuple(image_condition.shape)}")
_validate_bf16("image_condition_latent", image_condition)
text_valid_mask = tensors["text_valid_mask"]
token_count = None
for block_id in CONTEXT_BLOCK_IDS:
k_txt = tensors[block_key(block_id, "k_txt")]
v_txt = tensors[block_key(block_id, "v_txt")]
if k_txt.shape != v_txt.shape:
raise ValueError(f"Block {block_id} text K/V shape mismatch")
if (
k_txt.ndim != 4
or k_txt.shape[0] != 1
or k_txt.shape[1] != ATTENTION_HEADS
or k_txt.shape[3] != HEAD_DIM
):
raise ValueError(f"Unexpected block {block_id} text KV shape: {tuple(k_txt.shape)}")
if token_count is None:
token_count = int(k_txt.shape[2])
elif int(k_txt.shape[2]) != token_count:
raise ValueError("Text token count differs between context blocks")
_validate_bf16(block_key(block_id, "k_txt"), k_txt)
_validate_bf16(block_key(block_id, "v_txt"), v_txt)
assert token_count is not None
_validate_mask("text_valid_mask", text_valid_mask, (1, token_count))
valid_tokens = int(text_valid_mask.sum())
if valid_tokens <= 0:
raise ValueError("text_valid_mask must contain at least one valid token")
return valid_tokens
def validate_step_tensors_v2(tensors: Mapping[str, torch.Tensor]) -> None:
missing = expected_step_keys().difference(tensors)
if missing:
raise ValueError(f"Missing v2 step tensor keys: {sorted(missing)}")
if tuple(tensors["action_labels"].shape) != (1, CHUNK_LATENT_FRAMES):
raise ValueError(f"Unexpected action_labels shape: {tuple(tensors['action_labels'].shape)}")
if tuple(tensors["target_viewmats"].shape) != (1, CHUNK_LATENT_FRAMES, 4, 4):
raise ValueError(f"Unexpected target_viewmats shape: {tuple(tensors['target_viewmats'].shape)}")
if tuple(tensors["target_Ks"].shape) != (1, CHUNK_LATENT_FRAMES, 3, 3):
raise ValueError(f"Unexpected target_Ks shape: {tuple(tensors['target_Ks'].shape)}")
for step in range(NUM_STEPS):
noisy = tensors[f"step_{step}_noisy_sample"]
hidden = tensors[f"step_{step}_final_hidden"]
condition = tensors[f"step_{step}_frame_condition"]
velocity = tensors[f"step_{step}_velocity"]
timestep = tensors[f"step_{step}_timestep"]
if tuple(noisy.shape) != (1, LATENT_CHANNELS, CHUNK_LATENT_FRAMES, LATENT_HEIGHT, LATENT_WIDTH):
raise ValueError(f"Step {step} noisy shape: {tuple(noisy.shape)}")
if tuple(hidden.shape) != (1, TOKENS_PER_CHUNK, HIDDEN_SIZE):
raise ValueError(f"Step {step} hidden shape: {tuple(hidden.shape)}")
if tuple(condition.shape) != (1, CHUNK_LATENT_FRAMES, HIDDEN_SIZE):
raise ValueError(f"Step {step} condition shape: {tuple(condition.shape)}")
if tuple(velocity.shape) != tuple(noisy.shape):
raise ValueError(f"Step {step} velocity shape: {tuple(velocity.shape)}")
if timestep.numel() != 1 or timestep.dtype != torch.float32:
raise ValueError(f"Step {step} timestep must be one FP32 scalar")
for name, tensor in (
("noisy_sample", noisy),
("final_hidden", hidden),
("frame_condition", condition),
("velocity", velocity),
):
_validate_bf16(f"step_{step}_{name}", tensor)
_validate_bf16("target_viewmats", tensors["target_viewmats"])
_validate_bf16("target_Ks", tensors["target_Ks"])
def validate_vision_context_v2(
block_id: int,
tensors: Mapping[str, torch.Tensor],
) -> int:
required = {"k_vision", "v_vision", "context_valid_mask"}
missing = required.difference(tensors)
if missing:
raise ValueError(f"Block {block_id} missing vision KV keys: {sorted(missing)}")
k_vision = tensors["k_vision"]
v_vision = tensors["v_vision"]
if k_vision.shape != v_vision.shape:
raise ValueError(f"Block {block_id} vision K/V shape mismatch")
if (
k_vision.ndim != 4
or k_vision.shape[0] != 2
or k_vision.shape[1] != ATTENTION_HEADS
or k_vision.shape[3] != HEAD_DIM
):
raise ValueError(f"Unexpected block {block_id} vision KV shape: {tuple(k_vision.shape)}")
if k_vision.shape[2] % TOKENS_PER_FRAME:
raise ValueError(
f"Block {block_id} context tokens {k_vision.shape[2]} are not frame-aligned"
)
_validate_bf16(f"block_{block_id}_k_vision", k_vision)
_validate_bf16(f"block_{block_id}_v_vision", v_vision)
context_valid_mask = tensors["context_valid_mask"]
_validate_mask(
f"block_{block_id}_context_valid_mask",
context_valid_mask,
(1, int(k_vision.shape[2])),
)
valid_tokens = int(context_valid_mask.sum())
if valid_tokens % TOKENS_PER_FRAME:
raise ValueError(
f"Block {block_id} valid context tokens {valid_tokens} are not frame-aligned"
)
return valid_tokens // TOKENS_PER_FRAME
|