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58785a2 | 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 | from __future__ import annotations
from dataclasses import dataclass
import torch
import torch.nn.functional as F
from torch import nn
from .configuration import HrmPenguinConfig
from .recurrent.hrm_multimodal import MultimodalHrmTextModel
@dataclass
class HrmPenguinOutput:
logits: torch.Tensor | None
past_key_values: object | None = None
class HrmPenguinForConditionalGeneration(nn.Module):
def __init__(self, vision_encoder, language_model, projector, config: HrmPenguinConfig):
super().__init__()
self.vision_encoder = vision_encoder
self.language_model = language_model
self.projector = projector
self.config = config
self.last_effective_depth_metrics: dict[str, torch.Tensor] = {}
@property
def recurrent_model(self) -> MultimodalHrmTextModel:
return self.language_model.model
@staticmethod
def _replace_visual_tokens(
text_embeddings: torch.Tensor,
visual_embeddings: torch.Tensor,
visual_mask: torch.Tensor,
image_batch_indices: torch.LongTensor | None = None,
) -> torch.Tensor:
counts = visual_mask.sum(dim=1)
visual_rows = (counts > 0).nonzero(as_tuple=False).flatten()
if image_batch_indices is None:
image_batch_indices = visual_rows
image_batch_indices = image_batch_indices.to(device=visual_mask.device, dtype=torch.long)
if not torch.equal(visual_rows, image_batch_indices):
raise ValueError(
f"visual rows {visual_rows.tolist()} do not match image batch indices "
f"{image_batch_indices.tolist()}"
)
if visual_embeddings.ndim == 2:
if int(counts[visual_rows].sum()) != visual_embeddings.shape[0]:
raise ValueError(
f"visual_mask counts {counts.tolist()} do not match packed visual "
f"embeddings {tuple(visual_embeddings.shape)}"
)
elif visual_embeddings.ndim == 3:
if visual_rows.numel() != visual_embeddings.shape[0] or not torch.all(
counts[visual_rows] == visual_embeddings.shape[1]
):
raise ValueError(
f"visual_mask counts {counts.tolist()} do not match visual embeddings "
f"{tuple(visual_embeddings.shape)}"
)
else:
raise ValueError(
"visual_embeddings must be packed [sum(N_i), D] or dense [B, N, D]"
)
expanded_mask = visual_mask.unsqueeze(-1).expand_as(text_embeddings)
replacement = torch.zeros_like(text_embeddings).masked_scatter(
expanded_mask, visual_embeddings.to(text_embeddings.dtype).reshape(-1)
)
return torch.where(expanded_mask, replacement, text_embeddings)
def encode_images(self, pixel_values, grid_sizes, merge_sizes) -> torch.Tensor:
vision_parameter = next(self.vision_encoder.parameters())
pixel_values = pixel_values.to(device=vision_parameter.device, dtype=vision_parameter.dtype)
grid_sizes = grid_sizes.to(vision_parameter.device)
merge_sizes = merge_sizes.to(vision_parameter.device)
with torch.no_grad():
features = self.vision_encoder(pixel_values, grid_sizes, merge_sizes)
return self.projector(features)
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: torch.Tensor,
token_type_ids: torch.LongTensor,
position_ids: torch.LongTensor,
rope_position_ids: torch.LongTensor,
visual_mask: torch.BoolTensor,
instruction_mask: torch.BoolTensor,
image_batch_indices: torch.LongTensor | None = None,
visual_token_counts: torch.LongTensor | None = None,
grid_heights: torch.LongTensor | None = None,
grid_widths: torch.LongTensor | None = None,
pixel_values: torch.Tensor | None = None,
grid_sizes: torch.Tensor | None = None,
merge_sizes: torch.Tensor | None = None,
vision_features: torch.Tensor | None = None,
collect_visual_diagnostics: bool = False,
collect_depth_diagnostics: bool = False,
) -> HrmPenguinOutput:
mask_counts = visual_mask.sum(dim=1)
if visual_token_counts is not None and not torch.equal(
mask_counts, visual_token_counts.to(mask_counts.device)
):
raise ValueError("visual_mask does not match visual_token_counts")
if grid_heights is not None and grid_widths is not None:
expected_counts = grid_heights.to(mask_counts.device) * grid_widths.to(mask_counts.device)
if not torch.equal(mask_counts, expected_counts):
raise ValueError("visual token counts do not match dynamic grid areas")
text_embeddings = self.language_model.get_input_embeddings()(input_ids)
if bool(visual_mask.any()):
if vision_features is None:
if pixel_values is None or grid_sizes is None or merge_sizes is None:
raise ValueError(
"pixel_values/grid_sizes/merge_sizes or vision_features are required"
)
vision_features = self.encode_images(pixel_values, grid_sizes, merge_sizes)
# encode_images returns projected values for the regular path.
visual_embeddings = vision_features
else:
visual_embeddings = self.projector(vision_features)
inputs_embeds = self._replace_visual_tokens(
text_embeddings,
visual_embeddings,
visual_mask,
image_batch_indices=image_batch_indices,
)
else:
inputs_embeds = text_embeddings
outputs = self.recurrent_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
rope_position_ids=rope_position_ids,
visual_mask=visual_mask,
instruction_mask=instruction_mask,
use_cache=False,
collect_visual_diagnostics=collect_visual_diagnostics,
collect_depth_diagnostics=collect_depth_diagnostics,
)
logits = None
self.last_effective_depth_metrics = {}
diagnostics = self.recurrent_model.last_visual_diagnostics
if (
collect_depth_diagnostics
and diagnostics is not None
and diagnostics.logit_lens_response_hidden
and diagnostics.logit_lens_response_hidden[-1].numel()
):
with torch.no_grad():
final_hidden = diagnostics.logit_lens_response_hidden[-1]
final_log_probs = F.log_softmax(
self.language_model.lm_head(final_hidden).float(), dim=-1
)
final_probs = final_log_probs.exp()
for name, hidden in zip(
diagnostics.logit_lens_names,
diagnostics.logit_lens_response_hidden,
strict=True,
):
if hidden.shape != final_hidden.shape:
continue
intermediate_log_probs = F.log_softmax(
self.language_model.lm_head(hidden).float(), dim=-1
)
self.last_effective_depth_metrics[f"{name}_kl_to_final"] = (
F.kl_div(
intermediate_log_probs,
final_probs,
reduction="batchmean",
log_target=False,
)
)
logits = self.language_model.lm_head(outputs.last_hidden_state)
return HrmPenguinOutput(
logits=logits,
past_key_values=outputs.past_key_values,
)
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