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feat: add auto-generated CI documentation pre-commit hook (#2890)

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Our CI is entirely undocumented, this commit adds a README.md file with
a table of the current CI and what is does

---------

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
This commit is contained in:
Nathan Weinberg 2025-07-25 11:57:01 -04:00 • committed by Mustafa Elbehery
parent 7f834339ba
commit b381ed6d64
93 changed files with 495 additions and 477 deletions
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12
llama_stack/models/llama/llama3/multimodal/model.py
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@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import math
from collections.abc import Callable
from functools import partial
@ -22,6 +21,8 @@ from PIL import Image as PIL_Image
from torch import Tensor, nn
from torch.distributed import _functional_collectives as funcol
from llama_stack.log import get_logger
from ..model import ModelArgs, RMSNorm, apply_rotary_emb, precompute_freqs_cis
from .encoder_utils import (
build_encoder_attention_mask,
@ -34,9 +35,10 @@ from .encoder_utils import (
from .image_transform import VariableSizeImageTransform
from .utils import get_negative_inf_value, to_2tuple
logger = logging.getLogger(__name__)
MP_SCALE = 8
log = get_logger(name=__name__, category="core")
def reduce_from_tensor_model_parallel_region(input_):
"""All-reduce the input tensor across model parallel group."""
@ -415,7 +417,7 @@ class VisionEncoder(nn.Module):
)
state_dict[prefix + "gated_positional_embedding"] = global_pos_embed
state_dict[prefix + "gated_positional_embedding_gate"] = torch.zeros(1, dtype=global_pos_embed.dtype)
logger.info(f"Initialized global positional embedding with size {global_pos_embed.size()}")
log.info(f"Initialized global positional embedding with size {global_pos_embed.size()}")
else:
global_pos_embed = resize_global_position_embedding(
state_dict[prefix + "gated_positional_embedding"],
@ -423,7 +425,7 @@ class VisionEncoder(nn.Module):
self.max_num_tiles,
self.max_num_tiles,
)
logger.info(
log.info(
f"Resized global positional embedding from {state_dict[prefix + 'gated_positional_embedding'].size()} to {global_pos_embed.size()}"
)
state_dict[prefix + "gated_positional_embedding"] = global_pos_embed
@ -771,7 +773,7 @@ class TilePositionEmbedding(nn.Module):
if embed is not None:
# reshape the weights to the correct shape
nt_old, nt_old, _, w = embed.shape
logging.info(f"Resizing tile embedding from {nt_old}x{nt_old} to {self.num_tiles}x{self.num_tiles}")
log.info(f"Resizing tile embedding from {nt_old}x{nt_old} to {self.num_tiles}x{self.num_tiles}")
embed_new = TilePositionEmbedding._dynamic_resize(embed, self.num_tiles)
# assign the weights to the module
state_dict[prefix + "embedding"] = embed_new