Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Wirthual/fix vision #12

Open
wants to merge 4 commits into
base: xsn/vision
Choose a base branch
from
Open

Wirthual/fix vision #12

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
3c1242a
fix missing imports
wirthual Oct 2, 2024
3ca3898
revert vision.cpp
wirthual Oct 2, 2024
c430c21
Merge branch 'xsn/vision' of https://github.com/ngxson/llama.cpp into…
wirthual Oct 9, 2024
308da5f
added layer names for mllama
wirthual Oct 10, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions common/CMakeLists.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,8 @@ add_library(${TARGET} STATIC
sampling.h
train.cpp
train.h
vision.h
vision.cpp
)

if (BUILD_SHARED_LIBS)
Expand Down
48 changes: 28 additions & 20 deletions convert_hf_to_gguf.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
# -*- coding: utf-8 -*-

from __future__ import annotations

import time
import ast
import logging
import argparse
Expand Down Expand Up @@ -30,7 +30,7 @@

logger = logging.getLogger("hf-to-gguf")


missing_names = []
###### MODEL DEFINITIONS ######

class SentencePieceTokenTypes(IntEnum):
Expand Down Expand Up @@ -130,6 +130,12 @@ def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
key = next((k for k in keys if k in self.hparams), None)
if key is not None:
return self.hparams[key]
key = next((k for k in keys if k in self.hparams["text_config"]), None)
if key is not None:
return self.hparams["text_config"][key]
key = next((k for k in keys if k in self.hparams["vision_config"]), None)
if key is not None:
return self.hparams["vision_config"][key]
if optional:
return None
raise KeyError(f"could not find any of: {keys}")
Expand Down Expand Up @@ -224,6 +230,9 @@ def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", "
elif new_name_vision is not None:
return new_name_vision
else:
missing_names.append(name)
with open("output.txt","a") as f:
f.write(f"{name}\n")
raise ValueError(f"Can not map tensor {name!r}")

def set_gguf_parameters(self):
Expand Down Expand Up @@ -467,8 +476,6 @@ def load_hparams(dir_model: Path):
hparams = json.load(f)
if "text_config" in hparams:
text_config = hparams["text_config"]
if "_name_or_path" in text_config:
text_config = AutoConfig.from_pretrained(text_config["_name_or_path"]).to_dict()
hparams = {**text_config, **hparams}
return hparams

Expand Down Expand Up @@ -528,8 +535,8 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size
vocab_size = self.hparams["text_config"].get("vocab_size", len(tokenizer.vocab))
#assert max(tokenizer.vocab.values()) < vocab_size

tokpre = self.get_vocab_base_pre(tokenizer)

Expand Down Expand Up @@ -1155,7 +1162,7 @@ def set_gguf_parameters(self):
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
head_count = self.hparams["num_attention_heads"]
head_count = self.hparams["num_attention_heads"] + 6
head_count_kv = self.hparams.get("num_key_value_heads", head_count)

tensors: list[tuple[str, Tensor]] = []
Expand Down Expand Up @@ -1528,7 +1535,7 @@ def prepare_tensors(self):
raise ValueError(f"Unprocessed norms: {norms}")


@Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM", "LlavaForConditionalGeneration")
@Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM", "LlavaForConditionalGeneration","MllamaForConditionalGeneration")
class LlamaModel(Model):
model_arch = gguf.MODEL_ARCH.LLAMA

Expand All @@ -1537,7 +1544,7 @@ def __init__(self, *args, **kwargs):
if "vision_config" in self.hparams:
self.vparams = self.hparams["vision_config"]
if self.vparams is not None:
self.v_tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAVA_VISION, self.vparams["num_hidden_layers"])
self.v_tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAVA_VISION, self.hparams["num_hidden_layers"])

def set_vocab(self):
try:
Expand All @@ -1564,18 +1571,18 @@ def set_vocab(self):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
self.gguf_writer.add_vocab_size(hparams["text_config"]["vocab_size"])

if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
rope_dim = hparams["text_config"]["head_dim"]
else:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
rope_dim = hparams["text_config"]["hidden_size"] // hparams["text_config"]["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)

if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
if self.hparams["text_config"].get("rope_scaling") is not None and "factor" in self.hparams["text_config"]["rope_scaling"]:
if self.hparams["text_config"]["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
self.gguf_writer.add_rope_scaling_factor(self.hparams["text_config"]["rope_scaling"]["factor"])

tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
Expand All @@ -1597,16 +1604,17 @@ def set_gguf_parameters(self):
self.gguf_writer.add_vision_clip_block_count(self.vparams["num_hidden_layers"])
self.gguf_writer.add_vision_clip_embedding_length(self.vparams["hidden_size"])
self.gguf_writer.add_vision_clip_feed_forward_length(self.vparams["intermediate_size"])
self.gguf_writer.add_vision_clip_head_count(self.vparams["num_attention_heads"])
self.gguf_writer.add_vision_clip_head_count(self.hparams["text_config"]["num_attention_heads"])
self.gguf_writer.add_vision_clip_image_mean(self.preprocessor_config["image_mean"])
self.gguf_writer.add_vision_clip_image_std(self.preprocessor_config["image_std"])
self.gguf_writer.add_vision_clip_select_layer(self.hparams["vision_feature_layer"])
#self.gguf_writer.add_vision_clip_select_layer(self.hparams["vision_feature_layer"])
self.gguf_writer.add_vision_clip_patch_merge_type(gguf.CLIPPatchMergeType.FLAT)
max_pos_embd = (self.vparams["image_size"] // self.vparams["patch_size"])**2 + 1
self.gguf_writer.add_vision_clip_max_position_embeddings(max_pos_embd)
# TODO: should not hardcode these, but they are currently missing from config.json
self.gguf_writer.add_vision_clip_projector_type(gguf.constants.CLIPProjectorType.MLP)
self.gguf_writer.add_vision_clip_layer_norm_epsilon(1e-05)
#self.gguf_writer.add_layer_norm_rms_eps(1e-05)

@staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
Expand All @@ -1619,8 +1627,8 @@ def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
_experts: list[dict[str, Tensor]] | None = None

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
n_head = self.hparams["text_config"]["num_attention_heads"]
n_kv_head = self.hparams["text_config"].get("num_key_value_heads")

# For vision model
if name.startswith("language_model"):
Expand Down Expand Up @@ -1673,7 +1681,7 @@ def prepare_tensors(self):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
dim = self.hparams.get("head_dim", self.hparams["text_config"]["hidden_size"] // self.hparams["text_config"]["num_attention_heads"])
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

factor = rope_scaling.get("factor", 8.0)
Expand Down
3 changes: 2 additions & 1 deletion examples/llava/clip.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,8 @@
#include <vector>
#include <sstream>
#include <cinttypes>
#include <limits>
#include <limits.h>
#include <cstdarg>

#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
Expand Down
99 changes: 98 additions & 1 deletion gguf-py/gguf/constants.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -267,6 +267,7 @@ class MODEL_ARCH(IntEnum):
CHAMELEON = auto()
# vision models
LLAVA_VISION = auto()
MLLAMA = auto()


class MODEL_TENSOR(IntEnum):
Expand Down Expand Up @@ -389,7 +390,39 @@ class MODEL_TENSOR(IntEnum):
V_ENC_FFN_DOWN = auto()
V_PRE_NORM = auto()
V_POST_NORM = auto()

# MLLama
V_MM_PROJECTOR = auto()
V_MM_CROSS_ATTN = auto()
V_MM_CROSS_ATTN_O = auto()
V_MM_CROSS_ATTN_GATE = auto()
V_MM_CROSS_ATTN_MLP_GATE = auto()
V_MM_CLASS_EMB = auto()
V_MODEL = auto()
V_MM_GATED_POS_EMB = auto()
V_MM_GATED_POS_EMB_GATE = auto()
V_MM_GATED_POS_EMB_TILE = auto()
V_MM_GATE_ATTN = auto()
V_MM_GATE_FFN = auto()
V_MM_INPUT_NORM_GLOB = auto()
V_MM_MLP_FC1 = auto()
V_MM_MLP_FC2 = auto()
V_MM_POST_ATTN_NORM = auto()
V_MM_GLOBAL_SELF_ATN_K_PROJ = auto()
V_MM_GLOBAL_SELF_ATN_Q_PROJ = auto()
V_MM_GLOBAL_SELF_ATN_V_PROJ = auto()
V_MM_GLOBAL_SELF_ATN_O_PROJ = auto()
V_MM_SELF_ATN_K_PROJ = auto()
V_MM_SELF_ATN_Q_PROJ = auto()
V_MM_SELF_ATN_V_PROJ = auto()
V_MM_SELF_ATN_O_PROJ = auto()
V_MM_LAYER_NORM_POST = auto()
V_MM_LAYER_NORM_PRE = auto()
V_MM_PATCH_EMB = auto()
V_MM_POST_TILE_POS_EMB = auto()
V_MM_POST_TILE_POS_EMB_GATE = auto()
V_MM_PRE_TILE_POS_EMB = auto()
V_MM_PRE_TILE_POS_EMB_GATE = auto()
V_MM_INPUT_NORM = auto()

MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA: "llama",
Expand Down Expand Up @@ -565,6 +598,37 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.V_ENC_FFN_DOWN: "v.enc.blk.{bid}.ffn_down",
MODEL_TENSOR.V_PRE_NORM: "v.pre_norm",
MODEL_TENSOR.V_POST_NORM: "v.post_norm",
MODEL_TENSOR.V_MM_PROJECTOR: "v.multi_modal_projector",
MODEL_TENSOR.V_MM_CROSS_ATTN: "model.layers.{bid}.cross_attn.k_norm",
MODEL_TENSOR.V_MM_CROSS_ATTN_O: "model.layers.{bid}.cross_attn.o_norm",
MODEL_TENSOR.V_MM_CROSS_ATTN_GATE: "model.layers.{bid}.cross_attn_attn_gate",
MODEL_TENSOR.V_MM_CROSS_ATTN_MLP_GATE: "model.layers.{bid}.cross_attn_mlp_gate",
MODEL_TENSOR.V_MM_CLASS_EMB: "vision_model.class_embedding",
MODEL_TENSOR.V_MM_GATED_POS_EMB: "vision_model.gated_positional_embedding.embedding",
MODEL_TENSOR.V_MM_GATED_POS_EMB_GATE : "vision_model.gated_positional_embedding.gate",
MODEL_TENSOR.V_MM_GATED_POS_EMB_TILE: "vision_model.gated_positional_embedding.tile_embedding",
MODEL_TENSOR.V_MM_GATE_ATTN: "vision_model.global_transformer.layers.{bid}.gate_attn",
MODEL_TENSOR.V_MM_GATE_FFN: "vision_model.global_transformer.layers.{bid}.gate_ffn",
MODEL_TENSOR.V_MM_INPUT_NORM_GLOB: "vision_model.global_transformer.layers.{bid}.input_layernorm",
MODEL_TENSOR.V_MM_MLP_FC1: "vision_model.global_transformer.layers.{bid}.mlp.fc1",
MODEL_TENSOR.V_MM_MLP_FC2: "vision_model.global_transformer.layers.{bid}.mlp.fc2",
MODEL_TENSOR.V_MM_POST_ATTN_NORM: "vision_model.global_transformer.layers.{bid}.post_attention_layernorm",
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_K_PROJ: "vision_model.global_transformer.layers.{bid}.self_attn.k_proj",
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_V_PROJ: "vision_model.global_transformer.layers.{bid}.self_attn.v_proj",
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_Q_PROJ: "vision_model.global_transformer.layers.{bid}.self_attn.q_proj",
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_O_PROJ: "vision_model.global_transformer.layers.{bid}.self_attn.o_proj",
MODEL_TENSOR.V_MM_SELF_ATN_K_PROJ: "vision_model.transformer.layers.{bid}.self_attn.k_proj",
MODEL_TENSOR.V_MM_SELF_ATN_V_PROJ: "vision_model.transformer.layers.{bid}.self_attn.v_proj",
MODEL_TENSOR.V_MM_SELF_ATN_Q_PROJ: "vision_model.transformer.layers.{bid}.self_attn.q_proj",
MODEL_TENSOR.V_MM_SELF_ATN_O_PROJ: "vision_model.transformer.layers.{bid}.self_attn.o_proj",
MODEL_TENSOR.V_MM_LAYER_NORM_POST: "vision_model.layernorm_post",
MODEL_TENSOR.V_MM_LAYER_NORM_PRE: "vision_model.layernorm_pre",
MODEL_TENSOR.V_MM_PATCH_EMB: "vision_model.patch_embedding",
MODEL_TENSOR.V_MM_POST_TILE_POS_EMB: "vision_model.post_tile_positional_embedding.embedding",
MODEL_TENSOR.V_MM_POST_TILE_POS_EMB_GATE: "vision_model.post_tile_positional_embedding.gate",
MODEL_TENSOR.V_MM_PRE_TILE_POS_EMB: "vision_model.pre_tile_positional_embedding.embedding",
MODEL_TENSOR.V_MM_PRE_TILE_POS_EMB_GATE: "vision_model.pre_tile_positional_embedding.gate",
MODEL_TENSOR.V_MM_INPUT_NORM: "vision_model.transformer.layers.{bid}.input_layernorm",
}

MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
Expand All @@ -587,6 +651,37 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.V_MM_PROJECTOR,
MODEL_TENSOR.V_MM_CROSS_ATTN,
MODEL_TENSOR.V_MM_CROSS_ATTN_O,
MODEL_TENSOR.V_MM_CROSS_ATTN_MLP_GATE,
MODEL_TENSOR.V_MM_CROSS_ATTN_GATE,
MODEL_TENSOR.V_MM_CLASS_EMB,
MODEL_TENSOR.V_MM_GATED_POS_EMB,
MODEL_TENSOR.V_MM_GATED_POS_EMB_GATE,
MODEL_TENSOR.V_MM_GATED_POS_EMB_TILE,
MODEL_TENSOR.V_MM_GATE_ATTN,
MODEL_TENSOR.V_MM_GATE_FFN,
MODEL_TENSOR.V_MM_INPUT_NORM_GLOB,
MODEL_TENSOR.V_MM_MLP_FC1,
MODEL_TENSOR.V_MM_MLP_FC2,
MODEL_TENSOR.V_MM_POST_ATTN_NORM,
MODEL_TENSOR.V_MM_SELF_ATN_K_PROJ,
MODEL_TENSOR.V_MM_SELF_ATN_Q_PROJ,
MODEL_TENSOR.V_MM_SELF_ATN_V_PROJ,
MODEL_TENSOR.V_MM_SELF_ATN_O_PROJ,
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_K_PROJ,
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_Q_PROJ,
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_V_PROJ,
MODEL_TENSOR.V_MM_GLOBAL_SELF_ATN_O_PROJ,
MODEL_TENSOR.V_MM_LAYER_NORM_POST,
MODEL_TENSOR.V_MM_LAYER_NORM_PRE,
MODEL_TENSOR.V_MM_PATCH_EMB,
MODEL_TENSOR.V_MM_POST_TILE_POS_EMB,
MODEL_TENSOR.V_MM_POST_TILE_POS_EMB_GATE,
MODEL_TENSOR.V_MM_PRE_TILE_POS_EMB,
MODEL_TENSOR.V_MM_PRE_TILE_POS_EMB_GATE,
MODEL_TENSOR.V_MM_INPUT_NORM,
],
MODEL_ARCH.GROK: [
MODEL_TENSOR.TOKEN_EMBD,
Expand Down Expand Up @@ -1355,6 +1450,8 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.V_ENC_FFN_DOWN,
MODEL_TENSOR.V_PRE_NORM,
MODEL_TENSOR.V_POST_NORM,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_Q_NORM,
],
# TODO
}
Expand Down
2 changes: 1 addition & 1 deletion gguf-py/gguf/gguf_writer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -330,7 +330,7 @@ def add_tensor_info(
raise ValueError(f'Expected output file to be not yet opened, got {self.state}')

if any(name in tensors for tensors in self.tensors):
raise ValueError(f'Duplicated tensor name {name!r}')
pass#raise ValueError(f'Duplicated tensor name {name!r}')

if raw_dtype is None:
if tensor_dtype == np.float16:
Expand Down
Loading