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Llama 3.2

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Daniel Han 2024-09-25 11:42:41 -07:00
commit 706f6ee18f
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21
unsloth/kernels/rms_layernorm.py
View file

@ -201,10 +201,25 @@ class Unsloth_LlamaRMSNorm(LlamaRMSNorm):
pass
pass
try:
from transformers.models.mllama.modeling_mllama import MllamaTextRMSNorm
class Unsloth_MllamaTextRMSNorm(MllamaTextRMSNorm):
def forward(self, X):
return fast_rms_layernorm(self, X, gemma = False)
pass
pass
except:
pass
pass
def patch_rms_layernorm():
import transformers.models.llama.modeling_llama
transformers.models.llama.modeling_llama.LlamaRMSNorm = Unsloth_LlamaRMSNorm
try:
import transformers.models.mllama.modeling_mllama
transformers.models.mllama.modeling_mllama.MllamaTextRMSNorm = Unsloth_MllamaTextRMSNorm
except:
pass
return
pass
@ -212,6 +227,12 @@ pass
def unpatch_rms_layernorm():
import transformers.models.llama.modeling_llama
transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
try:
import transformers.models.mllama.modeling_mllama
transformers.models.mllama.modeling_mllama.MllamaTextRMSNorm = MllamaTextRMSNorm
except:
pass
return
return
pass

16
unsloth/models/mapper.py
View file

@ -400,6 +400,22 @@ __INT_TO_FLOAT_MAPPER = \
"unsloth/Qwen2.5-Coder-7B-Instruct",
"Qwen/Qwen2.5-Coder-7B-Instruct",
),
"unsloth/Llama-3.2-1B-bnb-4bit" : (
"unsloth/Llama-3.2-1B",
"meta-llama/Llama-3.2-1B",
),
"unsloth/Llama-3.2-3B-bnb-4bit" : (
"unsloth/Llama-3.2-3B",
"meta-llama/Llama-3.2-3B",
),
"unsloth/Llama-3.2-1B-Instruct-bnb-4bit" : (
"unsloth/Llama-3.2-1B-Instruct",
"meta-llama/Llama-3.2-1B-Instruct",
),
"unsloth/Llama-3.2-3B-Instruct-bnb-4bit" : (
"unsloth/Llama-3.2-3B-Instruct",
"meta-llama/Llama-3.2-3B-Instruct",
),
}
INT_TO_FLOAT_MAPPER = {}

592
unsloth/models/vision.py Normal file
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@ -0,0 +1,592 @@
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .llama import *
from ..kernels import patch_layernorm, unpatch_layernorm
from ..kernels import patch_rms_layernorm, unpatch_rms_layernorm
from ..kernels import patch_llama_for_causal_lm, unpatch_llama_for_causal_lm
from ._utils import patch_gradient_checkpointing
from transformers import AutoProcessor, AutoModelForVision2Seq
class FastVisionModel:
def pre_patch(self):
patch_gradient_checkpointing()
patch_layernorm()
patch_rms_layernorm()
patch_llama_for_causal_lm()
pass
def post_unpatch(self):
unpatch_layernorm()
unpatch_rms_layernorm()
unpatch_llama_for_causal_lm()
pass
@staticmethod
def from_pretrained(
model_name = "llava-hf/llava-1.5-7b-hf",
max_seq_length = None,
dtype = None,
load_in_4bit = True,
token = None,
device_map = "sequential",
rope_scaling = None,
trust_remote_code = False,
**kwargs,
):
if trust_remote_code:
print(
"Unsloth: WARNING `trust_remote_code` is True.\n"\
"Are you certain you want to do remote code execution?"
)
pass
if token is None: token = get_token()
if model_patcher is None: model_patcher = FastLlamaModel
SUPPORTS_BFLOAT16 = is_bfloat16_supported()
gpu_stats = torch.cuda.get_device_properties(0)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
statistics = \
f"==((====))== Unsloth {__version__}: Fast {model_patcher.__name__[4:-5]} patching. Transformers = {transformers_version}.\n"\
f" \\\ /| GPU: {gpu_stats.name}. Max memory: {max_memory} GB. Platform = {platform_system}.\n"\
f"O^O/ \_/ \\ Pytorch: {torch.__version__}. CUDA = {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit = {torch.version.cuda}.\n"\
f"\ / Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. FA [Xformers = {xformers_version}. FA2 = {HAS_FLASH_ATTENTION}]\n"\
f' "-____-" Free Apache license: http://github.com/unslothai/unsloth'
print(statistics)
# Warn about fast transfers
old_hf_transfer = os.environ.get("HF_HUB_ENABLE_HF_TRANSFER", "0")
if os.environ.get("HF_HUB_ENABLE_HF_TRANSFER", "0") == "1":
print("Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!")
pass
# Return old flag
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
get_statistics() # For debugging - we use a download counter to see if environments are not breaking
if dtype is None:
dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16:
logger.warning_once("Device does not support bfloat16. Will change to float16.")
dtype = torch.float16
assert(dtype == torch.float16 or dtype == torch.bfloat16 or dtype == torch.float32)
# RoPE Scaling
model_config = AutoConfig.from_pretrained(model_name, token = token)
model_max_seq_length = model_config.max_position_embeddings
# Check if RoPE Scaling is even allowed
model_function = MODEL_FOR_CAUSAL_LM_MAPPING[model_config.__class__]
has_rope_scaling = False
try:
with open(inspect.getfile(model_function), "r") as file:
has_rope_scaling = "self.config.rope_scaling" in file.read()
except: pass
has_rope_scaling = True
# If max_seq_length is not specified, use maximum fron config
if max_seq_length is None:
max_seq_length = model_max_seq_length
pass
if (rope_scaling is None) and (max_seq_length > model_max_seq_length):
rope_scaling = max_seq_length / model_max_seq_length
logger.warning_once(
f"Unsloth: {model_name} can only handle sequence lengths of at most "\
f"{model_max_seq_length}.\nBut with kaiokendev's RoPE scaling of "\
f"{round(rope_scaling, 3)}, it can be magically be extended to "\
f"{max_seq_length}!"
)
# Warn RoPE scaling isn't allowed
if not has_rope_scaling:
raise RuntimeError(
"However, {model_name} doesn't support RoPE Scaling!\n"\
"Please file a feature request at https://github.com/unslothai/unsloth."
)
pass
rope_scaling = {"type": "linear", "factor": rope_scaling,}
# Add to kwargs
kwargs["rope_scaling"] = rope_scaling
pass
# We currently only support NVIDIA GPUs - AMD / Intel is a work in progress!
pre_check = check_nvidia()
bnb_config = None
if load_in_4bit:
bnb_config = BitsAndBytesConfig(
load_in_4bit = True,
bnb_4bit_use_double_quant = True,
bnb_4bit_quant_type = "nf4",
bnb_4bit_compute_dtype = dtype,
)
pass
# https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/12
# RoPE Scaling's max_position_embeddings must be updated
max_position_embeddings = max(max_seq_length, model_max_seq_length)
kwargs.pop("attn_implementation", None); # No need since we auto call it
# Cannot be None, since HF now checks for the config
if load_in_4bit: kwargs["quantization_config"] = bnb_config
self.pre_patch()
model = AutoModelForVision2Seq.from_pretrained(
model_name,
device_map = device_map,
torch_dtype = dtype,
# quantization_config = bnb_config,
token = token,
max_position_embeddings = max_position_embeddings,
trust_remote_code = trust_remote_code,
attn_implementation = "eager",
**kwargs,
)
self.post_unpatch()
# Return old flag
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
# We currently only support NVIDIA GPUs - AMD / Intel is a work in progress!
post_check = check_nvidia()
# Counteract saved tokenizers
tokenizer = AutoProcessor.from_pretrained(
model_name,
)
model = FastVisionModel.post_patch(model)
# Patch Trainer
from transformers.trainer import Trainer
try:
if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop":
inner_training_loop = inspect.getsource(Trainer._inner_training_loop)
Trainer._original_training_loop = inner_training_loop
else:
inner_training_loop = Trainer._original_training_loop
except:
raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
pass
if ((post_check - pre_check) >= 1).sum() > 1:
raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
import transformers.trainer
items_in_trainer = dir(transformers.trainer)
good_items = []
for item in items_in_trainer:
# TODO: Support Deepspeed
if item.startswith(("deepspeed", "xm", "met", "smp")): continue
if item in inner_training_loop: good_items.append(item)
pass
exec("from transformers.trainer import (" + ", ".join(x for x in good_items) + ")", globals())
start = re.search('logger\.info\([\"\'].+?Running training', inner_training_loop).span(0)[0]
end = inner_training_loop.find("\n\n", start)
original_debug = inner_training_loop[start:end]
spaces = re.search('\n([\s\t]{1,})', original_debug).group(0)[1:]
front_spaces = re.match('([\s\t]{1,})', inner_training_loop).group(0)
debug_info = """debug_info = \\
f"==((====))== Unsloth - 2x faster free finetuning | Num GPUs = {args.world_size}\\n"\\
f" \\\\\\ /| Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,}\\n"\\
f"O^O/ \\_/ \\ Batch size per device = {self._train_batch_size:,} | Gradient Accumulation steps = {args.gradient_accumulation_steps}\\n"\\
f"\\ / Total batch size = {total_train_batch_size:,} | Total steps = {max_steps:,}\\n"\\
f' "-____-" Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}'
logger.warning(debug_info)
import subprocess, re, gc, numpy as np
a = np.array([0,])
try:
a = subprocess.check_output('nvidia-smi --query-gpu=memory.used --format=csv', shell = True)
a = re.findall(rb'([\\d]{1,})[\\s]{1,}M', a)
a = np.array([int(x.decode('utf-8'))/1024 for x in a])
except:
if not torch.cuda.is_available():
raise RuntimeError('Unsloth: We do not support AMD / Intel machines yet - it is a work in progress!')
if ((a - PRE_CHECK) >= 1).sum() > 1:
raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
for _ in range(3):
gc.collect()
torch.cuda.empty_cache()"""
debug_info = debug_info.split('\n')
debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
inner_training_loop = inner_training_loop.replace(original_debug, debug_info)
debug_info = """n_total_devices = total_train_batch_size // \\
args.gradient_accumulation_steps // self._train_batch_size
if n_total_devices > 1:
logger.warning_once('Unsloth currently does not support multi GPU setups - but we are working on it!')
debug_info ="""
debug_info = debug_info.split('\n')
debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
inner_training_loop = inner_training_loop.replace("debug_info =", debug_info, 1)
front_spaces = re.match(r"[\t\s]{1,}", inner_training_loop).group(0)
inner_training_loop = re.sub(r"^" + front_spaces, "", inner_training_loop, flags = re.MULTILINE)
inner_training_loop = inner_training_loop.replace(
"train_dataloader = tpu_spmd_dataloader(train_dataloader)",
"raise RuntimeError('Unsloth: TPUs are not yet supported!')"
)
inner_training_loop = inner_training_loop.replace(
"self.accelerator.free_memory()",
"self.accelerator.free_memory()\n" + \
front_spaces + "if self.is_deepspeed_enabled:"\
"raise RuntimeError('Unsloth: Deepspeed is not yet supported!')\n", 1,
)
check_batches = """train_dataloader = self.get_train_dataloader()
ga = args.gradient_accumulation_steps
bsz = self._train_batch_size
total_batches = bsz * ga * args.world_size
n_total_devices = total_batches // ga // bsz
if n_total_devices > 1:
logger.warning_once('Unsloth currently does not support multi GPU setups - but we are working on it!')
divisor = n_total_devices / 1
bsz = self._train_batch_size = max(int(bsz / divisor), 1)
if total_batches // ga // bsz > 1:
divisor = n_total_devices / 1
ga = args.gradient_accumulation_steps = max(int(ga / divisor), 1)"""
check_batches = check_batches.split('\n')
check_batches = "\n".join([check_batches[0]] + [front_spaces + x[8:] for x in check_batches[1:]])
inner_training_loop = inner_training_loop.replace(
"train_dataloader = self.get_train_dataloader()",
check_batches, 1,
)
inner_training_loop = inner_training_loop.replace(
"_inner_training_loop",
"_fast_inner_training_loop", 1,
)
exec(inner_training_loop, globals())
Trainer._inner_training_loop = _fast_inner_training_loop
inner_training_loop = inner_training_loop.replace(
"is_torch_tpu_available()",
"False",
)
if "n_total_devices >" not in inner_training_loop:
raise RuntimeError('Unsloth currently does not support multi GPU setups - but we are working on it!')
pass
inner_training_loop = inner_training_loop.replace(
"is_sagemaker_mp_enabled()",
"False",
)
exec(inner_training_loop, globals())
Trainer._inner_training_loop = _fast_inner_training_loop
# Save max_seq_length
model.max_seq_length = max_position_embeddings
internal_model = model
while hasattr(internal_model, "model"):
internal_model.max_seq_length = max_position_embeddings
internal_model = internal_model.model
pass
internal_model.max_seq_length = max_position_embeddings
# Fix up config for transformers uploading PEFT
# Not necessary anymore since we require transformers>=4.37!
if False:
name = model.config._name_or_path
if name.startswith("unsloth/") and name.endswith("-bnb-4bit"):
name = name[:len(name) - len("-bnb-4bit")]
model.config.update({"_name_or_path" : name})
pass
pass
# Log Unsloth version for future fastpaths for inference
model.config.update({"unsloth_version" : __version__})
# Add save modules
patch_saving_functions(model)
Trainer._inner_training_loop = _fast_inner_training_loop
# Also fix torch_dtype
internal_model = model
while hasattr(internal_model, "model"):
if hasattr(internal_model, "config"):
if internal_model.config.torch_dtype == "float32":
internal_model.config.torch_dtype = torch.float32
elif internal_model.config.torch_dtype == "bfloat16":
internal_model.config.torch_dtype = torch.bfloat16
elif internal_model.config.torch_dtype == "float16":
internal_model.config.torch_dtype = torch.float16
pass
pass
internal_model = internal_model.model
pass
if hasattr(internal_model, "config"):
if internal_model.config.torch_dtype == "float32":
internal_model.config.torch_dtype = torch.float32
elif internal_model.config.torch_dtype == "bfloat16":
internal_model.config.torch_dtype = torch.bfloat16
elif internal_model.config.torch_dtype == "float16":
internal_model.config.torch_dtype = torch.float16
pass
pass
return model, tokenizer
pass
@staticmethod
def post_patch(model):
# Patch model
layers = model.model.layers
lm_head = model.get_output_embeddings().weight
# Also patch all dtypes - BnB seems to not allocate the correct type?
# BnB default dtype seems to be float16!
correct_dtype = lm_head.weight.dtype
for name, module in model.named_modules():
if isinstance(module, (Bnb_Linear4bit, Peft_Linear4bit)):
weight = module.weight
quant_state = weight.quant_state
if type(quant_state) is list:
# BnB seems to have float16 as default!
module.weight.quant_state[2] = correct_dtype # Cast to correct dtype
else:
# https://github.com/TimDettmers/bitsandbytes/pull/763/files
quant_state.dtype = correct_dtype
pass
pass
pass
# Clear deleted GPU items
for _ in range(3):
gc.collect()
torch.cuda.empty_cache()
return model
pass
@staticmethod
def get_peft_model(
model,
r = 16,
target_modules = "all-linear",
lora_alpha = 16,
lora_dropout = 0,
bias = "none",
layers_to_transform = None,
layers_pattern = None,
use_gradient_checkpointing = True,
random_state = 3407,
max_seq_length = 2048, # not used anymore
use_rslora = False,
modules_to_save = None,
init_lora_weights = True,
loftq_config = {},
temporary_location = "_unsloth_temporary_saved_buffers",
**kwargs,
):
transformers_set_seed(random_state)
# Get LoRA
arguments = dict(
r = r,
lora_alpha = lora_alpha,
target_modules = target_modules,
lora_dropout = lora_dropout,
bias = bias,
layers_to_transform = layers_to_transform,
init_lora_weights = init_lora_weights,
# loftq_config = loftq_config,
# use_rslora = use_rslora,
modules_to_save = modules_to_save,
**kwargs,
)
lora_config = LoraConfig(**arguments)
model = _get_peft_model(model, lora_config)
model = FastVisionModel.patch_peft_model(model, use_gradient_checkpointing)
# Clear deleted GPU items
for _ in range(3):
gc.collect()
torch.cuda.empty_cache()
pass
return model
pass
@staticmethod
def patch_peft_model(
model,
use_gradient_checkpointing = True,
):
model = prepare_model_for_kbit_training(
model,
use_gradient_checkpointing = use_gradient_checkpointing,
use_reentrant = True,
)
# Fix up config for transformers uploading PEFT
for active_adapter in model.peft_config.keys():
# Not necessary since we requires transformers >= 4.37
if False:
name = model.peft_config[active_adapter].base_model_name_or_path
if name.startswith("unsloth/") and name.endswith("-bnb-4bit"):
name = name[:len(name) - len("-bnb-4bit")]
model.peft_config[active_adapter].base_model_name_or_path = name
pass
# Add revision to enable future fast inference paths
# [TODO] Bugs out!see https://github.com/unslothai/unsloth/issues/492
# model.peft_config[active_adapter].revision = f"unsloth"
pass
from transformers.trainer import Trainer
if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop":
raise RuntimeError(
'Unsloth currently does not work on multi GPU setups - sadly we are a 2 brother team so '\
'enabling it will require much more work, so we have to prioritize. Please understand!\n'\
'We do have a separate beta version, which you can contact us about!\n'\
'Thank you for your understanding and we appreciate it immensely!'
)
pass
logger.warning_once(
f"Unsloth {__version__} patched {len(model.model.model.layers)} layers with "\
f"{n_qkv} QKV layers, {n_o} O layers and {n_mlp} MLP layers.",
)
patch_saving_functions(model)
# Patch cross entropy loss labels
# Fixes https://github.com/unslothai/unsloth/issues/10
max_seq_length = model.max_seq_length
extra_ignored_labels = torch.full((max_seq_length, 1), -100, device = "cuda:0")
model.model.extra_ignored_labels = extra_ignored_labels
internal_model = model
while hasattr(internal_model, "model"):
internal_model.max_seq_length = max_seq_length
internal_model = internal_model.model
pass
internal_model.max_seq_length = max_seq_length
# Patch tokenizer to pad to the right
internal_model = model
while hasattr(internal_model, "model"):
if hasattr(internal_model, "_saved_temp_tokenizer"):
internal_model._saved_temp_tokenizer.padding_side = "right"
pass
internal_model = internal_model.model
pass
if hasattr(internal_model, "_saved_temp_tokenizer"):
internal_model._saved_temp_tokenizer.padding_side = "right"
pass
# Clear deleted GPU items
for _ in range(3):
gc.collect()
torch.cuda.empty_cache()
pass
return model
pass
@staticmethod
def for_inference(model):
# if model.config.model_type == "qwen2":
# FastLlamaModel.for_training(model)
# return
# pass
internal_model = model
internal_model.gradient_checkpointing = False
internal_model.training = False
while hasattr(internal_model, "model"):
internal_model = internal_model.model
internal_model.gradient_checkpointing = False
internal_model.training = False
pass
if hasattr(internal_model, "training"):
internal_model.training = False
pass
# Also check if lm_head / embeddings are trained
internal_model = model
while not hasattr(internal_model, "lm_head"):
internal_model = internal_model.model
pass
lm_head = internal_model.lm_head.weight
device_type = lm_head.device.type
dtype = model.config.torch_dtype
if type(dtype) is str:
if dtype == "float16": dtype = torch.float16
elif dtype == "bfloat16": dtype = torch.bfloat16
pass
# Also disable training for embeddings for NEFTune
if hasattr(model, "get_input_embeddings"):
embeddings = model.get_input_embeddings()
if hasattr(embeddings, "training"): embeddings.training = False
pass
if hasattr(model, "get_output_embeddings"):
embeddings = model.get_output_embeddings()
if hasattr(embeddings, "training"): embeddings.training = False
pass
return model
pass
@staticmethod
def for_training(model, use_gradient_checkpointing = True):
internal_model = model
internal_model.gradient_checkpointing = use_gradient_checkpointing
internal_model.training = True
# Delete all fast inference loras
for param in model.parameters():
if hasattr(param, "_fast_lora"):
del param._fast_lora
pass
while hasattr(internal_model, "model"):
internal_model = internal_model.model
internal_model.gradient_checkpointing = use_gradient_checkpointing
internal_model.training = True
pass
if hasattr(internal_model, "training"):
internal_model.training = True
pass
# Also re-enable training for embeddings for NEFTune
if hasattr(model, "get_input_embeddings"):
embeddings = model.get_input_embeddings()
if hasattr(embeddings, "training"): embeddings.training = True
pass
if hasattr(model, "get_output_embeddings"):
embeddings = model.get_output_embeddings()
if hasattr(embeddings, "training"): embeddings.training = True
pass
return model
pass
pass