7380a3b79a
* Model: Implement basic lora support * Add ability to load loras from config on launch * Supports loading multiple loras and lora scaling * Add function to unload loras * Colab: Update for basic lora support * Model: Test vram alloc after lora load, add docs * Git: Add loras folder to .gitignore * API: Add basic lora-related endpoints * Add /loras/ endpoint for querying available loras * Add /model/lora endpoint for querying currently loaded loras * Add /model/lora/load endpoint for loading loras * Add /model/lora/unload endpoint for unloading loras * Move lora config-checking logic to main.py for better compat with API endpoints * Revert bad CRLF line ending changes * API: Add basic lora-related endpoints (fixed) * Add /loras/ endpoint for querying available loras * Add /model/lora endpoint for querying currently loaded loras * Add /model/lora/load endpoint for loading loras * Add /model/lora/unload endpoint for unloading loras * Move lora config-checking logic to main.py for better compat with API endpoints * Model: Unload loras first when unloading model * API + Models: Cleanup lora endpoints and functions Condenses down endpoint and model load code. Also makes the routes behave the same way as model routes to help not confuse the end user. Signed-off-by: kingbri <bdashore3@proton.me> * Loras: Optimize load endpoint Return successes and failures along with consolidating the request to the rewritten load_loras function. Signed-off-by: kingbri <bdashore3@proton.me> --------- Co-authored-by: kingbri <bdashore3@proton.me> Co-authored-by: DocShotgun <126566557+DocShotgun@users.noreply.github.com>
468 lines
19 KiB
Python
468 lines
19 KiB
Python
import gc, time, pathlib
|
|
import torch
|
|
from exllamav2 import(
|
|
ExLlamaV2,
|
|
ExLlamaV2Config,
|
|
ExLlamaV2Cache,
|
|
ExLlamaV2Cache_8bit,
|
|
ExLlamaV2Tokenizer,
|
|
ExLlamaV2Lora
|
|
)
|
|
from exllamav2.generator import(
|
|
ExLlamaV2StreamingGenerator,
|
|
ExLlamaV2Sampler
|
|
)
|
|
from typing import List, Optional, Union
|
|
|
|
# Bytes to reserve on first device when loading with auto split
|
|
auto_split_reserve_bytes = 96 * 1024**2
|
|
|
|
class ModelContainer:
|
|
|
|
config: Optional[ExLlamaV2Config] = None
|
|
draft_config: Optional[ExLlamaV2Config] = None
|
|
model: Optional[ExLlamaV2] = None
|
|
draft_model: Optional[ExLlamaV2] = None
|
|
cache: Optional[ExLlamaV2Cache] = None
|
|
draft_cache: Optional[ExLlamaV2Cache] = None
|
|
tokenizer: Optional[ExLlamaV2Tokenizer] = None
|
|
generator: Optional[ExLlamaV2StreamingGenerator] = None
|
|
|
|
cache_fp8: bool = False
|
|
gpu_split_auto: bool = True
|
|
gpu_split: list or None = None
|
|
|
|
active_loras: List[ExLlamaV2Lora] = []
|
|
|
|
def __init__(self, model_directory: pathlib.Path, quiet = False, **kwargs):
|
|
"""
|
|
Create model container
|
|
|
|
Args:
|
|
model_dir (int): Model directory containing config.json, tokenizer.model etc.
|
|
quiet (bool): Suppress console output
|
|
load_progress_callback (function, optional): A function to call for each module loaded. Prototype:
|
|
def progress(loaded_modules: int, total_modules: int, loading_draft: bool)
|
|
**kwargs:
|
|
`cache_mode` (str): Sets cache mode, "FP16" or "FP8" (defaulf: "FP16")
|
|
'max_seq_len' (int): Override model's default max sequence length (default: 4096)
|
|
'rope_scale' (float): Set RoPE scaling factor for model (default: 1.0)
|
|
'rope_alpha' (float): Set RoPE alpha (NTK) factor for model (default: 1.0)
|
|
'chunk_size' (int): Sets the maximum chunk size for the model (default: 2048)
|
|
Inferencing in chunks reduces overall VRAM overhead by processing very long sequences in smaller
|
|
batches. This limits the size of temporary buffers needed for the hidden state and attention
|
|
weights.
|
|
'draft_model_dir' (str): Draft model directory
|
|
'draft_rope_scale' (float): Set RoPE scaling factor for draft model (default: 1.0)
|
|
'draft_rope_alpha' (float): RoPE alpha (NTK) factor for draft model.
|
|
By default, the draft model's alpha value is calculated automatically to scale to the size of the
|
|
full model.
|
|
'lora_dir' (str): Lora directory
|
|
'loras' (list[dict]): List of loras to be loaded, consisting of 'name' and 'scaling'
|
|
'gpu_split_auto' (bool): Automatically split model across available devices (default: True)
|
|
'gpu_split' (list[float]): Allocation for weights and (some) tensors, per device
|
|
'no_flash_attn' (bool): Turns off flash attention (increases vram usage) (default: False)
|
|
"""
|
|
|
|
self.quiet = quiet
|
|
|
|
self.cache_fp8 = "cache_mode" in kwargs and kwargs["cache_mode"] == "FP8"
|
|
self.gpu_split = kwargs.get("gpu_split")
|
|
self.gpu_split_auto = kwargs.get("gpu_split_auto") or True
|
|
|
|
self.config = ExLlamaV2Config()
|
|
self.config.model_dir = str(model_directory.resolve())
|
|
self.config.prepare()
|
|
|
|
# Grab the base model's sequence length before overrides for rope calculations
|
|
base_seq_len = self.config.max_seq_len
|
|
|
|
# Then override the max_seq_len if present
|
|
self.config.max_seq_len = kwargs.get("max_seq_len") or 4096
|
|
self.config.scale_pos_emb = kwargs.get("rope_scale") or 1.0
|
|
|
|
# Automatically calculate rope alpha
|
|
self.config.scale_alpha_value = kwargs.get("rope_alpha") or self.calculate_rope_alpha(base_seq_len)
|
|
|
|
# Turn off flash attention?
|
|
self.config.no_flash_attn = kwargs.get("no_flash_attn") or False
|
|
|
|
# low_mem is currently broken in exllamav2. Don't use it until it's fixed.
|
|
"""
|
|
if "low_mem" in kwargs and kwargs["low_mem"]:
|
|
self.config.set_low_mem()
|
|
"""
|
|
|
|
chunk_size = min(kwargs.get("chunk_size") or 2048, self.config.max_seq_len)
|
|
self.config.max_input_len = chunk_size
|
|
self.config.max_attn_size = chunk_size ** 2
|
|
|
|
draft_args = kwargs.get("draft") or {}
|
|
draft_model_name = draft_args.get("draft_model_name")
|
|
enable_draft = draft_args and draft_model_name
|
|
|
|
# Always disable draft if params are incorrectly configured
|
|
if draft_args and draft_model_name is None:
|
|
print("A draft config was found but a model name was not given. Please check your config.yml! Skipping draft load.")
|
|
enable_draft = False
|
|
|
|
if enable_draft:
|
|
|
|
self.draft_config = ExLlamaV2Config()
|
|
draft_model_path = pathlib.Path(draft_args.get("draft_model_dir") or "models")
|
|
draft_model_path = draft_model_path / draft_model_name
|
|
|
|
self.draft_config.model_dir = str(draft_model_path.resolve())
|
|
self.draft_config.prepare()
|
|
|
|
self.draft_config.scale_pos_emb = draft_args.get("draft_rope_scale") or 1.0
|
|
self.draft_config.scale_alpha_value = draft_args.get("draft_rope_alpha") or self.calculate_rope_alpha(self.draft_config.max_seq_len)
|
|
self.draft_config.max_seq_len = self.config.max_seq_len
|
|
|
|
if "chunk_size" in kwargs:
|
|
self.draft_config.max_input_len = kwargs["chunk_size"]
|
|
self.draft_config.max_attn_size = kwargs["chunk_size"] ** 2
|
|
|
|
|
|
def calculate_rope_alpha(self, base_seq_len):
|
|
ratio = self.config.max_seq_len / base_seq_len
|
|
|
|
# Default to a 1 alpha if the sequence length is ever less than or equal to 1
|
|
alpha = 1 if ratio <= 1.0 else -0.13436 + 0.80541 * ratio + 0.28833 * ratio ** 2
|
|
return alpha
|
|
|
|
def get_model_path(self):
|
|
model_path = pathlib.Path(self.config.model_dir)
|
|
return model_path
|
|
|
|
|
|
def load(self, progress_callback = None):
|
|
"""
|
|
Load model
|
|
|
|
Args:
|
|
progress_callback (function, optional): A function to call for each module loaded. Prototype:
|
|
def progress(loaded_modules: int, total_modules: int)
|
|
"""
|
|
for _ in self.load_gen(progress_callback): pass
|
|
|
|
def load_loras(self, lora_directory: pathlib.Path, **kwargs):
|
|
"""
|
|
Load loras
|
|
"""
|
|
|
|
loras = kwargs.get("loras") or []
|
|
success: List[str] = []
|
|
failure: List[str] = []
|
|
|
|
for lora in loras:
|
|
lora_name = lora.get("name") or None
|
|
lora_scaling = lora.get("scaling") or 1.0
|
|
|
|
if lora_name is None:
|
|
print("One of your loras does not have a name. Please check your config.yml! Skipping lora load.")
|
|
failure.append(lora_name)
|
|
continue
|
|
|
|
print(f"Loading lora: {lora_name} at scaling {lora_scaling}")
|
|
lora_path = lora_directory / lora_name
|
|
self.active_loras.append(ExLlamaV2Lora.from_directory(self.model, lora_path, lora_scaling))
|
|
print("Lora successfully loaded.")
|
|
success.append(lora_name)
|
|
|
|
# Return success and failure names
|
|
return { 'success': success, 'failure': failure }
|
|
|
|
def load_gen(self, progress_callback = None):
|
|
"""
|
|
Load model, generator function
|
|
|
|
Args:
|
|
progress_callback (function, optional): A function to call for each module loaded. Prototype:
|
|
def progress(loaded_modules: int, total_modules: int)
|
|
"""
|
|
|
|
# Load tokenizer
|
|
|
|
self.tokenizer = ExLlamaV2Tokenizer(self.config)
|
|
|
|
# Load draft model if a config is present
|
|
|
|
if self.draft_config:
|
|
|
|
self.draft_model = ExLlamaV2(self.draft_config)
|
|
if not self.quiet:
|
|
print("Loading draft model: " + self.draft_config.model_dir)
|
|
|
|
self.draft_cache = ExLlamaV2Cache(self.draft_model, lazy = True)
|
|
reserve = [auto_split_reserve_bytes] + [0] * 16
|
|
yield from self.draft_model.load_autosplit_gen(self.draft_cache, reserve_vram = reserve, last_id_only = True, callback_gen = progress_callback)
|
|
|
|
# Test VRAM allocation with a full-length forward pass
|
|
|
|
input_ids = torch.zeros((1, self.config.max_input_len), dtype = torch.long)
|
|
self.draft_model.forward(input_ids, cache = self.cache, preprocess_only = True)
|
|
|
|
# Load model
|
|
|
|
self.model = ExLlamaV2(self.config)
|
|
if not self.quiet:
|
|
print("Loading model: " + self.config.model_dir)
|
|
|
|
if not self.gpu_split_auto:
|
|
for value in self.model.load_gen(self.gpu_split, callback_gen = progress_callback):
|
|
if isinstance(value, str):
|
|
yield value
|
|
|
|
if self.cache_fp8:
|
|
self.cache = ExLlamaV2Cache_8bit(self.model, lazy = self.gpu_split_auto)
|
|
else:
|
|
self.cache = ExLlamaV2Cache(self.model, lazy = self.gpu_split_auto)
|
|
|
|
if self.gpu_split_auto:
|
|
reserve = [auto_split_reserve_bytes] + [0] * 16
|
|
yield from self.model.load_autosplit_gen(self.cache, reserve_vram = reserve, last_id_only = True, callback_gen = progress_callback)
|
|
|
|
# Test VRAM allocation with a full-length forward pass
|
|
|
|
input_ids = torch.zeros((1, self.config.max_input_len), dtype = torch.long)
|
|
self.model.forward(input_ids, cache = self.cache, preprocess_only = True)
|
|
|
|
# Create generator
|
|
|
|
self.generator = ExLlamaV2StreamingGenerator(self.model, self.cache, self.tokenizer, self.draft_model, self.draft_cache)
|
|
|
|
print("Model successfully loaded.")
|
|
|
|
|
|
def unload(self, loras_only: bool = False):
|
|
"""
|
|
Free all VRAM resources used by this model
|
|
"""
|
|
|
|
for lora in self.active_loras:
|
|
lora.unload()
|
|
|
|
self.active_loras = []
|
|
|
|
# Unload the entire model if not just unloading loras
|
|
if not loras_only:
|
|
if self.model: self.model.unload()
|
|
self.model = None
|
|
if self.draft_model: self.draft_model.unload()
|
|
self.draft_model = None
|
|
self.config = None
|
|
self.cache = None
|
|
self.tokenizer = None
|
|
self.generator = None
|
|
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
# Common function for token operations
|
|
def get_tokens(self, text: Optional[str], ids: Optional[List[int]], **kwargs):
|
|
if text:
|
|
# Assume token encoding
|
|
return self.tokenizer.encode(
|
|
text,
|
|
add_bos = kwargs.get("add_bos_token") or True,
|
|
encode_special_tokens = kwargs.get("encode_special_tokens") or True
|
|
)
|
|
if ids:
|
|
# Assume token decoding
|
|
ids = torch.tensor([ids])
|
|
return self.tokenizer.decode(ids, decode_special_tokens = kwargs.get("decode_special_tokens") or True)[0]
|
|
|
|
|
|
def generate(self, prompt: str, **kwargs):
|
|
gen = list(self.generate_gen(prompt, **kwargs))
|
|
reponse = "".join(map(lambda o: o[0], gen))
|
|
return reponse, gen[-1][1], gen[-1][2]
|
|
|
|
def generate_gen(self, prompt: str, **kwargs):
|
|
"""
|
|
Create generator function for prompt completion
|
|
|
|
Args:
|
|
prompt (str): Input prompt
|
|
**kwargs:
|
|
'token_healing' (bool): Use token healing (default: False)
|
|
'temperature' (float): Sampling temperature (default: 1.0)
|
|
'temperature_last' (bool): Apply temperature after all other samplers (default: False)
|
|
'top_k' (int): Sampling top-K (default: 0)
|
|
'top_p' (float): Sampling top-P (default: 1.0)
|
|
'min_p' (float): Sampling min-P (default: 0.0)
|
|
'tfs' (float): Tail-free sampling (default: 0.0)
|
|
'typical' (float): Sampling typical (default: 0.0)
|
|
'mirostat' (bool): Use Mirostat (default: False)
|
|
'mirostat_tau' (float) Mirostat tau parameter (default: 1.5)
|
|
'mirostat_eta' (float) Mirostat eta parameter (default: 0.1)
|
|
'repetition_penalty' (float): Token repetition/presence penalty (default: 1.15)
|
|
'repetition_range' (int): Repetition penalty range (default: whole context)
|
|
'repetition_decay' (int): Repetition penalty range (default: same as range)
|
|
'stop' (List[Union[str, int]]): List of stop strings/tokens to end response (default: [EOS])
|
|
'max_tokens' (int): Max no. tokens in response (default: 150)
|
|
'add_bos_token' (bool): Adds the BOS token to the start of the prompt (default: True)
|
|
'ban_eos_token' (bool): Bans the EOS token from generation (default: False)
|
|
'stream_interval' (float): Interval in seconds between each output chunk (default: immediate)
|
|
'generate_window' (int): Space to reserve at the end of the model's context when generating.
|
|
Rolls context window by the same amount if context length is exceeded to allow generating past
|
|
the models max_seq_len.
|
|
|
|
"""
|
|
|
|
token_healing = kwargs.get("token_healing") or False
|
|
max_tokens = kwargs.get("max_tokens") or 150
|
|
stream_interval = kwargs.get("stream_interval") or 0
|
|
generate_window = min(kwargs.get("generate_window") or 512, max_tokens)
|
|
|
|
# Sampler settings
|
|
|
|
gen_settings = ExLlamaV2Sampler.Settings()
|
|
|
|
# Warn of unsupported settings if the setting is enabled
|
|
|
|
if (kwargs.get("mirostat") or False) and not hasattr(gen_settings, "mirostat"):
|
|
print(" !! Warning: Currently installed ExLlamaV2 does not support Mirostat sampling")
|
|
|
|
if (kwargs.get("min_p") or 0.0) not in [0.0, 1.0] and not hasattr(gen_settings, "min_p"):
|
|
print(" !! Warning: Currently installed ExLlamaV2 does not support min-P sampling")
|
|
|
|
if (kwargs.get("tfs") or 0.0) not in [0.0, 1.0] and not hasattr(gen_settings, "tfs"):
|
|
print(" !! Warning: Currently installed ExLlamaV2 does not support tail-free sampling (TFS)")
|
|
|
|
if (kwargs.get("temperature_last") or False) and not hasattr(gen_settings, "temperature_last"):
|
|
print(" !! Warning: Currently installed ExLlamaV2 does not support temperature_last")
|
|
|
|
#Apply settings
|
|
|
|
gen_settings.temperature = kwargs.get("temperature") or 1.0
|
|
gen_settings.temperature_last = kwargs.get("temperature_last") or False
|
|
gen_settings.top_k = kwargs.get("top_k") or 0
|
|
gen_settings.top_p = kwargs.get("top_p") or 1.0
|
|
gen_settings.min_p = kwargs.get("min_p") or 0.0
|
|
gen_settings.tfs = kwargs.get("tfs") or 1.0
|
|
gen_settings.typical = kwargs.get("typical") or 1.0
|
|
gen_settings.mirostat = kwargs.get("mirostat") or False
|
|
|
|
# Default tau and eta fallbacks don't matter if mirostat is off
|
|
gen_settings.mirostat_tau = kwargs.get("mirostat_tau") or 1.5
|
|
gen_settings.mirostat_eta = kwargs.get("mirostat_eta") or 0.1
|
|
gen_settings.token_repetition_penalty = kwargs.get("repetition_penalty") or 1.0
|
|
gen_settings.token_repetition_range = kwargs.get("repetition_range") or self.config.max_seq_len
|
|
|
|
# Always make sure the fallback is 0 if range < 0
|
|
# It's technically fine to use -1, but this just validates the passed fallback
|
|
fallback_decay = 0 if gen_settings.token_repetition_range <= 0 else gen_settings.token_repetition_range
|
|
gen_settings.token_repetition_decay = kwargs.get("repetition_decay") or fallback_decay or 0
|
|
|
|
stop_conditions: List[Union[str, int]] = kwargs.get("stop") or []
|
|
ban_eos_token = kwargs.get("ban_eos_token") or False
|
|
|
|
|
|
# Ban the EOS token if specified. If not, append to stop conditions as well.
|
|
|
|
if ban_eos_token:
|
|
gen_settings.disallow_tokens(self.tokenizer, [self.tokenizer.eos_token_id])
|
|
else:
|
|
stop_conditions.append(self.tokenizer.eos_token_id)
|
|
|
|
# Override sampler settings for temp = 0
|
|
|
|
if gen_settings.temperature == 0:
|
|
gen_settings.temperature = 1.0
|
|
gen_settings.top_k = 1
|
|
gen_settings.top_p = 0
|
|
gen_settings.typical = 0
|
|
|
|
# Stop conditions
|
|
|
|
self.generator.set_stop_conditions(stop_conditions)
|
|
|
|
# Tokenized context
|
|
|
|
ids = self.tokenizer.encode(
|
|
prompt,
|
|
add_bos = kwargs.get("add_bos_token") or True,
|
|
encode_special_tokens = True
|
|
)
|
|
context_len = len(ids[0])
|
|
|
|
if context_len > self.config.max_seq_len:
|
|
print(
|
|
f"WARNING: The context length {context_len} is greater than the max_seq_len {self.config.max_seq_len}.",
|
|
"Generation is truncated and metrics may not be accurate."
|
|
)
|
|
|
|
prompt_tokens = ids.shape[-1]
|
|
|
|
# Begin
|
|
|
|
generated_tokens = 0
|
|
full_response = ""
|
|
start_time = time.time()
|
|
last_chunk_time = start_time
|
|
|
|
save_tokens = torch.empty((1, 0), dtype = torch.bool)
|
|
chunk_buffer = ""
|
|
chunk_tokens = 0
|
|
|
|
while True:
|
|
|
|
# Ingest prompt
|
|
|
|
if chunk_tokens == 0:
|
|
|
|
ids = torch.cat((ids, save_tokens), dim = - 1)
|
|
save_tokens = torch.empty((1, 0), dtype = torch.bool)
|
|
overflow = ids.shape[-1] + generate_window - self.config.max_seq_len
|
|
active_ids = ids[:, max(0, overflow):]
|
|
chunk_tokens = self.config.max_seq_len - active_ids.shape[-1]
|
|
|
|
self.generator.begin_stream(active_ids, gen_settings, token_healing = token_healing, loras = self.active_loras)
|
|
|
|
# Generate
|
|
|
|
chunk, eos, tokens = self.generator.stream()
|
|
|
|
if token_healing:
|
|
ids[:, -1] = self.generator.sequence_ids[:, -2] # Extract healed token
|
|
token_healing = False
|
|
|
|
save_tokens = torch.cat((save_tokens, tokens), dim=-1)
|
|
chunk_buffer += chunk
|
|
|
|
generated_tokens += 1
|
|
chunk_tokens -= 1
|
|
|
|
# Yield output
|
|
|
|
now = time.time()
|
|
elapsed = now - last_chunk_time
|
|
|
|
if chunk_buffer != "" and (elapsed > stream_interval or eos or generated_tokens == max_tokens):
|
|
yield chunk_buffer, prompt_tokens, generated_tokens
|
|
full_response += chunk_buffer
|
|
chunk_buffer = ""
|
|
last_chunk_time = now
|
|
|
|
if eos or generated_tokens == max_tokens: break
|
|
|
|
elapsed_time = last_chunk_time - start_time
|
|
|
|
initial_response = f"Response: {generated_tokens} tokens generated in {round(elapsed_time, 2)} seconds"
|
|
itemization = []
|
|
extra_parts = []
|
|
|
|
# Add tokens per second
|
|
itemization.append(f"{'Indeterminate' if elapsed_time == 0 else round(generated_tokens / elapsed_time, 2)} T/s")
|
|
|
|
# Add context (original token count)
|
|
if ids is not None:
|
|
itemization.append(f"context {context_len} tokens")
|
|
|
|
if context_len > self.config.max_seq_len:
|
|
extra_parts.append("<-- Not accurate (truncated)")
|
|
|
|
# Print output
|
|
print(initial_response + " (" + ", ".join(itemization) + ") " + " ".join(extra_parts))
|