295 lines
12 KiB
Python
295 lines
12 KiB
Python
import matmul_utils_4bit as mm4b
|
|
import torch
|
|
import torch.nn as nn
|
|
import time
|
|
import math
|
|
from torch.cuda.amp import custom_bwd, custom_fwd
|
|
from colorama import init, Fore, Back, Style
|
|
init(autoreset=True)
|
|
|
|
|
|
class AutogradMatmul4bitCuda(torch.autograd.Function):
|
|
|
|
@staticmethod
|
|
@custom_fwd(cast_inputs=torch.float16)
|
|
def forward(ctx, x, qweight, scales, zeros, g_idx, bits, maxq):
|
|
ctx.save_for_backward(qweight, scales, zeros, g_idx)
|
|
if g_idx is None:
|
|
output = mm4b._matmul4bit_v1_recons(x, qweight, scales, zeros)
|
|
else:
|
|
output = mm4b._matmul4bit_v2_recons(x, qweight, scales, zeros, g_idx)
|
|
output = output.clone()
|
|
return output
|
|
|
|
@staticmethod
|
|
@custom_bwd
|
|
def backward(ctx, grad_output):
|
|
qweight, scales, zeros, g_idx = ctx.saved_tensors
|
|
if ctx.needs_input_grad[0]:
|
|
if g_idx is None:
|
|
grad = mm4b._matmul4bit_v1_recons(grad_output, qweight, scales, zeros, transpose=True)
|
|
else:
|
|
grad = mm4b._matmul4bit_v2_recons(grad_output, qweight, scales, zeros, g_idx, transpose=True)
|
|
return grad, None, None, None, None, None, None
|
|
|
|
|
|
try:
|
|
import triton_utils as tu
|
|
|
|
class AutogradMatmul4bitTriton(torch.autograd.Function):
|
|
|
|
@staticmethod
|
|
@custom_fwd(cast_inputs=torch.float16)
|
|
def forward(ctx, x, qweight, scales, qzeros, g_idx, bits, maxq):
|
|
output = tu.triton_matmul(x, qweight, scales, qzeros, g_idx, bits, maxq)
|
|
ctx.save_for_backward(qweight, scales, qzeros, g_idx)
|
|
ctx.bits, ctx.maxq = bits, maxq
|
|
output = output.clone()
|
|
return output
|
|
|
|
@staticmethod
|
|
@custom_bwd
|
|
def backward(ctx, grad_output):
|
|
qweight, scales, qzeros, g_idx = ctx.saved_tensors
|
|
bits, maxq = ctx.bits, ctx.maxq
|
|
grad_input = None
|
|
|
|
if ctx.needs_input_grad[0]:
|
|
grad_input = tu.triton_matmul_transpose(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
|
|
return grad_input, None, None, None, None, None, None
|
|
|
|
except ImportError:
|
|
print('Triton not found. Please run "pip install triton".')
|
|
|
|
|
|
AutogradMatmul4bit = AutogradMatmul4bitCuda
|
|
backend = 'cuda'
|
|
|
|
|
|
def switch_backend_to(to_backend):
|
|
global AutogradMatmul4bit
|
|
global backend
|
|
if to_backend == 'cuda':
|
|
AutogradMatmul4bit = AutogradMatmul4bitCuda
|
|
backend = 'cuda'
|
|
print(Style.BRIGHT + Fore.GREEN + 'Using CUDA implementation.')
|
|
elif to_backend == 'triton':
|
|
# detect if AutogradMatmul4bitTriton is defined
|
|
if 'AutogradMatmul4bitTriton' not in globals():
|
|
raise ValueError('Triton not found. Please install triton')
|
|
AutogradMatmul4bit = AutogradMatmul4bitTriton
|
|
backend = 'triton'
|
|
print(Style.BRIGHT + Fore.GREEN + 'Using Triton implementation.')
|
|
else:
|
|
raise ValueError('Backend not supported.')
|
|
|
|
|
|
def matmul4bit_with_backend(x, qweight, scales, qzeros, g_idx, bits, maxq):
|
|
if backend == 'cuda':
|
|
return mm4b.matmul4bit(x, qweight, scales, qzeros, g_idx)
|
|
elif backend == 'triton':
|
|
assert qzeros.dtype == torch.int32
|
|
return tu.triton_matmul(x, qweight, scales, qzeros, g_idx, bits, maxq)
|
|
else:
|
|
raise ValueError('Backend not supported.')
|
|
|
|
|
|
# Assumes layer is perfectly divisible into 256 * 256 blocks
|
|
class Autograd4bitQuantLinear(nn.Module):
|
|
|
|
def __init__(self, in_features, out_features, groupsize=-1, is_v1_model=False):
|
|
super().__init__()
|
|
bits = 4
|
|
self.in_features = in_features
|
|
self.out_features = out_features
|
|
self.bits = bits
|
|
self.maxq = 2 ** self.bits - 1
|
|
groupsize = groupsize if groupsize != -1 else in_features
|
|
self.groupsize = groupsize
|
|
self.is_v1_model = is_v1_model
|
|
self.disable_bias = True
|
|
if is_v1_model:
|
|
self.register_buffer('zeros', torch.empty((out_features, 1)))
|
|
self.register_buffer('scales', torch.empty((out_features, 1)))
|
|
self.g_idx = None
|
|
else:
|
|
self.register_buffer('qzeros',
|
|
torch.empty((math.ceil(in_features/groupsize), out_features // 256 * (bits * 8)), dtype=torch.int32)
|
|
)
|
|
self.register_buffer('scales', torch.empty((math.ceil(in_features/groupsize), out_features)))
|
|
self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(in_features)], dtype = torch.int32))
|
|
self.register_buffer('bias', torch.empty(out_features))
|
|
self.register_buffer(
|
|
'qweight', torch.empty((in_features // 256 * (bits * 8), out_features), dtype=torch.int32)
|
|
)
|
|
|
|
|
|
def forward(self, x):
|
|
if torch.is_grad_enabled():
|
|
out = AutogradMatmul4bit.apply(x, self.qweight, self.scales,
|
|
self.qzeros if not self.is_v1_model else self.zeros,
|
|
self.g_idx, self.bits, self.maxq)
|
|
else:
|
|
out = matmul4bit_with_backend(x, self.qweight, self.scales,
|
|
self.qzeros if not self.is_v1_model else self.zeros,
|
|
self.g_idx, self.bits, self.maxq)
|
|
if not self.disable_bias:
|
|
out += self.bias
|
|
return out
|
|
|
|
|
|
def make_quant_for_4bit_autograd(module, names, name='', groupsize=-1, is_v1_model=False):
|
|
if isinstance(module, Autograd4bitQuantLinear):
|
|
return
|
|
for attr in dir(module):
|
|
tmp = getattr(module, attr)
|
|
name1 = name + '.' + attr if name != '' else attr
|
|
if name1 in names:
|
|
setattr(
|
|
module, attr, Autograd4bitQuantLinear(tmp.in_features, tmp.out_features, groupsize=groupsize, is_v1_model=is_v1_model)
|
|
)
|
|
for name1, child in module.named_children():
|
|
make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1, groupsize=groupsize, is_v1_model=is_v1_model)
|
|
|
|
|
|
def model_to_half(model):
|
|
model.half()
|
|
for n, m in model.named_modules():
|
|
if isinstance(m, Autograd4bitQuantLinear):
|
|
if m.is_v1_model:
|
|
m.zeros = m.zeros.half()
|
|
m.scales = m.scales.half()
|
|
m.bias = m.bias.half()
|
|
print(Style.BRIGHT + Fore.YELLOW + 'Converted as Half.')
|
|
|
|
|
|
def model_to_float(model):
|
|
model.float()
|
|
for n, m in model.named_modules():
|
|
if isinstance(m, Autograd4bitQuantLinear):
|
|
if m.is_v1_model:
|
|
m.zeros = m.zeros.float()
|
|
m.scales = m.scales.float()
|
|
m.bias = m.bias.float()
|
|
print(Style.BRIGHT + Fore.YELLOW + 'Converted as Float.')
|
|
|
|
|
|
def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
|
|
if type(module) in layers:
|
|
return {name: module}
|
|
res = {}
|
|
for name1, child in module.named_children():
|
|
res.update(find_layers(
|
|
child, layers=layers, name=name + '.' + name1 if name != '' else name1
|
|
))
|
|
return res
|
|
|
|
|
|
def load_llama_model_4bit_low_ram(config_path, model_path, groupsize=-1, half=False, device_map="auto", seqlen=2048, is_v1_model=False):
|
|
import accelerate
|
|
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
|
|
|
|
print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
|
|
t0 = time.time()
|
|
|
|
with accelerate.init_empty_weights():
|
|
config = LlamaConfig.from_pretrained(config_path)
|
|
model = LlamaForCausalLM(config)
|
|
model = model.eval()
|
|
layers = find_layers(model)
|
|
for name in ['lm_head']:
|
|
if name in layers:
|
|
del layers[name]
|
|
make_quant_for_4bit_autograd(model, layers, groupsize=groupsize, is_v1_model=is_v1_model)
|
|
model = accelerate.load_checkpoint_and_dispatch(
|
|
model=model,
|
|
checkpoint=model_path,
|
|
device_map=device_map,
|
|
no_split_module_classes=["LlamaDecoderLayer"]
|
|
)
|
|
|
|
model.seqlen = seqlen
|
|
|
|
if half:
|
|
model_to_half(model)
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained(config_path)
|
|
tokenizer.truncation_side = 'left'
|
|
|
|
print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
|
|
|
return model, tokenizer
|
|
|
|
def load_llama_model_4bit_low_ram_and_offload(config_path, model_path, lora_path=None, groupsize=-1, seqlen=2048, max_memory=None, is_v1_model=False):
|
|
import accelerate
|
|
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
|
|
|
|
if max_memory is None:
|
|
max_memory = {0: '24Gib', 'cpu': '48Gib'}
|
|
|
|
print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
|
|
t0 = time.time()
|
|
|
|
with accelerate.init_empty_weights():
|
|
config = LlamaConfig.from_pretrained(config_path)
|
|
model = LlamaForCausalLM(config)
|
|
model = model.eval()
|
|
layers = find_layers(model)
|
|
for name in ['lm_head']:
|
|
if name in layers:
|
|
del layers[name]
|
|
make_quant_for_4bit_autograd(model, layers, groupsize=groupsize, is_v1_model=is_v1_model)
|
|
accelerate.load_checkpoint_in_model(model, checkpoint=model_path, device_map={'': 'cpu'})
|
|
|
|
# rotary_emb fix
|
|
for n, m in model.named_modules():
|
|
if 'rotary_emb' in n:
|
|
cos_cached = m.cos_cached.clone().cpu()
|
|
sin_cached = m.sin_cached.clone().cpu()
|
|
break
|
|
|
|
if lora_path is not None:
|
|
from peft import PeftModel
|
|
from monkeypatch.peft_tuners_lora_monkey_patch import Linear4bitLt
|
|
model = PeftModel.from_pretrained(model, lora_path, device_map={'': 'cpu'}, torch_dtype=torch.float32, is_trainable=True)
|
|
print(Style.BRIGHT + Fore.GREEN + '{} Lora Applied.'.format(lora_path))
|
|
|
|
model.seqlen = seqlen
|
|
|
|
print('Apply half ...')
|
|
for n, m in model.named_modules():
|
|
if isinstance(m, Autograd4bitQuantLinear) or ((lora_path is not None) and isinstance(m, Linear4bitLt)):
|
|
if m.is_v1_model:
|
|
m.zeros = m.zeros.half()
|
|
m.scales = m.scales.half()
|
|
m.bias = m.bias.half()
|
|
|
|
print('Dispatching model ...')
|
|
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
|
|
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True, main_device=0)
|
|
torch.cuda.empty_cache()
|
|
print(Style.BRIGHT + Fore.YELLOW + 'Total {:.2f} Gib VRAM used.'.format(torch.cuda.memory_allocated() / 1024 / 1024))
|
|
|
|
# rotary_emb fix
|
|
for n, m in model.named_modules():
|
|
if 'rotary_emb' in n:
|
|
if getattr(m, '_hf_hook', None):
|
|
if isinstance(m._hf_hook, accelerate.hooks.SequentialHook):
|
|
hooks = m._hf_hook.hooks
|
|
else:
|
|
hooks = [m._hf_hook]
|
|
for hook in hooks:
|
|
if hook.offload:
|
|
if n + '.sin_cached' not in hook.weights_map.dataset.state_dict.keys():
|
|
hook.weights_map.dataset.state_dict[n + '.sin_cached'] = sin_cached.clone().cpu()
|
|
hook.weights_map.dataset.state_dict[n + '.cos_cached'] = cos_cached.clone().cpu()
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained(config_path)
|
|
tokenizer.truncation_side = 'left'
|
|
|
|
print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
|
|
|
return model, tokenizer
|
|
|
|
load_llama_model_4bit_low_ram_and_offload_to_cpu = load_llama_model_4bit_low_ram_and_offload
|