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alpaca_lora_4bit
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merge pull request in new branch

This commit is contained in:
John Smith 2023-04-07 10:40:24 +08:00
parent 85e9cf004a c991e2a091
commit 9351f49542
8 changed files with 476 additions and 228 deletions
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3
Finetune4bConfig.py
View File

@ -97,5 +97,6 @@ class Finetune4bConfig:
f"{self.warmup_steps=}\n{self.save_steps=}\n{self.save_total_limit=}\n" +\
f"{self.logging_steps=}\n" +\
f"{self.checkpoint=}\n{self.skip=}\n" +\
f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}"
f"{self.world_size=}\n{self.ddp=}\n{self.device_map=}\n" +\
f"{self.groupsize=}\n"
return s.replace("self.", "")

10
README.md
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@ -35,10 +35,20 @@ pip install -r requirements.txt
~The same finetune script from https://github.com/tloen/alpaca-lora can be used.~<br>
After installation, this script can be used:
GPTQv1:
```
python finetune.py
```
or
```
GPTQ_VERSION=1 python finetune.py
```
GPTQv2:
```
GPTQ_VERSION=2 python finetune.py
```
# Inference

21
autograd_4bit/__init__.py Normal file
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@ -0,0 +1,21 @@
import os
from colorama import init, Fore, Back, Style
init(autoreset=True)
try:
GPTQ_VERSION = int(os.environ["GPTQ_VERSION"])
except:
print(Style.BRIGHT + Fore.YELLOW + "GPTQ_VERSION environment not provided. Fallback to GPTQv1")
GPTQ_VERSION = 1 # Fallback version
loader = None
if GPTQ_VERSION == 1:
from .autograd_4bit_v1 import Autograd4bitQuantLinear, load_llama_model_4bit_low_ram
print(Style.BRIGHT + Fore.GREEN + "GPTQv1 set")
elif GPTQ_VERSION == 2:
from .autograd_4bit_v2 import Autograd4bitQuantLinear, load_llama_model_4bit_low_ram
print(Style.BRIGHT + Fore.GREEN + "GPTQv2 set")
else:
raise ValueError("GPTQ_VERSION not set or invalid")

32
autograd_4bit.py → autograd_4bit/autograd_4bit_v1.py
View File

@ -2,7 +2,6 @@ import matmul_utils_4bit as mm4b
import torch
import torch.nn as nn
import time
import math
class AutogradMatmul4bit(torch.autograd.Function):
@ -32,25 +31,17 @@ class AutogradMatmul4bit(torch.autograd.Function):
# Assumes layer is perfectly divisible into 256 * 256 blocks
class Autograd4bitQuantLinear(nn.Module):
def __init__(self, infeatures, outfeatures, groupsize=-1):
def __init__(self, in_features, out_features, groupsize=None):
super().__init__()
bits = 4
self.in_features = infeatures
self.out_features = outfeatures
self.in_features = in_features
self.out_features = out_features
self.bits = bits
self.groupsize = groupsize
if groupsize == -1:
self.register_buffer('zeros', torch.empty((outfeatures, 1)))
self.register_buffer('scales', torch.empty((outfeatures, 1)))
else:
self.register_buffer('qzeros',
torch.empty((math.ceil(infeatures/groupsize), outfeatures // 256 * (bits * 8)), dtype=torch.int)
)
self.register_buffer('scales', torch.empty((math.ceil(infeatures/groupsize), outfeatures)))
self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype = torch.int32))
self.register_buffer('bias', torch.empty(outfeatures))
self.register_buffer('zeros', torch.empty((out_features, 1)))
self.register_buffer('scales', torch.empty((out_features, 1)))
self.bias = nn.Parameter(torch.empty(out_features))
self.register_buffer(
'qweight', torch.empty((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
'qweight', torch.empty((in_features // 256 * (bits * 8), out_features), dtype=torch.int)
)
@ -84,8 +75,7 @@ def model_to_half(model):
model.half()
for n, m in model.named_modules():
if isinstance(m, Autograd4bitQuantLinear):
if m.groupsize == -1:
m.zeros = m.zeros.half()
m.zeros = m.zeros.half()
m.scales = m.scales.half()
m.bias = m.bias.half()
print('Converted as Half.')
@ -95,8 +85,7 @@ def model_to_float(model):
model.float()
for n, m in model.named_modules():
if isinstance(m, Autograd4bitQuantLinear):
if m.groupsize == -1:
m.zeros = m.zeros.float()
m.zeros = m.zeros.float()
m.scales = m.scales.float()
m.bias = m.bias.float()
print('Converted as Float.')
@ -187,8 +176,7 @@ def load_llama_model_4bit_low_ram_and_offload_to_cpu(config_path, model_path, lo
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.groupsize == -1:
m.zeros = m.zeros.half()
m.zeros = m.zeros.half()
m.scales = m.scales.half()
m.bias = m.bias.half()

221
autograd_4bit/autograd_4bit_v2.py Normal file
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@ -0,0 +1,221 @@
from colorama import init, Fore, Back, Style
import torch
import torch.nn as nn
import time
import math
import triton
from triton_utils import matmul_248_kernel, trans_matmul_248_kernel
class AutogradMatmul4bit(torch.autograd.Function):
@staticmethod
def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
matmul_248_kernel[grid](input, qweight, output,
scales, qzeros, g_idx,
input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
input.stride(0), input.stride(1),
qweight.stride(0), qweight.stride(1),
output.stride(0), output.stride(1),
scales.stride(0), qzeros.stride(0))
ctx.save_for_backward(qweight, scales, qzeros, g_idx)
ctx.input_shape, ctx.bits,ctx.maxq = input.shape,bits, maxq
return output
@staticmethod
def backward(ctx, grad_output):
input_shape, bits, maxq = ctx.input_shape, ctx.bits, ctx.maxq
qweight, scales, qzeros, g_idx = ctx.saved_tensors
grade_input = None
if ctx.needs_input_grad[0]:
grade_input = torch.empty((input_shape[0], input_shape[1]), device='cuda', dtype=torch.float32)
grid = lambda META: (triton.cdiv(input_shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(input_shape[1], META['BLOCK_SIZE_K']),)
trans_matmul_248_kernel[grid](grad_output, qweight, grade_input,
scales, qzeros, g_idx,
input_shape[0], qweight.shape[1], input_shape[1], bits, maxq,
grad_output.stride(0), grad_output.stride(1),
qweight.stride(0), qweight.stride(1),
grade_input.stride(0), grade_input.stride(1),
scales.stride(0), qzeros.stride(0))
return grade_input, None, None, None, None, None, None
class Autograd4bitQuantLinear(nn.Module):
def __init__(self, in_features, out_features, groupsize, bias=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.bits = 4 # Hardcoded 4-bits quantizations
self.maxq = 2 ** self.bits - 1
self.groupsize = groupsize if groupsize != -1 else in_features
self.register_buffer('qweight', torch.zeros((in_features // 32 * self.bits, out_features), dtype=torch.int32))
self.register_buffer('qzeros', torch.zeros((math.ceil(in_features / self.groupsize), out_features // 32 * self.bits), dtype=torch.int32))
self.register_buffer('scales', torch.zeros((math.ceil(in_features / self.groupsize), out_features), dtype=torch.float16))
self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(in_features)], dtype = torch.int32))
if bias:
self.register_buffer('bias', torch.zeros(out_features,dtype=torch.float16))
else:
self.bias = None
def forward(self, x):
out_shape = x.shape[:-1] + (self.out_features, )
out = AutogradMatmul4bit.apply(x.reshape(-1,x.shape[-1]), self.qweight, self.scales,
self.qzeros, self.g_idx, self.bits, self.maxq)
out = out + self.bias if self.bias is not None else out
return out.reshape(out_shape)
def make_quant_for_4bit_autograd(module, names, name='', groupsize=-1):
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)
)
for name1, child in module.named_children():
make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1, groupsize=groupsize)
def model_to_half(model):
model.half()
for n, m in model.named_modules():
if isinstance(m, Autograd4bitQuantLinear):
m.qzeros = m.qzeros.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):
m.qzeros = m.qzeros.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):
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)
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_to_cpu(config_path, model_path, lora_path=None, groupsize=-1, seqlen=2048, max_memory=None):
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)
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 peft.tuners.lora import Linear4bitLt
model = PeftModel.from_pretrained(model, lora_path, device_map={'': 'cpu'}, torch_dtype=torch.float32)
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)):
m.qzeros = m.qzeros.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

1
finetune.py
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@ -115,6 +115,7 @@ if not ft_config.skip:
per_device_train_batch_size=ft_config.mbatch_size,
gradient_accumulation_steps=ft_config.gradient_accumulation_steps,
warmup_steps=ft_config.warmup_steps,
optim="adamw_torch",
num_train_epochs=ft_config.epochs,
learning_rate=ft_config.lr,
fp16=True,

1
requirements.txt
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@ -5,6 +5,7 @@ datasets
sentencepiece
safetensors
flash-attn
triton
git+https://github.com/huggingface/transformers.git
git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
git+https://github.com/sterlind/peft.git

17
triton_utils.py
View File

@ -62,7 +62,7 @@ def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
g_ptrs = g_ptr + offs_k
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_bn[None, :]
zeros_ptrs = zeros_ptr + (offs_bn[None, :]// infearure_per_bits)
zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
shifter = (offs_k % infearure_per_bits) * bits
zeros_shifter = (offs_bn % infearure_per_bits) * bits
@ -78,12 +78,15 @@ def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
# ! Convert to fp16
b = b.to(tl.float16)
a = a.to(tl.float16)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K
@ -93,7 +96,7 @@ def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
c = accumulator.to(tl.float16)
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
tl.store(c_ptrs, accumulator, mask=c_mask)
tl.store(c_ptrs, c, mask=c_mask)
# code based https://github.com/fpgaminer/GPTQ-triton
@triton.autotune(
@ -157,7 +160,7 @@ def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
zeros_ptrs = zeros_ptr + (offs_n[None, :]// infearure_per_bits) + g_idx[:, None] * stride_zeros
zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
shifter = (offs_bk % infearure_per_bits) * bits
zeros_shifter = (offs_n % infearure_per_bits) * bits
@ -178,6 +181,9 @@ def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
b = tl.trans(b)
# ! Convert to fp16
b = b.to(tl.float16)
a = a.to(tl.float16)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_N
@ -188,7 +194,7 @@ def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
c = accumulator.to(tl.float16)
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
tl.store(c_ptrs, accumulator, mask=c_mask)
tl.store(c_ptrs, c, mask=c_mask)
def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
@ -202,4 +208,3 @@ def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
output.stride(0), output.stride(1),
scales.stride(0), qzeros.stride(0))
return output