GPTQv2 support
GPTQv2 support. 1. Adds dependency on `triton` 2. Refactors autograd_4bit to include both GPTQv1 and GPTQv2 3. Introduces new environment variable GPTQ_VERSION to select autograd_4bit version 4. Fixes triton kernels 5. Matrix multiplications are in fp16
This commit is contained in:
Andrey Glushenkov
committed by
GitHub
GitHub
parent
86387a0a35
commit
f20570343f
21
autograd_4bit/__init__.py
Normal file
21
autograd_4bit/__init__.py
Normal file
@@ -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")
|
||||
208
autograd_4bit/autograd_4bit_v1.py
Normal file
208
autograd_4bit/autograd_4bit_v1.py
Normal file
@@ -0,0 +1,208 @@
|
||||
import matmul_utils_4bit as mm4b
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import time
|
||||
|
||||
|
||||
class AutogradMatmul4bit(torch.autograd.Function):
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, x, qweight, scales, zeros, groupsize=-1):
|
||||
ctx.save_for_backward(qweight, scales, zeros)
|
||||
ctx.groupsize = groupsize
|
||||
if groupsize == -1:
|
||||
output = mm4b._matmul4bit_v1_recons(x, qweight, scales, zeros)
|
||||
else:
|
||||
output = mm4b._matmul4bit_v2_recons(x, qweight, scales, zeros, groupsize)
|
||||
output = output.clone()
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
qweight, scales, zeros = ctx.saved_tensors
|
||||
groupsize = ctx.groupsize
|
||||
if groupsize == -1:
|
||||
grad = mm4b._matmul4bit_v1_recons(grad_output, qweight, scales, zeros, transpose=True)
|
||||
else:
|
||||
grad = mm4b._matmul4bit_v2_recons(grad_output, qweight, scales, zeros, groupsize=groupsize, transpose=True)
|
||||
return grad, None, None, None, None
|
||||
|
||||
|
||||
# Assumes layer is perfectly divisible into 256 * 256 blocks
|
||||
class Autograd4bitQuantLinear(nn.Module):
|
||||
|
||||
def __init__(self, in_features, out_features, groupsize=None):
|
||||
super().__init__()
|
||||
bits = 4
|
||||
self.in_features = in_features
|
||||
self.out_features = out_features
|
||||
self.bits = bits
|
||||
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((in_features // 256 * (bits * 8), out_features), dtype=torch.int)
|
||||
)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
if torch.is_grad_enabled():
|
||||
out = AutogradMatmul4bit.apply(x, self.qweight, self.scales,
|
||||
self.qzeros if self.groupsize != -1 else self.zeros, self.groupsize)
|
||||
out.add_(self.bias)
|
||||
else:
|
||||
out = mm4b.matmul4bit(x, self.qweight, self.scales,
|
||||
self.qzeros if self.groupsize != -1 else self.zeros, self.groupsize)
|
||||
out.add_(self.bias)
|
||||
return out
|
||||
|
||||
|
||||
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.zeros = m.zeros.half()
|
||||
m.scales = m.scales.half()
|
||||
m.bias = m.bias.half()
|
||||
print('Converted as Half.')
|
||||
|
||||
|
||||
def model_to_float(model):
|
||||
model.float()
|
||||
for n, m in model.named_modules():
|
||||
if isinstance(m, Autograd4bitQuantLinear):
|
||||
m.zeros = m.zeros.float()
|
||||
m.scales = m.scales.float()
|
||||
m.bias = m.bias.float()
|
||||
print('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("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(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("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('{} 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.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('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(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
||||
|
||||
return model, tokenizer
|
||||
221
autograd_4bit/autograd_4bit_v2.py
Normal file
221
autograd_4bit/autograd_4bit_v2.py
Normal file
@@ -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
|
||||
Reference in New Issue
Block a user