222 lines
9.6 KiB
Python
222 lines
9.6 KiB
Python
from colorama import init, Fore, Back, Style
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import torch
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import torch.nn as nn
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import time
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import math
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import triton
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from triton_utils import matmul_248_kernel, trans_matmul_248_kernel
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class AutogradMatmul4bit(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
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grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
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matmul_248_kernel[grid](input, qweight, output,
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scales, qzeros, g_idx,
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input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0))
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ctx.save_for_backward(qweight, scales, qzeros, g_idx)
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ctx.input_shape, ctx.bits,ctx.maxq = input.shape,bits, maxq
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return output
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@staticmethod
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def backward(ctx, grad_output):
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input_shape, bits, maxq = ctx.input_shape, ctx.bits, ctx.maxq
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qweight, scales, qzeros, g_idx = ctx.saved_tensors
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grade_input = None
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if ctx.needs_input_grad[0]:
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grade_input = torch.empty((input_shape[0], input_shape[1]), device='cuda', dtype=torch.float32)
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grid = lambda META: (triton.cdiv(input_shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(input_shape[1], META['BLOCK_SIZE_K']),)
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trans_matmul_248_kernel[grid](grad_output, qweight, grade_input,
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scales, qzeros, g_idx,
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input_shape[0], qweight.shape[1], input_shape[1], bits, maxq,
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grad_output.stride(0), grad_output.stride(1),
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qweight.stride(0), qweight.stride(1),
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grade_input.stride(0), grade_input.stride(1),
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scales.stride(0), qzeros.stride(0))
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return grade_input, None, None, None, None, None, None
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class Autograd4bitQuantLinear(nn.Module):
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def __init__(self, in_features, out_features, groupsize, bias=True):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.bits = 4 # Hardcoded 4-bits quantizations
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self.maxq = 2 ** self.bits - 1
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self.groupsize = groupsize if groupsize != -1 else in_features
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self.register_buffer('qweight', torch.zeros((in_features // 32 * self.bits, out_features), dtype=torch.int32))
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self.register_buffer('qzeros', torch.zeros((math.ceil(in_features / self.groupsize), out_features // 32 * self.bits), dtype=torch.int32))
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self.register_buffer('scales', torch.zeros((math.ceil(in_features / self.groupsize), out_features), dtype=torch.float16))
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self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(in_features)], dtype = torch.int32))
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if bias:
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self.register_buffer('bias', torch.zeros(out_features,dtype=torch.float16))
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else:
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self.bias = None
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def forward(self, x):
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out_shape = x.shape[:-1] + (self.out_features, )
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out = AutogradMatmul4bit.apply(x.reshape(-1,x.shape[-1]), self.qweight, self.scales,
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self.qzeros, self.g_idx, self.bits, self.maxq)
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out = out + self.bias if self.bias is not None else out
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return out.reshape(out_shape)
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def make_quant_for_4bit_autograd(module, names, name='', groupsize=-1):
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if isinstance(module, Autograd4bitQuantLinear):
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return
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for attr in dir(module):
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tmp = getattr(module, attr)
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name1 = name + '.' + attr if name != '' else attr
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if name1 in names:
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setattr(
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module, attr, Autograd4bitQuantLinear(tmp.in_features, tmp.out_features, groupsize=groupsize)
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)
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for name1, child in module.named_children():
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make_quant_for_4bit_autograd(child, names, name + '.' + name1 if name != '' else name1, groupsize=groupsize)
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def model_to_half(model):
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model.half()
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear):
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m.qzeros = m.qzeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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print(Style.BRIGHT + Fore.YELLOW + 'Converted as Half.')
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def model_to_float(model):
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model.float()
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear):
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m.qzeros = m.qzeros.float()
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m.scales = m.scales.float()
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m.bias = m.bias.float()
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print(Style.BRIGHT + Fore.YELLOW + 'Converted as Float.')
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def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
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if type(module) in layers:
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return {name: module}
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res = {}
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for name1, child in module.named_children():
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res.update(find_layers(
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child, layers=layers, name=name + '.' + name1 if name != '' else name1
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))
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return res
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def load_llama_model_4bit_low_ram(config_path, model_path, groupsize=-1, half=False, device_map="auto", seqlen=2048):
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import accelerate
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
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t0 = time.time()
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with accelerate.init_empty_weights():
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config = LlamaConfig.from_pretrained(config_path)
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model = LlamaForCausalLM(config)
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model = model.eval()
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layers = find_layers(model)
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for name in ['lm_head']:
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if name in layers:
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del layers[name]
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make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
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model = accelerate.load_checkpoint_and_dispatch(
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model=model,
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checkpoint=model_path,
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device_map=device_map,
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no_split_module_classes=["LlamaDecoderLayer"]
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)
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model.seqlen = seqlen
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if half:
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model_to_half(model)
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tokenizer = LlamaTokenizer.from_pretrained(config_path)
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tokenizer.truncation_side = 'left'
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print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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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):
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import accelerate
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
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if max_memory is None:
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max_memory = {0: '24Gib', 'cpu': '48Gib'}
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print(Style.BRIGHT + Fore.CYAN + "Loading Model ...")
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t0 = time.time()
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with accelerate.init_empty_weights():
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config = LlamaConfig.from_pretrained(config_path)
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model = LlamaForCausalLM(config)
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model = model.eval()
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layers = find_layers(model)
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for name in ['lm_head']:
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if name in layers:
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del layers[name]
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make_quant_for_4bit_autograd(model, layers, groupsize=groupsize)
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accelerate.load_checkpoint_in_model(model, checkpoint=model_path, device_map={'': 'cpu'})
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# rotary_emb fix
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for n, m in model.named_modules():
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if 'rotary_emb' in n:
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cos_cached = m.cos_cached.clone().cpu()
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sin_cached = m.sin_cached.clone().cpu()
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break
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if lora_path is not None:
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from peft import PeftModel
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from peft.tuners.lora import Linear4bitLt
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model = PeftModel.from_pretrained(model, lora_path, device_map={'': 'cpu'}, torch_dtype=torch.float32)
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print(Style.BRIGHT + Fore.GREEN + '{} Lora Applied.'.format(lora_path))
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model.seqlen = seqlen
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print('Apply half ...')
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for n, m in model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear) or ((lora_path is not None) and isinstance(m, Linear4bitLt)):
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m.qzeros = m.qzeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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print('Dispatching model ...')
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device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
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model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True, main_device=0)
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torch.cuda.empty_cache()
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print(Style.BRIGHT + Fore.YELLOW + 'Total {:.2f} Gib VRAM used.'.format(torch.cuda.memory_allocated() / 1024 / 1024))
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# rotary_emb fix
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for n, m in model.named_modules():
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if 'rotary_emb' in n:
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if getattr(m, '_hf_hook', None):
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if isinstance(m._hf_hook, accelerate.hooks.SequentialHook):
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hooks = m._hf_hook.hooks
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else:
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hooks = [m._hf_hook]
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for hook in hooks:
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if hook.offload:
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if n + '.sin_cached' not in hook.weights_map.dataset.state_dict.keys():
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hook.weights_map.dataset.state_dict[n + '.sin_cached'] = sin_cached.clone().cpu()
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hook.weights_map.dataset.state_dict[n + '.cos_cached'] = cos_cached.clone().cpu()
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tokenizer = LlamaTokenizer.from_pretrained(config_path)
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tokenizer.truncation_side = 'left'
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print(Style.BRIGHT + Fore.GREEN + f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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