From 4a2d23aa293e61dd06b9294e5af05aa6789ce140 Mon Sep 17 00:00:00 2001 From: Andrey Glushenkov Date: Thu, 6 Apr 2023 02:31:06 +0300 Subject: [PATCH] Delete autograd_4bit.py File moved to autograd_4bit module --- autograd_4bit.py | 220 ----------------------------------------------- 1 file changed, 220 deletions(-) delete mode 100644 autograd_4bit.py diff --git a/autograd_4bit.py b/autograd_4bit.py deleted file mode 100644 index bb63cab..0000000 --- a/autograd_4bit.py +++ /dev/null @@ -1,220 +0,0 @@ -import matmul_utils_4bit as mm4b -import torch -import torch.nn as nn -import time -import math - - -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, infeatures, outfeatures, groupsize=-1): - super().__init__() - bits = 4 - self.in_features = infeatures - self.out_features = outfeatures - 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.bias = nn.Parameter(torch.empty(outfeatures)) - self.register_buffer( - 'qweight', torch.empty((infeatures // 256 * (bits * 8), outfeatures), 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): - if m.groupsize == -1: - 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): - if m.groupsize == -1: - 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)): - if m.groupsize == -1: - 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