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import time |
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import torch |
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import torch.nn as nn |
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from gptq import * |
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from modelutils import * |
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from quant import * |
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def get_llama(model): |
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import torch |
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def skip(*args, **kwargs): |
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pass |
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torch.nn.init.kaiming_uniform_ = skip |
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torch.nn.init.uniform_ = skip |
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torch.nn.init.normal_ = skip |
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from transformers import LlamaForCausalLM |
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model = LlamaForCausalLM.from_pretrained(model, torch_dtype='auto') |
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model.seqlen = 2048 |
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return model |
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@torch.no_grad() |
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def llama_sequential(model, dataloader, dev): |
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print('Starting ...') |
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use_cache = model.config.use_cache |
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model.config.use_cache = False |
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layers = model.model.layers |
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model.model.embed_tokens = model.model.embed_tokens.to(dev) |
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model.model.norm = model.model.norm.to(dev) |
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layers[0] = layers[0].to(dev) |
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dtype = next(iter(model.parameters())).dtype |
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inps = torch.zeros( |
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(args.nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev |
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) |
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cache = {'i': 0, 'attention_mask': None} |
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class Catcher(nn.Module): |
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def __init__(self, module): |
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super().__init__() |
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self.module = module |
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def forward(self, inp, **kwargs): |
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inps[cache['i']] = inp |
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cache['i'] += 1 |
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cache['attention_mask'] = kwargs['attention_mask'] |
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cache['position_ids'] = kwargs['position_ids'] |
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raise ValueError |
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layers[0] = Catcher(layers[0]) |
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for batch in dataloader: |
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try: |
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model(batch[0].to(dev)) |
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except ValueError: |
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pass |
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layers[0] = layers[0].module |
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layers[0] = layers[0].cpu() |
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model.model.embed_tokens = model.model.embed_tokens.cpu() |
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model.model.norm = model.model.norm.cpu() |
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torch.cuda.empty_cache() |
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outs = torch.zeros_like(inps) |
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attention_mask = cache['attention_mask'] |
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position_ids = cache['position_ids'] |
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print('Ready.') |
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quantizers = {} |
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for i in range(len(layers)): |
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layer = layers[i].to(dev) |
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full = find_layers(layer) |
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if args.true_sequential: |
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sequential = [ |
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['self_attn.k_proj', 'self_attn.v_proj', 'self_attn.q_proj'], |
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['self_attn.o_proj'], |
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['mlp.up_proj', 'mlp.gate_proj'], |
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['mlp.down_proj'] |
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] |
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else: |
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sequential = [list(full.keys())] |
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for names in sequential: |
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subset = {n: full[n] for n in names} |
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gptq = {} |
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for name in subset: |
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gptq[name] = GPTQ(subset[name]) |
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gptq[name].quantizer = Quantizer() |
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gptq[name].quantizer.configure( |
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args.wbits, perchannel=True, sym=args.sym, mse=False |
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) |
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def add_batch(name): |
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def tmp(_, inp, out): |
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gptq[name].add_batch(inp[0].data, out.data) |
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return tmp |
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handles = [] |
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for name in subset: |
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handles.append(subset[name].register_forward_hook(add_batch(name))) |
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for j in range(args.nsamples): |
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outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids = position_ids)[0] |
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for h in handles: |
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h.remove() |
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for name in subset: |
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print(f'Quantizing {name} in layer {i+1}/{len(layers)}...') |
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scale,zero,g_idx = gptq[name].fasterquant(percdamp=args.percdamp, groupsize=args.groupsize, actorder=args.act_order) |
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quantizers['model.layers.%d.%s' % (i, name)] = (gptq[name].quantizer.cpu(),scale.cpu(),zero.cpu(),g_idx.cpu()) |
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gptq[name].free() |
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for j in range(args.nsamples): |
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outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids = position_ids)[0] |
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layers[i] = layer.cpu() |
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del layer |
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del gptq |
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torch.cuda.empty_cache() |
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inps, outs = outs, inps |
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model.config.use_cache = use_cache |
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return quantizers |
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@torch.no_grad() |
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def llama_eval(model, testenc, dev): |
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print('Evaluating ...') |
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testenc = testenc.input_ids |
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nsamples = testenc.numel() // model.seqlen |
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use_cache = model.config.use_cache |
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model.config.use_cache = False |
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layers = model.model.layers |
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model.model.embed_tokens = model.model.embed_tokens.to(dev) |
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layers[0] = layers[0].to(dev) |
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dtype = next(iter(model.parameters())).dtype |
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inps = torch.zeros( |
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(nsamples, model.seqlen, model.config.hidden_size), dtype=dtype, device=dev |
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) |
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cache = {'i': 0, 'attention_mask': None} |
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class Catcher(nn.Module): |
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def __init__(self, module): |
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super().__init__() |
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self.module = module |
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def forward(self, inp, **kwargs): |
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inps[cache['i']] = inp |
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cache['i'] += 1 |
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cache['attention_mask'] = kwargs['attention_mask'] |
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cache['position_ids'] = kwargs['position_ids'] |
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raise ValueError |
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layers[0] = Catcher(layers[0]) |
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for i in range(nsamples): |
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batch = testenc[:, (i * model.seqlen):((i + 1) * model.seqlen)].to(dev) |
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try: |
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model(batch) |
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except ValueError: |
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pass |
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layers[0] = layers[0].module |
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layers[0] = layers[0].cpu() |
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model.model.embed_tokens = model.model.embed_tokens.cpu() |
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torch.cuda.empty_cache() |
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outs = torch.zeros_like(inps) |
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attention_mask = cache['attention_mask'] |
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position_ids = cache['position_ids'] |
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for i in range(len(layers)): |
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print(i) |
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layer = layers[i].to(dev) |
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if args.nearest: |
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subset = find_layers(layer) |
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for name in subset: |
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quantizer = Quantizer() |
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quantizer.configure( |
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args.wbits, perchannel=True, sym=args.sym, mse=False |
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) |
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W = subset[name].weight.data |
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quantizer.find_params(W, weight=True) |
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subset[name].weight.data = quantize( |
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W, quantizer.scale, quantizer.zero, quantizer.maxq |
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).to(next(iter(layer.parameters())).dtype) |
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for j in range(nsamples): |
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outs[j] = layer(inps[j].unsqueeze(0), attention_mask=attention_mask, position_ids = position_ids)[0] |
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layers[i] = layer.cpu() |
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del layer |
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torch.cuda.empty_cache() |
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inps, outs = outs, inps |
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if model.model.norm is not None: |
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model.model.norm = model.model.norm.to(dev) |
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model.lm_head = model.lm_head.to(dev) |
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testenc = testenc.to(dev) |
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nlls = [] |
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for i in range(nsamples): |
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hidden_states = inps[i].unsqueeze(0) |
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if model.model.norm is not None: |
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hidden_states = model.model.norm(hidden_states) |
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lm_logits = model.lm_head(hidden_states) |
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shift_logits = lm_logits[:, :-1, :].contiguous() |
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shift_labels = testenc[ |
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:, (i * model.seqlen):((i + 1) * model.seqlen) |
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][:, 1:] |
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loss_fct = nn.CrossEntropyLoss() |
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) |
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neg_log_likelihood = loss.float() * model.seqlen |
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nlls.append(neg_log_likelihood) |
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ppl = torch.exp(torch.stack(nlls).sum() / (nsamples * model.seqlen)) |
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print(ppl.item()) |
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model.config.use_cache = use_cache |
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def llama_pack(model, quantizers, wbits, groupsize): |
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layers = find_layers(model) |
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layers = {n: layers[n] for n in quantizers} |
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make_quant(model, quantizers, wbits, groupsize) |
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qlayers = find_layers(model, [QuantLinear]) |
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print('Packing ...') |
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for name in qlayers: |
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print(name) |
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quantizers[name],scale,zero,g_idx = quantizers[name] |
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qlayers[name].pack(layers[name], scale, zero, g_idx) |
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print('Done.') |
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return model |
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def load_quant(model, checkpoint, wbits, groupsize = -1, warmup_autotune = True): |
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from transformers import LlamaConfig, LlamaForCausalLM |
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config = LlamaConfig.from_pretrained(model) |
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def noop(*args, **kwargs): |
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pass |
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torch.nn.init.kaiming_uniform_ = noop |
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torch.nn.init.uniform_ = noop |
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torch.nn.init.normal_ = noop |
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torch.set_default_dtype(torch.half) |
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transformers.modeling_utils._init_weights = False |
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torch.set_default_dtype(torch.half) |
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model = LlamaForCausalLM(config) |
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torch.set_default_dtype(torch.float) |
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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(model, layers, wbits, groupsize) |
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del layers |
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print('Loading model ...') |
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if checkpoint.endswith('.safetensors'): |
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from safetensors.torch import load_file as safe_load |
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model.load_state_dict(safe_load(checkpoint), strict = False) |
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else: |
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model.load_state_dict(torch.load(checkpoint), strict = False) |
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if warmup_autotune: |
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autotune_warmup(model) |
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model.seqlen = 2048 |
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print('Done.') |
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return model |
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def llama_multigpu(model, gpus): |
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model.model.embed_tokens = model.model.embed_tokens.to(gpus[0]) |
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if hasattr(model.model, 'norm') and model.model.norm: |
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model.model.norm = model.model.norm.to(gpus[-1]) |
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import copy |
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model.lm_head = copy.deepcopy(model.lm_head).to(gpus[-1]) |
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cache = {'mask': None} |
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class MoveModule(nn.Module): |
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def __init__(self, module): |
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super().__init__() |
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self.module = module |
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self.dev = next(iter(self.module.parameters())).device |
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def forward(self, *inp, **kwargs): |
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inp = list(inp) |
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if inp[0].device != self.dev: |
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inp[0] = inp[0].to(self.dev) |
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if cache['mask'] is None or cache['mask'].device != self.dev: |
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cache['mask'] = kwargs['attention_mask'].to(self.dev) |
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kwargs['attention_mask'] = cache['mask'] |
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tmp = self.module(*inp, **kwargs) |
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return tmp |
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layers = model.model.layers |
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pergpu = math.ceil(len(layers) / len(gpus)) |
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for i in range(len(layers)): |
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layers[i] = MoveModule(layers[i].to(gpus[i // pergpu])) |
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model.gpus = gpus |
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def benchmark(model, input_ids, check=False): |
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input_ids = input_ids.to(model.gpus[0] if hasattr(model, 'gpus') else DEV) |
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torch.cuda.synchronize() |
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cache = {'past': None} |
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def clear_past(i): |
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def tmp(layer, inp, out): |
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if cache['past']: |
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cache['past'][i] = None |
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return tmp |
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for i, layer in enumerate(model.model.layers): |
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layer.register_forward_hook(clear_past(i)) |
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print('Benchmarking ...') |
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if check: |
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loss = nn.CrossEntropyLoss() |
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tot = 0. |
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def sync(): |
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if hasattr(model, 'gpus'): |
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for gpu in model.gpus: |
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torch.cuda.synchronize(gpu) |
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else: |
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torch.cuda.synchronize() |
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max_memory = 0 |
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with torch.no_grad(): |
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attention_mask = torch.ones((1, input_ids.numel()), device=DEV) |
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times = [] |
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for i in range(input_ids.numel()): |
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tick = time.time() |
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out = model( |
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input_ids[:, i:i+1], |
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past_key_values=cache['past'], |
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attention_mask=attention_mask[:, :(i + 1)].reshape((1, -1)) |
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) |
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sync() |
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times.append(time.time() - tick) |
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print(i, times[-1]) |
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max_memory = max(max_memory,torch.cuda.memory_allocated() / 1024 /1024) |
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if check and i != input_ids.numel() - 1: |
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tot += loss(out.logits[0].to(DEV), input_ids[:, (i + 1)].to(DEV)).float() |
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cache['past'] = list(out.past_key_values) |
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del out |
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sync() |
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import numpy as np |
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print('Median:', np.median(times)) |
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if check: |
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print('PPL:', torch.exp(tot / (input_ids.numel() - 1)).item()) |
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print('max memory(MiB):',max_memory) |
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if __name__ == '__main__': |
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import argparse |
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from datautils import * |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'model', type=str, |
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help='llama model to load' |
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) |
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parser.add_argument( |
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'dataset', type=str, choices=['wikitext2', 'ptb', 'c4'], |
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help='Where to extract calibration data from.' |
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) |
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parser.add_argument( |
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'--seed', |
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type=int, default=0, help='Seed for sampling the calibration data.' |
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) |
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parser.add_argument( |
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'--nsamples', type=int, default=128, |
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help='Number of calibration data samples.' |
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) |
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|
parser.add_argument( |
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'--percdamp', type=float, default=.01, |
|
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help='Percent of the average Hessian diagonal to use for dampening.' |
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|
) |
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|
parser.add_argument( |
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'--nearest', action='store_true', |
|
|
help='Whether to run the RTN baseline.' |
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) |
|
|
parser.add_argument( |
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'--wbits', type=int, default=16, choices=[2, 3, 4, 8, 16], |
|
|
help='#bits to use for quantization; use 16 for evaluating base model.' |
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|
) |
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|
parser.add_argument( |
|
|
'--trits', action='store_true', |
|
|
help='Whether to use trits for quantization.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--groupsize', type=int, default=-1, |
|
|
help='Groupsize to use for quantization; default uses full row.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--eval', action='store_true', |
|
|
help='evaluate quantized model.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--save', type=str, default='', |
|
|
help='Save quantized checkpoint under this name.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--save_safetensors', type=str, default='', |
|
|
help='Save quantized `.safetensors` checkpoint under this name.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--load', type=str, default='', |
|
|
help='Load quantized model.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--benchmark', type=int, default=0, |
|
|
help='Number of tokens to use for benchmarking.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--check', action='store_true', |
|
|
help='Whether to compute perplexity during benchmarking for verification.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--sym', action='store_true', |
|
|
help='Whether to perform symmetric quantization.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--act-order', action='store_true', |
|
|
help='Whether to apply the activation order GPTQ heuristic' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--true-sequential', action='store_true', |
|
|
help='Whether to run in true sequential model.' |
|
|
) |
|
|
parser.add_argument( |
|
|
'--new-eval', action='store_true', |
|
|
help='Whether to use the new PTB and C4 eval' |
|
|
) |
|
|
|
|
|
args = parser.parse_args() |
|
|
|
|
|
if type(args.load) is not str: |
|
|
args.load = args.load.as_posix() |
|
|
|
|
|
if args.load: |
|
|
model = load_quant(args.model, args.load, args.wbits, args.groupsize) |
|
|
else: |
|
|
model = get_llama(args.model) |
|
|
model.eval() |
|
|
|
|
|
dataloader, testloader = get_loaders( |
|
|
args.dataset, nsamples=args.nsamples, seed=args.seed, model=args.model, seqlen=model.seqlen |
|
|
) |
|
|
|
|
|
if not args.load and args.wbits < 16 and not args.nearest: |
|
|
tick = time.time() |
|
|
quantizers = llama_sequential(model, dataloader, DEV) |
|
|
print(time.time() - tick) |
|
|
|
|
|
if args.benchmark: |
|
|
gpus = [torch.device('cuda:%d' % i) for i in range(torch.cuda.device_count())] |
|
|
if len(gpus) > 1: |
|
|
llama_multigpu(model, gpus) |
|
|
else: |
|
|
model = model.to(DEV) |
|
|
if args.benchmark: |
|
|
input_ids = next(iter(dataloader))[0][:, :args.benchmark] |
|
|
benchmark(model, input_ids, check=args.check) |
|
|
|
|
|
if args.eval: |
|
|
datasets = ['wikitext2', 'ptb', 'c4'] |
|
|
if args.new_eval: |
|
|
datasets = ['wikitext2', 'ptb-new', 'c4-new'] |
|
|
for dataset in datasets: |
|
|
dataloader, testloader = get_loaders( |
|
|
dataset, seed=args.seed, model=args.model, seqlen=model.seqlen |
|
|
) |
|
|
print(dataset) |
|
|
llama_eval(model, testloader, DEV) |
|
|
|
|
|
if args.save: |
|
|
llama_pack(model, quantizers, args.wbits, args.groupsize) |
|
|
torch.save(model.state_dict(), args.save) |
|
|
|
|
|
if args.save_safetensors: |
|
|
llama_pack(model, quantizers, args.wbits, args.groupsize) |
|
|
from safetensors.torch import save_file as safe_save |
|
|
safe_save(model.state_dict(), args.save_safetensors) |
|
|
|
