mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-11 05:49:55 +00:00
[chat] fix bugs and add unit tests (#4213)
* style: rename replay buffer Experience replay is typically for off policy algorithms. Use this name in PPO maybe misleading. * fix: fix wrong zero2 default arg * test: update experience tests * style: rename zero_pad fn * fix: defer init in CycledDataLoader * test: add benchmark test * style: rename internal fn of generation * style: rename internal fn of lora * fix: remove unused loss fn * fix: remove unused utils fn * refactor: remove generate_with_actor fn * fix: fix type annotation * test: add models tests * fix: skip llama due to long execution time * style: modify dataset * style: apply formatter * perf: update reward dataset * fix: fix wrong IGNORE_INDEX in sft dataset * fix: remove DataCollatorForSupervisedDataset * test: add dataset tests * style: apply formatter * style: rename test_ci to test_train * feat: add llama in inference * test: add inference tests * test: change test scripts directory * fix: update ci * fix: fix typo * fix: skip llama due to oom * fix: fix file mod * style: apply formatter * refactor: remove duplicated llama_gptq * style: apply formatter * to: update rm test * feat: add tokenizer arg * feat: add download model script * test: update train tests * fix: modify gemini load and save pretrained * test: update checkpoint io test * to: modify nproc_per_node * fix: do not remove existing dir * fix: modify save path * test: add random choice * fix: fix sft path * fix: enlarge nproc_per_node to avoid oom * fix: add num_retry * fix: make lora config of rm and critic consistent * fix: add warning about lora weights * fix: skip some gpt2 tests * fix: remove grad ckpt in rm and critic due to errors * refactor: directly use Actor in train_sft * test: add more arguments * fix: disable grad ckpt when using lora * fix: fix save_pretrained and related tests * test: enable zero2 tests * revert: remove useless fn * style: polish code * test: modify test args
This commit is contained in:
Wenhao Chen
GitHub
@@ -4,8 +4,8 @@ import argparse
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from time import time
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import torch
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from llama_gptq import load_quant
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from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM
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from coati.quant import llama_load_quant, low_resource_init
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from transformers import AutoTokenizer, GenerationConfig, LlamaConfig, LlamaForCausalLM
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def generate_prompt(instruction, input=None):
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@@ -106,7 +106,10 @@ if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained(args.pretrained)
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if args.quant == '4bit':
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model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size)
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with low_resource_init():
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config = LlamaConfig.from_pretrained(args.pretrained)
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model = LlamaForCausalLM(config)
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model = llama_load_quant(model, args.gptq_checkpoint, 4, args.gptq_group_size)
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model.cuda()
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else:
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model = LlamaForCausalLM.from_pretrained(
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@@ -1,5 +0,0 @@
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from .loader import load_quant
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__all__ = [
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'load_quant',
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]
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@@ -1,41 +0,0 @@
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import torch
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import torch.nn as nn
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import transformers
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from transformers import LlamaConfig, LlamaForCausalLM
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from .model_utils import find_layers
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from .quant import make_quant
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def load_quant(pretrained: str, checkpoint: str, wbits: int, groupsize: int):
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config = LlamaConfig.from_pretrained(pretrained)
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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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print(f'Loading model with {wbits} bits...')
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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))
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else:
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model.load_state_dict(torch.load(checkpoint))
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model.seqlen = 2048
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print('Done.')
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return model
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@@ -1,13 +0,0 @@
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# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/modelutils.py
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import torch
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import torch.nn as nn
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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(child, layers=layers, name=name + '.' + name1 if name != '' else name1))
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return res
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@@ -1,283 +0,0 @@
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# copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/past/quant.py
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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def quantize(x, scale, zero, maxq):
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q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
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return scale * (q - zero)
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class Quantizer(nn.Module):
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def __init__(self, shape=1):
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super(Quantizer, self).__init__()
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self.register_buffer('maxq', torch.tensor(0))
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self.register_buffer('scale', torch.zeros(shape))
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self.register_buffer('zero', torch.zeros(shape))
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def configure(self, bits, perchannel=False, sym=True, mse=False, norm=2.4, grid=100, maxshrink=.8):
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self.maxq = torch.tensor(2**bits - 1)
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self.perchannel = perchannel
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self.sym = sym
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self.mse = mse
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self.norm = norm
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self.grid = grid
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self.maxshrink = maxshrink
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def find_params(self, x, weight=False):
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dev = x.device
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self.maxq = self.maxq.to(dev)
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shape = x.shape
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if self.perchannel:
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if weight:
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x = x.flatten(1)
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else:
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if len(shape) == 4:
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x = x.permute([1, 0, 2, 3])
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x = x.flatten(1)
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if len(shape) == 3:
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x = x.reshape((-1, shape[-1])).t()
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if len(shape) == 2:
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x = x.t()
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else:
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x = x.flatten().unsqueeze(0)
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tmp = torch.zeros(x.shape[0], device=dev)
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xmin = torch.minimum(x.min(1)[0], tmp)
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xmax = torch.maximum(x.max(1)[0], tmp)
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if self.sym:
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xmax = torch.maximum(torch.abs(xmin), xmax)
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tmp = xmin < 0
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if torch.any(tmp):
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xmin[tmp] = -xmax[tmp]
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tmp = (xmin == 0) & (xmax == 0)
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xmin[tmp] = -1
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xmax[tmp] = +1
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self.scale = (xmax - xmin) / self.maxq
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if self.sym:
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self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
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else:
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self.zero = torch.round(-xmin / self.scale)
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if self.mse:
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best = torch.full([x.shape[0]], float('inf'), device=dev)
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for i in range(int(self.maxshrink * self.grid)):
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p = 1 - i / self.grid
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xmin1 = p * xmin
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xmax1 = p * xmax
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scale1 = (xmax1 - xmin1) / self.maxq
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zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
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q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
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q -= x
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q.abs_()
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q.pow_(self.norm)
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err = torch.sum(q, 1)
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tmp = err < best
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if torch.any(tmp):
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best[tmp] = err[tmp]
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self.scale[tmp] = scale1[tmp]
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self.zero[tmp] = zero1[tmp]
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if not self.perchannel:
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if weight:
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tmp = shape[0]
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else:
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tmp = shape[1] if len(shape) != 3 else shape[2]
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self.scale = self.scale.repeat(tmp)
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self.zero = self.zero.repeat(tmp)
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if weight:
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shape = [-1] + [1] * (len(shape) - 1)
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self.scale = self.scale.reshape(shape)
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self.zero = self.zero.reshape(shape)
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return
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if len(shape) == 4:
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self.scale = self.scale.reshape((1, -1, 1, 1))
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self.zero = self.zero.reshape((1, -1, 1, 1))
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if len(shape) == 3:
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self.scale = self.scale.reshape((1, 1, -1))
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self.zero = self.zero.reshape((1, 1, -1))
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if len(shape) == 2:
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self.scale = self.scale.unsqueeze(0)
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self.zero = self.zero.unsqueeze(0)
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def quantize(self, x):
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if self.ready():
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return quantize(x, self.scale, self.zero, self.maxq)
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return x
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def enabled(self):
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return self.maxq > 0
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def ready(self):
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return torch.all(self.scale != 0)
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try:
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import quant_cuda
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except:
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print('CUDA extension not installed.')
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# Assumes layer is perfectly divisible into 256 * 256 blocks
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class QuantLinear(nn.Module):
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def __init__(self, bits, groupsize, infeatures, outfeatures):
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super().__init__()
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if bits not in [2, 3, 4, 8]:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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self.infeatures = infeatures
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self.outfeatures = outfeatures
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self.bits = bits
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if groupsize != -1 and groupsize < 32 and groupsize != int(math.pow(2, int(math.log2(groupsize)))):
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raise NotImplementedError("groupsize supports powers of 2 greater than 32. (e.g. : 32,64,128,etc)")
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groupsize = groupsize if groupsize != -1 else infeatures
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self.groupsize = groupsize
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self.register_buffer(
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'qzeros', torch.zeros((math.ceil(infeatures / groupsize), outfeatures // 256 * (bits * 8)),
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dtype=torch.int))
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self.register_buffer('scales', torch.zeros((math.ceil(infeatures / groupsize), outfeatures)))
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self.register_buffer('bias', torch.zeros(outfeatures))
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self.register_buffer('qweight', torch.zeros((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int))
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self._initialized_quant_state = False
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def pack(self, linear, scales, zeros):
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scales = scales.t().contiguous()
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zeros = zeros.t().contiguous()
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scale_zeros = zeros * scales
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self.scales = scales.clone()
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if linear.bias is not None:
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self.bias = linear.bias.clone()
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intweight = []
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for idx in range(self.infeatures):
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g_idx = idx // self.groupsize
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intweight.append(
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torch.round((linear.weight.data[:, idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:,
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None])
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intweight = torch.cat(intweight, dim=1)
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intweight = intweight.t().contiguous()
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intweight = intweight.numpy().astype(np.uint32)
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qweight = np.zeros((intweight.shape[0] // 256 * (self.bits * 8), intweight.shape[1]), dtype=np.uint32)
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i = 0
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row = 0
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while row < qweight.shape[0]:
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if self.bits in [2, 4, 8]:
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for j in range(i, i + (32 // self.bits)):
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qweight[row] |= intweight[j] << (self.bits * (j - i))
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i += 32 // self.bits
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row += 1
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elif self.bits == 3:
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for j in range(i, i + 10):
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qweight[row] |= intweight[j] << (3 * (j - i))
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i += 10
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qweight[row] |= intweight[i] << 30
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row += 1
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qweight[row] |= (intweight[i] >> 2) & 1
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i += 1
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for j in range(i, i + 10):
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qweight[row] |= intweight[j] << (3 * (j - i) + 1)
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i += 10
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qweight[row] |= intweight[i] << 31
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row += 1
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qweight[row] |= (intweight[i] >> 1) & 0x3
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i += 1
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for j in range(i, i + 10):
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qweight[row] |= intweight[j] << (3 * (j - i) + 2)
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i += 10
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row += 1
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else:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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qweight = qweight.astype(np.int32)
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self.qweight = torch.from_numpy(qweight)
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zeros -= 1
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zeros = zeros.numpy().astype(np.uint32)
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qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 256 * (self.bits * 8)), dtype=np.uint32)
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i = 0
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col = 0
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while col < qzeros.shape[1]:
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if self.bits in [2, 4, 8]:
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for j in range(i, i + (32 // self.bits)):
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qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
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i += 32 // self.bits
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col += 1
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elif self.bits == 3:
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for j in range(i, i + 10):
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qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
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i += 10
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qzeros[:, col] |= zeros[:, i] << 30
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col += 1
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qzeros[:, col] |= (zeros[:, i] >> 2) & 1
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i += 1
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for j in range(i, i + 10):
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qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
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i += 10
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qzeros[:, col] |= zeros[:, i] << 31
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col += 1
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qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
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i += 1
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for j in range(i, i + 10):
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qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
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i += 10
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col += 1
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else:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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qzeros = qzeros.astype(np.int32)
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self.qzeros = torch.from_numpy(qzeros)
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def forward(self, x):
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intermediate_dtype = torch.float32
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if not self._initialized_quant_state:
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# Do we even have a bias? Check for at least one non-zero element.
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if self.bias is not None and bool(torch.any(self.bias != 0)):
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# Then make sure it's the right type.
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self.bias.data = self.bias.data.to(intermediate_dtype)
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else:
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self.bias = None
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outshape = list(x.shape)
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outshape[-1] = self.outfeatures
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x = x.reshape(-1, x.shape[-1])
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if self.bias is None:
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y = torch.zeros(x.shape[0], outshape[-1], dtype=intermediate_dtype, device=x.device)
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else:
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y = self.bias.clone().repeat(x.shape[0], 1)
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output_dtype = x.dtype
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x = x.to(intermediate_dtype)
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if self.bits == 2:
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quant_cuda.vecquant2matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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elif self.bits == 3:
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quant_cuda.vecquant3matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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elif self.bits == 4:
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quant_cuda.vecquant4matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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elif self.bits == 8:
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quant_cuda.vecquant8matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
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else:
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raise NotImplementedError("Only 2,3,4,8 bits are supported.")
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y = y.to(output_dtype)
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return y.reshape(outshape)
|
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|
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|
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def make_quant(module, names, bits, groupsize, name=''):
|
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if isinstance(module, QuantLinear):
|
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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(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features))
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for name1, child in module.named_children():
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make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
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@@ -5,8 +5,7 @@ from locust import HttpUser, task
|
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samples = [[
|
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dict(
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instruction='Who is the best player in the history of NBA?',
|
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response=
|
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'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
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response='The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
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),
|
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dict(instruction='continue this talk', response=''),
|
||||
], [
|
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|
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@@ -1,19 +1,19 @@
|
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import argparse
|
||||
import os
|
||||
from threading import Lock
|
||||
from typing import Dict, Generator, List, Optional
|
||||
from typing import Generator, List, Optional
|
||||
|
||||
import torch
|
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import uvicorn
|
||||
from fastapi import FastAPI, HTTPException, Request
|
||||
from coati.quant import llama_load_quant, low_resource_init
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
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from llama_gptq import load_quant
|
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from pydantic import BaseModel, Field
|
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from slowapi import Limiter, _rate_limit_exceeded_handler
|
||||
from slowapi.errors import RateLimitExceeded
|
||||
from slowapi.util import get_remote_address
|
||||
from sse_starlette.sse import EventSourceResponse
|
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from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM
|
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from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM
|
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from utils import ChatPromptProcessor, Dialogue, LockedIterator, load_json, sample_streamingly, update_model_kwargs_fn
|
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|
||||
CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.'
|
||||
@@ -56,7 +56,7 @@ app.add_middleware(
|
||||
|
||||
def generate_streamingly(prompt, max_new_tokens, top_k, top_p, temperature):
|
||||
inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()}
|
||||
#TODO(ver217): streaming generation does not support repetition_penalty now
|
||||
# TODO(ver217): streaming generation does not support repetition_penalty now
|
||||
model_kwargs = {
|
||||
'max_generate_tokens': max_new_tokens,
|
||||
'early_stopping': True,
|
||||
@@ -162,7 +162,10 @@ if __name__ == '__main__':
|
||||
prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN, censored_words=censored_words)
|
||||
|
||||
if args.quant == '4bit':
|
||||
model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size)
|
||||
with low_resource_init():
|
||||
config = LlamaConfig.from_pretrained(args.pretrained)
|
||||
model = LlamaForCausalLM(config)
|
||||
model = llama_load_quant(model, args.gptq_checkpoint, 4, args.gptq_group_size)
|
||||
model.cuda()
|
||||
else:
|
||||
model = LlamaForCausalLM.from_pretrained(
|
||||
|
||||
@@ -10,37 +10,34 @@ samples = [
|
||||
([
|
||||
Dialogue(
|
||||
instruction='Who is the best player in the history of NBA?',
|
||||
response=
|
||||
'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
||||
response='The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
||||
),
|
||||
Dialogue(instruction='continue this talk', response=''),
|
||||
], 128,
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\nWho is the best player in the history of NBA?\n\n### Response:\nThe best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1\n\n### Instruction:\ncontinue this talk\n\n### Response:\n'
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\nWho is the best player in the history of NBA?\n\n### Response:\nThe best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1\n\n### Instruction:\ncontinue this talk\n\n### Response:\n'
|
||||
),
|
||||
([
|
||||
Dialogue(
|
||||
instruction='Who is the best player in the history of NBA?',
|
||||
response=
|
||||
'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
||||
response='The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
||||
),
|
||||
Dialogue(instruction='continue this talk', response=''),
|
||||
], 200,
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\ncontinue this talk\n\n### Response:\n'
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\ncontinue this talk\n\n### Response:\n'
|
||||
),
|
||||
([
|
||||
Dialogue(
|
||||
instruction='Who is the best player in the history of NBA?',
|
||||
response=
|
||||
'The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
||||
response='The best player in the history of the NBA is widely considered to be Michael Jordan. He is one of the most successful players in the league, having won 6 NBA championships with the Chicago Bulls and 5 more with the Washington Wizards. He is a 5-time MVP, 1'
|
||||
),
|
||||
Dialogue(instruction='continue this talk', response=''),
|
||||
], 211,
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\ncontinue this\n\n### Response:\n'
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\ncontinue this\n\n### Response:\n'
|
||||
),
|
||||
([
|
||||
Dialogue(instruction='Who is the best player in the history of NBA?', response=''),
|
||||
], 128,
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\nWho is the best player in the history of NBA?\n\n### Response:\n'
|
||||
'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.\n\n### Instruction:\nWho is the best player in the history of NBA?\n\n### Response:\n'
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
import json
|
||||
import re
|
||||
from threading import Lock
|
||||
from typing import Any, Callable, Generator, List, Optional
|
||||
import json
|
||||
import jieba
|
||||
|
||||
import jieba
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
@@ -127,7 +127,7 @@ STOP_PAT = re.compile(r'(###|instruction:).*', flags=(re.I | re.S))
|
||||
class ChatPromptProcessor:
|
||||
SAFE_RESPONSE = 'The input/response contains inappropriate content, please rephrase your prompt.'
|
||||
|
||||
def __init__(self, tokenizer, context: str, max_len: int = 2048, censored_words: List[str]=[]):
|
||||
def __init__(self, tokenizer, context: str, max_len: int = 2048, censored_words: List[str] = []):
|
||||
self.tokenizer = tokenizer
|
||||
self.context = context
|
||||
self.max_len = max_len
|
||||
@@ -182,6 +182,7 @@ class ChatPromptProcessor:
|
||||
intersection = set(jieba.cut(text.lower())) & self.censored_words
|
||||
return len(intersection) > 0
|
||||
|
||||
|
||||
class LockedIterator:
|
||||
|
||||
def __init__(self, it, lock: Lock) -> None:
|
||||
@@ -195,6 +196,7 @@ class LockedIterator:
|
||||
with self.lock:
|
||||
return next(self.it)
|
||||
|
||||
|
||||
def load_json(path: str):
|
||||
with open(path) as f:
|
||||
return json.load(f)
|
||||
return json.load(f)
|
||||
|
||||
Reference in New Issue
Block a user