| --- |
| inference: false |
| license: bigscience-bloom-rail-1.0 |
| language: |
| - ak |
| - ar |
| - as |
| - bm |
| - bn |
| - ca |
| - en |
| - es |
| - eu |
| - fon |
| - fr |
| - gu |
| - hi |
| - id |
| - ig |
| - ki |
| - kn |
| - lg |
| - ln |
| - ml |
| - mr |
| - ne |
| - nso |
| - ny |
| - or |
| - pa |
| - pt |
| - rn |
| - rw |
| - sn |
| - st |
| - sw |
| - ta |
| - te |
| - tn |
| - ts |
| - tum |
| - tw |
| - ur |
| - vi |
| - wo |
| - xh |
| - yo |
| - zh |
| - zu |
| pipeline_tag: text-generation |
| --- |
| ### Quantized bigscience/bloom with 8-bit weights |
|
|
| Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom) a ~176 billion parameters language model that you run and fine-tune with less memory. |
|
|
| Here, we also apply [LoRA (Low Rank Adaptation)](https://arxiv.org/abs/2106.09685) to reduce model size. The original version takes \~353GB memory, this version takes **\~180GB**. |
|
|
| Our main goal is to generate a model compressed enough to be deployed in a traditional Kubernetes cluster. |
|
|
| ### How to fine-tune |
|
|
| In this [notebook](https://nbviewer.org/urls/huggingface.co/joaoalvarenga/bloom-8bit/raw/main/fine-tuning-example.ipynb) you can find an adaptation from [Hivemind's GPT-J 8-bit fine-tuning notebook](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es) to fine-tune Bloom 8-bit with a 3x NVIDIA A100 instance. |
|
|
| ### How to use |
|
|
| This model can be used by adapting Bloom original implementation. This is an adaptation from [Hivemind's GPT-J 8-bit](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb): |
|
|
| ```python |
| import transformers |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| |
| from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise |
| from typing import Tuple |
| from torch.cuda.amp import custom_fwd, custom_bwd |
| |
| class FrozenBNBLinear(nn.Module): |
| def __init__(self, weight, absmax, code, bias=None): |
| assert isinstance(bias, nn.Parameter) or bias is None |
| super().__init__() |
| self.out_features, self.in_features = weight.shape |
| self.register_buffer("weight", weight.requires_grad_(False)) |
| self.register_buffer("absmax", absmax.requires_grad_(False)) |
| self.register_buffer("code", code.requires_grad_(False)) |
| self.adapter = None |
| self.bias = bias |
| |
| def forward(self, input): |
| output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias) |
| if self.adapter: |
| output += self.adapter(input) |
| return output |
| |
| @classmethod |
| def from_linear(cls, linear: nn.Linear) -> "FrozenBNBLinear": |
| weights_int8, state = quantize_blockise_lowmemory(linear.weight) |
| return cls(weights_int8, *state, linear.bias) |
| |
| def __repr__(self): |
| return f"{self.__class__.__name__}({self.in_features}, {self.out_features})" |
| |
| |
| class DequantizeAndLinear(torch.autograd.Function): |
| @staticmethod |
| @custom_fwd |
| def forward(ctx, input: torch.Tensor, weights_quantized: torch.ByteTensor, |
| absmax: torch.FloatTensor, code: torch.FloatTensor, bias: torch.FloatTensor): |
| weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code) |
| ctx.save_for_backward(input, weights_quantized, absmax, code) |
| ctx._has_bias = bias is not None |
| return F.linear(input, weights_deq, bias) |
| |
| @staticmethod |
| @custom_bwd |
| def backward(ctx, grad_output: torch.Tensor): |
| assert not ctx.needs_input_grad[1] and not ctx.needs_input_grad[2] and not ctx.needs_input_grad[3] |
| input, weights_quantized, absmax, code = ctx.saved_tensors |
| # grad_output: [*batch, out_features] |
| weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code) |
| grad_input = grad_output @ weights_deq |
| grad_bias = grad_output.flatten(0, -2).sum(dim=0) if ctx._has_bias else None |
| return grad_input, None, None, None, grad_bias |
| |
| |
| class FrozenBNBEmbedding(nn.Module): |
| def __init__(self, weight, absmax, code): |
| super().__init__() |
| self.num_embeddings, self.embedding_dim = weight.shape |
| self.register_buffer("weight", weight.requires_grad_(False)) |
| self.register_buffer("absmax", absmax.requires_grad_(False)) |
| self.register_buffer("code", code.requires_grad_(False)) |
| self.adapter = None |
| |
| def forward(self, input, **kwargs): |
| with torch.no_grad(): |
| # note: both quantuized weights and input indices are *not* differentiable |
| weight_deq = dequantize_blockwise(self.weight, absmax=self.absmax, code=self.code) |
| output = F.embedding(input, weight_deq, **kwargs) |
| if self.adapter: |
| output += self.adapter(input) |
| return output |
| |
| @classmethod |
| def from_embedding(cls, embedding: nn.Embedding) -> "FrozenBNBEmbedding": |
| weights_int8, state = quantize_blockise_lowmemory(embedding.weight) |
| return cls(weights_int8, *state) |
| |
| def __repr__(self): |
| return f"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})" |
| |
| |
| def quantize_blockise_lowmemory(matrix: torch.Tensor, chunk_size: int = 2 ** 20): |
| assert chunk_size % 4096 == 0 |
| code = None |
| chunks = [] |
| absmaxes = [] |
| flat_tensor = matrix.view(-1) |
| for i in range((matrix.numel() - 1) // chunk_size + 1): |
| input_chunk = flat_tensor[i * chunk_size: (i + 1) * chunk_size].clone() |
| quantized_chunk, (absmax_chunk, code) = quantize_blockwise(input_chunk, code=code) |
| chunks.append(quantized_chunk) |
| absmaxes.append(absmax_chunk) |
| |
| matrix_i8 = torch.cat(chunks).reshape_as(matrix) |
| absmax = torch.cat(absmaxes) |
| return matrix_i8, (absmax, code) |
| |
| |
| def convert_to_int8(model): |
| """Convert linear and embedding modules to 8-bit with optional adapters""" |
| for module in list(model.modules()): |
| for name, child in module.named_children(): |
| if isinstance(child, nn.Linear): |
| print(name, child) |
| setattr( |
| module, |
| name, |
| FrozenBNBLinear( |
| weight=torch.zeros(child.out_features, child.in_features, dtype=torch.uint8), |
| absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1), |
| code=torch.zeros(256), |
| bias=child.bias, |
| ), |
| ) |
| elif isinstance(child, nn.Embedding): |
| setattr( |
| module, |
| name, |
| FrozenBNBEmbedding( |
| weight=torch.zeros(child.num_embeddings, child.embedding_dim, dtype=torch.uint8), |
| absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1), |
| code=torch.zeros(256), |
| ) |
| ) |
| |
| class BloomBlock(transformers.models.bloom.modeling_bloom.BloomBlock): |
| def __init__(self, config, layer_number=None): |
| super().__init__(config, layer_number) |
| |
| convert_to_int8(self.self_attention) |
| convert_to_int8(self.mlp) |
| |
| |
| class BloomModel(transformers.models.bloom.modeling_bloom.BloomModel): |
| def __init__(self, config): |
| super().__init__(config) |
| convert_to_int8(self) |
| |
| |
| class BloomForCausalLM(transformers.models.bloom.modeling_bloom.BloomForCausalLM): |
| def __init__(self, config): |
| super().__init__(config) |
| convert_to_int8(self) |
| |
| transformers.models.bloom.modeling_bloom.BloomBlock = BloomBlock |
| |
| model = BloomForCausalLM.from_pretrained('joaoalvarenga/bloom-8bit', low_cpu_mem_usage=True) |
| tokenizer = BloomTokenizerFast.from_pretrained('joaoalvarenga/bloom-8bit') |
| |
| prompt = tokenizer("Given a table named salaries and columns id, created_at, salary, age. Creates a SQL to answer What is the average salary for 22 years old:", return_tensors='pt') |
| out = model.generate(**prompt, min_length=10, do_sample=True) |
| tokenizer.decode(out[0]) |
| ``` |
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