mia naomi
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Commit
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8e0992b
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Parent(s):
1caebaf
Delete shakespeare-inference.ipynb
Browse files- shakespeare-inference.ipynb +0 -255
shakespeare-inference.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "inference",
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU",
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"gpuClass": "standard"
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"!pip install transformers==4.14.1\n",
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"!pip install bitsandbytes-cuda111==0.26.0\n",
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"\n",
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"from IPython import display \n",
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"display.clear_output()"
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],
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"metadata": {
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"id": "Q8cuAdVDGXR6"
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},
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "8mkqaWlNGLKn"
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},
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"outputs": [],
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"source": [
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"import transformers\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"from torch import nn\n",
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"from torch.cuda.amp import custom_fwd, custom_bwd\n",
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"from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise\n",
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"from tqdm.auto import tqdm"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"#@title convert to 8bit\n",
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"class FrozenBNBLinear(nn.Module):\n",
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" def __init__(self, weight, absmax, code, bias=None):\n",
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" assert isinstance(bias, nn.Parameter) or bias is None\n",
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" super().__init__()\n",
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" self.out_features, self.in_features = weight.shape\n",
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" self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
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" self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
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" self.register_buffer(\"code\", code.requires_grad_(False))\n",
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" self.adapter = None\n",
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" self.bias = bias\n",
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" \n",
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" def forward(self, input):\n",
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" output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)\n",
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" if self.adapter:\n",
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" output += self.adapter(input)\n",
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" return output\n",
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" \n",
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" @classmethod\n",
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" def from_linear(cls, linear: nn.Linear) -> \"FrozenBNBLinear\":\n",
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" weights_int8, state = quantize_blockise_lowmemory(linear.weight)\n",
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" return cls(weights_int8, *state, linear.bias)\n",
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" \n",
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" def __repr__(self):\n",
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" return f\"{self.__class__.__name__}({self.in_features}, {self.out_features})\"\n",
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" \n",
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" \n",
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"class DequantizeAndLinear(torch.autograd.Function): \n",
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" @staticmethod\n",
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" @custom_fwd\n",
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" def forward(ctx, input: torch.Tensor, weights_quantized: torch.ByteTensor,\n",
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" absmax: torch.FloatTensor, code: torch.FloatTensor, bias: torch.FloatTensor):\n",
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" weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
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" ctx.save_for_backward(input, weights_quantized, absmax, code)\n",
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" ctx._has_bias = bias is not None\n",
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" return F.linear(input, weights_deq, bias)\n",
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" \n",
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" @staticmethod\n",
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" @custom_bwd\n",
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" def backward(ctx, grad_output: torch.Tensor):\n",
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" assert not ctx.needs_input_grad[1] and not ctx.needs_input_grad[2] and not ctx.needs_input_grad[3]\n",
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" input, weights_quantized, absmax, code = ctx.saved_tensors\n",
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" # grad_output: [*batch, out_features]\n",
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" weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
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" grad_input = grad_output @ weights_deq\n",
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" grad_bias = grad_output.flatten(0, -2).sum(dim=0) if ctx._has_bias else None\n",
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" return grad_input, None, None, None, grad_bias\n",
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" \n",
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" \n",
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"class FrozenBNBEmbedding(nn.Module):\n",
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" def __init__(self, weight, absmax, code):\n",
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" super().__init__()\n",
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" self.num_embeddings, self.embedding_dim = weight.shape\n",
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" self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
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" self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
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" self.register_buffer(\"code\", code.requires_grad_(False))\n",
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" self.adapter = None\n",
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" \n",
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" def forward(self, input, **kwargs):\n",
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" with torch.no_grad():\n",
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" # note: both quantuized weights and input indices are *not* differentiable\n",
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" weight_deq = dequantize_blockwise(self.weight, absmax=self.absmax, code=self.code)\n",
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" output = F.embedding(input, weight_deq, **kwargs)\n",
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" if self.adapter:\n",
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" output += self.adapter(input)\n",
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" return output \n",
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" \n",
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" @classmethod\n",
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" def from_embedding(cls, embedding: nn.Embedding) -> \"FrozenBNBEmbedding\":\n",
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" weights_int8, state = quantize_blockise_lowmemory(embedding.weight)\n",
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" return cls(weights_int8, *state)\n",
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" \n",
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" def __repr__(self):\n",
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" return f\"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})\"\n",
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" \n",
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" \n",
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"def quantize_blockise_lowmemory(matrix: torch.Tensor, chunk_size: int = 2 ** 20):\n",
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" assert chunk_size % 4096 == 0\n",
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" code = None\n",
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" chunks = []\n",
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" absmaxes = []\n",
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" flat_tensor = matrix.view(-1)\n",
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" for i in range((matrix.numel() - 1) // chunk_size + 1):\n",
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" input_chunk = flat_tensor[i * chunk_size: (i + 1) * chunk_size].clone()\n",
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" quantized_chunk, (absmax_chunk, code) = quantize_blockwise(input_chunk, code=code)\n",
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" chunks.append(quantized_chunk)\n",
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" absmaxes.append(absmax_chunk)\n",
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" \n",
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" matrix_i8 = torch.cat(chunks).reshape_as(matrix)\n",
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" absmax = torch.cat(absmaxes)\n",
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" return matrix_i8, (absmax, code)\n",
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" \n",
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" \n",
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"def convert_to_int8(model):\n",
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" \"\"\"Convert linear and embedding modules to 8-bit with optional adapters\"\"\"\n",
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" for module in list(model.modules()):\n",
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" for name, child in module.named_children():\n",
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" if isinstance(child, nn.Linear):\n",
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" setattr( \n",
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" module,\n",
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" name,\n",
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" FrozenBNBLinear(\n",
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" weight=torch.zeros(child.out_features, child.in_features, dtype=torch.uint8),\n",
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" absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
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" code=torch.zeros(256),\n",
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" bias=child.bias,\n",
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" ),\n",
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" )\n",
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" elif isinstance(child, nn.Embedding):\n",
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" setattr(\n",
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" module,\n",
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" name,\n",
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" FrozenBNBEmbedding(\n",
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" weight=torch.zeros(child.num_embeddings, child.embedding_dim, dtype=torch.uint8),\n",
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" absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
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" code=torch.zeros(256),\n",
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" )\n",
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" )\n",
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"class GPTJBlock(transformers.models.gptj.modeling_gptj.GPTJBlock):\n",
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" def __init__(self, config):\n",
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" super().__init__(config)\n",
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"\n",
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" convert_to_int8(self.attn)\n",
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" convert_to_int8(self.mlp)\n",
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"\n",
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"class GPTJModel(transformers.models.gptj.modeling_gptj.GPTJModel):\n",
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" def __init__(self, config):\n",
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" super().__init__(config)\n",
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" convert_to_int8(self)\n",
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" \n",
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"class GPTJForCausalLM(transformers.models.gptj.modeling_gptj.GPTJForCausalLM):\n",
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" def __init__(self, config):\n",
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" super().__init__(config)\n",
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" convert_to_int8(self)\n",
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"\n",
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"\n",
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"transformers.models.gptj.modeling_gptj.GPTJBlock = GPTJBlock # monkey-patch GPT-J"
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],
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"metadata": {
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"cellView": "form",
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"id": "fmpdVvfVG7Pc"
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},
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"tokenizer = transformers.AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
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"gpt = GPTJForCausalLM.from_pretrained(\"crumb/gpt-j-6b-shakespeare\", low_cpu_mem_usage=True)\n",
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"\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"gpt = gpt.to(device)"
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],
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"metadata": {
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"id": "ttKTRoUlG5YM"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"prompt = \"\"\"ROMEO: I would I were thy bird. \n",
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"JULIET: Sweet, so would I, Yet I should kill thee with much cherishing. Good night, good night! Parting is such sweet\"\"\"\n",
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"prompt = tokenizer(prompt, return_tensors='pt')\n",
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"prompt = {key: value.to(device) for key, value in prompt.items()}\n",
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"out = gpt.generate(**prompt, min_length=32, max_length=64, do_sample=True)\n",
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"out = tokenizer.decode(out[0])\n",
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"print(out)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "kSXSZz_kGcfm",
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"outputId": "d91dda66-88ab-4e52-bfb9-3df8092abe2f"
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},
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"ROMEO: I would I were thy bird. \n",
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"JULIET: Sweet, so would I, Yet I should kill thee with much cherishing. Good night, good night! Parting is such sweet sorrow, As a lost angel's song, in answer to An evil dream.\n",
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"\n",
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"ROMEO\n"
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]
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}
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]
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}
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]
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}
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