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