crumb
commited on
Commit
•
e6a91e0
1
Parent(s):
f5922a7
Upload shakespeare-inference.ipynb
Browse files- shakespeare-inference.ipynb +255 -0
shakespeare-inference.ipynb
ADDED
@@ -0,0 +1,255 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"nbformat": 4,
|
3 |
+
"nbformat_minor": 0,
|
4 |
+
"metadata": {
|
5 |
+
"colab": {
|
6 |
+
"name": "inference",
|
7 |
+
"provenance": []
|
8 |
+
},
|
9 |
+
"kernelspec": {
|
10 |
+
"name": "python3",
|
11 |
+
"display_name": "Python 3"
|
12 |
+
},
|
13 |
+
"language_info": {
|
14 |
+
"name": "python"
|
15 |
+
},
|
16 |
+
"accelerator": "GPU",
|
17 |
+
"gpuClass": "standard"
|
18 |
+
},
|
19 |
+
"cells": [
|
20 |
+
{
|
21 |
+
"cell_type": "code",
|
22 |
+
"source": [
|
23 |
+
"!pip install transformers==4.14.1\n",
|
24 |
+
"!pip install bitsandbytes-cuda111==0.26.0\n",
|
25 |
+
"\n",
|
26 |
+
"from IPython import display \n",
|
27 |
+
"display.clear_output()"
|
28 |
+
],
|
29 |
+
"metadata": {
|
30 |
+
"id": "Q8cuAdVDGXR6"
|
31 |
+
},
|
32 |
+
"execution_count": 1,
|
33 |
+
"outputs": []
|
34 |
+
},
|
35 |
+
{
|
36 |
+
"cell_type": "code",
|
37 |
+
"execution_count": 2,
|
38 |
+
"metadata": {
|
39 |
+
"id": "8mkqaWlNGLKn"
|
40 |
+
},
|
41 |
+
"outputs": [],
|
42 |
+
"source": [
|
43 |
+
"import transformers\n",
|
44 |
+
"import torch\n",
|
45 |
+
"import torch.nn.functional as F\n",
|
46 |
+
"from torch import nn\n",
|
47 |
+
"from torch.cuda.amp import custom_fwd, custom_bwd\n",
|
48 |
+
"from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise\n",
|
49 |
+
"from tqdm.auto import tqdm"
|
50 |
+
]
|
51 |
+
},
|
52 |
+
{
|
53 |
+
"cell_type": "code",
|
54 |
+
"source": [
|
55 |
+
"#@title convert to 8bit\n",
|
56 |
+
"class FrozenBNBLinear(nn.Module):\n",
|
57 |
+
" def __init__(self, weight, absmax, code, bias=None):\n",
|
58 |
+
" assert isinstance(bias, nn.Parameter) or bias is None\n",
|
59 |
+
" super().__init__()\n",
|
60 |
+
" self.out_features, self.in_features = weight.shape\n",
|
61 |
+
" self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
|
62 |
+
" self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
|
63 |
+
" self.register_buffer(\"code\", code.requires_grad_(False))\n",
|
64 |
+
" self.adapter = None\n",
|
65 |
+
" self.bias = bias\n",
|
66 |
+
" \n",
|
67 |
+
" def forward(self, input):\n",
|
68 |
+
" output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)\n",
|
69 |
+
" if self.adapter:\n",
|
70 |
+
" output += self.adapter(input)\n",
|
71 |
+
" return output\n",
|
72 |
+
" \n",
|
73 |
+
" @classmethod\n",
|
74 |
+
" def from_linear(cls, linear: nn.Linear) -> \"FrozenBNBLinear\":\n",
|
75 |
+
" weights_int8, state = quantize_blockise_lowmemory(linear.weight)\n",
|
76 |
+
" return cls(weights_int8, *state, linear.bias)\n",
|
77 |
+
" \n",
|
78 |
+
" def __repr__(self):\n",
|
79 |
+
" return f\"{self.__class__.__name__}({self.in_features}, {self.out_features})\"\n",
|
80 |
+
" \n",
|
81 |
+
" \n",
|
82 |
+
"class DequantizeAndLinear(torch.autograd.Function): \n",
|
83 |
+
" @staticmethod\n",
|
84 |
+
" @custom_fwd\n",
|
85 |
+
" def forward(ctx, input: torch.Tensor, weights_quantized: torch.ByteTensor,\n",
|
86 |
+
" absmax: torch.FloatTensor, code: torch.FloatTensor, bias: torch.FloatTensor):\n",
|
87 |
+
" weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
|
88 |
+
" ctx.save_for_backward(input, weights_quantized, absmax, code)\n",
|
89 |
+
" ctx._has_bias = bias is not None\n",
|
90 |
+
" return F.linear(input, weights_deq, bias)\n",
|
91 |
+
" \n",
|
92 |
+
" @staticmethod\n",
|
93 |
+
" @custom_bwd\n",
|
94 |
+
" def backward(ctx, grad_output: torch.Tensor):\n",
|
95 |
+
" assert not ctx.needs_input_grad[1] and not ctx.needs_input_grad[2] and not ctx.needs_input_grad[3]\n",
|
96 |
+
" input, weights_quantized, absmax, code = ctx.saved_tensors\n",
|
97 |
+
" # grad_output: [*batch, out_features]\n",
|
98 |
+
" weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
|
99 |
+
" grad_input = grad_output @ weights_deq\n",
|
100 |
+
" grad_bias = grad_output.flatten(0, -2).sum(dim=0) if ctx._has_bias else None\n",
|
101 |
+
" return grad_input, None, None, None, grad_bias\n",
|
102 |
+
" \n",
|
103 |
+
" \n",
|
104 |
+
"class FrozenBNBEmbedding(nn.Module):\n",
|
105 |
+
" def __init__(self, weight, absmax, code):\n",
|
106 |
+
" super().__init__()\n",
|
107 |
+
" self.num_embeddings, self.embedding_dim = weight.shape\n",
|
108 |
+
" self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
|
109 |
+
" self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
|
110 |
+
" self.register_buffer(\"code\", code.requires_grad_(False))\n",
|
111 |
+
" self.adapter = None\n",
|
112 |
+
" \n",
|
113 |
+
" def forward(self, input, **kwargs):\n",
|
114 |
+
" with torch.no_grad():\n",
|
115 |
+
" # note: both quantuized weights and input indices are *not* differentiable\n",
|
116 |
+
" weight_deq = dequantize_blockwise(self.weight, absmax=self.absmax, code=self.code)\n",
|
117 |
+
" output = F.embedding(input, weight_deq, **kwargs)\n",
|
118 |
+
" if self.adapter:\n",
|
119 |
+
" output += self.adapter(input)\n",
|
120 |
+
" return output \n",
|
121 |
+
" \n",
|
122 |
+
" @classmethod\n",
|
123 |
+
" def from_embedding(cls, embedding: nn.Embedding) -> \"FrozenBNBEmbedding\":\n",
|
124 |
+
" weights_int8, state = quantize_blockise_lowmemory(embedding.weight)\n",
|
125 |
+
" return cls(weights_int8, *state)\n",
|
126 |
+
" \n",
|
127 |
+
" def __repr__(self):\n",
|
128 |
+
" return f\"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})\"\n",
|
129 |
+
" \n",
|
130 |
+
" \n",
|
131 |
+
"def quantize_blockise_lowmemory(matrix: torch.Tensor, chunk_size: int = 2 ** 20):\n",
|
132 |
+
" assert chunk_size % 4096 == 0\n",
|
133 |
+
" code = None\n",
|
134 |
+
" chunks = []\n",
|
135 |
+
" absmaxes = []\n",
|
136 |
+
" flat_tensor = matrix.view(-1)\n",
|
137 |
+
" for i in range((matrix.numel() - 1) // chunk_size + 1):\n",
|
138 |
+
" input_chunk = flat_tensor[i * chunk_size: (i + 1) * chunk_size].clone()\n",
|
139 |
+
" quantized_chunk, (absmax_chunk, code) = quantize_blockwise(input_chunk, code=code)\n",
|
140 |
+
" chunks.append(quantized_chunk)\n",
|
141 |
+
" absmaxes.append(absmax_chunk)\n",
|
142 |
+
" \n",
|
143 |
+
" matrix_i8 = torch.cat(chunks).reshape_as(matrix)\n",
|
144 |
+
" absmax = torch.cat(absmaxes)\n",
|
145 |
+
" return matrix_i8, (absmax, code)\n",
|
146 |
+
" \n",
|
147 |
+
" \n",
|
148 |
+
"def convert_to_int8(model):\n",
|
149 |
+
" \"\"\"Convert linear and embedding modules to 8-bit with optional adapters\"\"\"\n",
|
150 |
+
" for module in list(model.modules()):\n",
|
151 |
+
" for name, child in module.named_children():\n",
|
152 |
+
" if isinstance(child, nn.Linear):\n",
|
153 |
+
" setattr( \n",
|
154 |
+
" module,\n",
|
155 |
+
" name,\n",
|
156 |
+
" FrozenBNBLinear(\n",
|
157 |
+
" weight=torch.zeros(child.out_features, child.in_features, dtype=torch.uint8),\n",
|
158 |
+
" absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
|
159 |
+
" code=torch.zeros(256),\n",
|
160 |
+
" bias=child.bias,\n",
|
161 |
+
" ),\n",
|
162 |
+
" )\n",
|
163 |
+
" elif isinstance(child, nn.Embedding):\n",
|
164 |
+
" setattr(\n",
|
165 |
+
" module,\n",
|
166 |
+
" name,\n",
|
167 |
+
" FrozenBNBEmbedding(\n",
|
168 |
+
" weight=torch.zeros(child.num_embeddings, child.embedding_dim, dtype=torch.uint8),\n",
|
169 |
+
" absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
|
170 |
+
" code=torch.zeros(256),\n",
|
171 |
+
" )\n",
|
172 |
+
" )\n",
|
173 |
+
"class GPTJBlock(transformers.models.gptj.modeling_gptj.GPTJBlock):\n",
|
174 |
+
" def __init__(self, config):\n",
|
175 |
+
" super().__init__(config)\n",
|
176 |
+
"\n",
|
177 |
+
" convert_to_int8(self.attn)\n",
|
178 |
+
" convert_to_int8(self.mlp)\n",
|
179 |
+
"\n",
|
180 |
+
"class GPTJModel(transformers.models.gptj.modeling_gptj.GPTJModel):\n",
|
181 |
+
" def __init__(self, config):\n",
|
182 |
+
" super().__init__(config)\n",
|
183 |
+
" convert_to_int8(self)\n",
|
184 |
+
" \n",
|
185 |
+
"class GPTJForCausalLM(transformers.models.gptj.modeling_gptj.GPTJForCausalLM):\n",
|
186 |
+
" def __init__(self, config):\n",
|
187 |
+
" super().__init__(config)\n",
|
188 |
+
" convert_to_int8(self)\n",
|
189 |
+
"\n",
|
190 |
+
"\n",
|
191 |
+
"transformers.models.gptj.modeling_gptj.GPTJBlock = GPTJBlock # monkey-patch GPT-J"
|
192 |
+
],
|
193 |
+
"metadata": {
|
194 |
+
"cellView": "form",
|
195 |
+
"id": "fmpdVvfVG7Pc"
|
196 |
+
},
|
197 |
+
"execution_count": 3,
|
198 |
+
"outputs": []
|
199 |
+
},
|
200 |
+
{
|
201 |
+
"cell_type": "code",
|
202 |
+
"source": [
|
203 |
+
"tokenizer = transformers.AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
|
204 |
+
"gpt = GPTJForCausalLM.from_pretrained(\"crumb/gpt-j-6b-shakespeare\", low_cpu_mem_usage=True)\n",
|
205 |
+
"\n",
|
206 |
+
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
|
207 |
+
"gpt = gpt.to(device)"
|
208 |
+
],
|
209 |
+
"metadata": {
|
210 |
+
"id": "ttKTRoUlG5YM"
|
211 |
+
},
|
212 |
+
"execution_count": null,
|
213 |
+
"outputs": []
|
214 |
+
},
|
215 |
+
{
|
216 |
+
"cell_type": "code",
|
217 |
+
"source": [
|
218 |
+
"prompt = \"\"\"ROMEO: I would I were thy bird. \n",
|
219 |
+
"JULIET: Sweet, so would I, Yet I should kill thee with much cherishing. Good night, good night! Parting is such sweet\"\"\"\n",
|
220 |
+
"prompt = tokenizer(prompt, return_tensors='pt')\n",
|
221 |
+
"prompt = {key: value.to(device) for key, value in prompt.items()}\n",
|
222 |
+
"out = gpt.generate(**prompt, min_length=32, max_length=64, do_sample=True)\n",
|
223 |
+
"out = tokenizer.decode(out[0])\n",
|
224 |
+
"print(out)"
|
225 |
+
],
|
226 |
+
"metadata": {
|
227 |
+
"colab": {
|
228 |
+
"base_uri": "https://localhost:8080/"
|
229 |
+
},
|
230 |
+
"id": "kSXSZz_kGcfm",
|
231 |
+
"outputId": "d91dda66-88ab-4e52-bfb9-3df8092abe2f"
|
232 |
+
},
|
233 |
+
"execution_count": 9,
|
234 |
+
"outputs": [
|
235 |
+
{
|
236 |
+
"output_type": "stream",
|
237 |
+
"name": "stderr",
|
238 |
+
"text": [
|
239 |
+
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
|
240 |
+
]
|
241 |
+
},
|
242 |
+
{
|
243 |
+
"output_type": "stream",
|
244 |
+
"name": "stdout",
|
245 |
+
"text": [
|
246 |
+
"ROMEO: I would I were thy bird. \n",
|
247 |
+
"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",
|
248 |
+
"\n",
|
249 |
+
"ROMEO\n"
|
250 |
+
]
|
251 |
+
}
|
252 |
+
]
|
253 |
+
}
|
254 |
+
]
|
255 |
+
}
|