Upload convert-gpt-j.ipynb

convert-gpt-j.ipynb

@@ -0,0 +1,406 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise\n",
+    "import transformers\n",
+    "%config Completer.use_jedi = False\n",
+    "\n",
+    "\n",
+    "model_name = \"EleutherAI/gpt-j-6B\"\n",
+    "gpt = transformers.AutoModelForCausalLM.from_pretrained(model_name)\n",
+    "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Tuple\n",
+    "from torch.cuda.amp import custom_fwd, custom_bwd\n",
+    "\n",
+    "\n",
+    "class DequantizeAndLinear(torch.autograd.Function):\n",
+    "    \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",
+    "        \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 BNBLinearWithAdapter(nn.Module):\n",
+    "    def __init__(self, weight, absmax, code,  bias=None, adapter_dim=0):\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.bias = bias\n",
+    "        \n",
+    "        if adapter_dim > 0:\n",
+    "            self.adapter = nn.Sequential(\n",
+    "                nn.Linear(self.in_features, adapter_dim, bias=False),\n",
+    "                nn.Linear(adapter_dim, self.out_features, bias=False),\n",
+    "            )\n",
+    "            \n",
+    "            nn.init.zeros_(self.adapter[1].weight)\n",
+    "        else:\n",
+    "            self.adapter = None\n",
+    "        \n",
+    "    def forward(self, input):\n",
+    "        out = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)\n",
+    "        \n",
+    "        if self.adapter:\n",
+    "            return self.adapter(input) + out\n",
+    "            \n",
+    "        return out\n",
+    "        \n",
+    "    \n",
+    "    @classmethod\n",
+    "    def from_linear(cls, linear: nn.Linear, **kwargs) -> \"FrozenBNBLinear\":\n",
+    "        weights_int8, state = quantize_blockise_lowmemory(linear.weight)\n",
+    "        return cls(weights_int8, *state, linear.bias, **kwargs)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return f\"{self.__class__.__name__}({self.in_features}, {self.out_features})\"\n",
+    "\n",
+    "\n",
+    "class BNBEmbeddingWithAdapter(nn.Module):\n",
+    "    def __init__(self, weight, absmax, code, adapter_dim=0):\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",
+    "        \n",
+    "        if adapter_dim > 0:\n",
+    "            self.adapter = nn.Sequential(\n",
+    "                nn.Embedding(self.num_embeddings, adapter_dim),\n",
+    "                nn.Linear(adapter_dim, self.embedding_dim, bias=False),\n",
+    "            )\n",
+    "            \n",
+    "            nn.init.zeros_(self.adapter[1].weight)\n",
+    "        else:\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",
+    "            out = F.embedding(input, weight_deq, **kwargs)\n",
+    "        if self.adapter:\n",
+    "            return out + self.adapter(input, **kwargs)\n",
+    "        \n",
+    "        return out\n",
+    "    \n",
+    "    @classmethod\n",
+    "    def from_embedding(cls, embedding: nn.Embedding, **kwargs) -> \"FrozenBNBEmbedding\":\n",
+    "        weights_int8, state = quantize_blockise_lowmemory(embedding.weight)\n",
+    "        return cls(weights_int8, *state, **kwargs)\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return f\"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bnbfy_(model, adapter_dim: int = 0):\n",
+    "    for module in list(model.modules()):\n",
+    "        for name, child in module.named_children():\n",
+    "            if isinstance(child, nn.Linear):\n",
+    "                print(name, child)\n",
+    "                setattr(module, name, BNBLinearWithAdapter.from_linear(child, adapter_dim=adapter_dim))\n",
+    "                \n",
+    "            elif isinstance(child, nn.Embedding):\n",
+    "                print(name, child)\n",
+    "                setattr(module, name, BNBEmbeddingWithAdapter.from_embedding(child, adapter_dim=adapter_dim))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lm_head Linear(in_features=4096, out_features=50400, bias=True)\n",
+      "wte Embedding(50400, 4096)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
+      "k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
+      "fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
+      "fc_out Linear(in_features=16384, out_features=4096, bias=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bnbfy_(gpt, adapter_dim=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = tokenizer(\"A cat sat on a mat and\", return_tensors='pt')\n",
+    "out = gpt.generate(**prompt, min_length=8, max_length=8, do_sample=True)\n",
+    "tokenizer.decode(out[0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py38",
+   "language": "python",
+   "name": "py38"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}