diff --git a/README.md b/README.md index 2530571..7ce5350 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,10 @@ + +Fork of IRIS, where instead of a new transformer as a world model we use an adapter on a pretrained LLM. The hypothesis is that the pretrained LLM will help the world model learn faster and in a more data effecient manner. + +See also: +- [AdaVAE](https://github.com/ImKeTT/AdaVAE) +- [bigvae](https://github.com/JD-P/minihf/blob/adavae-moe/vae_infer.py) + # Transformers are Sample-Efficient World Models (IRIS) [Transformers are Sample-Efficient World Models](https://openreview.net/forum?id=vhFu1Acb0xb)
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml index bfcbb31..7b70c6a 100644 --- a/config/world_model/default.yaml +++ b/config/world_model/default.yaml @@ -1,10 +1,2 @@ -_target_: models.TransformerConfig +_target_: models.BigVAEConfig tokens_per_block: 17 -max_blocks: 20 -attention: 'causal' -num_layers: 10 -num_heads: 4 -embed_dim: 256 -embed_pdrop: 0.1 -resid_pdrop: 0.1 -attn_pdrop: 0.1 diff --git a/journal.md b/journal.md new file mode 100644 index 0000000..2d351b0 --- /dev/null +++ b/journal.md @@ -0,0 +1,196 @@ +# 2023-11-03 11:36:39 + +Step 1 trying to get a VAE working + +ah bitsandbytes +so I needed to use a `poetry add https://github.com/TimDettmers/bitsandbytes/releases/download/0.41.0/bitsandbytes-0.41.0-py3-none-any.whl` to get it to work + +# 2023-11-09 07:29:27 + +Where am I up to? + +- [ ] I want to get bigVAE working with Mistral + - [ ] run + - [ ] Then simplify it +- [ ] then understand IRIS, and inser this model + + +Eror: OOM w minstral. It runs out of mem when set_adapter, with an input +- 7.6/24 at first stop point we have + - vae: DecoderOnlyTransformerVAE + - self.model + - and we seem to be running inputs through with grad + - which is werid as it's jsut + - a frozen model + - 2 adaptors (float32) + - and a vae head + - `next(iter(self.model.parameters()))` is bfloat16, cuda:0. As is vae + + $ self.model + PeftModel( + (base_model): LoraModel( + (model): MistralForCausalLM( + (model): MistralModel( + (embed_tokens): Embedding(32000, 4096) + + $ self.vae + DecoderOnlyTransformerVAE( + (model): PeftModel( + (base_model): LoraModel( + (model): MistralForCausalLM( + (model): MistralModel( + (embed_tokens): Embedding(32000, 4096) + (vae): VAEComponent( + (f): Linear(in_features=4096, out_features=1, bias=True) + (w_e): Linear(in_features=4096, out_features=768, bias=True) + (w_d): Linear(in_features=768, out_features=4096, bias=True) + ) + ) + + + +Hm JDP said he uses 8xH100 so 64GB*8. +- A p5.48xlarge is 80$ +- a o2.16x is 192GB and $14/h +we are using peft + +```py +self.model.print_trainable_parameters() +# trainable params: 83,886,080 || all params: 7,577,276,416 || trainable%: 1.107074302091819 + + +self.model +``` + + DecoderOnlyTransformerVAE( + (model): PeftModel( + (base_model): LoraModel( + (model): MistralForCausalLM( + (model): MistralModel( + (embed_tokens): Embedding(32000, 4096) + (layers): ModuleList( + (0-31): 32 x MistralDecoderLayer( + (self_attn): MistralAttention( ) + (mlp): MistralMLP() + (input_layernorm): MistralRMSNorm() + (post_attention_layernorm): MistralRMSNorm() + ) + ) + (norm): MistralRMSNorm() + ) + (lm_head): Linear(in_features=4096, out_features=32000, bias=False) + ) + ) + ) + (vae): VAEComponent( + (f): Linear(in_features=4096, out_features=1, bias=True) + (w_e): Linear(in_features=4096, out_features=768, bias=True) + (w_d): Linear(in_features=768, out_features=4096, bias=True) + ) + ) + +How much gpu ram should it take it train a 7B? +- 3B -> 14 +- 12B - 56GB +- so 7B should be ~30 :(. or 20 with cpu offloading +- batch size of 4... with 1 it takes 20GB then crashes + +# 2023-11-10 11:41:21 + +tldr: +- I can't use Mistral without a bigger gpu: 30GB+, or maybe I can use the deepspeed gpu offloading (batch=1) +- I I can just use GPT2 like adavae. Or stablelm + +Ideally I can use a small one for prototyping, and change to a large one if it works. + + +Models: +- mistral: the BigVAE code is setup for it +- gpt2: the AdaVAE code is setup for it... but it's also way messier. Roll your own adapter etc + + +TODO use no_grad! way better + + +now +- look at training code +- what is JP code actually doing? start with generate topic +- clean it up, there is so much repeeated code + + +# 2023-11-11 10:09:50 + +## Now how does VAE generate work inside? + +The VAE generate function takes in context, input embeddings, target embeddings, and other parameters in that order. The function performs the following steps: +- Encode: The input embeddings are encoded using an adapter on `self.model` and `vaecomponent.encoder`. This step involves using linear and pooling operations, followed by a softmax function to sample and obtain `z`. +- Decode/Embed: `z` is decoded to obtain `z_embed`, and the decoder is provided with target ID embeddings. This step is done using a linear operation and the model embeddings. +- Model: The model is run on the embeddings to obtain logits. The embeddings can be considered as latent states, and the latent state is expressed in the language of embeddings. + +# 2023-11-12 08:06:25 + +Now look at IRIS and TWM world models. + +- https://github.com/eloialonso/iris/blob/main/src/models/world_model.py + - Model(x, kv_cache). Where x is B, T, C. Batch, Time, Channels? + - if just takes in x and outputs x. The output are logits, from a linear layer. +- https://github.com/jrobine/twm/blob/main/twm/world_model.py + + + +# 2023-11-12 08:46:46 + +Adding BigVAe as transformer layer + +I can't tokenize, then pass in input id's. As I need to to be backpropable. So I need to by pass the embedding layers... +- perhaps I can encode actions by things I have previously embedded? + +# 2023-11-12 10:10:01 + +What the diff between BigVAERouter and DecoderOnlyTransformerVAE + +- DecoderOnlyTransformerVAE(prefix_ids, input_ids) -> outputs, mean +- BigVAERouter(prefix_ids, input_ids, target_ids,), + +OK I want to rename here: +- prefix_ids +- embed_ids -> input_ds +- target_ids +- decoder_prefix_ids -> prefix_ids + +Now how to reconcille it with the world model. what does the world model do? +- components + - transforer x->x: the part we are replacing + - embedder: a custom embedder + - heads for each output: obs, reward, end_of_episode +- when it goes foward + - tokens -> (x, obs, rewards, ends) + - where is is the output of the transformer/vae + - x = transformer(sequences) + - where the sequences are tokens embedded with the embedder. + - TODO: I will want to use the model embedder if possible? + +## Embedder deep dive + +First the code + +So the embedder, takes in `tokens` then breaks it up using a slices, into a seperate obs and action embedder. +We add them, plus positions, and add them into one of embed_dim + + self.pos_emb = nn.Embedding(config.max_tokens, config.embed_dim) + + self.embedder = Embedder( + max_blocks=config.max_blocks, + block_masks=[act_tokens_pattern, obs_tokens_pattern], + embedding_tables=nn.ModuleList([nn.Embedding(act_vocab_size, config.embed_dim), nn.Embedding(obs_vocab_size, config.embed_dim)]) + ) + + sequences = self.embedder(tokens, num_steps, prev_steps) + self.pos_emb(prev_steps + torch.arange(num_steps, device=tokens.device)) + + +Now how does the paper describe it? https://openreview.net/pdf?id=vhFu1Acb0xb + + +# IDEAS + +Could I just use a single transformer? Dreamer seem use encode and decode to a latent state, but IRIS doesn't? diff --git a/notebooks/mjc-002-vae_train.ipynb b/notebooks/mjc-002-vae_train.ipynb new file mode 100644 index 0000000..f43bb64 --- /dev/null +++ b/notebooks/mjc-002-vae_train.ipynb @@ -0,0 +1,486 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/media/wassname/SGIronWolf/projects5/worldmodels/vae_llm_worldmodel/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import argparse\n", + "from contextlib import contextmanager\n", + "from itertools import chain, islice\n", + "import json\n", + "import math\n", + "from pathlib import Path\n", + "import random\n", + "import os\n", + "import sys\n", + "import zipfile\n", + "\n", + "import accelerate\n", + "from datasets import load_dataset\n", + "import peft\n", + "import safetensors.torch as safetorch\n", + "import torch\n", + "from torch import nn, optim\n", + "from torch.nn import functional as F\n", + "from torch.utils import data\n", + "from tqdm import trange, tqdm\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n", + "from loguru import logger\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# autoreload import your package\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from vae_llm_worldmodels.models.bigvae.bigvae import set_adapter, DecoderOnlyTransformerVAE, VAERouter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Params" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['--rank', '16', '--context=96', '--vae_context=32', '--batch_size=1', '--output=./output/adapter']\n" + ] + }, + { + "data": { + "text/plain": [ + "Namespace(batch_size=1, dropout=0.0, epochs=1, gradient_accumulation_steps=1, gradient_checkpointing=False, lr=0.0001, model='stabilityai/stablelm-3b-4e1t', context=96, vae_context=32, output=PosixPath('output/adapter'), rank=16, save_every=1000, start_from=None, z_dim=768)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parser = argparse.ArgumentParser(description=__doc__)\n", + "parser.add_argument(\"--batch_size\", type=int, default=2, help=\"microbatch size\")\n", + "parser.add_argument(\"--dropout\", type=float, default=0.0, help=\"dropout rate\")\n", + "parser.add_argument(\"--epochs\", type=int, default=1, help=\"number of epochs\")\n", + "parser.add_argument(\n", + " \"--gradient_accumulation_steps\", type=int, default=1, help=\"gradient accumulation steps\"\n", + ")\n", + "parser.add_argument(\n", + " \"--gradient_checkpointing\",\n", + " action=\"store_true\",\n", + " default=False,\n", + " help=\"use gradient checkpointing\",\n", + ")\n", + "parser.add_argument(\"--lr\", type=float, default=1e-4, help=\"learning rate\")\n", + "parser.add_argument(\n", + " \"--model\",\n", + " type=str,\n", + " # default=\"mistralai/Mistral-7B-v0.1\",\n", + " # default=\"yichunkuo/stablelm-3b-4e1t-gptq\",\n", + " default=\"stabilityai/stablelm-3b-4e1t\", \n", + " # default=\"gpt2\", \n", + " # default=\"mlabonne/gpt2-GPTQ-4bit\",\n", + " help=\"model name\",\n", + ")\n", + "parser.add_argument(\"--context\", type=int, default=2048, help=\"context window length\")\n", + "parser.add_argument(\"--vae_context\", type=int, default=64, help=\"vae embed context\")\n", + "parser.add_argument(\"--output\", type=Path, required=True, help=\"path to save adapter\")\n", + "parser.add_argument(\"--rank\", type=int, default=32, help=\"the lora rank\")\n", + "parser.add_argument(\"--save_every\", type=int, default=1000, help=\"save every n steps\")\n", + "parser.add_argument(\"--start_from\", type=str, help=\"start from existing lora\")\n", + "parser.add_argument(\"--z_dim\", type=int, default=768, help=\"the latent dimension\")\n", + "\n", + "\n", + "argvs = \"\"\"\n", + "--rank 16 \n", + "--context=96 \n", + "--vae_context=32 \n", + "--batch_size=1 \n", + "--output=./output/adapter \n", + "\"\"\"\n", + "argvs = argvs.replace('\\n', ' ').strip()\n", + "argv = [s.strip() for s in argvs.split(\" \") if s and not s.startswith(\"#\")]\n", + "print(argv)\n", + "args = parser.parse_args(argv)\n", + "args\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "max_length = 32\n", + "tokenizer_args = dict(\n", + " padding='max_length', max_length=max_length,\n", + " truncation=True,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers.utils.logging import _get_library_root_logger\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "os.environ[\"TRANSFORMERS_VERBOSITY\"] = \"detail\"\n", + "\n", + "library_root_logger = _get_library_root_logger()\n", + "library_root_logger.propagate = True\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2023-11-12 09:44:45.920\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mcontextlib\u001b[0m:\u001b[36minner\u001b[0m:\u001b[36m81\u001b[0m - \u001b[1mLoading model: stabilityai/stablelm-3b-4e1t\u001b[0m\n", + "Using pad_token, but it is not set yet.\n" + ] + }, + { + "ename": "TypeError", + "evalue": "VAERouter.__init__() takes from 3 to 4 positional arguments but 5 were given", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/media/wassname/SGIronWolf/projects5/worldmodels/vae_llm_worldmodel/notebooks/mjc-002-vae_train.ipynb Cell 10\u001b[0m line \u001b[0;36m5\n\u001b[1;32m 57\u001b[0m vae_model\u001b[39m.\u001b[39mvae\u001b[39m.\u001b[39mrequires_grad_(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 58\u001b[0m vae_model\u001b[39m.\u001b[39mvae\u001b[39m.\u001b[39mw_d\u001b[39m.\u001b[39mrequires_grad_()\n\u001b[0;32m---> 59\u001b[0m router \u001b[39m=\u001b[39m VAERouter(base_model_peft, vae_model, device, peft_config)\n\u001b[1;32m 60\u001b[0m \u001b[39mif\u001b[39;00m args\u001b[39m.\u001b[39mstart_from:\n\u001b[1;32m 61\u001b[0m router\u001b[39m.\u001b[39mload_pretrained(args\u001b[39m.\u001b[39mstart_from, is_trainable\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", + "\u001b[0;31mTypeError\u001b[0m: VAERouter.__init__() takes from 3 to 4 positional arguments but 5 were given" + ] + } + ], + "source": [ + "accelerator = accelerate.Accelerator(\n", + " mixed_precision=\"bf16\", gradient_accumulation_steps=args.gradient_accumulation_steps\n", + ")\n", + "device = accelerator.device if accelerator.num_processes > 1 else \"cuda:0\"\n", + "is_main = accelerator.is_main_process\n", + "\n", + "print = tqdm.external_write_mode()(logger.info)\n", + "print0 = accelerator.on_main_process(print)\n", + "\n", + "if Path(args.model).exists():\n", + " model_name = Path(args.model).resolve()\n", + "else:\n", + " model_name = args.model\n", + "\n", + "print0(f\"Loading model: {model_name}\")\n", + "with accelerator.main_process_first():\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", + " tokenizer.padding_side = \"left\"\n", + " if tokenizer.pad_token is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + " bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_compute_dtype=torch.bfloat16,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_use_double_quant=True,\n", + " )\n", + " base_model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " device_map={\"\": device},\n", + " quantization_config=bnb_config,\n", + " torch_dtype=torch.bfloat16, \n", + " trust_remote_code=True\n", + " )\n", + " peft_config = peft.LoraConfig(\n", + " peft.TaskType.CAUSAL_LM,\n", + " inference_mode=False,\n", + " r=args.rank,\n", + " lora_alpha=8,\n", + " lora_dropout=args.dropout,\n", + " target_modules=[\n", + " \"self_attn.q_proj\",\n", + " \"self_attn.k_proj\",\n", + " \"self_attn.v_proj\",\n", + " \"self_attn.o_proj\",\n", + " \"mlp.gate_proj\",\n", + " \"mlp.up_proj\",\n", + " \"mlp.down_proj\",\n", + " ],\n", + " )\n", + " base_model_peft = peft.get_peft_model(base_model, peft_config)\n", + " vae_model = DecoderOnlyTransformerVAE(\n", + " base_model_peft, peft_config, device=device, z_dim=args.z_dim,\n", + " )\n", + " if args.start_from:\n", + " vae_model.load_pretrained(args.start_from)\n", + " base_model_peft.requires_grad_(False)\n", + " vae_model.vae.requires_grad_(False)\n", + " vae_model.vae.w_d.requires_grad_()\n", + " router = VAERouter(base_model_peft, vae_model, device)\n", + " if args.start_from:\n", + " router.load_pretrained(args.start_from, is_trainable=True)\n", + "accelerator.wait_for_everyone()\n", + "\n", + "router.train()\n", + "if args.gradient_checkpointing:\n", + " router.model.gradient_checkpointing_enable()\n", + " router.model.enable_input_require_grads()\n", + "\n", + "if is_main:\n", + " router.model.print_trainable_parameters()\n", + "\n", + "router.model.set_adapter(\"router\")\n", + "opt = optim.Adam(router.model.parameters(),\n", + " lr=args.lr,\n", + " betas=(0.9, 0.99))\n", + "accelerator.wait_for_everyone()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://github.com/JD-P/minihf/blob/adavae-moe/train_vae_router.py#L277\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# prepare dataset\n", + "input_ids_all, attention_mask_all = [], []\n", + "for shard_name in os.listdir(args.preprocessed):\n", + " data_path = os.path.join(args.preprocessed, shard_name)\n", + " data_file = safetorch.load_file(data_path)\n", + " input_ids = torch.split(data_file[\"input_ids\"], args.context, dim=1)\n", + " attention_mask = torch.split(data_file[\"attention_mask\"], args.context, dim=1)\n", + " if input_ids[-1].shape[1] != args.context:\n", + " input_ids = input_ids[:-1]\n", + " attention_mask = attention_mask[:-1]\n", + " input_ids_all.extend(input_ids)\n", + " attention_mask_all.extend(attention_mask)\n", + "del data_file, input_ids, attention_mask\n", + "input_ids_all = torch.cat(input_ids_all)\n", + "attention_mask_all = torch.cat(attention_mask_all)\n", + "valid_indices = attention_mask_all.sum(dim=1) == args.context\n", + "input_ids_all = input_ids_all[valid_indices]\n", + "attention_mask_all = attention_mask_all[valid_indices]\n", + "del valid_indices\n", + "\n", + "preprocessed = data.TensorDataset(input_ids_all, attention_mask_all)\n", + "\n", + "dataloader = data.DataLoader(\n", + " preprocessed,\n", + " batch_size=args.batch_size,\n", + " shuffle=True,\n", + " drop_last=True,\n", + ")\n", + "\n", + "router, opt, dataloader = accelerator.prepare(router, opt, dataloader)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vae_llm_worldmodels.utils import cosine_warmup\n", + "\n", + "\n", + "@torch.no_grad()\n", + "@torch.cuda.amp.autocast(dtype=torch.bfloat16)\n", + "def demo(model, input_ids, attention_mask, n_tokens):\n", + " \"\"\"inference.\"\"\"\n", + " bs = min(input_ids.shape[0], 2)\n", + " n_outputs = 2\n", + " tau = 0.8\n", + "\n", + " index = random.randrange(args.context - (args.vae_context * 2))\n", + " context_ids = input_ids[:,:index]\n", + " context_mask = attention_mask[:,:index]\n", + " embed_ids = input_ids[:,index:index + args.vae_context]\n", + " embed_mask = attention_mask[:,index:index + args.vae_context]\n", + " target_ids = input_ids[:,index:index + args.vae_context * 2]\n", + " target_mask = input_ids[:,index:index + args.vae_context * 2]\n", + "\n", + " in_texts = [tokenizer.decode(toks, skip_special_tokens=True)\n", + " for toks in torch.cat([context_ids, embed_ids], dim=1)]\n", + " mean = model.encode(embed_ids[:bs], embed_mask[:bs])\n", + " z = model.vae.vae.sample(mean.repeat_interleave(n_outputs, 0), tau=tau)\n", + " context_ids = context_ids[:bs].repeat_interleave(n_outputs, 0)\n", + " context_mask = context_mask[:bs].repeat_interleave(n_outputs, 0)\n", + " # empty = z.new_zeros([z.shape[0], 0], dtype=torch.long)\n", + " output_ids = model.generate(z, context_ids, context_mask, n_tokens, tau=tau)\n", + " out_texts = [tokenizer.decode(toks, skip_special_tokens=True) for toks in output_ids]\n", + " out_texts = list(batched(out_texts, n_outputs))\n", + " print(\"======\")\n", + " for in_text, out_batch in zip(in_texts, out_texts):\n", + " print(\"=== Input ===\")\n", + " print(in_text)\n", + " print(\"=== Outputs ===\")\n", + " for i, out_text in enumerate(out_batch):\n", + " print(out_text)\n", + " if i < len(out_batch) - 1:\n", + " print(\"===\")\n", + " print(\"======\")\n", + "\n", + "def save():\n", + " print0(f\"### Saving model to {args.output}\", file=sys.stderr)\n", + " accelerator.wait_for_everyone()\n", + " if accelerator.is_main_process:\n", + " unwrapped_model = accelerator.unwrap_model(router)\n", + " unwrapped_model.save_pretrained(args.output)\n", + " state_obj = {\"step\": i, \"last_kl_weight\": kl_sched(i)}\n", + " with open(args.output / \"state.json\", \"w\") as f:\n", + " json.dump(state_obj, f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# train\n", + "i = 0\n", + "kl_sched = cosine_warmup(5000, 0.01)\n", + "\n", + "\n", + "accelerator.wait_for_everyone()\n", + "for epoch in trange(args.epochs, disable=not is_main):\n", + " for input_ids, attention_mask in tqdm(dataloader, disable=not is_main):\n", + " input_ids = input_ids.long()\n", + " if is_main and i % 100 == 0:\n", + " demo(accelerator.unwrap_model(router), input_ids, attention_mask, args.vae_context)\n", + " pass\n", + " with accelerator.accumulate(router):\n", + " index = random.randrange(args.context - (args.vae_context * 2))\n", + " context_ids = input_ids[:,:index]\n", + " context_mask = attention_mask[:,:index]\n", + " embed_ids = input_ids[:,index:index + args.vae_context]\n", + " embed_mask = attention_mask[:,index:index + args.vae_context]\n", + " target_ids = input_ids[:,index:index + args.vae_context * 2]\n", + " target_mask = attention_mask[:,index:index + args.vae_context * 2]\n", + "\n", + " drop_mask = torch.rand([context_ids.shape[0], 1], device=device) < 0.5\n", + " context_ids = torch.where(drop_mask, torch.zeros_like(context_ids), context_ids)\n", + " context_mask = torch.where(drop_mask, torch.zeros_like(context_mask), context_mask)\n", + " outputs = router(embed_ids, embed_mask,\n", + " target_ids[:,:-1], target_mask[:,:-1],\n", + " context_ids, context_mask)\n", + " rec_losses = F.cross_entropy(\n", + " outputs.logits[:, -args.vae_context * 2:].transpose(-1, -2),\n", + " target_ids,\n", + " reduction=\"none\",\n", + " )\n", + " n_toks = target_mask.sum()\n", + " rec_loss = torch.sum(rec_losses * target_mask, dtype=torch.float32) / n_toks\n", + " # kl_loss = torch.sum(mean**2 / 2, dtype=torch.float32) * kl_sched(i) / n_toks\n", + " loss = rec_loss # + kl_loss\n", + "\n", + " # accelerator.backward(loss, inputs=list(p for p in accelerator.unwrap_model(router).model.parameters() if p.requires_grad))\n", + " accelerator.backward(loss)\n", + " # for n, p in router.named_parameters():\n", + " # if p.grad is not None:\n", + " # grad_norm = torch.norm(p.grad, dtype=torch.float32)\n", + " # if grad_norm != 0:\n", + " # print(f\"{n}: {grad_norm:g}\", file=sys.stderr)\n", + " opt.step()\n", + " opt.zero_grad()\n", + "\n", + " loss_global, rec_global = accelerator.reduce(\n", + " (loss, rec_loss), \"mean\"\n", + " )\n", + " print0(\n", + " f\"epoch: {epoch}, step: {i}, loss: {loss_global.item():g}, rec: {rec_global.item():g}\",\n", + " file=sys.stderr,\n", + " )\n", + " i += 1\n", + "\n", + " if i % args.save_every == 0:\n", + " save()\n", + "\n", + " save()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..ade2c0f --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3285 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = 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"poetry.core.masonry.api" diff --git a/src/models/__init__.py b/src/models/__init__.py index c1d02cb..7aab6a2 100644 --- a/src/models/__init__.py +++ b/src/models/__init__.py @@ -1 +1,2 @@ -from .transformer import Transformer, TransformerConfig +# from .transformer import Transformer, TransformerConfig +from .bigvae import BigVAE, BigVAEConfig diff --git a/src/models/bigvae.py b/src/models/bigvae.py new file mode 100644 index 0000000..4dd0f2b --- /dev/null +++ b/src/models/bigvae.py @@ -0,0 +1,419 @@ +""" +Fine-tunes a language model on pre-tokenized data. + + +From https://raw.githubusercontent.com/JD-P/minihf/adavae-moe/vae_infer.py +See https://huggingface.co/jdpressman/BigVAE-Mistral-7B-v0.1/blob/main/README.md + +BigVAE is an [AdaVAE](https://arxiv.org/abs/2205.05862) trained as a pair of LoRa finetunes on [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). +It is meant to be used with the [MiniHF VAE inference code](https://github.com/JD-P/minihf/blob/adavae-moe/vae_infer.py) and will not work if you try to load it.... +""" +from dataclasses import dataclass +import argparse +from contextlib import contextmanager +from itertools import chain, islice +import json +import math +from pathlib import Path +import random +import os +import sys +import zipfile + +import accelerate +from datasets import load_dataset +import peft +import safetensors.torch as safetorch +import torch +from torch import nn, optim +from torch.nn import functional as F +from torch.utils import data +from tqdm import trange, tqdm +from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig +from loguru import logger +from peft import PeftModel, LoraConfig + +logger.add(sys.stderr, format="{time} {level} {message}", filter="my_module", level="INFO") +print = tqdm.external_write_mode()(logger.info) + + +def cosine_warmup(steps, value=1.0): + return lambda i: value * math.sin(min(i / steps, 1) * math.pi / 2) ** 2 + + +@contextmanager +def set_adapter(model, adapter_name): + old_adapter_name = model.active_adapter + try: + if adapter_name is not None: + model.set_adapter(adapter_name) + yield model + else: + with model.disable_adapter(): + yield model + finally: + model.set_adapter(old_adapter_name) + + +def gumbel_like(x): + return torch.rand_like(x).log_().nan_to_num_().neg_().log_().neg_() + + +@contextmanager +def disable_causal_mask(): + raise NotImplementedError("FIXME") + import transformers.models.llama.modeling_llama as modeling + + decoder_fn = modeling._make_causal_mask + + def encoder_fn(*args, **kwargs): + return torch.zeros_like(decoder_fn(*args, **kwargs)) + + try: + modeling._make_causal_mask = encoder_fn + yield + finally: + modeling._make_causal_mask = decoder_fn + + +@contextmanager +def disable_causal_mask_mistral(): + raise NotImplementedError("FIXME") + import transformers.models.mistral.modeling_mistral as modeling + + decoder_fn = modeling._make_sliding_window_causal_mask + + def encoder_fn(*args, **kwargs): + return torch.zeros_like(decoder_fn(*args, **kwargs)) + + try: + modeling._make_sliding_window_causal_mask = encoder_fn + yield + finally: + modeling._make_sliding_window_causal_mask = decoder_fn + + +class VAEHead(nn.Module): + def __init__(self, d_model, z_dim): + super().__init__() + self.d_model = d_model + self.z_dim = z_dim + self.f = nn.Linear(d_model, 1) + self.w_e = nn.Linear(d_model, z_dim) + self.w_d = nn.Linear(z_dim, d_model) + nn.init.orthogonal_(self.w_e.weight) + with torch.no_grad(): + self.w_d.weight.copy_(self.w_e.weight.T) + + def encode(self, hidden_states, attention_mask): + scores = self.f(hidden_states) + scores = scores + attention_mask[:, :, None].log().nan_to_num() + weights = torch.softmax(scores, dim=1) + pooled = torch.sum(hidden_states * weights, dim=1) + return self.w_e(pooled) + + def sample(self, mean, tau=1.0): + return mean + torch.randn_like(mean) * tau**0.5 + + def decode(self, z): + return self.w_d(z) + + +class BigVAE(nn.Module): + """ + Version of AdaVAE with transformer. + """ + + def __init__(self, base_model_peft: PeftModel, peft_config: LoraConfig, z_dim: int=768, device: str = "cuda"): + super().__init__() + self.model = base_model_peft + self.model.add_adapter("encoder", peft_config) + self.model.add_adapter("decoder", peft_config) + self.model.config.output_hidden_states = True + self.vae_head = VAEHead(self.model.config.hidden_size, z_dim).to(device) + + def save_pretrained(self, path): + path = Path(path) + self.model.save_pretrained(path, safe_serialization=True) + safetorch.save_file(self.vae_head.state_dict(), path / "vae.safetensors") + + def load_pretrained(self, path, is_trainable=False): + path = Path(path) + self.model.delete_adapter("encoder") + self.model.load_adapter(path / "encoder", "encoder", is_trainable=is_trainable) + self.model.delete_adapter("decoder") + self.model.load_adapter(path / "decoder", "decoder", is_trainable=is_trainable) + self.vae_head.load_state_dict(safetorch.load_file(path / "vae.safetensors")) + + def encode(self, input_ids, attention_mask): + with set_adapter(self.model, "encoder"), disable_causal_mask_mistral(): + outputs = self.model( + input_ids=input_ids, attention_mask=attention_mask, use_cache=False + ) + return self.vae_head.encode(outputs.hidden_states[-1], attention_mask) + + def input_ids_to_embeds(self, input_ids): + embed_weight = self.model.get_input_embeddings().weight + input_one_hots = F.one_hot(input_ids, num_classes=self.model.config.vocab_size) + return input_one_hots.to(embed_weight) @ embed_weight + + @torch.no_grad() + def generate(self, z, input_ids, attention_mask, n_tokens, tau=1.0): + """ + Takes in a latent vector z from past tokens and generates next n_tokens tokens. + + Used in e.g. https://github.com/JD-P/minihf/blob/adavae-moe/train_vae_overlap.py#L335 + """ + z_embed = self.vae_head.decode(z)[:, None] + inputs_embeds = self.input_ids_to_embeds(input_ids) + inputs_embeds = torch.cat([z_embed, inputs_embeds], dim=1) + attention_mask = torch.cat( + [attention_mask.new_ones([attention_mask.shape[0], 1]), attention_mask], dim=1 + ) + new_embeds, past = None, None + with set_adapter(self.model, "decoder"): + for _ in range(n_tokens): + outputs = self.model( + inputs_embeds=inputs_embeds if past is None else new_embeds, + attention_mask=attention_mask, + use_cache=True, + past_key_values=past, + ) + logits = outputs.logits[:, -1:, :].float() + new_input_ids = torch.argmax(logits + gumbel_like(logits) * tau, dim=-1) + input_ids = torch.cat([input_ids, new_input_ids], dim=1) + new_embeds = self.input_ids_to_embeds(new_input_ids) + attention_mask = torch.cat( + [attention_mask, attention_mask.new_ones([attention_mask.shape[0], 1])], dim=1 + ) + past = outputs.past_key_values + return input_ids + + def forward(self, input_ids, attention_mask, prefix_ids, prefix_mask): + input_ids_all = torch.cat([prefix_ids, input_ids], dim=1) + attn_mask_all = torch.cat([prefix_mask, attention_mask], dim=1) + mean = self.encode(input_ids, attention_mask) + z = self.vae_head.sample(mean) + z_embed = self.vae_head.decode(z)[:, None] + inputs_embeds = self.input_ids_to_embeds(input_ids_all) + inputs_embeds = torch.cat([z_embed, inputs_embeds], dim=1) + attention_mask = torch.cat( + [attention_mask.new_ones([attn_mask_all.shape[0], 1]), attn_mask_all], dim=1 + ) + with set_adapter(self.model, "decoder"): + outputs = self.model( + inputs_embeds=inputs_embeds, attention_mask=attention_mask, use_cache=False + ) + return outputs, mean + +class BigVAERouter(nn.Module): + def __init__(self, base_model_peft: PeftModel, vae: BigVAE, device: str = "cuda"): + super().__init__() + peft_config = base_model_peft.peft_config["default"] # debug this + self.model = base_model_peft + self.model.add_adapter("router", peft_config) + self.model.config.output_hidden_states = True + self.vae = vae + + def save_pretrained(self, path): + path = Path(path) + self.model.save_pretrained(path, safe_serialization=True) + safetorch.save_file(self.model.state_dict(), path / "router.safetensors") + safetorch.save_file(self.vae.vae_head.state_dict(), path / "vae.safetensors") + + def load_pretrained(self, path, is_trainable=False): + path = Path(path) + self.model.delete_adapter("router") + if (path / "router").exists(): + self.model.load_adapter(path / "router", "router", is_trainable=is_trainable) + else: + self.model.load_adapter(path / "decoder", "router", is_trainable=is_trainable) + + def encode(self, input_ids, attention_mask): + with set_adapter(self.vae.model, "encoder"), disable_causal_mask_mistral(): + outputs = self.vae.model( + input_ids=input_ids, attention_mask=attention_mask, use_cache=False + ) + return self.vae.vae_head.encode(outputs.hidden_states[-1], attention_mask) + + def input_ids_to_embeds(self, input_ids): + embed_weight = self.model.get_input_embeddings().weight + input_one_hots = F.one_hot(input_ids, num_classes=self.model.config.vocab_size) + return input_one_hots.to(embed_weight) @ embed_weight + + def generate(self, z, input_ids, attention_mask, n_tokens, tau=1.0): + """ + predict next token given a latent vector z and previous tokens as input_ids + + e.g. https://github.com/JD-P/minihf/blob/adavae-moe/vae_infer.py#L428 + """ + z_embed = self.vae.vae_head.decode(z)[:, None] + inputs_embeds = self.input_ids_to_embeds(input_ids) + inputs_embeds = torch.cat([inputs_embeds, z_embed], dim=1) + attention_mask = torch.cat( + [attention_mask, attention_mask.new_ones([attention_mask.shape[0], 1])], dim=1 + ) + new_embeds, past = None, None + with set_adapter(self.vae.model, "router"): + for _ in range(n_tokens): + outputs = self.model( + inputs_embeds=inputs_embeds if past is None else new_embeds, + attention_mask=attention_mask, + use_cache=True, + past_key_values=past, + ) + logits = outputs.logits[:, -1:, :].float() + new_input_ids = torch.argmax(logits + gumbel_like(logits) * tau, dim=-1) + input_ids = torch.cat([input_ids, new_input_ids], dim=1) + new_embeds = self.input_ids_to_embeds(new_input_ids) + attention_mask = torch.cat( + [attention_mask, attention_mask.new_ones([attention_mask.shape[0], 1])], dim=1 + ) + past = outputs.past_key_values + return input_ids + + # def generate_cfg(self, z, input_ids, attention_mask, n_tokens, tau=1.0, cfg_scale=1): + # """ + # predict next tokens given a latent vector z and previous tokens as input_ids + + # but this one mixes base and router + # was used in topic modelling here https://github.com/JD-P/minihf/blob/adavae-moe/vae_infer.py#L614 + # I can soon delete it + # """ + # z_embed = self.vae.vae.decode(z)[:, None] + # inputs_embeds_base = self.input_ids_to_embeds(input_ids) + # inputs_embeds_router = torch.cat([inputs_embeds_base, z_embed], dim=1) + # attention_mask = torch.cat( + # [attention_mask, attention_mask.new_ones([attention_mask.shape[0], 1])], dim=1 + # ) + # new_embeds, base_past, router_past = None, None, None + # for _ in range(n_tokens): + # with set_adapter(self.vae.model, "router"): + # router_outputs = self.model( + # inputs_embeds=inputs_embeds_router if router_past is None else new_embeds, + # attention_mask=attention_mask, + # use_cache=True, + # past_key_values=router_past, + # ) + # with set_adapter(self.vae.model, None): + # base_outputs = self.model( + # inputs_embeds=inputs_embeds_base if base_past is None else new_embeds, + # attention_mask=attention_mask[:,:-1], + # use_cache=True, + # past_key_values=base_past, + # ) + # base_logits = base_outputs.logits[:, -1:, :].float() + # router_logits = router_outputs.logits[:, -1:, :].float() + + # # mix base and router prediction based on cfg_scale + # logits = base_logits + cfg_scale * (router_logits - base_logits) + # new_input_ids = torch.argmax(logits + gumbel_like(logits) * tau, dim=-1) + # input_ids = torch.cat([input_ids, new_input_ids], dim=1) + # new_embeds = self.input_ids_to_embeds(new_input_ids) + # attention_mask = torch.cat( + # [attention_mask, attention_mask.new_ones([attention_mask.shape[0], 1])], dim=1 + # ) + # base_past = base_outputs.past_key_values + # router_past = router_outputs.past_key_values + # return input_ids + + def forward(self, input_ids, input_mask, target_ids, target_mask, prefix_ids, prefix_mask): + """Like the decoder only, but with context/prefix.""" + mean = self.encode(input_ids, input_mask) + z = self.vae.vae_head.sample(mean) + z_embed = self.vae.vae_head.decode(z)[:, None] + prefix_embeds = self.input_ids_to_embeds(prefix_ids) + target_embeds = self.input_ids_to_embeds(target_ids) + inputs_embeds = torch.cat([prefix_embeds, z_embed, target_embeds], dim=1) + attention_mask = torch.cat( + [prefix_mask, + target_mask.new_ones([prefix_mask.shape[0], 1]), + target_mask], dim=1 + ) + outputs = self.model( + inputs_embeds=inputs_embeds, attention_mask=attention_mask, use_cache=False + ) + return outputs + +def batched(iterable, n): + "Batch data into tuples of length n. The last batch may be shorter." + # batched('ABCDEFG', 3) --> ABC DEF G + if n < 1: + raise ValueError("n must be at least one") + it = iter(iterable) + while batch := tuple(islice(it, n)): + yield batch + + +@dataclass +class BigVAEConfig: + model_name: str = "stabilityai/stablelm-3b-4e1t" + dropout: float = 0 + rank: int = 32 + z_dim: int = 768 + start_from: str = None + + tokens_per_block: int + max_blocks: int + attention: str + + num_layers: int + num_heads: int + embed_dim: int + + embed_pdrop: float + resid_pdrop: float + attn_pdrop: float + + @property + def max_tokens(self): + return self.tokens_per_block * self.max_blocks + +def load_model(config, device='cuda'): + tokenizer = AutoTokenizer.from_pretrained(config.model_name, trust_remote_code=True) + tokenizer.padding_side = "left" + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + bnb_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.bfloat16, + bnb_4bit_quant_type="nf4", + bnb_4bit_use_double_quant=True, + ) + base_model = AutoModelForCausalLM.from_pretrained( + config.model_name, + device_map={"": device}, + quantization_config=bnb_config, + torch_dtype=torch.bfloat16, + trust_remote_code=True + ) + peft_config = peft.LoraConfig( + peft.TaskType.CAUSAL_LM, + inference_mode=False, + r=config.rank, + lora_alpha=8, + lora_dropout=config.dropout, + target_modules=[ + "self_attn.q_proj", + "self_attn.k_proj", + "self_attn.v_proj", + "self_attn.o_proj", + "mlp.gate_proj", + "mlp.up_proj", + "mlp.down_proj", + ], + ) + base_model_peft = peft.get_peft_model(base_model, peft_config) + vae_model = BigVAE( + base_model_peft, device, peft_config, z_dim=config.z_dim, + ) + if config.start_from: + vae_model.load_pretrained(config.start_from) + base_model_peft.requires_grad_(False) + vae_model.vae_head.requires_grad_(False) + vae_model.vae_head.w_d.requires_grad_() + router = BigVAERouter(base_model_peft, vae_model, device, peft_config) + if config.start_from: + router.load_pretrained(config.start_from, is_trainable=True) + print(router.model.print_trainable_parameters()) + router.model.set_adapter("router") diff --git a/src/models/transformer.py b/src/models/transformer.py index 2aaac69..9ba5a96 100644 --- a/src/models/transformer.py +++ b/src/models/transformer.py @@ -1,120 +1,120 @@ -""" -Credits to https://github.com/karpathy/minGPT -""" +# """ +# Credits to https://github.com/karpathy/minGPT +# """ -from dataclasses import dataclass -import math -from typing import Optional +# from dataclasses import dataclass +# import math +# from typing import Optional -from einops import rearrange -import torch -import torch.nn as nn -from torch.nn import functional as F +# from einops import rearrange +# import torch +# import torch.nn as nn +# from torch.nn import functional as F -from .kv_caching import KeysValues, KVCache +# from .kv_caching import KeysValues, KVCache -@dataclass -class TransformerConfig: - tokens_per_block: int - max_blocks: int - attention: str +# @dataclass +# class TransformerConfig: +# tokens_per_block: int +# max_blocks: int +# attention: str - num_layers: int - num_heads: int - embed_dim: int +# num_layers: int +# num_heads: int +# embed_dim: int - embed_pdrop: float - resid_pdrop: float - attn_pdrop: float +# embed_pdrop: float +# resid_pdrop: float +# attn_pdrop: float - @property - def max_tokens(self): - return self.tokens_per_block * self.max_blocks +# @property +# def max_tokens(self): +# return self.tokens_per_block * self.max_blocks -class Transformer(nn.Module): - def __init__(self, config: TransformerConfig) -> None: - super().__init__() - self.config = config - self.drop = nn.Dropout(config.embed_pdrop) - self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)]) - self.ln_f = nn.LayerNorm(config.embed_dim) +# class Transformer(nn.Module): +# def __init__(self, config: TransformerConfig) -> None: +# super().__init__() +# self.config = config +# self.drop = nn.Dropout(config.embed_pdrop) +# self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)]) +# self.ln_f = nn.LayerNorm(config.embed_dim) - def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues: - device = self.ln_f.weight.device # Assumption that all submodules are on the same device - return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device) +# def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues: +# device = self.ln_f.weight.device # Assumption that all submodules are on the same device +# return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device) - def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor: - assert past_keys_values is None or len(past_keys_values) == len(self.blocks) - x = self.drop(sequences) - for i, block in enumerate(self.blocks): - x = block(x, None if past_keys_values is None else past_keys_values[i]) +# def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor: +# assert past_keys_values is None or len(past_keys_values) == len(self.blocks) +# x = self.drop(sequences) +# for i, block in enumerate(self.blocks): +# x = block(x, None if past_keys_values is None else past_keys_values[i]) - x = self.ln_f(x) - return x +# x = self.ln_f(x) +# return x -class Block(nn.Module): - def __init__(self, config: TransformerConfig) -> None: - super().__init__() - self.ln1 = nn.LayerNorm(config.embed_dim) - self.ln2 = nn.LayerNorm(config.embed_dim) - self.attn = SelfAttention(config) - self.mlp = nn.Sequential( - nn.Linear(config.embed_dim, 4 * config.embed_dim), - nn.GELU(), - nn.Linear(4 * config.embed_dim, config.embed_dim), - nn.Dropout(config.resid_pdrop), - ) +# class Block(nn.Module): +# def __init__(self, config: TransformerConfig) -> None: +# super().__init__() +# self.ln1 = nn.LayerNorm(config.embed_dim) +# self.ln2 = nn.LayerNorm(config.embed_dim) +# self.attn = SelfAttention(config) +# self.mlp = nn.Sequential( +# nn.Linear(config.embed_dim, 4 * config.embed_dim), +# nn.GELU(), +# nn.Linear(4 * config.embed_dim, config.embed_dim), +# nn.Dropout(config.resid_pdrop), +# ) - def forward(self, x: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor: - x_attn = self.attn(self.ln1(x), past_keys_values) - x = x + x_attn - x = x + self.mlp(self.ln2(x)) - return x +# def forward(self, x: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor: +# x_attn = self.attn(self.ln1(x), past_keys_values) +# x = x + x_attn +# x = x + self.mlp(self.ln2(x)) +# return x -class SelfAttention(nn.Module): - def __init__(self, config: TransformerConfig) -> None: - super().__init__() - assert config.embed_dim % config.num_heads == 0 - assert config.attention in ('causal', 'block_causal') - self.num_heads = config.num_heads - self.key = nn.Linear(config.embed_dim, config.embed_dim) - self.query = nn.Linear(config.embed_dim, config.embed_dim) - self.value = nn.Linear(config.embed_dim, config.embed_dim) - self.attn_drop = nn.Dropout(config.attn_pdrop) - self.resid_drop = nn.Dropout(config.resid_pdrop) - self.proj = nn.Linear(config.embed_dim, config.embed_dim) +# class SelfAttention(nn.Module): +# def __init__(self, config: TransformerConfig) -> None: +# super().__init__() +# assert config.embed_dim % config.num_heads == 0 +# assert config.attention in ('causal', 'block_causal') +# self.num_heads = config.num_heads +# self.key = nn.Linear(config.embed_dim, config.embed_dim) +# self.query = nn.Linear(config.embed_dim, config.embed_dim) +# self.value = nn.Linear(config.embed_dim, config.embed_dim) +# self.attn_drop = nn.Dropout(config.attn_pdrop) +# self.resid_drop = nn.Dropout(config.resid_pdrop) +# self.proj = nn.Linear(config.embed_dim, config.embed_dim) - causal_mask = torch.tril(torch.ones(config.max_tokens, config.max_tokens)) - block_causal_mask = torch.max(causal_mask, torch.block_diag(*[torch.ones(config.tokens_per_block, config.tokens_per_block) for _ in range(config.max_blocks)])) - self.register_buffer('mask', causal_mask if config.attention == 'causal' else block_causal_mask) +# causal_mask = torch.tril(torch.ones(config.max_tokens, config.max_tokens)) +# block_causal_mask = torch.max(causal_mask, torch.block_diag(*[torch.ones(config.tokens_per_block, config.tokens_per_block) for _ in range(config.max_blocks)])) +# self.register_buffer('mask', causal_mask if config.attention == 'causal' else block_causal_mask) - def forward(self, x: torch.Tensor, kv_cache: Optional[KVCache] = None) -> torch.Tensor: - B, T, C = x.size() - if kv_cache is not None: - b, nh, L, c = kv_cache.shape - assert nh == self.num_heads and b == B and c * nh == C - else: - L = 0 +# def forward(self, x: torch.Tensor, kv_cache: Optional[KVCache] = None) -> torch.Tensor: +# B, T, C = x.size() +# if kv_cache is not None: +# b, nh, L, c = kv_cache.shape +# assert nh == self.num_heads and b == B and c * nh == C +# else: +# L = 0 - q = self.query(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs) - k = self.key(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs) - v = self.value(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs) +# q = self.query(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs) +# k = self.key(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs) +# v = self.value(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2) # (B, nh, T, hs) - if kv_cache is not None: - kv_cache.update(k, v) - k, v = kv_cache.get() +# if kv_cache is not None: +# kv_cache.update(k, v) +# k, v = kv_cache.get() - att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) - att = att.masked_fill(self.mask[L:L + T, :L + T] == 0, float('-inf')) - att = F.softmax(att, dim=-1) - att = self.attn_drop(att) - y = att @ v - y = rearrange(y, 'b h t e -> b t (h e)') +# att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) +# att = att.masked_fill(self.mask[L:L + T, :L + T] == 0, float('-inf')) +# att = F.softmax(att, dim=-1) +# att = self.attn_drop(att) +# y = att @ v +# y = rearrange(y, 'b h t e -> b t (h e)') - y = self.resid_drop(self.proj(y)) +# y = self.resid_drop(self.proj(y)) - return y +# return y diff --git a/src/models/world_model.py b/src/models/world_model.py index 0691034..94bd119 100644 --- a/src/models/world_model.py +++ b/src/models/world_model.py @@ -10,7 +10,8 @@ from dataset import Batch from .kv_caching import KeysValues from .slicer import Embedder, Head from .tokenizer import Tokenizer -from .transformer import Transformer, TransformerConfig +# from .transformer import Transformer, TransformerConfig +from .bigvae import BigVAE, BigVAEConfig from utils import init_weights, LossWithIntermediateLosses @@ -23,11 +24,11 @@ class WorldModelOutput: class WorldModel(nn.Module): - def __init__(self, obs_vocab_size: int, act_vocab_size: int, config: TransformerConfig) -> None: + def __init__(self, obs_vocab_size: int, act_vocab_size: int, config: BigVAEConfig) -> None: super().__init__() self.obs_vocab_size, self.act_vocab_size = obs_vocab_size, act_vocab_size self.config = config - self.transformer = Transformer(config) + self.transformer = BigVAE(config) all_but_last_obs_tokens_pattern = torch.ones(config.tokens_per_block) all_but_last_obs_tokens_pattern[-2] = 0