{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "# ViT and MLP Singular Evolutions" ], "metadata": { "id": "ElW9o9ui1oa0" } }, { "cell_type": "code", "source": [ "!pip install scikit-rmt" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EBtEZm8nyIyB", "outputId": "e485bb4a-af41-45ee-da8a-35d3a515a37a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: scikit-rmt in /usr/local/lib/python3.11/dist-packages (1.0.0)\n", "Requirement already satisfied: numpy<=1.24.3,>=1.21.6 in /usr/local/lib/python3.11/dist-packages (from scikit-rmt) (1.24.3)\n", "Requirement already satisfied: scipy<1.12,>=1.7.3 in /usr/local/lib/python3.11/dist-packages (from scikit-rmt) (1.11.4)\n", "Requirement already satisfied: matplotlib<3.8,>=3.5.3 in /usr/local/lib/python3.11/dist-packages (from scikit-rmt) (3.7.5)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (1.3.2)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (4.58.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (1.4.8)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (24.2)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (11.2.1)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (3.2.3)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8,>=3.5.3->scikit-rmt) (2.9.0.post0)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib<3.8,>=3.5.3->scikit-rmt) (1.17.0)\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "emAyyh9dLvRc" }, "outputs": [], "source": [ "import os\n", "import math\n", "import random\n", "import pickle\n", "import tiktoken\n", "from datetime import datetime\n", "import inspect\n", "from dataclasses import dataclass\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.nn import functional as F\n", "\n", "from torchvision import datasets, transforms\n", "from torch.utils.data import DataLoader\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", "from skrmt.ensemble.spectral_law import MarchenkoPasturDistribution, TracyWidomDistribution\n", "import requests" ] }, { "cell_type": "code", "source": [ "# ------------------------------------------------------------------------------\n", "# CONFIGURATION\n", "# ------------------------------------------------------------------------------\n", "MODEL_TYPE = 'nanogpt' # Choose 'mlp', 'vit', or 'nanogpt'\n", "NUM_RUNS = 5\n", "NUM_EPOCHS = 10\n", "BATCH_SIZE = 16\n", "LEARNING_RATE = 1e-2\n", "MOMENTUM = 0.9\n", "LOG_EVERY_N_BATCHES = 200\n", "\n", "INPUT_SIZE = 28 * 28\n", "HIDDEN_SIZE = 1024\n", "OUTPUT_SIZE = 10\n", "NUM_HIDDEN_LAYERS = 2\n", "\n", "DATA_ROOT = './data'\n", "\n", "# ViT configuration parameters\n", "VIT_IMAGE_SIZE = 28\n", "VIT_PATCH_SIZE = 7\n", "VIT_EMBED_DIM = 64\n", "VIT_DEPTH = 2\n", "VIT_NUM_HEADS = 4\n", "VIT_MLP_RATIO = 2.0\n", "VIT_INPUT_CHANNELS = 1\n", "VIT_INIT_FC = True\n", "\n", "# nanoGPT configuration parameters\n", "NANOGPT_VOCAB_SIZE = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64\n", "NANOGPT_BLOCK_SIZE = 128\n", "NANOGPT_N_LAYER = 2\n", "NANOGPT_N_HEAD = 2\n", "NANOGPT_N_EMBD = 128\n", "NANOGPT_DROPOUT = 0.0\n", "NANOGPT_BIAS = False\n", "NANOGPT_WEIGHT_DECAY = 0.1\n", "NANOGPT_MAX_ITERS = 2000\n", "NANOGPT_WARMUP_ITERS = 200\n", "NANOGPT_LR_DECAY_ITERS = 2000\n", "NANOGPT_MIN_LR = 5e-5\n", "NANOGPT_DECAY_LR = True\n", "NANOGPT_GRAD_CLIP = 1.0" ], "metadata": { "id": "S7Qr5xwRx5t_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ------------------------------------------------------------------------------\n", "# TEXT DATA PREPARATION\n", "# ------------------------------------------------------------------------------\n", "enc = tiktoken.get_encoding(\"gpt2\")\n", "\n", "def download_text_sample(data_dir):\n", " \"\"\"Download a sample text dataset\"\"\"\n", " os.makedirs(data_dir, exist_ok=True)\n", "\n", " # Use a tiny Shakespeare dataset instead - it's reliable and small\n", " sample_url = \"https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\"\n", " output_file = os.path.join(data_dir, \"shakespeare.txt\")\n", "\n", " if not os.path.exists(output_file):\n", " print(f\"Downloading Shakespeare sample to {output_file}...\")\n", " with requests.get(sample_url, stream=True) as r:\n", " r.raise_for_status() # Ensure we catch HTTP errors\n", " total_size_in_bytes = int(r.headers.get('content-length', 0))\n", " progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)\n", " with open(output_file, 'wb') as f:\n", " for chunk in r.iter_content(chunk_size=8192):\n", " progress_bar.update(len(chunk))\n", " f.write(chunk)\n", " progress_bar.close()\n", " print(f\"Download complete: {output_file}\")\n", " else:\n", " print(f\"File already exists: {output_file}\")\n", "\n", " return output_file\n", "\n", "def prepare_text_data(data_dir, val_split=0.1, seed=42):\n", " \"\"\"Prepare text dataset for nanoGPT by tokenizing and converting to .bin files\"\"\"\n", " # Set random seed for reproducibility\n", " random.seed(seed)\n", " np.random.seed(seed)\n", "\n", " # Create output directory\n", " os.makedirs(data_dir, exist_ok=True)\n", "\n", " # Download the text sample\n", " text_file = download_text_sample(data_dir)\n", "\n", " # Verify the file exists and has content\n", " if not os.path.exists(text_file):\n", " raise FileNotFoundError(f\"Text file not found: {text_file}\")\n", "\n", " if os.path.getsize(text_file) == 0:\n", " raise ValueError(f\"Downloaded file is empty: {text_file}\")\n", "\n", " # Read the text file and split into documents\n", " print(\"Processing text data...\")\n", " with open(text_file, 'r', encoding='utf-8') as f:\n", " text = f.read()\n", "\n", " # For Shakespeare, split by newlines to get reasonable chunks\n", " raw_documents = [doc.strip() for doc in text.split('\\n') if doc.strip()]\n", "\n", " # Combine short lines to form more substantial documents\n", " documents = []\n", " current_doc = []\n", " for line in raw_documents:\n", " current_doc.append(line)\n", " # If we've accumulated about 100 tokens or hit a scene break, create a document\n", " if len(' '.join(current_doc)) > 400 or line.strip() == '':\n", " if current_doc: # Ensure we don't add empty documents\n", " documents.append(' '.join(current_doc))\n", " current_doc = []\n", "\n", " # Add any remaining lines\n", " if current_doc:\n", " documents.append(' '.join(current_doc))\n", "\n", " print(f\"Split text into {len(documents)} documents\")\n", "\n", " # Shuffle the documents\n", " random.shuffle(documents)\n", "\n", " # Split into train/val\n", " n_val = max(1, int(len(documents) * val_split))\n", " train_documents = documents[:-n_val]\n", " val_documents = documents[-n_val:]\n", "\n", " # Tokenize\n", " print(f\"Tokenizing {len(train_documents)} train documents...\")\n", " train_tokens = []\n", " for doc in tqdm(train_documents):\n", " train_tokens.extend(enc.encode(doc))\n", " # Add a separator token (newline)\n", " train_tokens.append(enc.eot_token) # EOT token\n", "\n", " print(f\"Tokenizing {len(val_documents)} validation documents...\")\n", " val_tokens = []\n", " for doc in tqdm(val_documents):\n", " val_tokens.extend(enc.encode(doc))\n", " # Add a separator token (newline)\n", " val_tokens.append(enc.eot_token) # EOT token\n", "\n", " print(f\"Train set: {len(train_tokens)} tokens\")\n", " train_tokens = np.array(train_tokens, dtype=np.uint16)\n", " save_path = os.path.join(data_dir, 'train.bin')\n", " train_tokens.tofile(save_path)\n", "\n", " print(f\"Validation set: {len(val_tokens)} tokens\")\n", " val_tokens = np.array(val_tokens, dtype=np.uint16)\n", " save_path = os.path.join(data_dir, 'val.bin')\n", " val_tokens.tofile(save_path)\n", "\n", " meta = {\n", " 'vocab_size': enc.n_vocab,\n", " 'train_tokens': len(train_tokens),\n", " 'val_tokens': len(val_tokens)\n", " }\n", " with open(os.path.join(data_dir, 'meta.pkl'), 'wb') as f:\n", " import pickle\n", " pickle.dump(meta, f)\n", "\n", " print(\"Done!\")" ], "metadata": { "id": "CCk2q9n0Y8pG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ------------------------------------------------------------------------------\n", "# MODEL DEFINITION\n", "# ------------------------------------------------------------------------------\n", "class SimpleMLP(nn.Module):\n", " def __init__(self, input_size, hidden_size, output_size, num_hidden_layers):\n", " super().__init__()\n", " self.fc_layers = nn.ModuleList()\n", " self.fc_layers.append(nn.Linear(input_size, hidden_size))\n", " for _ in range(num_hidden_layers):\n", " self.fc_layers.append(nn.Linear(hidden_size, hidden_size))\n", " self.fc_layers.append(nn.Linear(hidden_size, output_size))\n", " self.relu = nn.ReLU()\n", " self._init_gaussian_weights()\n", "\n", " def _init_gaussian_weights(self):\n", " for layer in self.fc_layers:\n", " fan_in = layer.weight.size(1)\n", " std = 1.0 / math.sqrt(fan_in)\n", " nn.init.normal_(layer.weight, mean=0.0, std=std)\n", " if layer.bias is not None:\n", " nn.init.zeros_(layer.bias)\n", "\n", " def forward(self, x):\n", " x = x.view(x.size(0), -1)\n", " for layer in self.fc_layers[:-1]:\n", " x = self.relu(layer(x))\n", " return self.fc_layers[-1](x)\n", "\n", " def get_target_layers(self):\n", " return [f\"fc_layers.{i}.weight\" for i in range(len(self.fc_layers))]\n", "\n", " def get_parameter(self, name):\n", " parts = name.split('.')\n", " obj = self\n", " for p in parts:\n", " obj = obj[int(p)] if p.isdigit() else getattr(obj, p)\n", " return obj\n", "\n", "class PatchEmbed(nn.Module):\n", " \"\"\"\n", " Split image into patches and embed them.\n", " \"\"\"\n", " def __init__(self, img_size=28, patch_size=7, in_chans=1, embed_dim=64):\n", " super().__init__()\n", " self.img_size = img_size\n", " self.patch_size = patch_size\n", " assert img_size % patch_size == 0, \"Image size must be divisible by patch size\"\n", " self.grid_size = (img_size // patch_size, img_size // patch_size)\n", " self.num_patches = self.grid_size[0] * self.grid_size[1]\n", " self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n", "\n", " def forward(self, x):\n", " x = self.proj(x)\n", " x = x.flatten(2)\n", " x = x.transpose(1, 2)\n", " return x\n", "\n", "class CustomViT(nn.Module):\n", " \"\"\"\n", " Very small Vision Transformer (ViT) wrapper for LossGeometry.\n", " Implements patch embedding, transformer encoder, and classification head.\n", " \"\"\"\n", " def __init__(self,\n", " num_classes=10,\n", " image_size=28,\n", " patch_size=7,\n", " embed_dim=64,\n", " depth=2,\n", " num_heads=4,\n", " mlp_ratio=2.0,\n", " input_channels=1,\n", " gaussian_init_fc=False):\n", " super().__init__()\n", " # Expose attributes for compatibility\n", " self.input_size = input_channels\n", " self.hidden_size = embed_dim\n", " self.output_size = num_classes\n", " self.num_hidden_layers = depth\n", "\n", " # Patch embedding module\n", " self.patch_embed = PatchEmbed(image_size, patch_size, input_channels, embed_dim)\n", " num_patches = self.patch_embed.num_patches\n", "\n", " # Class token and positional embeddings\n", " self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n", " self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))\n", " self.pos_dropout = nn.Dropout(p=0.0)\n", "\n", " # Transformer encoder layers\n", " encoder_layer = nn.TransformerEncoderLayer(\n", " d_model=embed_dim,\n", " nhead=num_heads,\n", " dim_feedforward=int(embed_dim * mlp_ratio),\n", " activation='gelu'\n", " )\n", " self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=depth)\n", "\n", " # Classification head\n", " self.head = nn.Linear(embed_dim, num_classes)\n", "\n", " # Initialization\n", " nn.init.xavier_uniform_(self.patch_embed.proj.weight)\n", " if self.patch_embed.proj.bias is not None:\n", " nn.init.zeros_(self.patch_embed.proj.bias)\n", " nn.init.zeros_(self.cls_token)\n", " nn.init.zeros_(self.pos_embed)\n", " if gaussian_init_fc:\n", " fan_in = self.head.weight.data.size(1)\n", " std = 1.0 / math.sqrt(fan_in)\n", " nn.init.normal_(self.head.weight.data, mean=0.0, std=std)\n", " if self.head.bias is not None:\n", " nn.init.zeros_(self.head.bias.data)\n", "\n", " def forward(self, x):\n", " batch_size = x.size(0)\n", " x = self.patch_embed(x)\n", " cls_tokens = self.cls_token.expand(batch_size, -1, -1)\n", " x = torch.cat((cls_tokens, x), dim=1)\n", " x = x + self.pos_embed\n", " x = self.pos_dropout(x).transpose(0, 1)\n", " x = self.transformer(x)\n", " x = x[0]\n", " x = self.head(x)\n", " return x\n", "\n", " def get_target_layers(self):\n", " layers = ['head.weight']\n", " for i in range(len(self.transformer.layers)):\n", " layers.append(f'transformer.layers.{i}.linear1.weight')\n", " layers.append(f'transformer.layers.{i}.linear2.weight')\n", " return layers\n", "\n", " def get_parameter(self, layer_name):\n", " parts = layer_name.split('.')\n", " obj = self\n", " for p in parts:\n", " if p.isdigit():\n", " obj = obj[int(p)]\n", " else:\n", " obj = getattr(obj, p)\n", " return obj\n", "\n", "class LayerNorm(nn.Module):\n", " \"\"\" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False \"\"\"\n", "\n", " def __init__(self, ndim, bias):\n", " super().__init__()\n", " self.weight = nn.Parameter(torch.ones(ndim))\n", " self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None\n", "\n", " def forward(self, input):\n", " return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)\n", "\n", "class CausalSelfAttention(nn.Module):\n", "\n", " def __init__(self, config):\n", " super().__init__()\n", " assert config.n_embd % config.n_head == 0\n", " # key, query, value projections for all heads, but in a batch\n", " self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)\n", " # output projection\n", " self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n", " # regularization\n", " self.attn_dropout = nn.Dropout(config.dropout)\n", " self.resid_dropout = nn.Dropout(config.dropout)\n", " self.n_head = config.n_head\n", " self.n_embd = config.n_embd\n", " self.dropout = config.dropout\n", " # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0\n", " self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n", " if not self.flash:\n", " print(\"WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0\")\n", " # causal mask to ensure that attention is only applied to the left in the input sequence\n", " self.register_buffer(\"bias\", torch.tril(torch.ones(config.block_size, config.block_size))\n", " .view(1, 1, config.block_size, config.block_size))\n", "\n", " def forward(self, x):\n", " B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)\n", "\n", " # calculate query, key, values for all heads in batch and move head forward to be the batch dim\n", " q, k, v = self.c_attn(x).split(self.n_embd, dim=2)\n", " k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)\n", " q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)\n", " v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)\n", "\n", " # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)\n", " if self.flash:\n", " # efficient attention using Flash Attention CUDA kernels\n", " y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)\n", " else:\n", " # manual implementation of attention\n", " att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n", " att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))\n", " att = F.softmax(att, dim=-1)\n", " att = self.attn_dropout(att)\n", " y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)\n", " y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side\n", "\n", " # output projection\n", " y = self.resid_dropout(self.c_proj(y))\n", " return y\n", "\n", "class MLP(nn.Module):\n", "\n", " def __init__(self, config):\n", " super().__init__()\n", " self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)\n", " self.gelu = nn.GELU()\n", " self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)\n", " self.dropout = nn.Dropout(config.dropout)\n", "\n", " def forward(self, x):\n", " x = self.c_fc(x)\n", " x = self.gelu(x)\n", " x = self.c_proj(x)\n", " x = self.dropout(x)\n", " return x\n", "\n", "class Block(nn.Module):\n", "\n", " def __init__(self, config):\n", " super().__init__()\n", " self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)\n", " self.attn = CausalSelfAttention(config)\n", " self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)\n", " self.mlp = MLP(config)\n", "\n", " def forward(self, x):\n", " x = x + self.attn(self.ln_1(x))\n", " x = x + self.mlp(self.ln_2(x))\n", " return x\n", "\n", "@dataclass\n", "class GPTConfig:\n", " block_size: int = 1024\n", " vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency\n", " n_layer: int = 12\n", " n_head: int = 12\n", " n_embd: int = 768\n", " dropout: float = 0.0\n", " bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster\n", "\n", "class GPTSpectral(nn.Module):\n", " \"\"\"GPT model extended with methods to expose layers for spectral analysis\"\"\"\n", "\n", " def __init__(self, config):\n", " super().__init__()\n", " assert config.vocab_size is not None\n", " assert config.block_size is not None\n", " self.config = config\n", "\n", " # Add these attributes to make compatible with save_analysis_data\n", " self.input_size = config.vocab_size\n", " self.hidden_size = config.n_embd\n", " self.output_size = config.vocab_size\n", " self.num_hidden_layers = config.n_layer\n", "\n", " self.transformer = nn.ModuleDict(dict(\n", " wte = nn.Embedding(config.vocab_size, config.n_embd),\n", " wpe = nn.Embedding(config.block_size, config.n_embd),\n", " drop = nn.Dropout(config.dropout),\n", " h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),\n", " ln_f = LayerNorm(config.n_embd, bias=config.bias),\n", " ))\n", " self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)\n", "\n", " # Weight tying - note this needs to be done after applying _init_weights or it will be overwritten\n", " self.apply(self._init_weights)\n", " # Apply special scaled init to the residual projections, per GPT-2 paper\n", " for pn, p in self.named_parameters():\n", " if pn.endswith('c_proj.weight'):\n", " torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))\n", "\n", " # Now set up weight tying\n", " self.transformer.wte.weight = self.lm_head.weight # weight tying\n", "\n", " # Report number of parameters\n", " print(\"number of parameters: %.2fM\" % (self.get_num_params()/1e6,))\n", "\n", " # Create a mapping of layer names to parameter names for spectral analysis\n", " self._target_layers = []\n", " self._build_target_layers()\n", "\n", " def get_num_params(self, non_embedding=True):\n", " \"\"\"\n", " Return the number of parameters in the model.\n", " For non-embedding count (default), the position embeddings get subtracted.\n", " The token embeddings would too, except due to the parameter sharing these\n", " params are actually used as weights in the final layer, so we include them.\n", " \"\"\"\n", " n_params = sum(p.numel() for p in self.parameters())\n", " if non_embedding:\n", " n_params -= self.transformer.wpe.weight.numel()\n", " return n_params\n", "\n", " def _init_weights(self, module):\n", " if isinstance(module, nn.Linear):\n", " torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n", " if module.bias is not None:\n", " torch.nn.init.zeros_(module.bias)\n", " elif isinstance(module, nn.Embedding):\n", " torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n", "\n", " def _build_target_layers(self):\n", " \"\"\"Build a list of target layers for spectral analysis\"\"\"\n", " # Add attention weight matrices\n", " for i in range(self.config.n_layer):\n", " self._target_layers.append(f\"transformer.h.{i}.attn.c_attn.weight\")\n", " self._target_layers.append(f\"transformer.h.{i}.attn.c_proj.weight\")\n", "\n", " # Add MLP weight matrices\n", " for i in range(self.config.n_layer):\n", " self._target_layers.append(f\"transformer.h.{i}.mlp.c_fc.weight\")\n", " self._target_layers.append(f\"transformer.h.{i}.mlp.c_proj.weight\")\n", "\n", " # Add embedding - but note that embedding weight is shared with lm_head\n", " self._target_layers.append(\"transformer.wte.weight\")\n", "\n", " # We don't need to add lm_head.weight since it shares parameters with transformer.wte.weight\n", " # due to weight tying, and will just cause confusion during analysis\n", "\n", " def get_target_layers(self):\n", " \"\"\"Return the list of target layers for spectral analysis\"\"\"\n", " return self._target_layers\n", "\n", " def get_parameter(self, param_name):\n", " \"\"\"Get a parameter by name\"\"\"\n", " for name, param in self.named_parameters():\n", " if name == param_name:\n", " return param\n", " raise AttributeError(f\"Parameter '{param_name}' not found in model\")\n", "\n", " def forward(self, idx, targets=None):\n", " device = idx.device\n", " b, t = idx.size()\n", " assert t <= self.config.block_size, f\"Cannot forward sequence of length {t}, block size is only {self.config.block_size}\"\n", " pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)\n", "\n", " # forward the GPT model itself\n", " tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)\n", " pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)\n", " x = self.transformer.drop(tok_emb + pos_emb)\n", " for block in self.transformer.h:\n", " x = block(x)\n", " x = self.transformer.ln_f(x)\n", "\n", " if targets is not None:\n", " # if we are given some desired targets also calculate the loss\n", " logits = self.lm_head(x)\n", " loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)\n", " else:\n", " # inference-time mini-optimization: only forward the lm_head on the very last position\n", " logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim\n", " loss = None\n", "\n", " return logits, loss\n", "\n", " def estimate_mfu(self, fwdbwd_per_iter, dt):\n", " \"\"\" estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS \"\"\"\n", " # The number of transformer layers\n", " L = self.config.n_layer\n", " # The hidden dimension\n", " H = self.config.n_embd\n", " # The number of heads\n", " Q = self.config.n_head\n", " # The batch size\n", " B = 1 # Assuming batch size of 1 for simplicity\n", " # The sequence length\n", " T = self.config.block_size\n", "\n", " # estimate the number of flops we do per iteration.\n", " # see the GPT-3 paper Appendix C as ref: https://arxiv.org/abs/2005.14165, GPT-3 training_flops\n", " flops_per_token = 6 * L * H**2 * (1 + T/H + (Q*T)/(4*H) + Q/4)\n", " flops_per_fwdbwd = flops_per_token * B * T\n", " flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter\n", " # express our flops throughput as ratio of A100 bfloat16 peak flops\n", " flops_achieved = flops_per_iter * (1.0/dt) # per second\n", " flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS\n", " mfu = flops_achieved / flops_promised\n", " return mfu\n", "\n", " def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n", " # start with all of the candidate parameters\n", " param_dict = {pn: p for pn, p in self.named_parameters()}\n", " # filter out those that do not require grad\n", " param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}\n", " # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.\n", " # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.\n", " decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]\n", " nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]\n", " optim_groups = [\n", " {'params': decay_params, 'weight_decay': weight_decay},\n", " {'params': nodecay_params, 'weight_decay': 0.0}\n", " ]\n", "\n", " print(f\"using SGD optimizer with momentum={betas[0]}\")\n", " return torch.optim.SGD(optim_groups, lr=learning_rate, momentum=betas[0])\n", "\n", " @classmethod\n", " def from_pretrained(cls, model_type, override_args=None):\n", " \"\"\"Initialize a pretrained GPT model by copying over the weights from a GPT-2 HuggingFace model\"\"\"\n", " assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}\n", " from model import GPT\n", "\n", " # Start with a standard GPT model\n", " gpt = GPT.from_pretrained(model_type, override_args)\n", "\n", " # Create our spectral-enabled model with the same config\n", " config = gpt.config\n", " model = cls(config)\n", "\n", " # Copy over the weights\n", " model.load_state_dict(gpt.state_dict())\n", "\n", " return model\n", "\n", " def crop_block_size(self, block_size):\n", " # model surgery to decrease the block size if necessary\n", " # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)\n", " # but want to use a smaller block size for some smaller, simpler model\n", " assert block_size <= self.config.block_size\n", " self.config.block_size = block_size\n", " self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])\n", " for block in self.transformer.h:\n", " if hasattr(block.attn, 'bias'):\n", " block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]" ], "metadata": { "id": "qXBAtPGlyII9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "if MODEL_TYPE == 'mlp':\n", " _reference_model = SimpleMLP(INPUT_SIZE, HIDDEN_SIZE, OUTPUT_SIZE, NUM_HIDDEN_LAYERS)\n", "elif MODEL_TYPE == 'vit':\n", " _reference_model = CustomViT(\n", " num_classes=OUTPUT_SIZE,\n", " image_size=VIT_IMAGE_SIZE,\n", " patch_size=VIT_PATCH_SIZE,\n", " embed_dim=VIT_EMBED_DIM,\n", " depth=VIT_DEPTH,\n", " num_heads=VIT_NUM_HEADS,\n", " mlp_ratio=VIT_MLP_RATIO,\n", " input_channels=VIT_INPUT_CHANNELS,\n", " gaussian_init_fc=VIT_INIT_FC\n", " )\n", "elif MODEL_TYPE == 'nanogpt':\n", " _nanogpt_config = GPTConfig(\n", " block_size=NANOGPT_BLOCK_SIZE,\n", " vocab_size=NANOGPT_VOCAB_SIZE,\n", " n_layer=NANOGPT_N_LAYER,\n", " n_head=NANOGPT_N_HEAD,\n", " n_embd=NANOGPT_N_EMBD,\n", " dropout=NANOGPT_DROPOUT,\n", " bias=NANOGPT_BIAS\n", " )\n", " _reference_model = GPTSpectral(_nanogpt_config)\n", "else:\n", " raise ValueError(f\"Unsupported MODEL_TYPE: {MODEL_TYPE}\")\n", "LAYER_NAMES = _reference_model.get_target_layers()\n", "LAYER_SHAPES = [tuple(_reference_model.get_parameter(name).shape) for name in LAYER_NAMES]" ], "metadata": { "id": "J-SmJlURz7Y0", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a8a86c26-5ee2-4082-afa1-84af1843958e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "number of parameters: 6.83M\n" ] } ] }, { "cell_type": "code", "source": [ "# Helper function for loading text data for nanoGPT\n", "def get_batch_text(split, block_size, batch_size, device):\n", " \"\"\"Load training and validation data for nanoGPT\"\"\"\n", " # We recreate np.memmap every batch to avoid a memory leak\n", " data_dir = DATA_ROOT\n", " if split == 'train':\n", " data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')\n", " else:\n", " data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')\n", " ix = torch.randint(len(data) - block_size, (batch_size,))\n", " x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])\n", " y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])\n", " if device.type == 'cuda':\n", " # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)\n", " x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)\n", " else:\n", " x, y = x.to(device), y.to(device)\n", " return x, y\n", "\n", "# Learning rate decay scheduler (cosine with warmup)\n", "def get_lr(it, learning_rate, warmup_iters, lr_decay_iters, min_lr):\n", " # 1) linear warmup for warmup_iters steps\n", " if it < warmup_iters:\n", " return learning_rate * (it + 1) / (warmup_iters + 1)\n", " # 2) if it > lr_decay_iters, return min learning rate\n", " if it > lr_decay_iters:\n", " return min_lr\n", " # 3) in between, use cosine decay down to min learning rate\n", " decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)\n", " assert 0 <= decay_ratio <= 1\n", " coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1\n", " return min_lr + coeff * (learning_rate - min_lr)\n", "\n", "# Helps estimate an arbitrarily accurate loss over either split using many batches\n", "@torch.no_grad()\n", "def estimate_loss_nanogpt(model, device, eval_iters=50):\n", " \"\"\"Estimate loss for nanoGPT model on train/val splits\"\"\"\n", " out = {}\n", " model.eval()\n", " for split in ['train', 'val']:\n", " losses = torch.zeros(eval_iters)\n", " for k in range(eval_iters):\n", " X, Y = get_batch_text(split, NANOGPT_BLOCK_SIZE, BATCH_SIZE, device)\n", " logits, loss = model(X, Y)\n", " losses[k] = loss.item()\n", " out[split] = losses.mean()\n", " model.train()\n", " return out" ], "metadata": { "id": "ia7_QeWcZKxa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ------------------------------------------------------------------------------\n", "# TRAIN + AGGREGATE SINGULAR VALUES\n", "# ------------------------------------------------------------------------------\n", "def train_and_aggregate():\n", " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", " print(f\"Using device: {device}\")\n", "\n", " # Prepare data loaders based on model type\n", " if MODEL_TYPE == 'nanogpt':\n", " # Ensure text data is prepared\n", " prepare_text_data(DATA_ROOT)\n", " print(\"Text data prepared for nanoGPT training\")\n", " else:\n", " # MNIST loader (exact same normalize as main.py)\n", " transform = transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,))\n", " ])\n", " mnist = datasets.MNIST(root=DATA_ROOT, train=True, download=True, transform=transform)\n", "\n", " aggregated_sv = None\n", " reference_batches = None\n", "\n", " for run in range(NUM_RUNS):\n", " print(f\"\\n--- Run {run+1}/{NUM_RUNS} ---\")\n", " if MODEL_TYPE == 'mlp':\n", " model = SimpleMLP(INPUT_SIZE, HIDDEN_SIZE, OUTPUT_SIZE, NUM_HIDDEN_LAYERS).to(device)\n", " optimizer = optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n", " criterion = nn.CrossEntropyLoss()\n", " elif MODEL_TYPE == 'vit':\n", " model = CustomViT(\n", " num_classes=OUTPUT_SIZE,\n", " image_size=VIT_IMAGE_SIZE,\n", " patch_size=VIT_PATCH_SIZE,\n", " embed_dim=VIT_EMBED_DIM,\n", " depth=VIT_DEPTH,\n", " num_heads=VIT_NUM_HEADS,\n", " mlp_ratio=VIT_MLP_RATIO,\n", " input_channels=VIT_INPUT_CHANNELS,\n", " gaussian_init_fc=VIT_INIT_FC\n", " ).to(device)\n", " optimizer = optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)\n", " criterion = nn.CrossEntropyLoss()\n", " elif MODEL_TYPE == 'nanogpt':\n", " # Create nanoGPT model\n", " nanogpt_config = GPTConfig(\n", " block_size=NANOGPT_BLOCK_SIZE,\n", " vocab_size=NANOGPT_VOCAB_SIZE,\n", " n_layer=NANOGPT_N_LAYER,\n", " n_head=NANOGPT_N_HEAD,\n", " n_embd=NANOGPT_N_EMBD,\n", " dropout=NANOGPT_DROPOUT,\n", " bias=NANOGPT_BIAS\n", " )\n", " model = GPTSpectral(nanogpt_config).to(device)\n", " # Use nanoGPT-specific optimizer settings\n", " optimizer = model.configure_optimizers(\n", " weight_decay=NANOGPT_WEIGHT_DECAY,\n", " learning_rate=LEARNING_RATE,\n", " betas=(MOMENTUM, 0.95),\n", " device_type=device.type,\n", " )\n", " criterion = None # nanoGPT computes loss internally\n", " else:\n", " raise ValueError(f\"Unsupported MODEL_TYPE: {MODEL_TYPE}\")\n", "\n", " # Set up data loader based on model type\n", " if MODEL_TYPE == 'nanogpt':\n", " # nanoGPT doesn't use traditional DataLoader, we'll manually iterate\n", " loader = None\n", " total_batches = NANOGPT_MAX_ITERS\n", " else:\n", " loader = DataLoader(mnist, batch_size=BATCH_SIZE, shuffle=True)\n", " total_batches = NUM_EPOCHS * len(loader)\n", "\n", " # collect per-layer snapshots\n", " layer_names = model.get_target_layers()\n", " sv_snapshots = {name: [] for name in layer_names}\n", " batch_list = []\n", " batch_idx = 0\n", "\n", " if MODEL_TYPE == 'nanogpt':\n", " pbar = tqdm(range(NANOGPT_MAX_ITERS), desc=f\"Training GPT Run {run+1}\")\n", " for iter_num in pbar:\n", " X, Y = get_batch_text('train', NANOGPT_BLOCK_SIZE, BATCH_SIZE, device)\n", "\n", " # Determine and set the learning rate for this iteration\n", " if NANOGPT_DECAY_LR:\n", " lr = get_lr(iter_num, LEARNING_RATE, NANOGPT_WARMUP_ITERS,\n", " NANOGPT_LR_DECAY_ITERS, NANOGPT_MIN_LR)\n", " for param_group in optimizer.param_groups:\n", " param_group['lr'] = lr\n", "\n", " # Forward pass\n", " optimizer.zero_grad()\n", " logits, loss = model(X, Y)\n", " loss.backward()\n", "\n", " # Gradient clipping\n", " if NANOGPT_GRAD_CLIP > 0.0:\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), NANOGPT_GRAD_CLIP)\n", "\n", " # snapshot\n", " if batch_idx % LOG_EVERY_N_BATCHES == 0:\n", " for name in layer_names:\n", " with torch.no_grad():\n", " W_cpu = model.get_parameter(name).data.cpu()\n", " # center the matrix\n", " W_center = W_cpu - W_cpu.mean()\n", " sigma = W_center.std()\n", " if sigma > 1e-15:\n", " # normalize by sigma * sqrt(max_dim) to match MP theory\n", " max_dim = float(max(W_center.shape))\n", " W_norm = W_center / (sigma * math.sqrt(max_dim))\n", " sv = torch.linalg.svdvals(W_norm).numpy()\n", " # filter out any non-finite values\n", " sv = sv[np.isfinite(sv)]\n", " else:\n", " sv = np.array([], dtype=float)\n", " sv_snapshots[name].append(sv)\n", " batch_list.append(batch_idx)\n", "\n", " optimizer.step()\n", " batch_idx += 1\n", "\n", " # Periodic evaluation (every 100 iterations)\n", " if iter_num % 100 == 0 and iter_num > 0:\n", " losses = estimate_loss_nanogpt(model, device, eval_iters=20)\n", " print(f\"\\nRun {run+1}, iter {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}\")\n", "\n", " # Update progress bar with loss and learning rate\n", " if NANOGPT_DECAY_LR:\n", " pbar.set_postfix({'loss': f'{loss.item():.4f}', 'lr': f'{lr:.2e}'})\n", " else:\n", " pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n", " else:\n", " # Original training loop for MLP/ViT\n", " for epoch in range(NUM_EPOCHS):\n", " pbar = tqdm(loader, desc=f\"Epoch {epoch+1}/{NUM_EPOCHS}\", leave=False)\n", " for imgs, labels in pbar:\n", " imgs, labels = imgs.to(device), labels.to(device)\n", " optimizer.zero_grad()\n", " logits = model(imgs)\n", " loss = criterion(logits, labels)\n", " loss.backward()\n", "\n", " # snapshot\n", " if batch_idx % LOG_EVERY_N_BATCHES == 0:\n", " for name in layer_names:\n", " with torch.no_grad():\n", " W_cpu = model.get_parameter(name).data.cpu()\n", " # center the matrix\n", " W_center = W_cpu - W_cpu.mean()\n", " sigma = W_center.std()\n", " if sigma > 1e-15:\n", " # normalize by sigma * sqrt(max_dim) to match MP theory\n", " max_dim = float(max(W_center.shape))\n", " W_norm = W_center / (sigma * math.sqrt(max_dim))\n", " sv = torch.linalg.svdvals(W_norm).numpy()\n", " # filter out any non-finite values\n", " sv = sv[np.isfinite(sv)]\n", " else:\n", " sv = np.array([], dtype=float)\n", " sv_snapshots[name].append(sv)\n", " batch_list.append(batch_idx)\n", "\n", " optimizer.step()\n", " batch_idx += 1\n", "\n", " # first run initializes aggregator\n", " if aggregated_sv is None:\n", " aggregated_sv = {name: [sv_snapshots[name]] for name in sv_snapshots}\n", " reference_batches = batch_list\n", " else:\n", " for name in sv_snapshots:\n", " aggregated_sv[name].append(sv_snapshots[name])\n", "\n", " # concatenate across runs\n", " final_sv = {}\n", " for name, runs_list in aggregated_sv.items():\n", " num_snaps = len(runs_list[0])\n", " combined = []\n", " for snap in range(num_snaps):\n", " arrays = [ runs_list[r][snap] for r in range(NUM_RUNS) ]\n", " combined.append(np.concatenate(arrays))\n", " final_sv[name] = combined\n", "\n", " # Calculate layer shapes from the actual model used\n", " layer_shapes = [tuple(model.get_parameter(name).shape) for name in layer_names]\n", "\n", " return final_sv, reference_batches, layer_names, layer_shapes" ], "metadata": { "id": "JFeinPKN0Cmp" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ------------------------------------------------------------------------------\n", "# PLOTTING 20 EQUALLY-SPACED HISTOGRAMS PER LAYER\n", "# ------------------------------------------------------------------------------\n", "def plot_singular_evolution(final_sv, batches, layer_names, layer_shapes):\n", " for name in layer_names:\n", " # Advanced singular-value plotting with MP & TW overlays\n", " results_data = final_sv[name]\n", " num_plots = min(20, len(results_data))\n", " indices = np.linspace(0, len(results_data)-1, num_plots, dtype=int)\n", " # Determine common range robustly, ignoring NaNs\n", " all_vals = np.concatenate([results_data[i] for i in indices if len(results_data[i]) > 0])\n", " finite_vals = all_vals[np.isfinite(all_vals)]\n", " if finite_vals.size > 0:\n", " max_val = np.max(finite_vals)\n", " max_plot = max(max_val * 1.1, 3.0)\n", " else:\n", " max_plot = 3.0\n", " common_range = (0, max_plot)\n", " ncols = 5\n", " nrows = math.ceil(num_plots / ncols)\n", " fig, axes = plt.subplots(nrows, ncols, figsize=(ncols*4, nrows*3), squeeze=False)\n", " axes = axes.flatten()\n", " # Get layer shape\n", " idx = layer_names.index(name)\n", " m, n = layer_shapes[idx]\n", " ratio = min(m,n)/max(m,n)\n", " is_square = (m==n)\n", " for pi, snap in enumerate(indices):\n", " ax = axes[pi]\n", " sv_vals = results_data[snap]\n", " nb = max(min(len(sv_vals)//10, 100), 30)\n", " counts, bins, _ = ax.hist(sv_vals, bins=nb, density=False,\n", " alpha=0.7, color='royalblue',\n", " label='Histogram', range=common_range)\n", " w = bins[1]-bins[0]\n", " centers = (bins[:-1]+bins[1:])/2\n", " # MP overlay\n", " mp = MarchenkoPasturDistribution(beta=1, ratio=ratio, sigma=1.0)\n", " x_mp = np.linspace(common_range[0], common_range[1], 500)\n", " mp_d = np.array([2*x*mp.pdf(x*x) if x>0 else 0 for x in x_mp])\n", " ax.plot(x_mp, mp_d * len(sv_vals)*w, 'r--', linewidth=1.5, label=f'MP (λ={ratio:.2f})')\n", " # TW overlay\n", " tw = TracyWidomDistribution(beta=1)\n", " edge = 2.0\n", " scale = 0.5*(np.sqrt(m)+np.sqrt(n))**(-1/3)\n", " x_tw = np.linspace(edge-4*scale, edge+4*scale, 300)\n", " args = (x_tw-edge)/scale\n", " pdf_tw = tw.pdf(args)/scale\n", " mpv = np.max(pdf_tw)\n", " mask = (centers>edge-scale)&(centers epsilon else np.sign(d + 1e-20) * epsilon\n", " s = si + sj\n", " denom2 = s if abs(s) > epsilon else np.sign(s + 1e-20) * epsilon\n", " if abs(denom2) < epsilon:\n", " denom2 = epsilon\n", "\n", " ugv_ji = UGV[j, i]\n", " ugv_ij = UGV[i, j]\n", " coeff_u = ugv_ji / denom1 + ugv_ij / denom2\n", " coeff_v = ugv_ij / denom1 + ugv_ji / denom2\n", "\n", " sum_u += coeff_u * U_pred[:, j:j+1]\n", " sum_v += coeff_v * V_pred[:, j:j+1]\n", "\n", " drift_U[:, i:i+1] = sum_u\n", " drift_V[:, i:i+1] = sum_v\n", "\n", " return drift_S, drift_U, drift_V\n" ], "metadata": { "id": "WIHv4gz6yfJ6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "class SimpleCNN(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " # Convolutional feature extractor\n", " self.conv1 = nn.Conv2d(3, 16, kernel_size=5, padding=2)\n", " self.relu1 = nn.ReLU()\n", " self.pool1 = nn.MaxPool2d(2)\n", " self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)\n", " self.relu2 = nn.ReLU()\n", " self.pool2 = nn.MaxPool2d(2)\n", " # Fully-connected layers\n", " self.fc1 = nn.Linear(32*8*8, 120)\n", " self.relu3 = nn.ReLU()\n", " self.fc2 = nn.Linear(120, 10)\n", "\n", " def forward(self, x):\n", " x = self.pool1(self.relu1(self.conv1(x)))\n", " x = self.pool2(self.relu2(self.conv2(x)))\n", " x = x.view(-1, 32*8*8)\n", " x = self.relu3(self.fc1(x))\n", " return self.fc2(x)\n" ], "metadata": { "id": "T0wROtSqyzmN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Hyperparameters\n", "eta = 0.01\n", "num_epochs = 1\n", "batch_size = 64\n", "num_steps_svd_analysis = 782\n", "k_components_to_track = 50\n", "target_layer_name = 'fc1' # <-- analyze fc1\n", "\n", "# Device setup\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(\"Using device:\", device)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o047LjUPy1-e", "outputId": "cd11e6a7-eba0-4d1a-f4a5-0db024d0df91" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Using device: cuda\n" ] } ] }, { "cell_type": "code", "source": [ "transform = transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5,)*3, (0.5,)*3)\n", "])\n", "\n", "trainset = torchvision.datasets.CIFAR10(\n", " root='data/', train=True, download=True, transform=transform\n", ")\n", "trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)\n", "print(f\"Loaded CIFAR-10 training set: {len(trainset)} samples\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "igWBeOiHy3Zi", "outputId": "b2315241-e243-433f-8e23-ba472de64cd2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loaded CIFAR-10 training set: 50000 samples\n" ] } ] }, { "cell_type": "code", "source": [ "# Model, Loss, Optimizer\n", "model = SimpleCNN().to(device)\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.SGD(model.parameters(), lr=eta)\n", "\n", "# Extract fc1 weights and shape\n", "target_layer = getattr(model, target_layer_name)\n", "assert isinstance(target_layer, nn.Linear), f\"{target_layer_name} must be nn.Linear\"\n", "W0 = target_layer.weight.data.clone().cpu().numpy() # shape [out, in]\n", "m, n = W0.shape\n", "print(f\"{target_layer_name} weight shape: {m}x{n}\")\n", "\n", "# Full SVD at t=0\n", "U0, S0_full, Vt0 = np.linalg.svd(W0, full_matrices=False)\n", "V0 = Vt0.T\n", "k = min(k_components_to_track, len(S0_full), m, n)\n", "print(f\"Tracking {k} components\")\n", "\n", "# Initialize predicted SVD histories\n", "U_pred = U0[:, :k].copy()\n", "S_pred = S0_full[:k].copy()\n", "V_pred = V0[:, :k].copy()\n", "U_hist = [U_pred.copy()]\n", "S_hist = [S_pred.copy()]\n", "V_hist = [V_pred.copy()]\n", "\n", "# Store actual weights for post-SVD\n", "W_history = [W0.copy()]\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8hSP0K2gy5Wl", "outputId": "bcb287bc-8a44-4d96-8366-fdeea7c03065" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "fc1 weight shape: 120×2048\n", "Tracking 50 components\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"Starting training + SVD analysis...\")\n", "for epoch in range(num_epochs):\n", " model.train()\n", " for t, (inputs, labels) in enumerate(trainloader):\n", " inputs, labels = inputs.to(device), labels.to(device)\n", "\n", " # Save current weights\n", " Wt = target_layer.weight.data.clone().cpu().numpy()\n", " if t > 0:\n", " W_history.append(Wt.copy())\n", "\n", " # Forward/backward\n", " optimizer.zero_grad()\n", " outputs = model(inputs)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", "\n", " # Predict SVD drift for first num_steps_svd_analysis\n", " if t < num_steps_svd_analysis and target_layer.weight.grad is not None:\n", " grad = target_layer.weight.grad.data.clone().cpu().numpy()\n", " G = -eta * grad\n", " dS, dU, dV = calculate_bootstrapped_svd_drifts(U_pred, S_pred, V_pred, G)\n", "\n", " # Update predicted components\n", " S_next = np.maximum(S_pred + dS, 1e-16)\n", " U_next = orthogonalize_vectors(U_pred + dU)\n", " V_next = orthogonalize_vectors(V_pred + dV)\n", "\n", " # Sign alignment\n", " for i in range(k):\n", " if np.dot(U_next[:,i], U_pred[:,i]) < 0:\n", " U_next[:,i] *= -1\n", " if np.dot(V_next[:,i], V_pred[:,i]) < 0:\n", " V_next[:,i] *= -1\n", "\n", " U_hist.append(U_next.copy())\n", " S_hist.append(S_next.copy())\n", " V_hist.append(V_next.copy())\n", " U_pred, S_pred, V_pred = U_next, S_next, V_next\n", "\n", " optimizer.step()\n", "print(\"Training complete.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "evHqEE7Cy6oE", "outputId": "f5049ebc-3c35-414a-b424-46d3942314d4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Starting training + SVD analysis...\n", "Training complete.\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"Computing actual SVD trajectory from stored weights...\")\n", "S_act_hist = []\n", "U_act_hist = []\n", "\n", "for W in W_history:\n", " Ua, Sa_full, Vta = np.linalg.svd(W, full_matrices=False)\n", " sa = np.full(k, np.nan)\n", " sa[:min(len(Sa_full), k)] = Sa_full[:k]\n", " S_act_hist.append(sa)\n", "\n", " Ua_aligned = np.full((m, k), np.nan)\n", " Ua_aligned[:, :Ua.shape[1]] = Ua[:, :k]\n", " U_act_hist.append(Ua_aligned)\n", "\n", "print(\"Actual SVD trajectory computed.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bAnUOBhRy8Fq", "outputId": "f8e67b57-afc0-4d40-9f1b-61232b4161b6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Computing actual SVD trajectory from stored weights...\n", "Actual SVD trajectory computed.\n" ] } ] }, { "cell_type": "code", "source": [ "plot_steps = min(len(S_hist)-1, len(S_act_hist))\n", "steps = np.arange(plot_steps)\n", "num_to_plot = min(k, 8)\n", "\n", "S_pred_arr = np.array(S_hist[:plot_steps])\n", "S_act_arr = np.array(S_act_hist[:plot_steps])\n", "\n", "# Compute alignments\n", "align_arr = np.full((plot_steps, k), np.nan)\n", "for t in range(plot_steps):\n", " for i in range(k):\n", " up, ua = U_hist[t][:,i], U_act_hist[t][:,i]\n", " if not np.all(np.isnan(up)) and not np.all(np.isnan(ua)):\n", " align_arr[t,i] = abs(np.dot(up, ua))\n", "\n", "fig, axes = plt.subplots(2, 1, figsize=(14,12), sharex=True)\n", "\n", "# --------- Top: Singular Values ----------\n", "cmap = plt.cm.viridis(np.linspace(0,0.9,num_to_plot))\n", "ax1 = axes[0]\n", "for idx in range(num_to_plot):\n", " if np.any(~np.isnan(S_pred_arr[:,idx])):\n", " ax1.plot(steps, S_pred_arr[:,idx], '--', color=cmap[idx],\n", " label=f\"Pred σ{idx+1}\", alpha=0.8)\n", " if np.any(~np.isnan(S_act_arr[:,idx])):\n", " ax1.plot(steps, S_act_arr[:,idx], '-', color=cmap[idx],\n", " label=f\"Act σ{idx+1}\", alpha=0.8)\n", "\n", "ax1.set_yscale('log')\n", "ax1.axvline(300, color='red', linestyle='--', linewidth=1)\n", "ax1.set_xlim(0,600)\n", "ax1.set_title(f\"Top {num_to_plot} Singular Values ({target_layer_name})\", fontsize=14)\n", "ax1.set_ylabel(\"σ (log)\")\n", "ax1.legend(loc='upper right', fontsize=10)\n", "ax1.grid(True, which='both', ls='--', alpha=0.5)\n", "\n", "# ------ Bottom: Vector Alignment ------\n", "ax2 = axes[1]\n", "for idx in range(num_to_plot):\n", " if np.any(~np.isnan(align_arr[:,idx])):\n", " ax2.plot(steps, align_arr[:,idx], color=cmap[idx],\n", " label=f\"Align u{idx+1}\", alpha=0.8)\n", "\n", "ax2.axvline(300, color='red', linestyle='--', linewidth=1)\n", "ax2.set_xlim(0,600)\n", "ax2.set_ylim(0,1.05)\n", "ax2.set_title(f\"Left-vector Alignment for Top {num_to_plot} ({target_layer_name})\", fontsize=14)\n", "ax2.set_xlabel(\"Step t\")\n", "ax2.set_ylabel(\"|uᵀu|\")\n", "ax2.legend(loc='upper right', fontsize=10)\n", "ax2.grid(True, which='both', ls='--', alpha=0.5)\n", "\n", "fig.suptitle(\"Predicted vs Actual SVD Dynamics (Top Components)\", fontsize=16, y=0.98)\n", "fig.subplots_adjust(top=0.88, hspace=0.4)\n", "\n", "plt.show()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "gBiRS3AXy9Dn", "outputId": "f07fb88d-70f2-42fd-f8e4-cb51d7085881" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Neural Network Spectral Analysis: Singular Value Tail Dynamics\n", "\n", "## What This Does\n", "\n", "Track singular values of neural network weight matrices during training and detect peaks in the **tail region** (beyond the bulk distribution). Fit **Equation 13** theoretical curves to explain these peaks.\n", "\n", "## Key Idea\n", "\n", "- **Bulk**: Most singular values follow random matrix predictions\n", "- **Tail**: Values beyond Tracy-Widom edge may show structured peaks\n", "- **Theory**: Equation 13 models these peaks using drift (D) and noise (β) parameters\n", "\n", "## Equation 13\n", "p_σ(σ) = 2 × (β/4ηD)^((m-n+3)/4) × σ^((m-n+3)/2-1) / Γ((m-n+3)/4) × exp(-(β/4ηD)σ²)\n", "\n", "May take longer than 3 hours to run due to need for significant ensembling N = 1024\n" ], "metadata": { "id": "aSx1iSE125PP" } }, { "cell_type": "code", "source": [ "# Install required packages\n", "!pip install torch torchvision scikit-learn matplotlib numpy scipy\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import torchvision\n", "import torchvision.transforms as transforms\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.signal import find_peaks\n", "from scipy.special import gamma\n", "import math" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A3U0bK8D1Iaw", "outputId": "254a2864-f875-4b4d-ee27-6c4ccff824e7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n", "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (0.21.0+cu124)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.10.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (2.0.2)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.15.3)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.18.0)\n", "Requirement 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\u001b[31m106.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n", " Attempting uninstall: nvidia-nvjitlink-cu12\n", " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", " Attempting uninstall: nvidia-curand-cu12\n", " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", " Attempting uninstall: nvidia-cufft-cu12\n", " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", " Attempting uninstall: nvidia-cuda-runtime-cu12\n", " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-cupti-cu12\n", " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", " Attempting uninstall: nvidia-cublas-cu12\n", " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", " Attempting uninstall: nvidia-cusparse-cu12\n", " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", " Attempting uninstall: nvidia-cudnn-cu12\n", " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", " Attempting uninstall: nvidia-cusolver-cu12\n", " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n" ] } ] }, { "cell_type": "code", "source": [ "class SimpleMLP(nn.Module):\n", " def __init__(self, input_size=784, hidden_sizes=[1024, 1024, 1024], num_classes=10):\n", " super(SimpleMLP, self).__init__()\n", "\n", " layers = []\n", " prev_size = input_size\n", "\n", " for hidden_size in hidden_sizes:\n", " layers.append(nn.Linear(prev_size, hidden_size))\n", " layers.append(nn.ReLU())\n", " prev_size = hidden_size\n", "\n", " layers.append(nn.Linear(prev_size, num_classes))\n", "\n", " self.network = nn.Sequential(*layers)\n", "\n", " # Initialize weights with Gaussian N(0, 1/fan_in)\n", " for module in self.modules():\n", " if isinstance(module, nn.Linear):\n", " fan_in = module.weight.size(1)\n", " std = 1.0 / math.sqrt(fan_in)\n", " nn.init.normal_(module.weight, mean=0.0, std=std)\n", " nn.init.zeros_(module.bias)\n", "\n", " def forward(self, x):\n", " return self.network(x)\n", "\n", " def get_target_layers(self):\n", " \"\"\"Return the linear layers for analysis\"\"\"\n", " target_layers = {}\n", " for name, module in self.named_modules():\n", " if isinstance(module, nn.Linear):\n", " target_layers[name] = module\n", " return target_layers" ], "metadata": { "id": "f_uEFy9J2IZf" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Load MNIST dataset\n", "transform = transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,))\n", "])\n", "\n", "train_dataset = torchvision.datasets.MNIST(root='./data', train=True,\n", " download=True, transform=transform)\n", "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n", "\n", "print(f\"Loaded MNIST dataset with {len(train_dataset)} training samples\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cAR9Upd62Jdc", "outputId": "b4c973e7-4d0b-4670-bd26-a19251405c4f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loaded MNIST dataset with 60000 training samples\n" ] } ] }, { "cell_type": "code", "source": [ "def compute_singular_values(weight_matrix):\n", " \"\"\"Compute singular values of a weight matrix\"\"\"\n", " if weight_matrix.dim() == 2:\n", " matrix = weight_matrix.detach().cpu().numpy()\n", " else:\n", " matrix = weight_matrix.view(weight_matrix.size(0), -1).detach().cpu().numpy()\n", "\n", " # Center and normalize the matrix\n", " matrix_centered = matrix - np.mean(matrix, axis=1, keepdims=True)\n", " matrix_std = np.std(matrix_centered)\n", " if matrix_std > 0:\n", " matrix_normalized = matrix_centered / matrix_std\n", " # Additional normalization by sqrt(max dimension)\n", " max_dim = max(matrix_normalized.shape)\n", " matrix_normalized = matrix_normalized / np.sqrt(max_dim)\n", " else:\n", " matrix_normalized = matrix_centered\n", "\n", " # Compute SVD\n", " try:\n", " _, singular_values, _ = np.linalg.svd(matrix_normalized, full_matrices=False)\n", " return singular_values\n", " except np.linalg.LinAlgError:\n", " print(\"SVD failed, returning empty array\")\n", " return np.array([])\n", "\n", "def compute_loss_gradient_magnitude(model, data_loader, criterion, device):\n", " \"\"\"Compute the magnitude of the loss gradient\"\"\"\n", " model.eval()\n", "\n", " data_iter = iter(data_loader)\n", " inputs, targets = next(data_iter)\n", " inputs, targets = inputs.to(device), targets.to(device)\n", "\n", " outputs = model(inputs.view(inputs.size(0), -1))\n", " loss = criterion(outputs, targets)\n", "\n", " model.zero_grad()\n", " loss.backward()\n", "\n", " total_grad_norm = 0.0\n", " for param in model.parameters():\n", " if param.grad is not None:\n", " param_norm = param.grad.data.norm(2)\n", " total_grad_norm += param_norm.item() ** 2\n", "\n", " total_grad_norm = total_grad_norm ** 0.5\n", " model.train()\n", "\n", " return total_grad_norm" ], "metadata": { "id": "5iBzcX092NiC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def compute_theoretical_distribution_eq13(sigma_values, eta, m, n, D, beta):\n", " \"\"\"\n", " Compute theoretical distribution using formula 13:\n", " p_σ(σ) = 2 * (β/(4ηD))^((m-n+3)/4) * σ^((m-n+3)/2 - 1) / Γ((m-n+3)/4) * exp(-(β/(4ηD)) σ²)\n", " \"\"\"\n", " if D <= 0 or beta <= 0:\n", " print(f\"Warning: Invalid parameters for eq13: D={D}, beta={beta}\")\n", " return np.zeros_like(sigma_values)\n", "\n", " # Formula 13 parameters\n", " exponent = (m - n + 3) / 4\n", " power_term = (m - n + 3) / 2 - 1\n", "\n", " # Scale factor from eq13\n", " scale_factor = beta / (4 * eta * D)\n", "\n", " if exponent <= 0:\n", " print(f\"Warning: Invalid exponent {exponent:.3f} for dimensions m={m}, n={n}\")\n", " return np.zeros_like(sigma_values)\n", "\n", " # Compute the distribution\n", " try:\n", " normalization = 2 * (scale_factor ** exponent) / gamma(exponent)\n", " except (ValueError, OverflowError) as e:\n", " print(f\"Warning: Gamma function error for exponent {exponent:.3f}: {e}\")\n", " return np.zeros_like(sigma_values)\n", "\n", " # Handle potential numerical issues\n", " sigma_values = np.maximum(sigma_values, 1e-12)\n", "\n", " # Compute PDF with numerical stability\n", " try:\n", " log_pdf = (np.log(normalization) +\n", " power_term * np.log(sigma_values) -\n", " scale_factor * sigma_values**2)\n", " pdf_values = np.exp(log_pdf)\n", " except (OverflowError, RuntimeWarning):\n", " pdf_values = normalization * (sigma_values ** power_term) * np.exp(-scale_factor * sigma_values**2)\n", "\n", " # Handle any NaN or inf values\n", " pdf_values = np.nan_to_num(pdf_values, nan=0.0, posinf=0.0, neginf=0.0)\n", "\n", " return pdf_values" ], "metadata": { "id": "Y1jwdzcI2PoF" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def plot_tail_with_peaks_and_theory(singular_values, loss_gradient, batch_num, layer_name,\n", " layer_shape, learning_rate=0.01, sv_min=2.0, sv_max=6.5):\n", " \"\"\"Plot full histogram and focused tail histogram with peak detection and theoretical curves\"\"\"\n", "\n", " # Filter singular values to the specified range for tail analysis\n", " mask = (singular_values >= sv_min) & (singular_values <= sv_max)\n", " tail_values = singular_values[mask]\n", "\n", " print(f\"Total singular values: {len(singular_values)}\")\n", " print(f\"Singular values in range [{sv_min}, {sv_max}]: {len(tail_values)}\")\n", " print(f\"Full SV range: [{np.min(singular_values):.3f}, {np.max(singular_values):.3f}]\")\n", "\n", " # =============================================================================\n", " # PLOT 1: FULL HISTOGRAM\n", " # =============================================================================\n", " fig1, ax1 = plt.subplots(figsize=(12, 8))\n", "\n", " # Full histogram\n", " full_bins = max(min(len(singular_values) // 20, 150), 50)\n", " counts_full, bin_edges_full, patches_full = ax1.hist(singular_values, bins=full_bins,\n", " density=False, alpha=0.7,\n", " color='lightblue',\n", " label='Full Singular Value Distribution')\n", "\n", " # Highlight the tail region we'll analyze\n", " ax1.axvspan(sv_min, sv_max, alpha=0.2, color='red',\n", " label=f'Tail Analysis Region [{sv_min}, {sv_max}]')\n", " ax1.axvline(x=sv_min, color='red', linestyle='--', alpha=0.8, linewidth=2)\n", " ax1.axvline(x=sv_max, color='red', linestyle='--', alpha=0.8, linewidth=2)\n", "\n", " # Add bulk edge marker (approximate)\n", " bulk_edge = 2.0\n", " ax1.axvline(x=bulk_edge, color='black', linestyle=':', alpha=0.7, linewidth=2,\n", " label=f'Approximate Bulk Edge = {bulk_edge}')\n", "\n", " # Styling for full plot\n", " ax1.set_xlabel(\"Singular Value (σ)\", fontsize=14)\n", " ax1.set_ylabel(\"Count\", fontsize=14)\n", " ax1.set_title(f\"Complete Singular Value Distribution\\n{layer_name} - Batch {batch_num}\", fontsize=16)\n", " ax1.legend(fontsize=12)\n", " ax1.grid(True, alpha=0.3)\n", "\n", " # Add statistics for full distribution\n", " full_stats_text = f\"Total values: {len(singular_values)}\\n\"\n", " full_stats_text += f\"Mean: {np.mean(singular_values):.3f}\\n\"\n", " full_stats_text += f\"Std: {np.std(singular_values):.3f}\\n\"\n", " full_stats_text += f\"Max: {np.max(singular_values):.3f}\\n\"\n", " full_stats_text += f\"Median: {np.median(singular_values):.3f}\"\n", "\n", " ax1.text(0.75, 0.98, full_stats_text, transform=ax1.transAxes, fontsize=11,\n", " verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # =============================================================================\n", " # PLOT 2: TAIL-ONLY ANALYSIS WITH PEAK DETECTION\n", " # =============================================================================\n", "\n", " if len(tail_values) == 0:\n", " print(f\"No singular values in range [{sv_min}, {sv_max}] for detailed analysis\")\n", " return 0\n", "\n", " # Create focused tail histogram\n", " fig2, ax2 = plt.subplots(figsize=(12, 8))\n", " tail_bins = max(min(len(tail_values) // 5, 100), 20)\n", " counts, bin_edges, patches = ax2.hist(tail_values, bins=tail_bins, density=False,\n", " alpha=0.7, color='royalblue',\n", " label=f'Tail Histogram (σ ∈ [{sv_min}, {sv_max}])')\n", " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", "\n", " # Peak detection with adjusted parameters for the specified range\n", " min_peak_height = np.max(counts) * 0.05 # Lower threshold for better detection\n", " min_distance = max(1, len(counts) // 30) # Closer peaks allowed\n", " peaks, peak_properties = find_peaks(counts,\n", " height=min_peak_height,\n", " distance=min_distance,\n", " prominence=np.max(counts) * 0.02) # Lower prominence threshold\n", "\n", " # Plot detected peaks\n", " if len(peaks) > 0:\n", " peak_positions = bin_centers[peaks]\n", " peak_heights = counts[peaks]\n", "\n", " # Mark peaks with red dots\n", " ax2.scatter(peak_positions, peak_heights,\n", " color='red', s=150, zorder=5,\n", " label=f'Detected Peaks ({len(peaks)})', edgecolors='black', linewidth=1)\n", "\n", " # Add vertical lines at peak positions\n", " for i, (pos, height) in enumerate(zip(peak_positions, peak_heights)):\n", " ax2.axvline(x=pos, color='red', linestyle='--', alpha=0.6, linewidth=1)\n", " # Annotate peak with its position\n", " ax2.annotate(f'Peak {i+1}\\nσ={pos:.3f}',\n", " xy=(pos, height),\n", " xytext=(10, 15),\n", " textcoords='offset points',\n", " fontsize=10,\n", " bbox=dict(boxstyle='round,pad=0.3',\n", " facecolor='yellow', alpha=0.8),\n", " arrowprops=dict(arrowstyle='->',\n", " connectionstyle='arc3,rad=0.1'))\n", "\n", " # Add theoretical curves for each peak\n", " if layer_shape is not None:\n", " m, n = layer_shape\n", "\n", " # Compute D (drift): square of (max_sv - median_sv) divided by batch number\n", " max_sv = np.max(singular_values) # Use full singular value array for D calculation\n", " median_sv = np.median(singular_values)\n", " batch_num_for_drift = max(1, batch_num)\n", " D = ((max_sv - median_sv) ** 2) / batch_num_for_drift\n", "\n", " # Use loss gradient as beta\n", " beta = loss_gradient if loss_gradient > 0 else 1.0\n", "\n", " print(f\"\\nTheoretical Parameters for {layer_name} batch {batch_num}:\")\n", " print(f\" Matrix dimensions: {m} × {n}\")\n", " print(f\" D (drift) = {D:.6f}\")\n", " print(f\" β (loss grad) = {beta:.6f}\")\n", " print(f\" Learning rate η = {learning_rate}\")\n", " print(f\" Detected peaks at: {[f'{pos:.3f}' for pos in peak_positions]}\")\n", "\n", " # Plot theoretical curve for each peak\n", " colors = ['green', 'orange', 'purple', 'brown', 'pink', 'cyan', 'olive', 'navy']\n", " for i, (peak_pos, peak_height) in enumerate(zip(peak_positions, peak_heights)):\n", " # Create sigma range around the peak, constrained to our analysis range\n", " curve_width = (sv_max - sv_min) * 0.3 # Use 30% of total range around each peak\n", "\n", " sigma_start = max(sv_min, peak_pos - curve_width)\n", " sigma_end = min(sv_max, peak_pos + curve_width)\n", " sigma_range = np.linspace(sigma_start, sigma_end, 500)\n", "\n", " # Compute theoretical distribution\n", " theoretical_pdf = compute_theoretical_distribution_eq13(sigma_range, learning_rate, m, n, D, beta)\n", "\n", " if np.max(theoretical_pdf) > 0:\n", " # Find theoretical peak and shift to match detected peak\n", " theoretical_peak_idx = np.argmax(theoretical_pdf)\n", " theoretical_peak_pos = sigma_range[theoretical_peak_idx]\n", " shift_amount = peak_pos - theoretical_peak_pos\n", " shifted_sigma = sigma_range + shift_amount\n", "\n", " # Scale to match peak height\n", " scale_factor = peak_height / np.max(theoretical_pdf)\n", " scaled_pdf = theoretical_pdf * scale_factor\n", "\n", " # Only plot points within our analysis range\n", " visible_mask = (shifted_sigma >= sv_min) & (shifted_sigma <= sv_max)\n", " if np.any(visible_mask):\n", " color = colors[i % len(colors)]\n", " ax2.plot(shifted_sigma[visible_mask], scaled_pdf[visible_mask],\n", " '-.', linewidth=3, alpha=0.9,\n", " label=f'Eq13 Peak {i+1} (D={D:.2e}, β={beta:.2e})',\n", " color=color)\n", "\n", " print(f\" Plotted Eq13 curve for peak {i+1}: center σ={peak_pos:.3f}, shift={shift_amount:.3f}\")\n", " else:\n", " print(f\" Warning: Zero theoretical distribution for peak {i+1}\")\n", " else:\n", " print(f\" No significant peaks detected in range [{sv_min}, {sv_max}]\")\n", "\n", " # Styling for tail plot\n", " ax2.set_xlabel(f\"Singular Value (σ ∈ [{sv_min}, {sv_max}])\", fontsize=14)\n", " ax2.set_ylabel(\"Count\", fontsize=14)\n", " ax2.set_title(f\"Tail Analysis with Equation 13 Theory\\n{layer_name} - Batch {batch_num}\", fontsize=16)\n", " ax2.legend(fontsize=11, loc='upper right')\n", " ax2.grid(True, alpha=0.3)\n", "\n", " # Set x-axis limits to our analysis range\n", " ax2.set_xlim(sv_min, sv_max)\n", "\n", " # Add detailed statistics text box for tail region\n", " if len(tail_values) > 0:\n", " tail_stats_text = f\"Tail Analysis Range: [{sv_min}, {sv_max}]\\n\"\n", " tail_stats_text += f\"Values in range: {len(tail_values)}\\n\"\n", " tail_stats_text += f\"% of total: {100*len(tail_values)/len(singular_values):.1f}%\\n\"\n", " tail_stats_text += f\"Mean: {np.mean(tail_values):.3f}\\n\"\n", " tail_stats_text += f\"Std: {np.std(tail_values):.3f}\\n\"\n", " tail_stats_text += f\"Max: {np.max(tail_values):.3f}\"\n", "\n", " ax2.text(0.02, 0.98, tail_stats_text, transform=ax2.transAxes, fontsize=10,\n", " verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightcyan', alpha=0.9))\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " return len(peaks) if len(peaks) > 0 else 0" ], "metadata": { "id": "xgGs-Bcd2S5y" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Set device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Using device: {device}\")\n", "\n", "# Ensembling parameters\n", "num_ensemble_models = 1024\n", "analysis_batches = [200, 400, 800] # Analyze at these specific batches\n", "total_batches = 1000 # Train each model for 1000 batches\n", "target_layer_name = 'network.0' # First linear layer\n", "\n", "print(\"=\"*80)\n", "print(\"ENSEMBLE TRAINING SETUP\")\n", "print(\"=\"*80)\n", "print(f\"Number of ensemble models: {num_ensemble_models}\")\n", "print(f\"Analysis batches: {analysis_batches}\")\n", "print(f\"Training batches per model: {total_batches}\")\n", "print(f\"Target layer: {target_layer_name}\")\n", "print(f\"Device: {device}\")\n", "print(\"=\"*80)\n", "\n", "# Storage for ensemble results\n", "ensemble_results = {\n", " 'batch_numbers': analysis_batches,\n", " 'loss_values': {batch: [] for batch in analysis_batches}, # Store losses from all models\n", " 'loss_gradients': {batch: [] for batch in analysis_batches}, # Store gradients from all models\n", " 'singular_values_data': {batch: [] for batch in analysis_batches} # Store SVs from all models\n", "}\n", "\n", "# Get layer shape from a temporary model\n", "temp_model = SimpleMLP().to(device)\n", "target_layers = temp_model.get_target_layers()\n", "if target_layer_name not in target_layers:\n", " print(f\"Available layers: {list(target_layers.keys())}\")\n", " target_layer_name = list(target_layers.keys())[0]\n", " print(f\"Using layer: {target_layer_name}\")\n", "\n", "target_layer = target_layers[target_layer_name]\n", "layer_shape = (target_layer.weight.size(0), target_layer.weight.size(1))\n", "print(f\"🔍 Target layer shape: {layer_shape}\")\n", "del temp_model # Clean up" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xnnc71gz2Vwh", "outputId": "fd07acc4-31b5-4a59-adcd-24f1b1e0ceb8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Using device: cuda\n", "================================================================================\n", "🎯 ENSEMBLE TRAINING SETUP\n", "================================================================================\n", "🔢 Number of ensemble models: 1\n", "📊 Analysis batches: [200, 400, 800]\n", "🚂 Training batches per model: 1000\n", "🎯 Target layer: network.0\n", "💻 Device: cuda\n", "================================================================================\n", "🔍 Target layer shape: (1024, 784)\n" ] } ] }, { "cell_type": "code", "source": [ "import time\n", "\n", "print(\"STARTING ENSEMBLE TRAINING\")\n", "print(\"=\"*80)\n", "\n", "start_time = time.time()\n", "\n", "for ensemble_idx in range(num_ensemble_models):\n", " progress_pct = (ensemble_idx / num_ensemble_models) * 100\n", " print(f\"\\nTraining Model {ensemble_idx + 1:4d}/{num_ensemble_models} ({progress_pct:5.1f}%)\")\n", "\n", " torch.manual_seed(42 + ensemble_idx)\n", " np.random.seed(42 + ensemble_idx)\n", "\n", " model = SimpleMLP().to(device)\n", " criterion = nn.CrossEntropyLoss()\n", " optimizer = optim.SGD(model.parameters(), lr=0.1,momentum=0.9)\n", "\n", " # Get target layer for this model\n", " target_layers = model.get_target_layers()\n", " target_layer = target_layers[target_layer_name]\n", "\n", " # Create fresh data iterator for this model\n", " data_iter = iter(train_loader)\n", " batch_count = 0\n", "\n", " # Training loop for this model\n", " for batch_idx in range(total_batches):\n", " try:\n", " inputs, targets = next(data_iter)\n", " except StopIteration:\n", " # Reset iterator when we finish an epoch\n", " data_iter = iter(train_loader)\n", " inputs, targets = next(data_iter)\n", "\n", " inputs, targets = inputs.to(device), targets.to(device)\n", " inputs = inputs.view(inputs.size(0), -1) # Flatten for MLP\n", "\n", " # Forward pass\n", " outputs = model(inputs)\n", " loss = criterion(outputs, targets)\n", "\n", " # Backward pass\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # Analysis at specific batches\n", " if batch_count in analysis_batches:\n", " # Compute loss gradient magnitude\n", " loss_grad_magnitude = compute_loss_gradient_magnitude(model, train_loader, criterion, device)\n", "\n", " # Compute singular values for target layer\n", " with torch.no_grad():\n", " singular_values = compute_singular_values(target_layer.weight)\n", "\n", " # Store results for this model\n", " ensemble_results['loss_values'][batch_count].append(loss.item())\n", " ensemble_results['loss_gradients'][batch_count].append(loss_grad_magnitude)\n", " ensemble_results['singular_values_data'][batch_count].append(singular_values)\n", "\n", " # Brief progress update for analysis batches\n", " if ensemble_idx % 100 == 0: # Print details every 100 models\n", " print(f\" Batch {batch_count}: Loss={loss.item():.4f}, \"\n", " f\"Grad={loss_grad_magnitude:.4f}, SVs={len(singular_values)}\")\n", "\n", " batch_count += 1\n", "\n", " # Progress update every 50 models\n", " if (ensemble_idx + 1) % 50 == 0:\n", " elapsed = time.time() - start_time\n", " models_per_sec = (ensemble_idx + 1) / elapsed\n", " eta_minutes = (num_ensemble_models - ensemble_idx - 1) / models_per_sec / 60\n", " print(f\" Completed {ensemble_idx + 1} models | \"\n", " f\"Rate: {models_per_sec:.1f} models/sec | \"\n", " f\"ETA: {eta_minutes:.1f} min\")\n", "\n", "total_time = time.time() - start_time\n", "print(f\"\\n ENSEMBLE TRAINING COMPLETED!\")\n", "print(f\"Total training time: {total_time/60:.1f} minutes\")\n", "print(f\"Models trained: {num_ensemble_models}\")\n", "print(f\"Analysis batches: {analysis_batches}\")\n", "\n", "# Print ensemble data summary\n", "print(f\"\\nENSEMBLE DATA SUMMARY:\")\n", "for batch_num in analysis_batches:\n", " num_models = len(ensemble_results['loss_values'][batch_num])\n", " total_sv_count = sum(len(svs) for svs in ensemble_results['singular_values_data'][batch_num])\n", " print(f\" Batch {batch_num}: {num_models} models, {total_sv_count:,} total singular values\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1PX-vOTR2Yn5", "outputId": "27aaad7b-fd79-4053-dfc7-c6e79e475ba0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🚀 STARTING ENSEMBLE TRAINING\n", "================================================================================\n", "\n", "🤖 Training Model 1/1 ( 0.0%)\n", " 📊 Batch 200: Loss=0.1456, Grad=1.1232, SVs=784\n", " 📊 Batch 400: Loss=0.4359, Grad=1.5567, SVs=784\n", " 📊 Batch 800: Loss=0.2268, Grad=0.8109, SVs=784\n", "\n", "🎉 ENSEMBLE TRAINING COMPLETED!\n", "⏱️ Total training time: 0.3 minutes\n", "🤖 Models trained: 1\n", "📊 Analysis batches: [200, 400, 800]\n", "\n", "📈 ENSEMBLE DATA SUMMARY:\n", " Batch 200: 1 models, 784 total singular values\n", " Batch 400: 1 models, 784 total singular values\n", " Batch 800: 1 models, 784 total singular values\n" ] } ] }, { "cell_type": "code", "source": [ "def plot_ensemble_tail_analysis(batch_num, ensemble_singular_values, ensemble_loss_gradients,\n", " layer_name, layer_shape, learning_rate=0.01, sv_min=2.0, sv_max=6.5):\n", " \"\"\"Plot ensemble analysis with aggregated data\"\"\"\n", "\n", " # Aggregate all singular values from ensemble\n", " all_singular_values = np.concatenate(ensemble_singular_values)\n", "\n", " # Use mean loss gradient across ensemble\n", " mean_loss_gradient = np.mean(ensemble_loss_gradients)\n", "\n", " print(f\"\\nENSEMBLE ANALYSIS - BATCH {batch_num}\")\n", " print(f\"Models in ensemble: {len(ensemble_singular_values)}\")\n", " print(f\"Total aggregated singular values: {len(all_singular_values):,}\")\n", " print(f\"Mean loss gradient: {mean_loss_gradient:.6f}\")\n", " print(f\"Loss gradient std: {np.std(ensemble_loss_gradients):.6f}\")\n", "\n", " # Filter to analysis range\n", " mask = (all_singular_values >= sv_min) & (all_singular_values <= sv_max)\n", " tail_values = all_singular_values[mask]\n", "\n", " print(f\"Values in range [{sv_min}, {sv_max}]: {len(tail_values):,}\")\n", " print(f\"Coverage: {100*len(tail_values)/len(all_singular_values):.2f}% of total\")\n", "\n", " # =============================================================================\n", " # PLOT 1: FULL ENSEMBLE HISTOGRAM\n", " # =============================================================================\n", " fig1, ax1 = plt.subplots(figsize=(14, 8))\n", "\n", " # Full histogram with more bins due to large dataset\n", " full_bins = max(min(len(all_singular_values) // 1000, 200), 100)\n", " counts_full, bin_edges_full, patches_full = ax1.hist(all_singular_values, bins=full_bins,\n", " density=False, alpha=0.7,\n", " color='lightblue',\n", " label=f'Ensemble Distribution ({len(ensemble_singular_values)} models)')\n", "\n", " # Highlight the tail region\n", " ax1.axvspan(sv_min, sv_max, alpha=0.2, color='red',\n", " label=f'Tail Analysis Region [{sv_min}, {sv_max}]')\n", " ax1.axvline(x=sv_min, color='red', linestyle='--', alpha=0.8, linewidth=2)\n", " ax1.axvline(x=sv_max, color='red', linestyle='--', alpha=0.8, linewidth=2)\n", "\n", " # Add bulk edge marker\n", " bulk_edge = 2.0\n", " ax1.axvline(x=bulk_edge, color='black', linestyle=':', alpha=0.7, linewidth=2,\n", " label=f'Bulk Edge = {bulk_edge}')\n", "\n", " # Styling\n", " ax1.set_xlabel(\"Singular Value (σ)\", fontsize=14)\n", " ax1.set_ylabel(\"Count\", fontsize=14)\n", " ax1.set_title(f\"Ensemble Singular Value Distribution (N={len(ensemble_singular_values)} models)\\n{layer_name} - Batch {batch_num}\", fontsize=16)\n", " ax1.legend(fontsize=12)\n", " ax1.grid(True, alpha=0.3)\n", "\n", " # Enhanced statistics\n", " ensemble_stats = f\"Ensemble Stats (N={len(ensemble_singular_values)}):\\n\"\n", " ensemble_stats += f\"Total values: {len(all_singular_values):,}\\n\"\n", " ensemble_stats += f\"Mean: {np.mean(all_singular_values):.3f}\\n\"\n", " ensemble_stats += f\"Std: {np.std(all_singular_values):.3f}\\n\"\n", " ensemble_stats += f\"Max: {np.max(all_singular_values):.3f}\\n\"\n", " ensemble_stats += f\"99th percentile: {np.percentile(all_singular_values, 99):.3f}\"\n", "\n", " ax1.text(0.70, 0.98, ensemble_stats, transform=ax1.transAxes, fontsize=11,\n", " verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # =============================================================================\n", " # PLOT 2: ENSEMBLE TAIL ANALYSIS WITH PEAK DETECTION\n", " # =============================================================================\n", "\n", " if len(tail_values) == 0:\n", " print(\"No values in tail range for analysis\")\n", " return 0\n", "\n", " fig2, ax2 = plt.subplots(figsize=(14, 8))\n", "\n", " # Tail histogram with more bins for ensemble data\n", " tail_bins = max(min(len(tail_values) // 50, 150), 30)\n", " counts, bin_edges, patches = ax2.hist(tail_values, bins=tail_bins, density=False,\n", " alpha=0.7, color='royalblue',\n", " label=f'Ensemble Tail (N={len(ensemble_singular_values)} models)')\n", " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", "\n", " # Peak detection with adjusted parameters for ensemble data\n", " min_peak_height = np.max(counts) * 0.02 # Lower threshold for ensemble data\n", " min_distance = max(1, len(counts) // 50)\n", " peaks, peak_properties = find_peaks(counts,\n", " height=min_peak_height,\n", " distance=min_distance,\n", " prominence=np.max(counts) * 0.01)\n", "\n", " print(f\"Peak detection: Found {len(peaks)} peaks\")\n", "\n", " if len(peaks) > 0:\n", " peak_positions = bin_centers[peaks]\n", " peak_heights = counts[peaks]\n", "\n", " # Plot peaks\n", " ax2.scatter(peak_positions, peak_heights,\n", " color='red', s=200, zorder=5,\n", " label=f'Detected Peaks ({len(peaks)})',\n", " edgecolors='black', linewidth=2)\n", "\n", " # Theoretical analysis\n", " if layer_shape is not None:\n", " m, n = layer_shape\n", "\n", " # Compute ensemble statistics for D and beta\n", " max_sv = np.max(all_singular_values)\n", " median_sv = np.median(all_singular_values)\n", " D = ((max_sv - median_sv) ** 2) / max(1, batch_num)\n", " beta = mean_loss_gradient\n", "\n", " print(f\"Theoretical parameters:\")\n", " print(f\" Matrix dimensions: {m} × {n}\")\n", " print(f\" D (ensemble drift): {D:.6f}\")\n", " print(f\" β (mean loss grad): {beta:.6f}\")\n", " print(f\" Peak positions: {[f'{pos:.3f}' for pos in peak_positions]}\")\n", "\n", " # Plot theoretical curves\n", " colors = ['green', 'orange', 'purple', 'brown', 'pink', 'cyan', 'olive', 'navy']\n", " for i, (peak_pos, peak_height) in enumerate(zip(peak_positions, peak_heights)):\n", " curve_width = (sv_max - sv_min) * 0.2\n", " sigma_start = max(sv_min, peak_pos - curve_width)\n", " sigma_end = min(sv_max, peak_pos + curve_width)\n", " sigma_range = np.linspace(sigma_start, sigma_end, 500)\n", "\n", " theoretical_pdf = compute_theoretical_distribution_eq13(sigma_range, learning_rate, m, n, D, beta)\n", "\n", " if np.max(theoretical_pdf) > 0:\n", " theoretical_peak_idx = np.argmax(theoretical_pdf)\n", " theoretical_peak_pos = sigma_range[theoretical_peak_idx]\n", " shift_amount = peak_pos - theoretical_peak_pos\n", " shifted_sigma = sigma_range + shift_amount\n", "\n", " scale_factor = peak_height / np.max(theoretical_pdf)\n", " scaled_pdf = theoretical_pdf * scale_factor\n", "\n", " visible_mask = (shifted_sigma >= sv_min) & (shifted_sigma <= sv_max)\n", " if np.any(visible_mask):\n", " color = colors[i % len(colors)]\n", " ax2.plot(shifted_sigma[visible_mask], scaled_pdf[visible_mask],\n", " '-.', linewidth=4, alpha=0.9,\n", " label=f'Eq13 Peak {i+1} (D={D:.2e}, β={beta:.2e})',\n", " color=color)\n", "\n", " # Styling\n", " ax2.set_xlabel(f\"Singular Value (σ ∈ [{sv_min}, {sv_max}])\", fontsize=14)\n", " ax2.set_ylabel(\"Count\", fontsize=14)\n", " ax2.set_title(f\"Ensemble Tail Analysis with Equation 13 Theory\\n{layer_name} - Batch {batch_num} (N={len(ensemble_singular_values)} models)\", fontsize=16)\n", " ax2.legend(fontsize=11, loc='upper right')\n", " ax2.grid(True, alpha=0.3)\n", " ax2.set_xlim(sv_min, sv_max)\n", "\n", " # Enhanced tail statistics\n", " tail_stats = f\"Ensemble Tail Analysis:\\n\"\n", " tail_stats += f\"Models: {len(ensemble_singular_values)}\\n\"\n", " tail_stats += f\"Range: [{sv_min}, {sv_max}]\\n\"\n", " tail_stats += f\"Values: {len(tail_values):,}\\n\"\n", " tail_stats += f\"Density: {len(tail_values)/len(ensemble_singular_values):.1f} per model\\n\"\n", " tail_stats += f\"Mean: {np.mean(tail_values):.3f}\\n\"\n", " tail_stats += f\"Std: {np.std(tail_values):.3f}\"\n", "\n", " ax2.text(0.02, 0.98, tail_stats, transform=ax2.transAxes, fontsize=11,\n", " verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightcyan', alpha=0.9))\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " return len(peaks) if len(peaks) > 0 else 0\n", "\n", "# Generate ensemble analysis plots\n", "print(\"\\n\" + \"=\"*80)\n", "print(\"ENSEMBLE ANALYSIS VISUALIZATION\")\n", "print(\"=\"*80)\n", "\n", "total_ensemble_peaks = 0\n", "ensemble_summary = {}\n", "\n", "for batch_num in analysis_batches:\n", " print(f\"\\n{'='*60}\")\n", " print(f\"ENSEMBLE BATCH {batch_num} ANALYSIS\")\n", " print(f\"{'='*60}\")\n", "\n", " ensemble_svs = ensemble_results['singular_values_data'][batch_num]\n", " ensemble_grads = ensemble_results['loss_gradients'][batch_num]\n", " ensemble_losses = ensemble_results['loss_values'][batch_num]\n", "\n", " print(f\"Mean loss across ensemble: {np.mean(ensemble_losses):.6f} ± {np.std(ensemble_losses):.6f}\")\n", "\n", " num_peaks = plot_ensemble_tail_analysis(\n", " batch_num=batch_num,\n", " ensemble_singular_values=ensemble_svs,\n", " ensemble_loss_gradients=ensemble_grads,\n", " layer_name=target_layer_name,\n", " layer_shape=layer_shape,\n", " learning_rate=0.01,\n", " sv_min=2.0,\n", " sv_max=6.5\n", " )\n", "\n", " total_ensemble_peaks += num_peaks\n", " ensemble_summary[batch_num] = {\n", " 'peaks': num_peaks,\n", " 'models': len(ensemble_svs),\n", " 'total_svs': sum(len(svs) for svs in ensemble_svs),\n", " 'mean_loss': np.mean(ensemble_losses)\n", " }\n", "\n", " print(f\"Batch {batch_num} complete: {num_peaks} peaks detected\")\n", "\n", "print(f\"\\n\" + \"=\"*80)\n", "print(\"FINAL ENSEMBLE SUMMARY\")\n", "print(\"=\"*80)\n", "print(f\"Ensemble size: {num_ensemble_models} models\")\n", "print(f\"Analysis batches: {analysis_batches}\")\n", "print(f\"Total peaks across all batches: {total_ensemble_peaks}\")\n", "\n", "print(f\"\\nDetailed Summary by Batch:\")\n", "for batch_num, summary in ensemble_summary.items():\n", " print(f\" Batch {batch_num:3d}: {summary['peaks']} peaks | \"\n", " f\"{summary['models']} models | \"\n", " f\"{summary['total_svs']:,} SVs | \"\n", " f\"Loss: {summary['mean_loss']:.4f}\")\n", "\n", "print(f\"\\nEnsemble analysis complete! Statistical power: {num_ensemble_models:,} models\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "3Zfrh57K2aUl", "outputId": "0f44e719-fc85-406e-ce3c-261f39786cd8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "================================================================================\n", "🎯 ENSEMBLE ANALYSIS VISUALIZATION\n", "================================================================================\n", "\n", "============================================================\n", "📊 ENSEMBLE BATCH 200 ANALYSIS\n", "============================================================\n", "📈 Mean loss across ensemble: 0.145618 ± 0.000000\n", "\n", "🔍 ENSEMBLE ANALYSIS - BATCH 200\n", "📊 Models in ensemble: 1\n", "🔢 Total aggregated singular values: 784\n", "📈 Mean loss gradient: 1.123240\n", "📉 Loss gradient std: 0.000000\n", "🎯 Values in range [2.0, 6.5]: 16\n", "📊 Coverage: 2.04% of total\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🎯 Peak detection: Found 6 peaks\n", "🧮 Theoretical parameters:\n", " Matrix dimensions: 1024 × 784\n", " D (ensemble drift): 0.086073\n", " β (mean loss grad): 1.123240\n", " Peak positions: ['2.595', '2.786', '2.978', '3.265', '3.457', '3.744']\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Batch 200 complete: 6 peaks detected\n", "\n", "============================================================\n", "📊 ENSEMBLE BATCH 400 ANALYSIS\n", "============================================================\n", "📈 Mean loss across ensemble: 0.435931 ± 0.000000\n", "\n", "🔍 ENSEMBLE ANALYSIS - BATCH 400\n", "📊 Models in ensemble: 1\n", "🔢 Total aggregated singular values: 784\n", "📈 Mean loss gradient: 1.556685\n", "📉 Loss gradient std: 0.000000\n", "🎯 Values in range [2.0, 6.5]: 19\n", "📊 Coverage: 2.42% of total\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🎯 Peak detection: Found 6 peaks\n", "🧮 Theoretical parameters:\n", " Matrix dimensions: 1024 × 784\n", " D (ensemble drift): 0.053856\n", " β (mean loss grad): 1.556685\n", " Peak positions: ['2.692', '2.913', '3.356', '3.578', '3.799', '4.021']\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Batch 400 complete: 6 peaks detected\n", "\n", "============================================================\n", "📊 ENSEMBLE BATCH 800 ANALYSIS\n", "============================================================\n", "📈 Mean loss across ensemble: 0.226771 ± 0.000000\n", "\n", "🔍 ENSEMBLE ANALYSIS - BATCH 800\n", "📊 Models in ensemble: 1\n", "🔢 Total aggregated singular values: 784\n", "📈 Mean loss gradient: 0.810938\n", "📉 Loss gradient std: 0.000000\n", "🎯 Values in range [2.0, 6.5]: 23\n", "📊 Coverage: 2.93% of total\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🎯 Peak detection: Found 5 peaks\n", "🧮 Theoretical parameters:\n", " Matrix dimensions: 1024 × 784\n", " D (ensemble drift): 0.032960\n", " β (mean loss grad): 0.810938\n", " Peak positions: ['2.194', '2.580', '3.094', '3.737', '4.380']\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Batch 800 complete: 5 peaks detected\n", "\n", "================================================================================\n", "🏆 FINAL ENSEMBLE SUMMARY\n", "================================================================================\n", "🤖 Ensemble size: 1 models\n", "📊 Analysis batches: [200, 400, 800]\n", "🎯 Total peaks across all batches: 17\n", "\n", "📋 Detailed Summary by Batch:\n", " Batch 200: 6 peaks | 1 models | 784 SVs | Loss: 0.1456\n", " Batch 400: 6 peaks | 1 models | 784 SVs | Loss: 0.4359\n", " Batch 800: 5 peaks | 1 models | 784 SVs | Loss: 0.2268\n", "\n", "🎉 Ensemble analysis complete! Statistical power: 1 models\n" ] } ] } ] }