{ "cells": [ { "cell_type": "markdown", "id": "abc5cf0b", "metadata": {}, "source": [ "# Addestra il tuo GPTux" ] }, { "cell_type": "markdown", "id": "cd1dfd38", "metadata": {}, "source": [ "Questo è un breve notebook in cui è presentata la costruzione ed addestramento di un transformer \"giocattolo\", sia per le sue dimensioni che per il piccolo dataset utilizzato.\n", "Lo scopo è quindi puramente didattico, in quanto il notebook nasce come parte integrante della presentazione per l'evento organizzato dall'associazione POuL per il Linux Day 2025.\n", "\n" ] }, { "cell_type": "markdown", "id": "8dc7c592", "metadata": {}, "source": [ "## Tokenizer\n", "\n", "\n", "Così come i pixel con la loro rappresentazione tramite vettore 3 dimensionale (RGB), vanno a formare una rappresentazione delle immagini adatta all'elaborazione digitale; anche il testo ha bisogno di essere suddiviso in parole o sotto-parole che possano essere rappresentabili univocamente tramite un vettore multi dimensionale.\n", "\n", "
\n", "\n", "$pixel : immagine = token : testo$\n", "\n", "
\n", "\n", "Un tokenizzatore fa proprio l'operazione di dividere un documento in \"pixel di testo\", chiamati **token** (tasselli). \n", "\n", "Sarà poi compito del modello capire quale vettore rappresenti al meglio ciascun token." ] }, { "cell_type": "code", "execution_count": null, "id": "9b9e3e41-d2b1-4d25-9940-4dcfb8b690e9", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 34, "id": "e373fcb3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original text: `this is a Tokenizer!`\n", "Input IDs: [2520, 310, 247, 35097, 6081, 2]\n", "Tokens: ['this', 'Ġis', 'Ġa', 'ĠToken', 'izer', '!']\n", "Converted back: this is a Tokenizer!\n", "\n", "\n", "\n", "Notes:\n", " - the tokenizer uses 50254 different tokens!\n", " - Some words like 'tokenizes' are split into multiple tokens: ['token', 'izes']!\n", " - Characters like spaces are tokenized with special characters: ['Ġ']\n", " - The token `John` is different from ` John` and `john`: ['John'] vs ['ĠJohn'] vs ['john']\n", " - The tokenizer uses special tokens come {'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>'} per rappresentare inizio/fine frase, padding, parole sconosciute, ecc.\n" ] } ], "source": [ "from transformers import AutoTokenizer\n", "import transformers\n", "import matplotlib.pyplot as plt\n", "import torch\n", "import torch.nn as nn\n", "import math\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import gc\n", "\n", "# Download and initialize the tokenizer used by Pythia\n", "tokenizer = AutoTokenizer.from_pretrained('EleutherAI/pythia-160m')\n", "\n", "# Show an example of tokenization\n", "text = \"this is a Tokenizer!\"\n", "tokenized_input = tokenizer(text)\n", "\n", "print(f\"Original text: `{text}`\")\n", "print(\"Input IDs: \", tokenized_input['input_ids'])\n", "print(\"Tokens: \", tokenizer.convert_ids_to_tokens(tokenized_input['input_ids']))\n", "print(\"Converted back: \", tokenizer.decode(tokenized_input['input_ids']), end=\"\\n\\n\\n\\n\")\n", "print(\"Notes:\")\n", "print(f\" - the tokenizer uses {tokenizer.vocab_size} different tokens!\")\n", "print(f\" - Some words like 'tokenizes' are split into multiple tokens: {tokenizer.tokenize('tokenizes')}!\")\n", "print(\" - Characters like spaces are tokenized with special characters: \", tokenizer.tokenize(\" \"))\n", "print(f\" - The token `John` is different from ` John` and `john`: {tokenizer.tokenize('John')} vs {tokenizer.tokenize(' John')} vs {tokenizer.tokenize('john')}\")\n", "print(f\" - The tokenizer uses special tokens come {tokenizer.special_tokens_map} per rappresentare inizio/fine frase, padding, parole sconosciute, ecc.\")" ] }, { "cell_type": "markdown", "id": "b2ca4287", "metadata": {}, "source": [ "## Transformer di Esempio: Pythia / GPT NeoX\n", "\n", "Useremo questo modello come esempio, completamente open source sia lato software che lato pesi e dati. Fa parte di un progetto di ricerca promosso da [EleutherAI](https://www.eleuther.ai/).\n", "I modelli sono basati sull'architettura [GPT-NeoX](https://github.com/EleutherAI/gpt-neox), simile a GPT-3 di OpenAI. La famiglia di modelli [Pythia](https://github.com/EleutherAI/pythia) parte dai 14 milioni di parametri fino ad arrivare a modelli con 12 miliardi di parametri. \n", "Noi useremo un modello molto piccolo da 160 milioni di parametri: **pythia-160m**.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "c1304a66", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GPTNeoXForCausalLM(\n", " (gpt_neox): GPTNeoXModel(\n", " (embed_in): Embedding(50304, 768)\n", " (emb_dropout): Dropout(p=0.0, inplace=False)\n", " (layers): ModuleList(\n", " (0-11): 12 x GPTNeoXLayer(\n", " (input_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (post_attention_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (post_attention_dropout): Dropout(p=0.0, inplace=False)\n", " (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n", " (attention): GPTNeoXAttention(\n", " (query_key_value): Linear(in_features=768, out_features=2304, bias=True)\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (mlp): GPTNeoXMLP(\n", " (dense_h_to_4h): Linear(in_features=768, out_features=3072, bias=True)\n", " (dense_4h_to_h): Linear(in_features=3072, out_features=768, bias=True)\n", " (act): GELUActivation()\n", " )\n", " )\n", " )\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (rotary_emb): GPTNeoXRotaryEmbedding()\n", " )\n", " (embed_out): Linear(in_features=768, out_features=50304, bias=False)\n", ")" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hf_model = transformers.AutoModelForCausalLM.from_pretrained('EleutherAI/pythia-160m')\n", "\n", "hf_model # Show the model architecture" ] }, { "cell_type": "markdown", "id": "b740bd99", "metadata": {}, "source": [ "## Embedding and Unembedding" ] }, { "cell_type": "markdown", "id": "99c361c4", "metadata": {}, "source": [ "#### Embedding\n", "\n", "L'operazione di embedding consiste nel mappare ogni token in un vettore di dimensione fissa, che rappresenta il token in uno spazio multi-dimensionale.\n", "Essenzialmente associa ad ogni elemento nel vocabolario un vettore.\n", "\n", "1. Decidiamo la **dimensionalità dello spazio** che il modello userà per rappresentare il suo ragionamento (d_model)\n", "2. Definiamo un embedding layer, in realtà non è altro che una matrice che proietta ogni token nello spazio d_model\n", "\n", "#### Unembedding / Language Modeling Head\n", "\n", "L'operazione di unembedding consiste nel mappare ogni vettore di dimensione fissa (d_model) in un token. Spesso questa operazione è implementata come la trasposizione della matrice di embedding. \n", "Essenzialmente associa un \"punteggio\" ad ogni token nel vocabolario, in base alla sua vicinanza al vettore nello spazio d_model.\n", "\n", "Anche in questo caso, non è altro che una matrice che proietta ogni vettore nello spazio del vocabolario." ] }, { "cell_type": "code", "execution_count": 3, "id": "17d4cade", "metadata": {}, "outputs": [], "source": [ "class GPTuxEmbedding(nn.Module):\n", " def __init__(self, vocab_size, hidden_size, weights=None):\n", " super().__init__()\n", " self.token_emb = nn.Embedding(vocab_size, hidden_size)\n", " if weights is not None:\n", " self.token_emb.weight = nn.Parameter(weights)\n", "\n", " def forward(self, x):\n", " return self.token_emb(x)\n", "\n", "\n", "class GPTuxLMH(nn.Module):\n", " def __init__(self, vocab_size, hidden_size, weights=None, bias=None):\n", " super().__init__()\n", " if bias is None:\n", " self.lm_head = nn.Linear(hidden_size, vocab_size, bias=False)\n", " else:\n", " self.lm_head = nn.Linear(hidden_size, vocab_size, bias=True)\n", " self.lm_head.bias = nn.Parameter(bias)\n", " if weights is not None:\n", " self.lm_head.weight = nn.Parameter(weights)\n", "\n", " def forward(self, x):\n", " return self.lm_head(x)" ] }, { "cell_type": "code", "execution_count": 4, "id": "c15002de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input shape: torch.Size([1, 1])\n", "Embedding output shape: torch.Size([1, 1, 768])\n", "LM Head output shape: torch.Size([1, 1, 50304])\n" ] } ], "source": [ "tokenized_input = tokenizer(\"test\", return_tensors='pt')\n", "\n", "embedding_layer = GPTuxEmbedding(vocab_size=50254, hidden_size=768, weights=hf_model.gpt_neox.embed_in.weight)\n", "lm_head = GPTuxLMH(vocab_size=50254, hidden_size=768, weights=hf_model.embed_out.weight, bias=hf_model.embed_out.bias)\n", "x = embedding_layer(x = tokenized_input['input_ids'])\n", "logits = lm_head(x)\n", "\n", "print(f\"Input shape: {tokenized_input['input_ids'].shape}\")\n", "print(f\"Embedding output shape: {x.shape}\")\n", "print(f\"LM Head output shape: {logits.shape}\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "2b442b15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Embedding sono compatibili? True\n", "LM Head sono compatibili? True\n" ] } ], "source": [ "# Test Embedding e LM Head\n", "# Embedding\n", "sample_input = tokenizer(\"Hello GPTux!\", return_tensors='pt')['input_ids']\n", "embedding_layer = GPTuxEmbedding(vocab_size=hf_model.config.vocab_size, hidden_size=hf_model.config.hidden_size,\n", " weights=hf_model.gpt_neox.embed_in.weight)\n", "embedded_output = embedding_layer(sample_input)\n", "hf_embedded_output = hf_model.gpt_neox.embed_in(sample_input)\n", "print(\"Embedding sono compatibili?\", torch.allclose(embedded_output, hf_embedded_output, atol=1e-6))\n", "\n", "# LM Head\n", "lm_head = GPTuxLMH(vocab_size=hf_model.config.vocab_size, hidden_size=hf_model.config.hidden_size,\n", " weights=hf_model.embed_out.weight, bias=hf_model.embed_out.bias)\n", "lm_output = lm_head(embedded_output)\n", "hf_lm_output = hf_model.embed_out(embedded_output)\n", "print(\"LM Head sono compatibili?\", torch.allclose(lm_output, hf_lm_output, atol=1e-6))\n" ] }, { "cell_type": "markdown", "id": "80bbc7f5", "metadata": {}, "source": [ "## Decoding\n", "\n", "Il decoding è il processo di generazione di testo a partire dai token previsti dal modello. Esistono diversi metodi per effettuare il decoding, tra cui:\n", "- **Greedy Decoding**: seleziona il token con la probabilità più alta ad ogni passo.\n", "- **Beam Search**: mantiene un certo numero di sequenze candidate e seleziona la migliore alla fine.\n", "- **Sampling**: seleziona i token in base alla loro distribuzione di probabilità, permettendo una maggiore varietà nel testo generato.\n", "- **Top-k Sampling**: limita la selezione ai k token più probabili.\n", "\n", "Noi useremo o il greedy decoding o un sampling basato sulla probabilità (temperature sampling)." ] }, { "cell_type": "code", "execution_count": 6, "id": "bb54f391", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([1, 6, 50304])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenized_input = tokenizer(\"My name is Bond, James\", return_tensors='pt')\n", "logits = hf_model(**tokenized_input).logits\n", "logits.shape # Questo è l'output del modello: un vettore multi-dimensionale, non ancora testo" ] }, { "cell_type": "code", "execution_count": 7, "id": "303e51fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Decoded output: Bond\n" ] } ], "source": [ "def greedy_decode(logits):\n", " logits = logits[:, -1] # Prendiamo solo l'ultimo token predetto\n", " predicted_token_ids = torch.argmax(logits, dim=-1) # Prende il massimo (Greedy Decoding)\n", " return tokenizer.decode(predicted_token_ids[0]).replace(\"Ġ\", \"_\")\n", "\n", "print(\"Decoded output:\", greedy_decode(logits)) " ] }, { "cell_type": "code", "execution_count": 8, "id": "70dbd6db", "metadata": {}, "outputs": [], "source": [ "def plot_topk_logits(logits, tokenizer, topk=10, title=\"Top-k Token Probability Distribution\"):\n", " \"\"\"\n", " Plots the probability distribution of the top-k tokens from logits.\n", "\n", " Args:\n", " logits (torch.Tensor or np.ndarray): The logits for a single position (1D array).\n", " tokenizer: HuggingFace tokenizer to convert token ids to strings.\n", " topk (int): Number of top tokens to display.\n", " title (str): Title for the plot.\n", " \"\"\"\n", " if isinstance(logits, torch.Tensor):\n", " logits = logits.detach().cpu().numpy()\n", " probs = torch.softmax(torch.tensor(logits), dim=-1).numpy()\n", " topk_indices = probs.argsort()[-topk:][::-1]\n", " topk_probs = probs[topk_indices]\n", " topk_tokens = [s.replace(\"Ġ\", \"_\") for s in tokenizer.convert_ids_to_tokens(topk_indices)]\n", "\n", " plt.figure(figsize=(6, 1 + 0.2 * topk))\n", " bars = plt.barh(range(topk), topk_probs, color='steelblue')\n", " plt.yticks(range(topk), topk_tokens)\n", " plt.xlabel(\"Probability\")\n", " plt.title(title)\n", " plt.xlim(0,1)\n", " plt.gca().invert_yaxis()\n", " for i, bar in enumerate(bars):\n", " plt.text(bar.get_width(), bar.get_y() + bar.get_height()/2, f\"{topk_probs[i]:.2%}\", va='center')\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "id": "739d3421", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AVERAGE TEMPERATURE (0.7):\n" ] }, { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "' Bond'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def probabilistic_decode(logits, temperature=1.0, verbose=False):\n", " scaled_logits = logits[:, -1] / temperature\n", " probabilities = torch.softmax(scaled_logits, dim=-1)\n", " predicted_token_id = torch.multinomial(probabilities, num_samples=1)\n", " if verbose:\n", " plot_topk_logits(scaled_logits[0], tokenizer, topk=10, title=f\"Top-10 Token Probability Distribution (Temp={temperature})\")\n", " return tokenizer.decode(predicted_token_id[0]).replace(\"Ġ\", \"_\")\n", "\n", "print(\"AVERAGE TEMPERATURE (0.7):\")\n", "probabilistic_decode(logits, temperature=0.7, verbose=True)\n", "\n", "print(\"\\nHIGH TEMPERATURE (3):\")\n", "probabilistic_decode(logits, temperature=3.0, verbose=True)\n", "\n", "print(\"\\nLOW TEMPERATURE (0.1):\")\n", "probabilistic_decode(logits, temperature=0.2, verbose=True)\n" ] }, { "cell_type": "markdown", "id": "619d26ce", "metadata": {}, "source": [ "## LayerNorm\n", "\n", "La layer normalizzazione è il primo \"componente\" che troviamo in ogni blocco del transformer. Il suo scopo è quello di normalizzare l'input del layer successivo, in modo da stabilizzare e accelerare l'addestramento del modello. Non inserisce quindi particulari informazioni, semplicemente si usa per motivi di ottimizzazione.\n", "\n", "L'operazione che compie è la seguente:\n", "1. Calcola la media e la deviazione standard dell'input\n", "2. Normalizza l'input sottraendo la media e dividendo per la deviazione\n", "3. Applica una trasformazione lineare con parametri appresi (gamma e beta) (notare che questi parametri non sono strettamente necessari ma nella pratica aiutano a dare importanza a certe feature)\n", "\n", "Matematicamente, per un input \\(x\\) dove x è un vettore di dimensione d_model, la LayerNorm è definita come:\n", "$$\n", "\\text{LayerNorm}(x) = \\gamma \\cdot \\frac{x - \\mu}{\\sigma} + \\beta\n", "$$" ] }, { "cell_type": "code", "execution_count": 10, "id": "1d33e8db", "metadata": {}, "outputs": [], "source": [ "class GPTuxLayerNorm(nn.Module):\n", " def __init__(self, dim, gamma=None, beta=None, eps=1e-5):\n", " super().__init__()\n", " self.eps = eps\n", "\n", " self.gamma = nn.Parameter(gamma if gamma is not None else torch.ones(dim))\n", " self.beta = nn.Parameter(beta if beta is not None else torch.zeros(dim))\n", "\n", " def forward(self, x):\n", " mean = x.mean(-1, keepdim=True)\n", " var = x.var(-1, keepdim=True, unbiased=False)\n", " x = (x - mean) / torch.sqrt(var + self.eps)\n", " return self.gamma * x + self.beta\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "2dec33ca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LayerNorm((768,), eps=1e-05, elementwise_affine=True)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hf_model.gpt_neox.layers[0].input_layernorm" ] }, { "cell_type": "code", "execution_count": 12, "id": "e6077894", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sono compatibili? True\n" ] } ], "source": [ "# Test\n", "sample_input = torch.randn(2, 8, hf_model.config.hidden_size)\n", "layer_norm = GPTuxLayerNorm(dim=hf_model.config.hidden_size,\n", " gamma=hf_model.gpt_neox.layers[0].input_layernorm.weight,\n", " beta=hf_model.gpt_neox.layers[0].input_layernorm.bias)\n", "out = layer_norm(sample_input)\n", "hf_out = hf_model.gpt_neox.layers[0].input_layernorm(sample_input)\n", "print(\"Sono compatibili?\", torch.allclose(out, hf_out, atol=1e-6))" ] }, { "cell_type": "markdown", "id": "3187ed88", "metadata": {}, "source": [ "Visualizziamo brevemente cosa succede all'interno di una LayerNorm:" ] }, { "cell_type": "code", "execution_count": 13, "id": "3a437f43", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = (torch.randn(768)-0.5) * 12 + 2\n", "\n", "basic_layernorm = GPTuxLayerNorm(768)\n", "y1 = basic_layernorm(x)\n", "\n", "gamma_layernorm = GPTuxLayerNorm(768, gamma=5*torch.ones(768))\n", "y2 = gamma_layernorm(x)\n", "\n", "beta_layernorm = GPTuxLayerNorm(768, beta=5*torch.ones(768))\n", "y3 = beta_layernorm(x)\n", "\n", "# Plot the probability distribution (histogram) of the values in x, y1, y2, y3\n", "fig, axs = plt.subplots(1, 4, figsize=(22, 5))\n", "\n", "axs[0].hist(x.detach().numpy(), bins=40, color='C0', alpha=0.7)\n", "axs[0].set_title('Distribution of x')\n", "axs[0].set_xlabel('Value')\n", "axs[0].set_ylabel('Frequency')\n", "axs[0].grid(True)\n", "mean = x.mean().item()\n", "std = x.std().item()\n", "axs[0].axvline(mean, color='red', linestyle='dashed', linewidth=1)\n", "axs[0].axvline(mean + std, color='red', linestyle='dashed', linewidth=1)\n", "axs[0].axvline(mean - std, color='red', linestyle='dashed', linewidth=1)\n", "axs[0].text(mean + 0.1, axs[0].get_ylim()[1]*0.9, f'Mean: {mean:.2f}', color='red')\n", "axs[0].text(mean + std + 0.1, axs[0].get_ylim()[1]*0.8, f'Std: {std:.2f}', color='red')\n", "\n", "\n", "axs[1].hist(y1.detach().numpy(), bins=40, color='C1', alpha=0.7)\n", "axs[1].set_title('Distribution of y1 (Basic LayerNorm)')\n", "axs[1].set_xlabel('Value')\n", "axs[1].set_ylabel('Frequency')\n", "axs[1].grid(True)\n", "mean = y1.mean().item()\n", "std = y1.std().item()\n", "axs[1].axvline(mean, color='red', linestyle='dashed', linewidth=1)\n", "axs[1].axvline(mean + std, color='red', linestyle='dashed', linewidth=1)\n", "axs[1].axvline(mean - std, color='red', linestyle='dashed', linewidth=1)\n", "axs[1].text(mean + 0.1, axs[1].get_ylim()[1]*0.9, f'Mean: {mean:.2f}', color='red')\n", "axs[1].text(mean + std + 0.1, axs[1].get_ylim()[1]*0.8, f'Std: {std:.2f}', color='red')\n", "\n", "axs[2].hist(y2.detach().numpy(), bins=40, color='orange', alpha=0.7)\n", "axs[2].set_title('Distribution of y2 (Gamma=2)')\n", "axs[2].set_xlabel('Value')\n", "axs[2].set_ylabel('Frequency')\n", "axs[2].grid(True)\n", "mean = y2.mean().item()\n", "std = y2.std().item()\n", "axs[2].axvline(mean, color='red', linestyle='dashed', linewidth=1)\n", "axs[2].axvline(mean + std, color='red', linestyle='dashed', linewidth=1)\n", "axs[2].axvline(mean - std, color='red', linestyle='dashed', linewidth=1)\n", "axs[2].text(mean + 0.1, axs[2].get_ylim()[1]*0.9, f'Mean: {mean:.2f}', color='red')\n", "axs[2].text(mean + std + 0.1, axs[2].get_ylim()[1]*0.8, f'Std: {std:.2f}', color='red')\n", "\n", "axs[3].hist(y3.detach().numpy(), bins=40, color='green', alpha=0.7)\n", "axs[3].set_title('Distribution of y3 (Beta=2)')\n", "axs[3].set_xlabel('Value')\n", "axs[3].set_ylabel('Frequency')\n", "axs[3].grid(True)\n", "mean = y3.mean().item()\n", "std = y3.std().item()\n", "axs[3].axvline(mean, color='red', linestyle='dashed', linewidth=1)\n", "axs[3].axvline(mean + std, color='red', linestyle='dashed', linewidth=1)\n", "axs[3].axvline(mean - std, color='red', linestyle='dashed', linewidth=1)\n", "axs[3].text(mean + 0.1, axs[3].get_ylim()[1]*0.9, f'Mean: {mean:.2f}', color='red')\n", "axs[3].text(mean + std + 0.1, axs[3].get_ylim()[1]*0.8, f'Std: {std:.2f}', color='red')\n", "\n", "for ax in axs:\n", " ax.set_xlim([-35, 35])\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f657fcb1", "metadata": {}, "source": [ "## Multi Layer Perceptron" ] }, { "cell_type": "markdown", "id": "5529f230", "metadata": {}, "source": [ "Nell'architettuara transformer, i Multi-Layer Perceptron (MLP) vengono utilizzati all’interno di ogni blocco del transformer, subito dopo il meccanismo dell'attenzione. L’MLP è tipicamente composto da due layer lineari con una funzione di attivazione non lineare (come GELU o ReLU) tra di essi.\n", "\n", "L’MLP aumenta la capacità del modello di rappresentare **relazioni non lineari** e in uno **spazio dimensionale superiore** affinando la rappresentazione del residuo prima di passarla al layer successivo.\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "c78c3a68", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate circle data\n", "x = np.random.rand(1000) - 0.5\n", "y = np.random.rand(1000) - 0.5\n", "mask = x**2 + y**2 <= 0.1\n", "\n", "# Add a third dimension, non linear with the other two\n", "z = x**2 + y**2 # <-- THIS IS A NON-LINEAR NEW FEATURE\n", "\n", "separation_plane = np.linspace(-0.5, 0.5, 10)\n", "X, Y = np.meshgrid(separation_plane, separation_plane)\n", "Z = 0.1 * np.ones_like(X) # <-- THIS IS A LINEAR PLANE IN X,Y,Z\n", "\n", "fig = plt.figure(figsize=(12, 4))\n", "\n", "# 2D scatter\n", "ax1 = fig.add_subplot(1, 3, 1)\n", "ax1.scatter(x[~mask], y[~mask], color='orange')\n", "ax1.scatter(x[mask], y[mask], color='steelblue')\n", "ax1.set_title(\"2D FEATURES\")\n", "ax1.axis('off')\n", "\n", "# 3D scatter\n", "ax2 = fig.add_subplot(1, 3, 2, projection='3d')\n", "ax2.scatter(x[~mask], y[~mask], z[~mask], color='orange')\n", "ax2.scatter(x[mask], y[mask], z[mask], color='steelblue')\n", "ax2.view_init(elev=30., azim=30)\n", "ax2.set_title(\"3D FEATURES\")\n", "ax2.set_axis_off()\n", "\n", "# 3D scatter + plane\n", "ax3 = fig.add_subplot(1, 3, 3, projection='3d')\n", "ax3.scatter(x[~mask], y[~mask], z[~mask], color='orange')\n", "ax3.scatter(x[mask], y[mask], z[mask], color='steelblue')\n", "ax3.plot_surface(X, Y, Z, alpha=0.6, color='gray')\n", "ax3.view_init(elev=30., azim=30)\n", "ax3.set_title(\"3D FEATURES + SEPARATION PLANE\")\n", "ax3.set_axis_off()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "20cc64dd", "metadata": {}, "source": [ "### Layer Lineari\n", "\n", "#### Cos’è un Layer Lineare?\n", "\n", "Un layer lineare (detto anche fully connected o denso) è uno dei mattoni fondamentali delle reti neurali. Prende un vettore di input e lo trasforma in un vettore di output moltiplicandolo per una matrice di pesi e aggiungendo un bias. Matematicamente, questa è l’operazione più semplice per esprimere una trasformazione lineare da uno spazio di input a uno spazio di output. \n", "Dato un vettore di input $x$, una matrice di pesi $W$ e un vettore di bias $b$, l’output $y$ si calcola come:\n", "\n", "$$y = Wx + b$$\n", "\n", "#### Implementazione:\n", "\n", "In PyTorch, un layer lineare si implementa con nn.Linear(in_features, out_features). Internamente, memorizza una matrice di pesi di forma (out_features, in_features) e un vettore di bias di forma (out_features). \n", "Questa operazione equivale a una moltiplicazione tra matrici seguita da una somma vettoriale, che può essere eseguita con torch.einsum(), torch.nn.Linear() oppure semplicemente $y = x @ W^T + b$." ] }, { "cell_type": "code", "execution_count": 15, "id": "374febb9", "metadata": {}, "outputs": [], "source": [ "# Define an example input, weights, and biases\n", "x = torch.randn(4, 10, 768)\n", "w = torch.randn(768, 4 * 768)\n", "b = torch.randn(4 * 768)\n", "\n", "# Plain implementation\n", "y_plain = x @ w + b\n", "\n", "# Using the torch.nn module (industry standard)\n", "linear = nn.Linear(768, 4 * 768)\n", "linear.weight = nn.Parameter(w.T)\n", "linear.bias = nn.Parameter(b)\n", "y_nn = linear(x)\n", "\n", "# Using Einstein summation notation (tensor algebra)\n", "y_torch = torch.einsum('bsd,dh->bsh', x, w) + b # ('batch seq d_model, d_model hidden -> batch seq hidden') <-- We will use this for clarity!\n", "\n", "assert torch.allclose(y_plain, y_nn, atol=1e-5)\n", "assert torch.allclose(y_plain, y_torch, atol=1e-5)" ] }, { "cell_type": "markdown", "id": "b367d820", "metadata": {}, "source": [ "### Attivazione Non Lineare\n", "\n", "Una delle caratteristiche più importanti delle reti neurali è la capacità di modellare relazioni non lineari. Questo è reso possibile dall’uso di funzioni di attivazione non lineari, che vengono applicate dopo le trasformazioni lineari.\n", "\n", "Essenzialmente, una funzione di attivazione introduce delle non-linearità che aumentano la capacità rappresentativa del modello. \n", "Senza di esse, una rete composta solo da layer lineari sarebbe equivalente a un singolo layer lineare, indipendentemente dal numero di layer (W_single = W1 @ W2 @ W3 => x @ W_single = x @ W1 @ W2 @ W3).\n", "\n", "La scelta della funzione di attivazione è principalmente dettata da magia nera ed evidenze empiriche. Alcune delle funzioni di attivazione più comuni sono:\n", "- ReLU (Rectified Linear Unit): $f(x) = max(0, x)$\n", "- GELU (Gaussian Error Linear Unit): $f(x) = x * P(X \\leq x)$, dove $P(X \\leq x)$ è la funzione di distribuzione cumulativa della distribuzione normale standard.\n", "- Sigmoid: $f(x) = \\frac{1}{1 + e^{-x}}$\n", "- Tanh: $f(x) = \\frac{e^x - e^{-x}}{e^x + e^{-x}}$\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "1d292eac", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-4, 4, 200)\n", "x_torch = torch.from_numpy(x)\n", "\n", "relu = np.maximum(0, x)\n", "gelu = torch.nn.functional.gelu(x_torch).numpy()\n", "sigmoid = 1 / (1 + np.exp(-x))\n", "tanh = np.tanh(x)\n", "\n", "fig, axs = plt.subplots(1, 4, figsize=(18, 4))\n", "\n", "axs[0].plot(x, relu, label='ReLU', color='C0', linewidth=2)\n", "axs[0].set_title('ReLU')\n", "axs[0].grid(True)\n", "\n", "axs[1].plot(x, gelu, label='GELU', color='C1', linewidth=2)\n", "axs[1].set_title('GELU')\n", "axs[1].grid(True)\n", "\n", "axs[2].plot(x, sigmoid, label='Sigmoid', color='C2', linewidth=2)\n", "axs[2].set_title('Sigmoid')\n", "axs[2].grid(True)\n", "\n", "axs[3].plot(x, tanh, label='Tanh', color='C3', linewidth=2)\n", "axs[3].set_title('Tanh')\n", "axs[3].grid(True)\n", "\n", "for ax in axs:\n", " ax.set_xlabel('x')\n", " ax.set_ylabel('f(x)')\n", " ax.set_ylim(-1.5, 4.5)\n", " ax.plot(x, x*0, color='black', linewidth=2, linestyle='--', alpha=0.5)\n", " ax.legend()\n", "\n", "plt.ylim(-1.5, 4.5)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6f51e801", "metadata": {}, "source": [ "### Implementazione MLP" ] }, { "cell_type": "code", "execution_count": 17, "id": "0ea1c711", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GPTNeoXMLP(\n", " (dense_h_to_4h): Linear(in_features=768, out_features=3072, bias=True)\n", " (dense_4h_to_h): Linear(in_features=3072, out_features=768, bias=True)\n", " (act): GELUActivation()\n", ")" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hf_model.gpt_neox.layers[0].mlp" ] }, { "cell_type": "code", "execution_count": 18, "id": "cbaad17f", "metadata": {}, "outputs": [], "source": [ "class GPTuxMLP(nn.Module):\n", " def __init__(self, config, W1=None, b1=None, W2=None, b2=None, dropout=0):\n", " super().__init__()\n", " #self.dropout = nn.Dropout(dropout) # Utile se si fa training, non per l'inferenza\n", " self.gelu = nn.GELU()\n", " self.l1 = nn.Linear(config.hidden_size, 4 * config.hidden_size)\n", " if W1 is not None: \n", " self.l1.weight = nn.Parameter(W1)\n", " if b1 is not None: \n", " self.l1.bias = nn.Parameter(b1)\n", " self.l2 = nn.Linear(4 * config.hidden_size, config.hidden_size)\n", " if W2 is not None: \n", " self.l2.weight = nn.Parameter(W2)\n", " if b2 is not None: \n", " self.l2.bias = nn.Parameter(b2)\n", "\n", " def forward(self, x):\n", " x = self.l1(x)\n", " x = self.gelu(x)\n", " x = self.l2(x)\n", " #x = self.dropout(x) # Utile se si fa training, non per l'inferenza\n", " return x\n", " " ] }, { "cell_type": "code", "execution_count": 19, "id": "e6c538e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gli output sono compatibili? True\n" ] } ], "source": [ "# Test GPTuxMLP\n", "mlp = GPTuxMLP(config=hf_model.config,\n", " W1=hf_model.gpt_neox.layers[0].mlp.dense_h_to_4h.weight,\n", " b1=hf_model.gpt_neox.layers[0].mlp.dense_h_to_4h.bias,\n", " W2=hf_model.gpt_neox.layers[0].mlp.dense_4h_to_h.weight,\n", " b2=hf_model.gpt_neox.layers[0].mlp.dense_4h_to_h.bias)\n", "\n", "sample_input = torch.randn(2, 8, hf_model.config.hidden_size)\n", "out = mlp(sample_input)\n", "hf_out = hf_model.gpt_neox.layers[0].mlp(sample_input)\n", "\n", "# Check\n", "print(\"Gli output sono compatibili?\", torch.allclose(out, hf_out, atol=1e-5))" ] }, { "cell_type": "markdown", "id": "a796bac3", "metadata": {}, "source": [ "## Positional Embedding (RoPE)\n", "\n", "(approfondimento)\n", "\n", "Nei transformer non esiste alcuna nozione intrinseca di ordine/posizione nella frase di un token. D'altra parte, il significato di una frase dipende fortemente dall'ordine delle parole. Per esempio, \"Il gatto insegue il topo\" ha un significato completamente diverso da \"Il topo insegue il gatto\", nonostante contengano le stesse parole. Per questo motivo è **strettamente necessario** fornire al modello delle informazioni sulla posizione dei token nella sequenza. Esistono diversi metodi per incorporare queste informazioni, tra cui:\n", "- **Positional Encoding**: Aggiunge un vettore di posizione pre-calcolato ad ogni embedding di token.\n", "- **Learned Positional Embeddings**: Utilizza un layer di embedding separato per imparare le rappresentazioni di posizione durante l'addestramento.\n", "- **Rotary Positional Embeddings (RoPE)**: Integra le informazioni di posizione direttamente nei meccanismi di attenzione, ruotando gli spazi di embedding in base alla posizione dei token.\n", "\n", "### RoPE\n", "\n", "L'idea alla base di RoPE è quella di applicare una rotazione agli spazi di embedding dei token in base alla loro posizione nella sequenza. Questo viene fatto utilizzando funzioni trigonometriche (seno e coseno) per creare una matrice di rotazione che dipende dalla posizione del token. In questo modo, le relazioni tra i token possono essere modulate in base alla loro posizione relativa, migliorando la capacità del modello di catturare le dipendenze a lungo raggio nel testo.\n", "\n", "Matematicamente, per un token in posizione \\(pos\\) con embedding \\(x\\), la rotazione viene applicata come segue:\n", "$$\n", "x_{rotated} = R(pos) \\cdot x\n", "$$\n", "dove \\(R(pos)\\) è la matrice di rotazione calcolata utilizzando le funzioni seno e coseno basate sulla posizione \\(pos\\).\n", "\n", "Questo metodo è usato in molti modelli moderni, tra cui Pythia, Qwen, Llama etc. \n", "\n", "La proprietà matematica chiave di questa rotazione è che il prodotto scalare tra due vettori ruotati dipende solo dalla differenza tra le loro posizioni:\n", "$$\n", " \\langle R(\\theta_1)\\vec{v}_1, R(\\theta_2)\\vec{v}_2 \\rangle = \\langle \\vec{v}_1, \\vec{v}_2 \\rangle \\cdot \\cos(\\theta_1 - \\theta_2)\n", "$$\n", "Essenzialmente il modello può **recuperare la distanza relativa tra due token** semplicemente confrontando i loro vettori ruotati. La rotazione non altera la lunghezza dei vettori, ma ne cambia la direzione in modo controllato, permettendo al meccanismo di attenzione di essere sensibile alla posizione relativa tra token.\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "9b95fa56", "metadata": {}, "outputs": [], "source": [ "class GPTuxRotaryEmbedding(torch.nn.Module):\n", " def __init__(self, dim: int, max_seq_len: int, base: int = 10000, precision: torch.dtype = torch.float32):\n", " super().__init__()\n", " self.dim = dim\n", " self.max_seq_len = max_seq_len\n", " self.base = base\n", " \n", " # Calcola l'inveso delle frequenze in fp32\n", " inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.float32) / self.dim))\n", "\n", " # Calcola i valori di coseno e seno per tutte le posizioni fino a max_seq_len\n", " t = torch.arange(self.max_seq_len, dtype=torch.float32)\n", " freqs = torch.einsum(\"i,j->ij\", t, inv_freq)\n", " emb = torch.cat((freqs, freqs), dim=-1)\n", "\n", " # Salva tali valori in una cache come attributi\n", " self.cos_cached = emb.cos().to(precision)\n", " self.sin_cached = emb.sin().to(precision)\n", "\n", " @staticmethod\n", " def _rotate_half(x: torch.Tensor) -> torch.Tensor:\n", " \"\"\"\n", " Rotates half of the hidden dimensions of the input tensor.\n", " This is the core operation for applying the rotary embedding.\n", " \"\"\"\n", " x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]\n", " return torch.cat((-x2, x1), dim=-1)\n", "\n", " def forward(self, q: torch.Tensor, k: torch.Tensor, offset: int = 0) -> tuple[torch.Tensor, torch.Tensor]:\n", " \"\"\"\n", " Applies rotary positional embedding to the query and key tensors.\n", "\n", " Args:\n", " q (torch.Tensor): The query tensor. Shape: [bsz, n_heads, seq_len, head_dim]\n", " k (torch.Tensor): The key tensor. Shape: [bsz, n_heads, seq_len, head_dim]\n", " offset (int, optional): The starting position offset, used for KV caching during\n", " inference. Defaults to 0.\n", "\n", " Returns:\n", " tuple[torch.Tensor, torch.Tensor]: A tuple containing the rotated query and key tensors.\n", " \"\"\"\n", " seq_len = q.shape[2]\n", "\n", " q = q.permute(2, 0, 1, 3)\n", " k = k.permute(2, 0, 1, 3)\n", "\n", " # Ensure that the requested sequence length is within the cached limits\n", " if offset + seq_len > self.max_seq_len:\n", " raise ValueError(\n", " f\"Sequence length {offset + seq_len} exceeds the maximum sequence length \"\n", " f\"of {self.max_seq_len} this model supports.\"\n", " )\n", "\n", " # Retrieve the pre-computed cos and sin values for the current sequence length\n", " # Slicing is done based on the offset and current sequence length\n", " # Reshape to [seq_len, 1, 1, dim] to be broadcastable with q and k\n", " cos = self.cos_cached[offset : offset + seq_len].unsqueeze(1).unsqueeze(1)\n", " sin = self.sin_cached[offset : offset + seq_len].unsqueeze(1).unsqueeze(1)\n", " \n", " # Apply the rotation to the query and key tensors (apply only to the first rotary_pct of dimensions)\n", " q_rotated = (q[..., :self.dim] * cos) + (self._rotate_half(q[..., :self.dim]) * sin)\n", " k_rotated = (k[..., :self.dim] * cos) + (self._rotate_half(k[..., :self.dim]) * sin)\n", "\n", " # Apply the rotary embedding only to the first rotary dim dimensions\n", " q_rotated = torch.cat((q_rotated, q[..., self.dim:]), dim=-1)\n", " k_rotated = torch.cat((k_rotated, k[..., self.dim:]), dim=-1)\n", "\n", " q_rotated = q_rotated.permute(1, 2, 0, 3)\n", " k_rotated = k_rotated.permute(1, 2, 0, 3)\n", "\n", " return q_rotated, k_rotated" ] }, { "cell_type": "markdown", "id": "aecf9b28", "metadata": {}, "source": [ "## Attenzione\n", "\n", "### Cos'è il Meccanismo di Attenzione?\n", "\n", "L'attenzione è il cuore dei transformer. È il meccanismo che permette al modello di \"concentrarsi\" su quali parti della sequenza di input sono più rilevanti per ogni posizione.\n", "\n", "**Metafora intuitiva:** Immagina di leggere una frase. Quando leggi la parola \"banca\", il cervello automaticamente \"presta attenzione\" a parole circostanti come \"fiume\" o \"denaro\" per capire il significato nel contesto. L'attenzione nel transformer funziona allo stesso modo: per ogni token, il modello calcola quanto dovrebbe \"guardare\" a ogni altro token della sequenza.\n", "\n", "### Come Funziona l'Attenzione?\n", "\n", "L'attenzione calcula tre cose per ogni token:\n", "1. **Query (Q):** \"Che cosa sto cercando?\" — una rappresentazione di ciò che il token corrente vuole sapere\n", "2. **Key (K):** \"Ecco chi sono io\" — una firma identificativa di ogni token nella sequenza\n", "3. **Value (V):** \"Ecco l'informazione che porto\" — il contenuto effettivo di ogni token\n", "\n", "Il processo è fondamentalmente un'operazione di **ricerca e recupero**:\n", "- Calcoli la somiglianza tra la Query del token corrente e le Key di tutti i token (incluso se stesso)\n", "- Questa somiglianza (attention score) ti dice quanto importante è ogni altro token\n", "- Infine, ponderi i Values usando questi punteggi e li sommi insieme\n", "\n", "Matematicamente, per ogni posizione, l'output è:\n", "$$\\text{Attention}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n", "\n", "Dove $d_k$ è la dimensione della key (fattore di normalizzazione).\n", "\n", "### Attenzione Causale (Causal Attention)\n", "\n", "Nel nostro GPTux e nei modelli di linguaggio generativi, usiamo **attenzione causale**: durante la predizione del token $t$, il modello può solo \"guardare\" a token in posizioni $1, 2, ..., t$. **Non può vedere il futuro**. Questo è essenziale durante l'addestramento e l'inferenza per evitare leak di informazione.\n", "\n", "### Attention Multi-Head\n", "\n", "Una singola attenzione potrebbe non catturare tutte le relazioni interessanti. Per questo motivo, usiamo **più heads** in parallelo:\n", "- Ogni head apprende a concentrarsi su diversi tipi di relazioni (sintattiche, semantiche, posizionali, ecc.)\n", "- Gli output di tutte le head vengono concatenati e proiettati\n", "- Il modello combina questi diversi \"punti di vista\" per una comprensione più ricca\n", "\n", "### Implementazione Coincisa\n", "\n", "1. Proietta l'input in Q, K, V tramite tre layer lineari\n", "2. Calcola attention scores: $\\text{scores} = \\frac{QK^T}{\\sqrt{d_k}}$\n", "3. Applica la maschera causale (setta a $-\\infty$ i la similitudine ai token del futuro / evita che il modello \"veda\" i token futuri)\n", "4. Applica softmax per normalizzare i punteggi\n", "5. Moltiplica per i Values e somma: $\\text{output} = \\text{scores} \\cdot V$\n", "6. Proietta l'output con un layer lineare" ] }, { "cell_type": "code", "execution_count": 21, "id": "011de79c", "metadata": {}, "outputs": [], "source": [ "class GPTuxAttention(nn.Module):\n", " def __init__(self, config, Wqkv=None, bqkv=None, Wo=None, bo=None):\n", " super().__init__()\n", " self.n_head = config.num_attention_heads\n", " self.n_embd = config.hidden_size\n", " self.dropout = config.attention_dropout\n", " self.head_dim = config.hidden_size // config.num_attention_heads\n", " # Pythia come molti altri modelli usa Rotary Embeddings ma applicati solo a una parte dei vettori di query e key\n", " self.rotary_pct = config.rotary_pct\n", "\n", " # Layer di proiezione QKV (uniti per compatibilità con i pesi di HuggingFace / parallelizzazione)\n", " self.qkv_proj = nn.Linear(self.n_embd, 3 * self.n_embd)\n", " if Wqkv is not None: \n", " self.qkv_proj.weight = nn.Parameter(Wqkv)\n", " if bqkv is not None: \n", " self.qkv_proj.bias = nn.Parameter(bqkv)\n", "\n", " # Layer di proiezione in uscita\n", " self.Wo = nn.Linear(self.n_embd, self.n_embd)\n", " if Wo is not None: \n", " self.Wo.weight = nn.Parameter(Wo)\n", " if bo is not None: \n", " self.Wo.bias = nn.Parameter(bo)\n", "\n", " # Maschera causale per l'attenzione\n", " self.causal_mask = torch.tril(torch.ones(2048, 2048))\n", "\n", " # Rotary Embedding\n", " self.rotary_dim = int(self.head_dim * self.rotary_pct)\n", " self.rotary_emb = GPTuxRotaryEmbedding(self.rotary_dim, max_seq_len=config.max_position_embeddings)\n", "\n", " def forward(self, x, position_ids, show=False):\n", " bs, seq, d_model = x.size() # batch size, sequence length, embedding dimensionality (n_embd)\n", "\n", " qkv = self.qkv_proj(x)\n", " q, k, v = qkv.view(bs, seq, self.n_head, 3 * self.head_dim).chunk(3, dim=-1)\n", " k = k.transpose(1,2)\n", " q = q.transpose(1,2)\n", " v = v.transpose(1,2)\n", "\n", " # Applica gli embedding RoPE a query e key\n", " q, k = self.rotary_emb(q, k, offset=position_ids[0,0].item())\n", "\n", "\n", " # Calcola i punteggi di attenzione usando einsum\n", " # (bs, n_head, seq, head_dim) @ (bs, n_head, head_dim, seq) -> (bs, n_head, seq, seq)\n", " z = torch.einsum('bnsh,bnth->bnst', q, k) / math.sqrt(self.head_dim)\n", " if show:\n", " self.plot_attention_matrix(z, title=\"Attention Scores before Causal Masking\")\n", "\n", " # Usa la maschera causale per mascherare i punteggi di attenzione futuri\n", " z = z.masked_fill(self.causal_mask[:seq, :seq] == 0, float('-inf'))\n", " if show:\n", " self.plot_attention_matrix(z, title=\"Attention Scores after Causal Masking\")\n", "\n", " scores = torch.softmax(z, dim=-1)\n", " # scores = torch.nn.functional.dropout(scores, p=self.dropout, training=self.training)\n", " if show:\n", " self.plot_attention_matrix(scores, title=\"Attention Weights after Softmax\")\n", "\n", " y = torch.einsum('bnst,bnth->bsnh', scores, v).reshape(bs, seq, d_model)\n", "\n", " y = self.Wo(y)\n", "\n", " return y\n", "\n", " def plot_attention_matrix(self, matrix, title=\"Attention Matrix\"):\n", " matrix = matrix[0, 0].detach().cpu().numpy() # Solo primo batch/head\n", " plt.figure(figsize=(6, 6))\n", " plt.imshow(matrix, cmap='viridis')\n", " for (i, j), val in np.ndenumerate(matrix):\n", " plt.text(j, i, f\"{val:.2f}\", ha='center', va='center', color='white' if val < matrix.max()/2 else 'black', fontsize=8)\n", " plt.title(title)\n", " plt.xlabel('Key Positions')\n", " plt.ylabel('Query Positions')\n", " plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "b4028109", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GPTNeoXAttention(\n", " (query_key_value): Linear(in_features=768, out_features=2304, bias=True)\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", ")" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hf_model.gpt_neox.layers[0].attention" ] }, { "cell_type": "code", "execution_count": 23, "id": "d42adb21", "metadata": {}, "outputs": [ { "data": { "image/png": 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OaDayoiivHENWZMfr29ra4uvri6+vL66urnz55Zds27aNvn37ZimW50cyXiS9OxYy+767u7tz/vx5duzYwbZt29i2bRtLliyhT58+LFu2LEvxpufpAf/jjz9WjXI8ld0H43+/H09jWLFiBZ6enqryL7sttkyZMpw/f57k5OR0J6E+LzIykgYNGuDg4MBXX31FiRIlsLa25uzZs4wbN87oBJhR++p0OqN+WLZsWW7cuMHmzZvZvn07GzZsYP78+UyaNIkvv/wSSJs7s2TJEj766CNq165t+KKp7t27v/QTemb07NmTwYMH8/jxY1q1apXhnSaHDh2iffv21K9fn/nz5+Pl5YWFhQVLliwxmlRpY2PDwYMH2bdvH1u2bGH79u2sXbuWxo0bs3PnTqP6d+jQgTVr1vDtt9+yfPlyzMwy9xnV1MeS/68kQXiDrFq1Cnd3d8OdAc/buHEjf/75Jz///DM2NjYvPGFltK5EiRLExsYaPrlmh8yeON3c3LC1teXGjRuqddevX8fMzAxvb+9siys9TycdBgYGAmnvx44dOwgPD89wFKFIkSLo9Xr8/f0Nk0Eh7Yt6IiMjDbP9XyQr77ulpSXt2rWjXbt26PV6hg4dysKFC/n8889fevJ++un8eX5+foZJf08vC1hYWLxSH8iONixRogSQlgy9Sgzt2rXj2LFjbNiwgR49eryw7P79+wkLC2Pjxo3Ur1/fsPz27duqss7Ozul+wdLdu3eNLqcA2NnZ0a1bN7p160ZycjKdOnVi6tSpjB8/Hmtra9avX0/fvn2ZNWuW4TmJiYnZ9gVOHTt2ZMiQIRw/fvyFE3Y3bNiAtbU1O3bsMJrot2TJElVZMzMzmjRpQpMmTZg9ezbffPMNEydOZN++fUbt9M4779C8eXP69euHvb09CxYsyJY6Qdq+dvPmTdXy9JaJzJFLDG+IhIQENm7cSNu2bencubPqb/jw4cTExBhuR3t6P3l6Bx07O7t0l3ft2pVjx46xY8cO1brIyEhSU1OzHPeL4nieVqulefPmbNq0yeh2paCgIMOXvzg4OGT59dOT0aWYp9finw6Rv/vuuyiKYvjk97ynn2xat24NoLrDY/bs2QCGmeAvktn3PSwszGidmZkZlSpVAlDdUpmev/76y2gOwcmTJzlx4oRhxru7uzsNGzZk4cKFhiTpeS+7hTQ72rBFixY4ODjwzTffkJKSkuUY/ve//+Hl5cWYMWPw8/NTrQ8ODmbKlCmGeMH4U2pycjLz589XPa9EiRIcP36c5ORkw7LNmzerLpn8u40sLS0pV64ciqIY6qPValWfjH/88cfXnkP0VL58+ViwYAFffPEF7dq1y7CcVqtFo9EYve6dO3dUX5kdHh6ueu7TeQDp9bs+ffowd+5cfv75Z8aNG/dqlUhHixYtOHbsGOfPnzeK7d+3eYvMkxGEN8Tff/9NTEwM7du3T3d9rVq1cHNzY9WqVYZvCdRqtUyfPp2oqCisrKxo3Lgx7u7uVKtWjQULFjBlyhR8fHxwd3encePGfPLJJ/z999+0bdvWcItRXFwcly5dYv369dy5cwdXV9csxf30W+xGjBhBixYt0Gq1dO/ePd2yU6ZMMdxvPXToUMzNzVm4cCFJSUnMmDEja2/YC3To0IFixYrRrl07SpQoQVxcHLt37+aff/6hRo0ahoNqo0aN6N27N3PnzsXf35+WLVui1+s5dOgQjRo1Yvjw4VSuXJm+ffvyyy+/GIasT548ybJly3jnnXcMk0lfJLPv+6BBgwgPD6dx48YUKlSIu3fv8uOPP1KlShWj0YuM+Pj4UK9ePT744AOSkpL4/vvvcXFxMbq08dNPP1GvXj0qVqzI4MGDKV68OEFBQRw7dowHDx5w4cKFF77G67ahg4MDCxYsoHfv3rz11lt0794dNzc37t27x5YtW6hbty7z5s3L8PnOzs78+eeftG7dmipVqhh9k+LZs2f5/fffqV27NgB16tTB2dmZvn37MmLECDQaDStWrEh3WHvQoEGsX7+eli1b0rVrVwICAli5cqVhxOOp5s2b4+npSd26dfHw8ODatWvMmzePNm3aGCZPtm3blhUrVuDo6Ei5cuU4duwYu3fvxsXF5aXvT2Zl5hJZmzZtmD17Ni1btqRnz54EBwfz008/4ePjw8WLFw3lvvrqKw4ePEibNm0oUqQIwcHBzJ8/n0KFCqm+tfGp4cOHEx0dzcSJE3F0dHzpdyZkxtixY1m5ciXNmjXjww8/NNzmWLhwYcLDwzM9WimeY6rbJ0T2ateunWJtba3ExcVlWKZfv36KhYWFEhoaqiiKovz6669K8eLFFa1Wa3S71OPHj5U2bdoo9vb2CmB0m1ZMTIwyfvx4xcfHR7G0tFRcXV2VOnXqKN99952SnJysKErGt7gpivrWxdTUVOXDDz9U3NzcFI1GY3TL47/LKoqinD17VmnRooWSL18+xdbWVmnUqJFy9OhRozLP39b1vMzeFvb7778r3bt3V0qUKKHY2Ngo1tbWSrly5ZSJEycq0dHRRmVTU1OVmTNnKmXKlFEsLS0VNzc3pVWrVsqZM2cMZVJSUpQvv/xSKVasmGJhYaF4e3sr48ePN7pNTlHSv83sqcy87+vXr1eaN2+uuLu7K5aWlkrhwoWVIUOGKIGBgS+s7/PtNWvWLMXb21uxsrJSfH19lQsXLqjKBwQEKH369FE8PT0VCwsLpWDBgkrbtm2V9evXp7vNf3udNnxq3759SosWLRRHR0fF2tpaKVGihNKvXz/l9OnTL6zrU48ePVJGjRqllCpVSrG2tlZsbW2VatWqKVOnTlWioqIM5Y4cOaLUqlVLsbGxUQoUKKCMHTtW2bFjR7r9aNasWUrBggUVKysrpW7dusrp06dVtzkuXLhQqV+/vuLi4qJYWVkpJUqUUD755BOj14yIiFD69++vuLq6Kvny5VNatGihXL9+XXUr5avc5vgi6fW/RYsWKSVLllSsrKyUMmXKKEuWLFEmT55stJ/u2bNH6dChg1KgQAHF0tJSKVCggNKjRw+j23Kfv83xeWPHjlUAZd68eUax/vs2x/T2i3+/t4qiKOfOnVN8fX0VKysrpVChQsq0adOUuXPnKoDy+PHjF9ZfqGkURWZ5CCGEeDN99NFHLFy4kNjYWPka5yySOQhCCCHeCAkJCUaPw8LCWLFiBfXq1ZPk4BXIHAQhhBBvhNq1a9OwYUPKli1LUFAQixYtIjo6ms8//9zUof0nSYIghBDijdC6dWvWr1/PL7/8gkaj4a233mLRokVGt6mKzJM5CEIIIYRQkTkIQgghhFCRBEEIIYQQKv/pOQh6vZ5Hjx5hb28vX4IhhBBCvISiKMTExFCgQIGX/hbGfzpBePToUY5//74QQgjxprl//z6FChV6YZn/dILw9KtJ69t2xlxjYeJoclZCnTKmDiHHWe16vd+6/69IbFnN1CHkCqvwl//+w5tAb/Xm319vGRJn6hByRVwxR1OHkONSUxI5vfsbw/nzRf7TCcLTywrmGgvMNS/+6db/OnMLa1OHkOPe9CTvqf8PbQlgbv7/47Kf3vzNTxDMtVn/Ibb/ov8v+yZk7pd0ZZKiEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqkiAIIYQQQsXc1AHkJR/M6EWt1lXxLOLKB3UmcevSvXTLVapXhikbR/PA/7Fh2UdNviY5MYWyNUvw4Zy+AJhbaLl8zI8Fn6wiJTk1V+rwMoUKODNhdGscHWyIjUti2pyt3LkXlmH577/pRskSHrTpNle1bvyoVrRqWpHWXX8gNi4pJ8N+JUN/6E/tdtXxLOrO/6p+QsCFO+mW02g0vP9dH2q0qIIuVUd0WAxz3l/Io4DHVG9emUHfvmco6+TuQPjjSIZWH5dLtXixQl5OTPioNU4ONsTGJ/HN99u4c9+4PatU8Oa7ye9y72GEYdn/xq4i+UmfLF7ElY/eb4Kzkx0Av648xMFj/rlXiUwoWMiZsZ+1x9HRlri4JGZM/Zu7t0NV5Tw8HRk7sT0+pTwIDIzkf/1+M6yr8lZRBn3QCBsbSxTgxFF/fluwF0XJxYq8QMGCznz6aVscHJ7UccZm7twxrqOHhyPjxrXBx8eDx4+jeP/9xYZ1VasWYfDghlhbWwIKx48H8Ouv+/JM/Z5XoLALH3/zLg5OdsTHJjJr4gbuBgSrylWqUYyvF/TlwXPvw6heP5OclIpGo2Hwxy2pVq8Uep2e6Mh4vp/8J4H3w3OzKhky7Jv2T/bNH9T7JjzZ/wb/a/87brz/ff91V0oV96B1rx9zJfan8kSC8NNPPzFz5kweP35M5cqV+fHHH6lZs2aux3Fo0ynWfb+VWTsnvrTsA//HDK07SbX81qX7fNjgS3SpOjQaDZ+vGk7bwY3586edORFyln08vDl/b7/A9t2XaVC3FONHtWbIqBXplu36TnUeBkZSsoSHal39OiVJTdXndLiv5dD64/wxYxNzDn39wnK121enfJ3SDKnyMbpUHT0ndmLA1B5M6T6H0zsvcHrnBUPZr//+lAv7L+d06Jn28bDm/LPjAtv2XqFhnVJM+KgV749ZqSp372EEAz5aplpuZWnOtIkdmTJnK5euPcTMTINDPuvcCD1LPhrbhi1/n2Pn1ov4NizD2IntGTZosapcfFwSS37dj52dFf2HNDRaFxuTwNTJfxL4KBILSy0zv3+PZq0qsXPrxVyqxYuNHt2SzZvPs2PHJerXL83YsW0ZOnSpUZn4+CQWLz6InZ0VAwc2MFoXE5PI119vIjAwEgsLLd9914PmzSuyY8elXKxF5oyY3IFt606xa9M56jUrz5ip7zKi+4J0yz64E8qwzvNUy2s1KkO5qkUY+u6P6FL19Hi/If1HNuebj9fkdPiZ8vHQf+2bI1vx/sfG+6aVpTnTJnRkyvcZ73/d2lfn0eNIShVXH4dzmskvMaxdu5bRo0czefJkzp49S+XKlWnRogXBwepsMqddPuJH6KOIlxd8gaSEZHSpOgDMLc2xsraEPJLBOznaUrqkJ7v2XgHgwBE/3N3sKejlpCpbtLALvrVLsmrdCdU6Zydb3utam3m/7cvpkF/LpUPXCH348k8TiqJgaWWBpbUFALYOtoSk8zwXL2eqNqnI7hUHsz3WV+HkaEsZH0927r8KwP6jfri7OqTbnhlp1qAsV2484tK1hwDo9QqR0Qk5Ee4rc3KypVQZL3Y/OdEd2n8dN3cHChR0VpWNiUnk8sX7JCamqNbd9A8i8FEkACnJOm7efIynp1NOhp5pTk62lCrlxa5dacnnwYM3cHe3p0AB4zrGxCRy+fKD9Ot3M4jAwEgAUlJ0BAQE4+npmOOxZ5VjfjtKli/Ins1piffhXVdw9XTEyzt/lrajKGBhaY6lZdrnXNt8VoQGRWV7vK8iw33zX/3tZftfUW8X6tXyYeWGk7kW+/NMPoIwe/ZsBg8eTP/+/QH4+eef2bJlC4sXL+bTTz81cXQZ8yrmxrxDX6DX6dm58jCbf9trWOdR2JUv1ozAq5g7J3dc4J9f95gw0mfc3ewJC49Dp3+WsQQHx+Dh5sDDJwcWAK3WjLEjWjL9h23o9epRgrEjWvLz4v0kJCTnRtg57vg/Z6jSqAJrA38lISaR0IfhjGk4WVWueb+GnNx6lsiQaBNEqebuqm7PoJBoVXsCFPR0YtGcPuj0erbuvsxf284DUNTblZQUHdM/74Sbiz0Bd0L4afG+PJUkuHk4EB4Wi173XL8NisLdw5FHD18toXfOb0f9hmX5bOza7Arztbi5ORAeHoveaN+MxsPDgUev8KHF2dmO+vVLM2HCuuwMM1u4eToSERKDXvfs2BISGIW7l1O6lwe8vPMz749h6PV6dv55ls1r0z60nNh/nco1i/H7/vHExycRFhTNJ89dUjIld1d7wiIy2DcfRxqWFfV2JSVVx/TPnux/d5/tf1qtGWOHt2D6j9vTPQ7nBpOOICQnJ3PmzBmaNm1qWGZmZkbTpk05duyYqnxSUhLR0dFGf6Zw88IdepUZzXDfL/iq54+0GdiI+h1rGNYH3QvlgzqT6O4zEgsrC+q2r26SOF9V/551OHjUj7vp7KxtmlciKDiasxfTn5/xX1SqegmKlvemR6EhdC/4Puf2XmLkgsGqci36N2b74r3pbCFv8wsIotOABQwctZyJ3/zFO62q0KhuaQC0Wg3VKhdh5k87GfDRMkLDYxjzQTMTR5yzbG0tmTKjG2tXHcPveqCpw8l2traWTJ3ahTVrjuPn9/jlT8jDbl59xHtNpjO86098NXIVbbrVxLdFBQBKli9IUR8PejWZTq9G0zl/IoARkzuYOOKsMex/83cyYNQyQsOe7X/9u9fh4DF/7j4w3ZwKkyYIoaGh6HQ6PDyMr614eHjw+LG6Y0+bNg1HR0fDn7e392u9ftMedZh/5CvmH/mK5u/Vy/Tz4mMSiX/yCSv0UQT71x+nQp3SqnKJcUnsX3+Cxt1qv1ac2SU4JAaX/HZozTSGZe7u9gT96xNx5QredGr3FmsXD2HezF7Y2VqxdvEQHB1seKtSYerV8mHt4iGsXTwEgCXz+lOyuHuu1iU9TXvX5+ezM/n57Exa9GuY6ec169OA8/suExcVj6Io7Fq2nyqNKhiVqdSgHJbWFpzecSGDreS+4FB1e3q4OajaMz4hmbj4tNGekLBYdh+8RuXyhQAIConh3KX7hIbHArBj31XKlS6QSzXIWLOWFfl56SB+XjqIt6oXI79LPsy0z/VbD0eCX2E42cbWkmmze3D0kB8b1qovn5lKSEg0+fPnw8xo33QgKChrH4JsbCyZPr0bR4/6sX79qewO85U1aV+Fn9YP56f1w6laqwTObvaYaZ+dfty8HAn+16gXpM0piY9NmwAdGhTN/q0XqPBWUQCatq/K+ZO3iItJRFEUdm86R6UaxXOjOi8VHBqDi/PL903V/rf/KuVKpe1/Vcp7826bqvzxy/v8NK0ndrZW/PHL+zg52ORaPUx+iSErxo8fz+jRow2Po6OjXytJ2P37UXb/fjTLz8vv4UhEcDSKomCTz5q3W1Zh+/K069IFirsTdC8MXaoOcwstddu9xe3L9185xuwUGRWP380gmjUub5ikGBIaqxqO/nDc74b/Pd0dWPRjP7oNWAjA199tNip7cMtY+g9fkifuYti94uArzQ8IvBVEzVZVWffdP6SmpFKrbTXuXDYeIWk1oAk7l+032VBfeiKj4vELCKJ5w3KGiVAhoTGq9nRxtiM8Mg5FARsbC+rUKMHmXWkT8/Yevk6bZhWxtbEkPiGZ2tWLE3A79+f//Nuu7ZfYtf3Z5LqatXxo2qKiYZJiSEh0li8vWNtYMG1WD06duMWqZYezO+TXEhkZj7//Y5o1q2CYpBgSEpOlywvW1hZMn96NkydvsXJl1o9rOWnP3+fZ8/d5w+MavqVo0rayYZJiaFB0upcX8rvaExEWm3astbWkZoMy7Nh4BoDHD8Kp4VuKDUsOk5qqo2aD0ty9GZRbVXqhdPfNsBijywvwZP9r+q/9707a/jd8gvFxePGcvnR9/5fcrIZpEwRXV1e0Wi1BQcaNGhQUhKenp6q8lZUVVlZWORbPiB/6UrNFZfJ7OPLNX2NIiEmkf5W029k+mtef41vPcXzreep1qE7bQY3RperQmms5+Ocpdq44BEDlBmV553/N0Ov0aM21nNt/lVXT/86xmLPqu3k7GT+qFb271iIuPplv52wF0uYVHDlxkyMnbpo4wuwz8uf3ebv1W+T3dGLa9onExyTSr9SHAIz+9X8c+/s0x/45zd8/badw2YIsPD+T1BQdEY8j+f6DZzuirYMtdTvV5P1KY0xVlQzNnL+TCSNb07tLWntOm7sNgHHDW3D45E2OnAygQZ1SvNOqCjqdHq3WjH1HbrB1d9pkuODQGFauO86CGT1RFAgJi2FmHrnj5nlzZm5h7MT29Oxdl7j4JL6b+o9h3ehP23DssB/HDvtjZWXO0jVDsbDQYpfPmt//HMHuHZdY9PM+OnWpSZlyBbC2saBeg7QRv4N7r7F6+RFTVcvInDnbGTu2LT171iE+PokZM7YAMGZMK44d8+fo0ZtYWZmzfPkQLCzMsbOzYu3aYezadZnffjvAu+/WoEwZL6ytLfD1TavfgQPXWbUqbyULAHO/3MSYKe/SbXBD4uOSmP3ZBsO6j77syPF91zi+/zp1m5Wnbbeahr57aOdldv6ZliD88/txvIu7MX/jcHSpesJDY/jxq02mqpLKzAU7mTCiNb071yIuIf19Mzg0hpXrj7Ng+nP73/y8s/9pFMW0d8m+/fbb1KxZkx9/TLu/U6/XU7hwYYYPH/7SSYrR0dE4OjrS2K4H5hrL3AjXZBLqlzN1CDnOalveGRLNSYltc/8WXlOwCjP9qFJu0FtpTR1CjrMMjjV1CLkiroSTqUPIcakpiRzfNomoqCgcHBxeWNbklxhGjx5N3759qV69OjVr1uT7778nLi7OcFeDEEIIIXKfyROEbt26ERISwqRJk3j8+DFVqlRh+/btqomLQgghhMg9Jk8QAIYPH87w4cNNHYYQQgghnjD5NykKIYQQIu+RBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVzUweQHT44cg5be62pw8hR8x44mDqEHDfixyumDiFXVLE6YuoQcoXv0Q9MHUKuuOG72NQh5Liut5qYOoRc8b33ElOHkONiY/TU2Ja5sjKCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqJib8sUPHjzIzJkzOXPmDIGBgfz555+88847uR5HdEQqE3rfNTxOStDz+H4yq0+Wxt7p2Vv0+H4y3wy7j14P+lSFQj5WfDi1APaOWqPtzf7kIXs2RrL2XBnyORivM7X93ZZiZqHFzCqtXiV6VcOrcSmjMmHnHnB67N/YFXY2LKs9vwtaq2fvhaIonBz1F9H+wTTbMiR3gs+k6IhUxve+Z3iclKAQeD+ZNSdLYe/0rD0e309m6rCH6PUKulTw9rFkxFQvQ3v+sTCU3RujsLDQYGGl4YNJnpSubJPr9XmRPXsS+XZmDIoeUnUw9H92dO1ia1QmLk7PwPcjuHgxBZ0Oblz1NFq/a3ciX34djV4HZcqY88McJ+zt895nh+h9Zwle8BeeH/cgX82yGZYL+mkjMQfOU2zJeLR2ae2li00gZPEWkgIeotGaYVutNK69mudW6JmydU8ck74NR68opKbCmKFO9O3qkGH5/iODWP5HDGHXi+H0pM+uWBfN7J8j0enAw03LojnuFC5kkVtVyJRt767CzEJrOJ6U7l0F76Y+RmXubLnOzT8uGx4nhMThWtmT2tNaEBUQxvlZh0mMSMRMq8G5nDtVx9QzOj6ZWkSEngE9wgyPExIUHtzTcficB05OxvvWr/Nj2bQ+AQtLsLTSMPFLBypVsQSgbOFASpY2R/vksDXxS0eqv22Za/Uw6TsaFxdH5cqVGTBgAJ06dTJZHA7O5szbXMLweMOvoVw+GWeUHAC4uJsz849iWFmnNfDCrwJZ/UMwQyZ5Gcoc2RGNed7pp+mqMrklDiXdXljGrrAz9Rb1yHD9nT/OY1vAgWj/4OwO77U5OJvz0+bihsfrfw3j0sl4o+QAIL+7Od/9UcTQnj9/9ZhVP4Twv0meBFxNZMvKCH7eXgIbOzP2/hXF/C8e88OfxXK1Li+iKArDR0SyYZ0L5cpZcP9+Kr4NQ2jdypp8+Z4dhMzNNQwfmg8nJzPe7RJmtI24OD2jP45i43oXSvqYM2FiFHO+j2XS5xmfmEwhJTiC6D1nsCpZ6IXlYk9cRaNVJ+XBC/7CurQ3niM6A5AaGZMjcb4qRVHoMzyIvRsKUqmcFXfup1DO9x6dWufDPp86Wdu4JRYLc43Rsuv+yYz7Oowzu7zx8jBn5foYhn4awuaVBXKrGpn29ldNcSrlmuH6om3KULRNGcPjXe/9QeHmJQEwszSnyuh6OPq4oOj0nPxiDzdWnqfcwOo5HndmOTub8ef2Z8fYxQtjOXU8WZUcXLuSwu8r4vlntyt2dmb8vTGeKZ9H88c/z96bletdcHA0TcJu0o8JrVq1YsqUKXTs2NGUYajsXBdB8y7OquUWVmaGk4lOp5CYoEfz3D4aEZrKH/NDGDTRU/XcN0nM7TCCDt+ieK+8s0O+yM51kbTo4qRabplOe/KkPTUaSE0lbRkQG63D1TNvfRKDtDijotNijIlRcHYyw9LS+MRhZaWhXl0rHB00qufv3ZdEhfIWlPRJy2r79rXlr00JOR94Fih6PcELN+E6oA0ai4yz79TIWCL+PIhr35ZGy5Mfh5F06yFObesYlpk72edYvK9Ko4HIqLS2jI7R4+JshpWlus2CQlL5dm4Es740PsFevpFMxbKWeHmkvUetm9iyfW88YeG6nA8+B4VfCSIpIgEv3yIA2Hs74ujjAoBGa4ZzWXfiA/NWwvdvG9bG8253W9VyjQZSUxQS4hUAYqIVPLzyzuhdHv+saywpKYmkpCTD4+jo6Gx/jatn4omN0lOzcfoHkJRkPaM63Sb4YTLFylgz6ZfChnVzJzxiwDgPbPPlrcsK/3bxm10oCjiV9aDUkDpYOamHzeMfRnFk0Bo0ZhoKtipLkY6VANCn6rg8cy8VxzZBY6Y+eOU1ae2p4+3G+dJdn5Ks8FGn2wQ/TKFoGWu++CXtE2rxstZ07J+f/g1uks9Ji4Wlhpm/F8nN0F9Ko9Hw8wJnBg6OwNbWjKgoPYt+dVYlCC/y8KGOQoWe9Vdvb3OCgvWkpiqYm+eN9o3cfBTr0oWxLv7iT8LBCzfh8l5zzGysjJanPAhBm9+BkN82kxTwEDN7W1x7NceqmFcGW8p9Go2G33/2pPPAQOxszYiI0rF+kVe6bfn+x8F8+7mLamShcjlLzl1Kwi8gmVIlLFm1IQZFgbsPUnDJn7eOSaem7AMF8pdzo8L/3sbKOeNLd7c3X6dwy1KYmavrkJqQwu1/rlPhfzVzMtzXcu50MtFRCg2bWKnWlSlnQd9BdjSrG4zjk+R+xXoXozL9e4Sh00GtulaM+Dgftra5l0DknVQlE6ZNm4ajo6Phz9vbO9tfY+e6CJp0dESbwcHRwtKMeZtLsOpEaQoVt2Lb6ggAdqyNwL2ABZXrpH8iyivenvsu9Zb0pO5v3bBwtObSN7tUZRxKudNofX/q/tadqlPacP/vywTu9Qfg5tKTeNQvQb6i+XM79FeyY13kS9pTw0+bi7P6RCm8i1uydXUkkDY/4cjOGBbtLcHKIyXp2D8/00Y8zMXIXy41VeH7H2JZ9Kszp0+4s25Nfj4cGUlYuN7UoWWbpHtBxJ64Sv5ODV5YLmrPGSxcHbGtUFy1TtHpSbr5kHx1KuA9/QOc2tTm0bcrUVLzzifr1FSFb74PZ/0iL26fLsqudQXp+2EQoWHGMf62KorCBS1oXE/9abRkcUvmz3Cn34ggara4T1iEDidHszyT6D3V4Kf2NFvehSZLOmHpaJ2WLGQgNSGFB7sDKNq2jGqdPkXHiUm78ahZiIIN8s6lv39bvzae9p1s0m2HB/dS2bU9kR0H3dl/0oO+g+wYPTTCsH7PMXc2bHVj9Z8uhIfpmTk1d0dK/lMJwvjx44mKijL83b9/P1u3nxCn4/DWaJqlc3nh3ywszWjW2Ym9f0UCcPF4HMd3RdO/vh/96/sBMLx1AAFX8tZwrY1H2siImbmWol2qEH7xkaqMhZ0lFvnSsl0b93x4NSlFxJNy4ecfcnfjRfZ3W8rxD9eTGpfM/m5LSYrMW/UESIjTc2hrDM3TubzwbxaWmiftGQXA4e0xFC1lhYtH2mWF5p2duHomgZRkJSdDzpLLV1IICtJRu1ZaW1WpYomXl5bLl1MyvY2CBbU8ePDsJHT/fioe7nnnpJJ4/S6pIZHcHfkDd4bNJsn/ASG//E3UzpNG5RKu3Cbu9HXuDJvNnWGzAbj/8XySbgdi7uqIeX4HQ/JgV7UUSqqOlNDI3K5Ohs5fTuJRkI76tdM+SdeoYk0hL3POXU4yKrf/SAJ/74ijeI07FK9xB4Aqje9z7lJauc5t83F0izcnd3jzv76OJCQq+BTNW5fGbD2fHYN8ulYk7MLjDMs+2HsLh2LOOBQzPibrU9OSA2sXWyp/VCeDZ5teXJye7ZsTebebOqED2LktkVKlzXH3TBsd6djVhrOnU0h+cpwpUDBtua2tGT362HLmZHLuBP7Ef+oSg5WVFVZW6mGa7HJwSzTFyljjXSL91wh+mIxDfnOsbczQ6xUOb0srD/DJHOPJU21KXGHe1hJ56i6G1IQUlFQ9FvZp9Qvc7ZfuZMXEsDisnG3RmGlIjU8m+NgdCrUuB0CteZ0N5eIDozky6Hcaru2XK/FnVVp7WmXYnkEPU3DMrzVqz6Jl0sp6FbZg14ZIEuL02NiZcWJvLAWLWWKRheH7nFawgJagYD1+/imUKmnB7dup3L2TSokSmd+tGzW0YvzEaPxvplLSx5xly+Lp0CHv3Knh2Lwmjs2fDR8/+GIxTq1rq+5ieDr58KmbXSfh/d1QtHY2KIqCma0VSXcfY1XEk8SbDwCwcHHM+QpkkndBcwKDUrnml0zZUpbcvJ1MwN0USpcwPrmvnG88v0nrdZPze70NdzEEBqXi5WGOTqfw6ZQwhvZzzNUh6ZdJTUhBn6rH8skx6P7uAJxKuWRY/s7m66rRA32qnhOT9mDpYMVb4+qj0eSdffLftv2TSJmy5hT3SX+fLFRYy8Y/EoiL02NnZ8b+3UkULa7F0lJDVKQeSysNNjYa9HqFbf8kULZ87iZ7/6kEIaft/COClt2NM9UVc4Jx8TCndc/83L6exPJZabfPKXooUd6aIZP+OxMSkyPiOfv5VtArKArYFnCg0oRmAFyasQf3usXwqFucoAM3ubfpMhqtBkWn4NnQh0KtM76tLK/a8UckLbs7GS1bPicEFw9z2vR05vb1RJbNCgHS2tOnvDUfTPIAoE5ze/wuJjLindtYWGqwtjFj3Jy8NRvczU3LzOmODPlfJGZmoNfD1CmOFCqoZcbMGDw8zejb2w6Axk1DCAvXExOj8Fb1IOrUsWLeXCfy5TNj1kxH+g8MR5cKpcuYM3eOk2krlklha/dg7uyAY/MaLyyn0WhwH9aJ4IWbUJJT0Vho8RrT7YUTHnObh5s5P890p/uQx4a2nDvVjcKFLJg8IwwvD3P+1/flCc3AUcHce5BCUrJC66Z2TB2f8cnXFBLDEzg+cSeKXgEF7ArYU/3zxgCcmXYAr3pFKOBbFICYu5FE+YdR6LsSRtt4sCeARwdu4+iTnz39NgDgUsmDqmN8c7UumbFhbTxdehiPHsydFYO7uxnde9vRrKU1ly+k0KVtKJaWGmxsNXw3N+0cdCsglS/GRxkmTJerYMGEL3P37iKNoigmGzONjY3l5s2bAFStWpXZs2fTqFEj8ufPT+HChV/y7LRJio6Ojqw7XwZb+7zzST0nzHvQ2NQh5LgRhXabOoRcUcUq0tQh5Arfox+YOoRcccN3ualDyHFdbzUxdQi5Yor336YOIcfFxuipUT6IqKgoHBxenHCYNIU+ffo0jRo1MjwePXo0AH379mXp0qUmikoIIYQQJk0QGjZsiAkHMIQQQgiRgbwze0UIIYQQeYYkCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVjaIoiqmDeFXR0dE4OjpSYeBUtJbWpg4nR1lH/mebKdMSnTSmDiFXONxNNXUIuSLBzdzUIeSKmMJvfr8t09zf1CHkCr+tJU0dQo7TJSXiN3sCUVFRODg4vLCsjCAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAq5qZ88WnTprFx40auX7+OjY0NderUYfr06ZQuXTrXYxnbpRENKxWngIsj3b5ZwY0HIemW8yngyqddG5HfwRaAeX8fYe/5m3SoVZ6ejaoayrk723P25gPG/PJPrsSfWaP7NMK3Wgm83BzpPX45/nfTr2e7hhXo074mGo2GM1fuMWPJHnQ6PV6uDnz+v5aUKurOo+Ao+kxYkcs1eLn/L20JUKiAMxNGt8bRwYbYuCSmzdnKnXthGZb//ptulCzhQZtucw3L3N3sGfVBM7wL5kev1/PX1vNs/OdsboSfKWN6NaJ+1eIUcHWk16QV+N1Tt2f1st4M7+KLjZUFoHD4wm3mrTuEokCtCkX4sKuvoayzvS1hUXH0/mJVLtbixSzNtczu0hofNxcSU1MJi4vny3/2cC88yqhcQScHdn7UH7+gUMOyEWs2cz8irVzDUsUY26I+ZmYa/IJCGf/nTuKSknO1Li+zs8tKzCzM0FqlnX5KvfcWBZv4qMpFB4Rx8fvDJEXEA1B28NsUaFCc8MuPuTDrIABKqp78lbyoOLIeWktt7lXiJSzNtczq3poS7i4kpqQSHhvPV5vSaU9nB77v2RatRoNWa8at4HAm/7mb6MQkABqULsYnreuj1aS154T1udueJk0QDhw4wLBhw6hRowapqalMmDCB5s2bc/XqVezs7HI1lt3n/Fi66xRLx3TLsIy1hTnf/689ny3bzvmAR5hpNDjaWQOw6fgVNh2/Yii7/rM+bD15Pcfjzqq9J/1YsfkUv0zunmEZLzcH3u9Sl74TVhAeFc/MMe/wTuNKbNh1nriEZBauO4KdjSX/61ovFyPPvP8vbQnw8fDm/L39Att3X6ZB3VKMH9WaIaPST9q6vlOdh4GRlCzhYbR86mcdWbXuBPsP3wDA2ck2x+POir2n/Fix9RS/Tsy4PaPjEpm4YAsPQ6KwtNDy0yedaVO3HJsPX+X45bscv3zXUHb2R+9w5vr93Ag9S/44fYmD/ncA6FWzMlM6NKPPkvWqcnFJyXRcoE5ubC0tmPJOM3ovXsft0Ag+b9OIoQ3eZubOQzkdepbV+LI5jiVdM1yfmpjCifHbeOuzJrhU8kLR6UmOTjtpOvi40ODXdzEz16LoFU5+toM7f16mRLfKuRV+pqw7eYmDfncA6FmrMl91aka/34zbMzg6jvcWriUpVQfA+LYNGda0NtM278fW0oKv321G31/XcTskgontGvFBo7f5bnvutadJLzFs376dfv36Ub58eSpXrszSpUu5d+8eZ86cyfVYzt58SHBk7AvLtKpRhku3H3M+4BEAekUhIjZBVa5CUU/y29ty4GJAjsT6Os5ff0hI+Ivr2fjtUhw6E0B4VFrmvnHPBZrXKQOkHYgv3HhIYlJKjsf6qv6/tKWToy2lS3qya29aMnPgiB/ubvYU9HJSlS1a2AXf2iVZte6E0fJqVYqQkqIzJAcAEZHxORp3Vp3ze0hwxIvb0+9eCA9D0j6dJafo8LsXgpero6qcq5MdNcoVZuuRqzkS66tKTtUZkgOACw8eU9DJIUvb8C1ZlGuBIdwOjQBg9ckLtKmY+6Ox2eHhLn+cy3vgUskLAI3WDCtnGwDMrS0wM08bLdCn6NAnpYLGZKGmKzlVZ0gOAC7cf0xBZ3V7puh0huTATKPBxsIcRVEA8C1VlGuPQrgdktaea05coHXl3G1Pk44g/FtUVNoOnj9//nTXJyUlkZSUZHgcHR2dK3E9VdzLheTUVOZ+8A4eTvnwexTK7A0HVCeWjnUqsPnEVVL1+lyNL7t4ujjwOPTZexsYEoWHi70JI8p+b0JburvZExYeh06vGJYFB8fg4ebAw8BIwzKt1oyxI1oy/Ydt6P9Vj6LeLkRGxTN5bDu8C+XncVAUPy3aR+Bj46HQ/xIXR1ua1CjJqDl/qda1rVeeoxdvExGjTgbzkt61qrLn+q1019lYWrBuSA/MNBr2XA/g5wMn0SsKBRzteRT5bL99GBmNm70dWjONUR/JC85O2YMCOJd1p9yQWoaT/1MxdyIws9ByfOxWEkJicSjhQoVhdQzl4gOjOTF+O3GPovCoXYRiHSuYoBaZ17tOVfZeS789LbRmrP2gB17ODvg9DmXY8k0AeDnZE/h8e0bkfnvmmUmKer2ejz76iLp161KhQvqNPW3aNBwdHQ1/3t7euRqj1syMt8sUYcrq3XSbtpLgyFgmdG9iVMba0pwW1Urz19HLuRqbyJr/T23Zv2cdDh714+79cNU6rdaMtyoVYdmaowwasYyTZ+/w5acdTBBl9rCztmT2yHdYvvUU1+4Eqda3963ApoN5uz2H1K9BERdHZu8+rFoXHBNHg+9+pcvC3xmwbAPVihSkf91qJojy1dWb14FGy7rRcFFnLB2tOfvNXlUZvU4h5MwDKn9Sn4aLu2DjameYdwBg6+VAo6VdaflXP/TJOh4dSP/kmxe836AGhV0cmbND3Z4AKTo9neatov43C7kdEk7XmpVyOcKM5ZkEYdiwYVy+fJk1a9ZkWGb8+PFERUUZ/u7fz93riI8jojnld5/gqLThzq0nr1GpmJdRmeZvlSIgMIxbj9UH4/+Kx2HReLo+Gw7zcnMkKCzGhBFlvzehLYNDYnDJn/aJ4il3d3uCQoxH1ipX8KZTu7dYu3gI82b2ws7WirWLh+DoYENQSDT+t4IMExt37r1CqRIeaLV55tCQabbWFswd04kD5wJYvUM9yfKtMoWwtNBy/NKd3A8ukwbUrUazsiUZvOIvElNSVetTdDrC49JGP6ISkth49grVixQE4FFUDAWeuyxR0MmBkJi4PDd6YOuRNhppZq6lRJdKhF0ITKdMPlyrFsTGLR8ajYZCLUoRcVWd8JnbWlCwiQ8PdvnneNyvon+9ajQtX5IhS9Nvz+el6PRsPHOF9lXLAhAYGYPX8+3pnPvtmSeOAsOHD2fz5s3s27ePQoUKZVjOysoKBwcHo7/ctPOMH+WLeGBnbQlAvfLF8HtoPKP6nToV/vOfOPed9Me3WgnyO6ZNVuvUpDK7juXNSXqv6k1oy8ioePxuBtGscXkAGtQtRUhorNHlBYAPx/1O1/4L6TZgIcM/WUVcfBLdBiwkKjqBE6dv4+Zqj6tLPgBq1SjO3fth6HR575LKi9hYpSUHxy7dYfE/J9It06F+BTYfvoJeyVsnzKf61XmLNhVLM2DZBmISk9Itk9/OBnOztMO2hVZLs7I+XA0MBuCw/x3KeblTzNUZgJ41K7P18o10t2MqqQkppMQ8q9uD3TdxKqWerFigcQkirweTEpc2Yz/o2F0cS7gAEPsgCv2T6/b6FB2Bh27j8GRdXtK37lu0rlyaQYszbs8CTvZYW6Rd6ddooGWFUvg9TjsOHfK7Q7kC7hRzS2vP7m9XZtvF3G1PjaKYbm9RFIUPP/yQP//8k/3791OyZMksPT86OhpHR0cqDJyK1tL6tWL5rEdTfCsUw8XBjqi4BOISU2j/xWIm9WrGgYsBHLiUNoTVpmZZ+jevgV5RCI6M5evVuwh6MoGqiLszqz/tRbPxC4nP5kl81pHZ00zjBjalbpXi5HeyIzo2gbiEZLqMXsyEwc05dCaAQ2fTJuN1aFSR3u1rAnD26n2mL96NTqfHytKcdbMGYGGhJZ+tFRFR8Ww7fJUFa9MfPsuKRKfsmWmU19vS4e6LP0lkhXfB/Iwf1QpHBxvi4pP5ds5Wbt0NZeyIlhw5cZMjJ24alfd0d2DRj/2MbnOsUbUo/xvQAA0a4uKTmDN/F7fuhv77pbIswS17pjiN79uUupWL4eJoR1RsAvGJKXQat5iJ/Ztx6FwAB8/fon+7mrzfoTa3Hj27xXP3KT+W/HMSADsbS7Z9P4Qeny03TGbMLjGFX7/fejjk48DHg7kXHmm4jS1Zp6PbL2v4sHFtgqPjWHv6Is3K+vBh49roFQWtmRknbt1j+o5DpOjSTpiNShfnk+a+aM3M8A8O5dONO4jNhtviyjTPnk/ocY+iOfXZDhSdHgWwK+BAxRF1sfVy4Ny3+/CsVxSvesUAuL/9Bv6rz6HRaLB2s6PKJw2x8cjHnb+vcmv9RTRmZig6Pa7VClH+g1qG2yZfh9/WrJ2DMuLhkI99nw7mXlgk8c+1Z/cFaxjetDYh0XGsPXmRhmWKM7JZHQDMzDRcfRjMt1sOEJWQCECjMsUZ08oXczMz/INCGb/u9dtTl5SI3+wJREVFvfRDtkkThKFDh7J69Wo2bdpk9N0Hjo6O2NjYvOCZabIzQcjrsitByMuyK0HI67IzQcjLsitByOuyI0HI67IrQcjrsitByMuykiCY9BLDggULiIqKomHDhnh5eRn+1q5da8qwhBBCiP/3TJrim3DwQgghhBAvkCcmKQohhBAib5EEQQghhBAqkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKFibuoAsoP7sUjMtVamDiNHhb/lbOoQcpxlrGLqEHJFnNcbsdu9VIK7xtQh5ArHW3pTh5DjQuYUN3UIucL7VoSpQ8hxqbok/DJZVkYQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqWU4Qli1bxpYtWwyPx44di5OTE3Xq1OHu3bvZGpwQQgghTCPLCcI333yDjY0NAMeOHeOnn35ixowZuLq6MmrUqGwPUAghhBC5L8s3ZN+/fx8fHx8A/vrrL959913ef/996tatS8OGDbM7PiGEEEKYQJZHEPLly0dYWBgAO3fupFmzZgBYW1uTkJCQvdEJIYQQwiSyPILQrFkzBg0aRNWqVfHz86N169YAXLlyhaJFi2Z3fEIIIYQwgSyPIPz000/Url2bkJAQNmzYgIuLCwBnzpyhR48e2R6gEEIIIXJflkcQnJycmDdvnmr5l19+mS0BCSGEEML0XulXYyIjIzl58iTBwcHo9c9+qESj0dC7d+9sC04IIYQQppHlBOGff/6hV69exMbG4uDggEbz7BfbJEEQQggh3gxZnoMwZswYBgwYQGxsLJGRkURERBj+wsPDcyJGIYQQQuSyLCcIDx8+ZMSIEdja2uZEPEIIIYTIA7KcILRo0YLTp0/nRCxCCCGEyCOyPAehTZs2fPLJJ1y9epWKFStiYWFhtL59+/bZFpwQQgghTCPLCcLgwYMB+Oqrr1TrNBoNOp3u9aMSQgghhEllOUF4/rZGIYQQQryZsjwHQQghhBBvvldKEA4cOEC7du3w8fHBx8eH9u3bc+jQoeyOTQghhBAmkuUEYeXKlTRt2hRbW1tGjBjBiBEjsLGxoUmTJqxevTonYhRCCCFELsvyHISpU6cyY8YMRo0aZVg2YsQIZs+ezddff03Pnj2zNcDcVKBwfj6Z8i4OTrbExSYy6/M/uRsQrCpXqXpRpszvw4M7oYZlH/X+heSkVABadHyLbgPqozHTcOHkLX6c+g+61Lwxd2NMr0bUr1qcAq6O9Jq0Ar97Iaoy1ct6M7yLLzZWFoDC4Qu3mbfuEIqStt4jvz3j+jSmsIczOr3Chn0X+GP3+Vytx4uMea8RvlVLUMDNkV6fLcc/nTp6uTowaXBLShdx51FIFO99vsKwTqOBD7s1oHalomjNzLjo/5Bvl+4mVZc32vCpT3o0on7ltLbs8eUK/O6r6/m8n8d0pkwRdxqOmA+AjZUFMz9oR9kiHmi1GsPyvMbSXMus7q0p4e5CYkoq4bHxfLVpD/fCo4zKFXR24PuebdFqNGi1ZtwKDmfyn7uJTkx64bq8YnTvRvi+ldZv35uQcb/9fMizftt74gqj9e0aVKBvu5poNBpOX73HjKV70OWxflvIy4nPhrfC0cGGuPhkps7bxu37YUZlWjeqQNc2bxkeu7nk48LVB0yY+Teebg788dMgbt17dvydOHMTD4OM+4OpFSicn0++6vTkfJLErEkbuXvrxfvo9IX98Cnrxbv1pwFQ1Med4ePb4uRsh06n58blh8z7drPhXJPTsjyCcOvWLdq1a6da3r59e27fvp2lbS1YsIBKlSrh4OCAg4MDtWvXZtu2bVkNKduMnNSBretPM7D9D/yx5DBjvu6YYdkHd0IZ2nW+4e9pg3kUdKLvsCaM6fcb/dvMwcklH63frZ5bVXipvaf8GDx1LY9CM96ZouMSmbhgC90mLqP3F6uo5FOANnXLGdbPHNGeLUeu0nn8UrpNXMbuk365EXqm7T3lx/tT1vAoJOM6xiUk8/OGI3y+YItqXfsGFSlT1J3en6+g66dL0CsK3Vu8lc5WTGv3aT8GTn9xWz7Vq9lbPPjX+5Gq07Ns+yk+mL0+p0LMNutOXqL17KV0+nEle68F8FWnZqoywdFxvLdwLZ3mraLDDysIjoljWNPaL12XV+w96ceQr1/ebxeuO8Kk+ep+6+XmwJDOdXn/6zW8O2YR+R3t6NioUk6G/ErGDmnO37sv0uPDxaz88yQTh7dSldm67zL9Pl5u+AuPjGfnoWuG9fGJyUbr81pyADByYnu2bjjNwHfm8sfSQ4z5qtMLy3d6rw6BD4y/jTg5OZWfvt3CoE4/8kG3+VjbWNC1n29Ohm0kywmCt7c3e/bsUS3fvXs33t7eWdpWoUKF+Pbbbzlz5gynT5+mcePGdOjQgStXrmQ1rNfmmN+OkuUKsGfLBQAO77qCm6cjBbzzZ2k7vs3Kc3z/dSLCYgHY8sdJGrbKOzvpOb+HBEfEvrCM370QHj45SCWn6PC7F4KXqyMANcsVJiVFx55T/oby4dHxORfwKzh34+V1jI5L5ILfQxKSUlTrSnq7cfLKXcOIwdGLt2lVp5yqnKmd8395PQGKF3ChYVUflm47abQ8JVXHqev3iYnPO5+i05OcquOg3x3D4wv3H1PQ2UFVLkWnIyk17TZrM40GGwtzlCfDXi9al1ecv/GQ4PBX77dNapbi0NkAwqPS9sc/91ygee0yORLrq3JysKVMCQ92HLgKwP7jfri72FPQ0ynD55Qr6Ymzoy2HTgXkUpSvz9H5yflk60UADu++ipuHQ4bnkyLF3ajTqAxrlxjP5Xt0L5zb/kEA6PUKflce4VHAKUdjf16WLzGMGTOGESNGcP78eerUqQPAkSNHWLp0KT/88EOWtvXvkYipU6eyYMECjh8/Tvny5bMa2mtx83AkPDQW/XPDcSGBUbh5OfLovvo3Jry88zNv7QfodXp2bjrH5rVpB193TyeCAp9ls0GPInH3csz5CuQQF0dbmtQoyag5fwFQrIALETHxTP2gNUU88/MoNJof1hwwJBRvgut3gujYqBLrdp0nMSWVpjVL4+WmPiH9F5hrzfisTzO+WrYTnT5vnRBfVe86Vdl77Va66yy0Zqz9oAdezg74PQ5l2PJNmVr3JvBwceBxaLThcWBIFB4u9iaMSM3D1Z6wiDijvhgUGo2Hqz0PH0em+5y2TSqy/cBVo0slNlYW/Db9PczMNBw6eZNlG46jz0P9283TQX0+eRyFm6f6fKI1N+OjSR2Y/eVf6HUZ18HK2oKWHd9i8Y+7cyzuf8tygvDBBx/g6enJrFmz+OOPPwAoW7Ysa9eupUOHDq8ciE6nY926dcTFxVG7dvpDf0lJSSQlPfu0Ex0dnW65nHbzWiC9ms0kPjYJVw8Hvv6pN9ER8Rzcedkk8eQUO2tLZo98h+VbT3HtTloWq9VqqF62MAO+/p1bj8Lo1KgS3wxtQ98v35wJqpsPXcHL1YGfJ3YjKTmVk1fu8naFIqYO65UMbleLfWf9uRMYjpfLfzPJed77DWpQ2MWRAYvSP0im6PR0mrcKC60ZE9s1omvNSiw+dPql60TeZG1lQdO6ZXh//LPjS1hEHB0GLyQyOh77fNZ8Pbot3dtVZ/WmUyaM9NW9934jjuy5xv3boXh4OaVbxtxcy8TpXTlzPICj+66lWyYnZDlBAOjYsSMdO2Z8fT4rLl26RO3atUlMTCRfvnz8+eeflCuX/nDutGnT+PLLL7PldQGatqtCp95poyD7t10iv2s+zLRmhqzPzcuRkED1J+P4uGdJSmhQNPu3XaLCW0U4uPMywY8jKVDo2TCSRwEngtPZRl5na23B3DGdOHAugNU7zhqWPw6L4ca9YG49SptUtPXIVcb1boJWa5bnJkO9jl//PMavfx4DoNnbpbn9MOwlz8ibqpUqhKeLA10bV0FrZoadtRX/fDuQ3lNWExmbYOrwsqR/vWo0LV+SgYs3kJjy4klaKTo9G89c4auOzVRJwIvW/ZcFhUVT0N3J8NjLzZGgsBjTBZSOoNAYXJzt0JppDKMIHq4OBIWmH2fjOqW4fT+MOw+e7X8pqToin1zWjIlNZPPeyzTzLWvyBKFp28p0eu/J+WR7OucTT0dCHqvPBZWqFcXNy5F23Wui1Zpha2fFsi2jGPHeQqIi4tGamzFhehfCQ2NYMGNrrtbplRKE7FS6dGnOnz9PVFQU69evp2/fvhw4cCDdJGH8+PGMHj3a8Dg6OjrL8x6et/uf8+z+57zhcfV6JWnSpjK7/j5HvWblCQ2KTvfyQn7XfESExaEoCja2lrxdvxTb/0w7iR7edZXZywaxYsE+IsJiadO1Jge2X3rlGE3BxiotOTh26Q6L/zlhtO7oxduM6OqLm1M+QiJjqVu5GHcCw96o5MDSQouVhTkx8Uk45rOhb9ua/LzhiKnDeiWDZvxh+N/LxYHfJ79Hu08XmTCiV9O37lu0rlyagYs2EJPBnQcFnOwJj0sgMSUVjQZaViiF3+OQl657U+w96c8vk7rz68ajhEfF07FJZXYev27qsIxERsdz41YwLRqUY+u+KzSsVYqQsJiMLy80rsjmPcbHTycHW2LiEtHp9FiYa2nwdkn8b6vvNsttuzdfYPfmC4bH1euWpEnrSuz65zz1mpYjNDj988mYgc/2Rw8vJ+av/YC+beYAYKY1Y8K3XYiJTuD7r//O+Ur8i0bJxEyd/Pnz4+fnh6urK87Ozmg0mgzLhoer34CsaNq0KSVKlGDhwoUvLRsdHY2joyONy32CudbqtV4XoFBRV8Z83REHR1vi45KYNelP7jyZIPLRFx04vv8Gx/dfp333t2nbtSY6nR6t1oyDuy6zcsE+w3ZavVuNrgPqA3Dx1G3mTvn7tW9zDH/L+bWe/9T4vk2pW7kYLo52RMUmEJ+YQqdxi5nYvxmHzgVw8Pwt+reryfsdahtGCQB2n/JjyT9p8yzeLl+EEd18AQ2xCUnMWLGXgAehGbxi5mmy6Rrip/2aUrdK8efqmMy7nyxm4oDmHDwXwKFzAVhZmrN+xgAszbXks7UiPDqebUeuMn/dYfI72LJgQlcUvYLGTMPaHWfZuO9itsQGoLPMeP/Jigm9m1Kv4pO2jEtry3cmLObzvs04cD6AgxeMr9M/TRCev51xzRe9cc5nQ34HO0IiYzl94z6TFm3PlvgS3LOnnh4O+dj36WDuhUUSn5QMQLJOR/cFaxjetDYh0XGsPXmRhmWKM7JZ2ic4MzMNVx8G8+2WA0QlJL5w3euyv5c9yfGnA9L6bX5HO6JjE4hLTKbzmMVMGNScQ2cDOHT2Sb/9bgAWT/ptRHQ82w5fZf4fhwHo0LAifdrVBODstft8u2R3tiTv5gnZd32/cAFnJg5vhYO9NfHxyUz9aTu37oXy6QfNOXwqgMOnAwzlFs3oTYdBC4hPfDYps8HbJRnUvS46vR5zrRlnLt1j3rIDpKS+/u8A5buVfZetCxVxYcxXnXBwtEk7n0z+kzs30xKZjyZ14PiB6xw/cMPoOU8ThKe3OTZqVYlPv+nMLb/Hhkm1V87f46dv1XexZFaqLom9l2YQFRWFg8OLLztmKkFYtmwZ3bt3x8rKiqVLl74wQejbt2/WI35O48aNKVy4MEuXLn1p2exOEPKy7EoQ8rLsShDyuuxKEPK67EoQ8rrsShDysuxMEPKy7EwQ8qqsJAiZusTw/Em/X79+rxXc88aPH0+rVq0oXLgwMTExrF69mv3797Njx45sew0hhBBCZF2W5yBotVoCAwNxd3c3Wh4WFoa7u3uWfu45ODiYPn36EBgYiKOjI5UqVWLHjh00a6b+EhQhhBBC5J4sJwgZXZFISkrC0tIyS9tatOi/N1lKCCGE+P8g0wnC3LlzAdBoNPz222/ky5fPsE6n03Hw4EHKlMlb39olhBBCiFeT6QRhzpy02y4UReHnn39Gq9Ua1llaWlK0aFF+/vnn7I9QCCGEELku0wnC0x9iatSoERs3bsTZ+c2fVS+EEEL8f5XlOQj79u17eSEhhBBC/KdlKkEYPXo0X3/9NXZ2dkbfZJie2bNnZ0tgQgghhDCdTCUI586dIyUlxfB/Rl70BUpCCCGE+O/IVILw/GUFucQghBBCvPnMXncD0dHR/PXXX1y/nrd+FEQIIYQQry7LCULXrl2ZN28eAAkJCVSvXp2uXbtSsWJFNmzYkO0BCiGEECL3ZTlBOHjwIL6+vgD8+eefKIpCZGQkc+fOZcqUKdkeoBBCCCFyX5YThKioKPLnzw/A9u3beffdd7G1taVNmzb4+/tne4BCCCGEyH1ZThC8vb05duwYcXFxbN++nebNmwMQERGBtbV1tgcohBBCiNyX5S9K+uijj+jVqxf58uWjSJEiNGzYEEi79FCxYsXsjk8IIYQQJpDlBGHo0KHUrFmT+/fv06xZM8zM0gYhihcvLnMQhBBCiDdElhMEgOrVq1O9enUURUFRFDQaDW3atMnu2IQQQghhIq/0PQjLly+nYsWK2NjYYGNjQ6VKlVixYkV2xyaEEEIIE8nyCMLs2bP5/PPPGT58OHXr1gXg8OHD/O9//yM0NJRRo0Zle5BCCCGEyF1ZThB+/PFHFixYQJ8+fQzL2rdvT/ny5fniiy8kQRBCCCHeAFm+xBAYGEidOnVUy+vUqUNgYGC2BCWEEEII08pyguDj48Mff/yhWr527VpKliyZLUEJIYQQwrSyfInhyy+/pFu3bhw8eNAwB+HIkSPs2bMn3cRBCCGEEP89WU4Q3n33XU6ePMns2bP566+/AChbtiwnT56katWq2R1fpih+t1A0FiZ57dxi71bJ1CHkuOgiVqYOIVeEVdObOoRcUXxdiqlDyBVRxd78fvu4/v+TPvuHralDyHGpqZm/cJClBCE6OpoTJ06QnJzMnDlzcHNzy3JwQgghhMj7Mp0gnD9/ntatWxMUFISiKNjb2/PHH3/QokWLnIxPCCGEECaQ6bGGcePGUaxYMQ4fPsyZM2do0qQJw4cPz8nYhBBCCGEimR5BOHPmDDt37uStt94CYPHixeTPn5/o6GgcHBxyLEAhhBBC5L5MjyCEh4dTqFAhw2MnJyfs7OwICwvLkcCEEEIIYTpZmqR49epVHj9+bHisKArXrl0jJibGsKxSpTd/tr0QQgjxpstSgtCkSRMURTFa1rZtWzQajeFXHXU6XbYGKIQQQojcl+kE4fbt2zkZhxBCCCHykEwnCEWKFMnJOIQQQgiRh2T5txiEEEII8eaTBEEIIYQQKpIgCCGEEEJFEgQhhBBCqGQ5QZg8eTJ3797NiViEEEIIkUdkOUHYtGkTJUqUoEmTJqxevZqkpKSciEsIIYQQJpTlBOH8+fOcOnWK8uXLM3LkSDw9Pfnggw84depUTsQnhBBCCBN4pTkIVatWZe7cuTx69IhFixbx4MED6tatS6VKlfjhhx+IiorK7jiFEEIIkYtea5KioiikpKSQnJyMoig4Ozszb948vL29Wbt2bXbFKIQQQohc9koJwpkzZxg+fDheXl6MGjWKqlWrcu3aNQ4cOIC/vz9Tp05lxIgR2R2rEEIIIXJJlhOEihUrUqtWLW7fvs2iRYu4f/8+3377LT4+PoYyPXr0ICQkJFsDFUIIIUTuydKvOQJ07dqVAQMGULBgwQzLuLq6otfrXyswIYQQQphOlkYQUlJSWLp0KdHR0TkVjxBCCCHygCwlCBYWFiQmJuZULEIIIYTII7I8B2HYsGFMnz6d1NTUnIhHCCGEEHlAlucgnDp1ij179rBz504qVqyInZ2d0fqNGzdmW3C5bejsvtRqWw3Pom78r8an3LqQ/ldKN+/TgI4ftjQ8di3owqXD1/iq6xw8i7rx+ZqPMNOaoTXXcu/6Q77/4DdiI+NyqxovVLCgM5+Oa4uDoy1xsUnMmLGZO3dDjcp4eDgybmwbfHw8ePw4iveHLDasa9miIp061TA8dnOz5+LF+0z+Iu+0+8c9G1G/SnEKuDrSc/IK/O6rJ8xWLOHFp72bAGCuNeOC/yNmrt5HSqruhevymuVtO+Nma4eiKMSmJPPl4b1cCQ02KlOrgDdL23TiVmSEYVnHjatJ0qXSpXQF+ld6y7Dc0y4fJwMf8L8df+daHV6mYEFnPv20LQ4OtsTFPemzd9Lps+Oe67PvP+uzVasWYfDghlhbWwIKx48H8Ouv+1CUXK7IS3zS41m/7fFF+v32eT9/3JkyRdxp+OF8wzLfSsX4qGsDzMw03HwQyheLdxCXmJzToWfJitZdcLOxQ68oxKUk88XRPVwJM+6zXUpVoH+FaobHXvnS+uWQXZuwNbfg52YdqOjmgVZjRqVlP+Z2FTKlYEFnPh3bBkfHtH47fcYW1bG2apUiDB7UABsbS1AUjp8I4Jff9qMo4OnpyJeTOmJmpkGrNePuvTBmzdlGbGzufXuxRlGytpv079//heuXLFnySoF8++23jB8/npEjR/L9999n6jnR0dE4OjrSyPxdzDUWr/S6z6tYrwyBt4OZve8LvugyK8ME4d9+OTeD5V+t5/CfJ7GwNEdjpiE5MQWAD2b1AWDBmOWvFVuqb6XXev5Ts77rwc5dl9mx4xL165eme7faDB221KiMvb01RYq4YmdnxcABDYwShH9b9Nsgli47xKFDN147tugiVq+9DYCqpQryMCSK38Z34+Mf/073QGtlaU6qTo9Op0ejgRnD2nPuxgNW7zr7wnXZIbRG9k3gdbC0Ijo57YDRopgPH1WvQ6t1xn2tVgFvJtVtROt1L++DO7r1Y86pI2y/5f/asRVfl/La2wCYNasHO3c+12e712bo0KVGZYz67MAGRgmCj48HcXFJBAZGYmGh5bvverB16wV27LiULfFFFcvefrvo026MmZd+v32qV/O3KOblQtPqJQ0Jgo2VBZumDeD9GX9w53EEY3s2JikllR/WHXzt2ELfzr7k2KjPFi3JR9Xq0GrDshc+Z2fnfsw5c5Rtt/2wNNNS3bMgkUmJrGnbLVsThOJ/ZF89Z818cqzdeYn6vqXp0b0WHwwzrmda30wkMDAKCwsts2Z2Z8vWi+zYeQkLCy0ajYbk5LTR+uFDmwIwb/7u14orNTWRwwe+JCoqCgcHhxeWzfIIwqsmAC9y6tQpFi5cSKVK2XMSfFWXDl/P8nPK1CiBk5sDx/45A0BK8rNLL2ZmGqztrEjIxYzvRZycbClVyotPxq4B4ODBG4z4sDkFCjjz6NGzT5cxMYlcvvyAypULv3B7ZcoUwMnJlqNHX/9kkp3O+T18aZmk59rJQqvFysIcBeWl6/KapwdaAHtLq9eKsoq7Jy42tuy+E/D6gWUTQ5/95Lk+OyJrffbmzSDD/ykpOgICgvH0dMz54LMoM/0WoHgBFxpW9eHLxTtoWr2kYXndikW5cS+EO4/T3pd1+87z0+h3syVByE7GfdbypSM5Vdy8cLGxZdedmwAk63UcfXSPQvlefHIzJScnW0qX8uSTcU/67aEbjPywGQUKOPHoUaSh3L/75s2bz/pmSsqzZMXMTIO1tQUJCbk7GpTlBAEgNTWV/fv3ExAQQM+ePbG3t+fRo0c4ODiQL1++LG0rNjaWXr168euvvzJlypRXCcekWvZvxO5Vh9E9N/xsbqHlx6NTcC/syu1L95nUaaYJI3zGzc2B8PBY9Ppne2RwcDQe7g5GB9vMat2qErt2X0an+2/e0url4sCsER0o5ObI4Yu3Wbf3QqbW5TWzGreidkFvAPpvSf9ST2EHJzZ37o1OUVh3/TIrr5xXlelatiJ/3rhKah66RTnDPuvxan3W2dmO+vVLM2HCuuwMM9eYa834rG8zvlq6E53e+Mzqmd+BwLBnd5gFhkXj6mSH1kyjKmtqsxu2pnaBtD7bb9uGF5btVqYif/pfJVXJO/3yZdzd7An7V78NCo7Gw93RKEF4nrOzHQ3ql2b8Z+sNy8zNzVgwry8eHo7cuhXMxEkvfq+yW5YnKd69e5eKFSvSoUMHhg0bZvhCpOnTp/Pxxx9nOYBhw4bRpk0bmjZt+tKySUlJREdHG/2ZkrWtFQ261mb70n1Gy1NTdHxQYzzdCv2P+zce0mbwy+v2X2NtbUGjRuXYti3vnjhfJjAsmp6TV9Bi1EIszbU0rlYyU+vymjF7t1FnxS/MOnmET2vVV62/HBJE7RU/03b9CoZs/4te5SvTpkRpozI25ha08ynD2uvZM+yeF9naWjJ1ahfWrDmOn99jU4fzSga3r8W+s/7cCQw3dSivZfT+rdRevZDvTh1m/NsNMixnY25BuxJvdr+EtL75zZTOrFl7wqhvpqbqGfy/JXTqMpd798No16ZKrsaV5QRh5MiRVK9enYiICGxsbAzLO3bsyJ49e7K0rTVr1nD27FmmTZuWqfLTpk3D0dHR8Oft7Z2l1/u3pu/5suDUNBacmkbzPhl30oz4vvs2d68+4N619IcGU1N07Fh2gKa96r1WnNklJCSa/PnzYWamMSxzd3cgKDjriVaDBmW4czeUu3fDsjNEk0hISmHnyRu0rFUmS+vymg03rlC7oDdOVtZGy2NTkolJThuafBwXyz/+16jhZfxFZ21KlMI/PIybEXmrPTPss0FZ67M2NpZMn96No0f9WL/+v/vLs9VKFaJbk6r8M30giz7thp21Ff9MH4hTPhseh0fj5fJs2N3LxYHQyLg8N3rwvA3+V6hdQN1nn2pTvBT+EWH4R+atfvkywSExuPyr33q4OxAUrP4hQxsbS6ZP68qRo/6s25B+30xN1bNtxyWaNSufYzGnJ8sJwqFDh/jss8+wtLQ0Wl60aFEePszcNTSA+/fvM3LkSFatWoW1dfqd49/Gjx9PVFSU4e/+/ftZiv3fdq88xAc1xvNBjfHsXH4gy89v2b8R25fsN1rmXtgVK5u090aj0VD/3VrcvnTvteLMLpGR8fj7P6ZZswoA1K9fmpCQmFcaqm3VqvJ/evSgkLsTWm1a9zfXmtHwLR9uPgh96bq8xMHSCnfbZ3cRNS/qQ0RiIpFJxt9V4mZrx9PDlJ2FBY2LlFDd6dC1bMU8+SktO/qstbUF06d34+TJW6xceTSnQs0Vg6b/Qduxv9Fu3CIGfruWuMQk2o1bRGRsAkcv36FMEXeKejoD0KVRFXaeev3Jw9lJ1WeLpN9nn+pWuhJrr1/MrfCyTWRkPP43g2jW9Em/9X3abyONyllbWzBjWldOnbrNylXGfdPD3QErq7RZABoNNKxfhlu3cvcnDLI8B0Gv16PTqWd6PnjwAHt7+0xv58yZMwQHB/PWW89usdLpdBw8eJB58+aRlJSEVqs1eo6VlRVWVtkzYzg9I38aSM1WVcnv6cS0zeOJj0mgf7lRAIz6eTDHNp/l+Oa0yYiFSnlRonIRPmt/zGgbxSsWpt9X3YC0iSX+527z06gXz9DNTXPmbGfsuLb07FmH+LgkZszcAsCYMa04dtSfo8duYmVlzvJlQ7CwMMfOzoq1a4axa9dlfluUlkR5F8qPTwl3xu+7ZsqqZGhCn6bUrVQMF0c7fhzdifjEFDqOX8xn/Zpx8HwAB8/fokZZb7o3qYpOUdCaaTh17T6//X0c4IXr8hJ7Syt+at4Oa3NzFEUhLDGBgVvT5iB827A5u+8EsPtOAK2Kl+K98pXR6fVozczYGuDHuuuXDdsp7uRMORd3+t/M3eubmTVnznbGjn3SZ+OTmDHjuT57zJ+jR5/02eXP9dm1T/rsbwd4990alCnjhbW1Bb6+aZdWDhy4zqpVeStZmNC7KfWe9Nt5o9L67TsTFvN532YcOB/AwQu3Xvj8+MQUvl66k1nDO6A1MyPgUSiTF23Ppegzx97SivlN22Ntbo5eUQhPSGDAjrQ+O71+C3bdvcnuu2mTZIs7pvXLfrfUk8e3v9sPFxsb7C2tON7zfxwLvMeofVtztS4vM3vOdsaNbUOvnrWJj0ti+ndp8X08uhVHj6Uda9/tVP1Z36xXCoD9B6+zavUxihd3Z+CAtEuGZhoN/jeD+PGnXblahyzf5titWzccHR355ZdfsLe35+LFi7i5udGhQwcKFy6c6bscYmJiuHvX+DbC/v37U6ZMGcaNG0eFChVeuo3svs0xL8uu2xzzsuy6zTGvy87bHPOy7LrNMa/Lrtsc87LsvM0xL8vO2xzzqhy9zXHWrFm0aNGCcuXKkZiYSM+ePfH398fV1ZXff/8909uxt7dXJQF2dna4uLhkKjkQQgghRM7JcoJQqFAhLly4wJo1a7h48SKxsbEMHDiQXr16GU1aFEIIIcR/1yt9D4K5uTnvvfdedsfC/v37s32bQgghhMi6LCcIy5e/+Ota+/Tp88rBCCGEECJvyHKCMHLkSKPHKSkpxMfHY2lpia2trSQIQgghxBsgy9+DEBERYfQXGxvLjRs3qFevXpYmKQohhBAi78pygpCekiVL8u2336pGF4QQQgjx35QtCQKkTVx89OhRdm1OCCGEECaU5TkIf//9t9FjRVEIDAxk3rx51K1bN9sCE0IIIYTpZDlBeOedd4weazQa3NzcaNy4MbNmzcquuIQQQghhQq/0WwxCCCGEeLO98hyE0NBQoqOz/jPBQgghhMj7spQgREZGMmzYMFxdXfHw8MDZ2RlPT0/Gjx9PfHx8TsUohBBCiFyW6UsM4eHh1K5dm4cPH9KrVy/Kli0LwNWrV/nxxx/ZtWsXhw8f5uLFixw/fpwRI0bkWNBCCCGEyFmZThC++uorLC0tCQgIwMPDQ7WuefPm9O7dm507dzJ37txsD1QIIYQQuSfTCcJff/3FwoULVckBgKenJzNmzKB169ZMnjyZvn37ZmuQQgghhMhdmZ6DEBgYSPny5TNcX6FCBczMzJg8eXK2BCaEEEII08l0guDq6sqdO3cyXH/79m3c3d2zIyYhhBBCmFimE4QWLVowceJEkpOTVeuSkpL4/PPPadmyZbYGJ4QQQgjTyNIkxerVq1OyZEmGDRtGmTJlUBSFa9euMX/+fJKSkli+fHlOxiqEEEKIXJLpBKFQoUIcO3aMoUOHMn78eBRFAdK+arlZs2bMmzePwoUL51igQgghhMg9Wfqq5WLFirFt2zYiIiLw9/cHwMfHh/z58+dIcEIIIYQwjSz/FgOAs7MzNWvWzO5YhBBCCJFHvPJvMQghhBDizfVKIwh5TfDAamgtrU0dRo6K9nnzf0XT6/CbX0cAz4MaU4eQKxSz/x/1NEs1dQQ5zzxaa+oQcoVZSoqpQ8hxZqmZP87KCIIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKFibsoX/+KLL/jyyy+NlpUuXZrr16/neizj3m1IgwrFKejiSNfpK7nxMERVRqOBMe/Up07Zouh0eqLiE/ny913cD42iYH4HvhvYFjONBnMzM24FhfPVmt3EJCTlel1eZPk77+Jma4deUYhLTuaLg/u4GhJsVKaqpxdTGjUFwNzMjNOPHvLlwX0k63RogPH1GlC/SFHMNWacDnzI5/t2k6LXm6A2GSvk6cTnw1rhaG9DXHwyU+Zv4/aDMKMyGg182Lshb1cpltaeMQl8u3AnD4MijcpNHNqSNg0r0Lzfj8TG56329PZ04vMPWuFkb01sfDJf/7w93XoO79mAWpWLojUz46LfQ2Ys2k2qLq3NererQev65UlJ1ZGcksrsZfu4GvDYFNVJV8GCznw6tg2OjrbExSUxfcYW7twNNSpTtUoRBg9qgI2NJSgKx08E8Mtv+1EUKFbMjZEfNsfZyRadTs/1G4F8P3cnycmpJqpR+sb0akT9qsUp4OZIr89X4HdPfQyqXtab4V19sbGyABQOn7/NvHWHUBTjcpMHtaCtb3kaffBTnuuzSzt3ws3u2THoq737uBpsXNeCDg7MaNWC8u7u3I+Kot3ylelua2XXzpR3d6fqvPm5EXqWFCzozLgJ7XB0tCE2LokZ0zZz906oqlyr1pXp3qs2ZhoN587d4YfZO9DpjI+n383pScmSnnRoOzu3wgfywAhC+fLlCQwMNPwdPnzYJHHsOu9Pvx/+4GFYVIZlGlYoQZViBej67Uq6TF/JiRv3+LBdPQCCo+Po9/1aus1YxbvfriAkKo4PWtXOrfAzbdjWzbRavZw2v6/gt3Nn+K5pC1WZa6EhdFi7ija/r6DlqmW42NryXsXKAHQrX5Hybu60+30FTVcuQVEU+ld5K7er8VLj3m/Opt0X6f7RYlZuOslnQ1upyvhW96Fi6YL0+WQZfT5ZxpnL9/hfT1+jMg1qllTtrHnJuEHN2LTnIl1HL2HF3yf5/H8tVWXaN6pI6WLu9B2/gu4fp7VZt1ZpbVayiBudmlVhwGer6DN+Bet3nmdMv8a5XY0XGv1RSzZvuUCffr/w+5rjjBvbRlUmJjaRr6duov/A33j/g6WUL1+Q5s0qApCcnMrcH3fSd8CvDBqyGGtrC3p0r5Xb1Xipvaf9GDx1LY9CMj4GRcclMnH+FrpNWEbvyauoVLIAbeqWMyrTqJqPIfnLiz78Zwttlq2g3fKVLD59lhkt1X02NjmZ2YeP8NGWrRluZ0C1t7gXmfF7ZWqjPm7Fln/O0fe9haxdfZyx49uqynh6OtJvYH0++nAFvXsuwNnZjrbtqhqV6dy1Jo8eReRW2EZMniCYm5vj6elp+HN1dTVJHGcDHhIcGfvCMgoKFuZaLC20ANhZWxEcGQNASqqOpBQdAGYaDTZW5igoGW7LVGKSn32asLeySjfCxNRUUp+MCFhotVibmxvKlXV148j9u4YRg/13b9OxTLl0tmI6zg62lCnuwY5DVwHYd8IPd1d7Cno4GZVTFAULCy1WT9rT1saSkLCYZ9txtKVPx7eZu2xfrsWeFc4ONpQt5sH2w0/qedIfDxd7Cv2rnj6F3Th16a7hpHHs/G1a+aa1maKAubkZNtYWAOSztSIk/MX7QW5ycrKldClPdu2+DMDBQzdwd7OnQAEno3I3bwYRGJh2skhJ0XHzZjCeno4APHwYwa3baZ9Q9XqF6zcC8fRwzL1KZNK5Gw8Jjnjxe+93L4SHTxKI5BQdfvdC8HJ9Vpf8Drb0a1eTOb/vz8lQX0tM0vPHIMt0j5NRiYmcefiIhJSUdLdR0sWFZj4+/HziZI7F+TqcnGwpVdqLXbue9NsD13F3c6BAQWejcvUbluHYEX8iwuMA+GfTORo1eXY8LVLUlbr1SvH7qmO5F/xzTHqJAcDf358CBQpgbW1N7dq1mTZtGoULF063bFJSEknPda7o6OjcChOAA5dvUaOkN3unDCEuKZngqFgG/LDOsN5ca8aqMT0okN8Bv0ehjPxlU67Gl1mzmrWkVqG093jA3xvTLVPQ3oFf23agsKMT++7cZuXF8wBcCg6iZ8VKLL94nsTUVNqULE1BB4fcCj1T3F3sCY2MQ6d/duAJCo3G09Xe6PLB4TMBvFW+MP/8MpT4xGRCwmMYNnmtYf2nQ5ozf+UB4hPTP0iZWnr1fBwajYerPQ+eq+f120F0bFKJdTvPk5ScSpNapfFyTWuzm/dCWLP1DBt/GER0bCLJqTo++HLtv1/KZNzd7AkLj0X/fFsGR+Ph7sijR5HpPsfZ2Y4G9Usz/rP1qnXW1ha0aVWZXxcdyKmQc42Loy1Nqpdk1Jy/DMsmDmjGj2sP5dk++9R3rVpSy9sbgAEb/8zSc83NzPimeTM+3bET3b+vreQRbu4OhIfFotc9iy84OBp3dwcePXw2GuDu4UhQ0LNRkKDHUbh7pO2bWq0ZYz5pzXczthj1/9xk0hGEt99+m6VLl7J9+3YWLFjA7du38fX1JSYmJt3y06ZNw9HR0fDn/aSD5Zby3h74eLnSbNKvNP38F07cuM/n3ZoY1qfq9HSbsYpGExdyJyicznUr5Wp8mTVm13bqLvmFWccOM66ub7plHsZE0/r3FdRc9DOWWi0tSpQEYP21Kxy4e4c173ZjzbvduB0RgS6PzT/IrDLFPSnu7UqH//1M+yELOH3pHmPfbwZAu8YVCQqN4cyV+yaO8vVtOXCF4xfusGBSNxZM6sa9wAjDCJGXmwMNa5Sk86hFtB/+C2u2nmHKCPVQ6H+Fra0l30zpzJq1J/DzM55HYW5uxqTPOnD6zG0OH/EzUYTZw87aktkfvcPyrae4dicIgA4NKvA4LIbT1/J+n/1423bq/fIrs48cYVz99I9BGRlRuxY7/P0JCA/Poejyhj79fDl08Ab37oa9vHAOMWmC0KpVK7p06UKlSpVo0aIFW7duJTIykj/++CPd8uPHjycqKsrwd/9+7u4I7WqW46TfPWISklAU+OfkVWqUVCcpqTo9f524QtsaZXM1vqzaeP0qtQt542RtnWGZ+JQU/vG7zjtlntXlhxPHaPv7Cjqv+x3/8DD8w0zXgdMTHBaDq5MdWjONYZmHqwOPQ40Tz1YNynPmyj1i49Pac9uBK7xVPq093yrvjW/1EmyYN5gN8wYDsPy7vpQq6p57FXmJ9Orp6epAUKg6wf5twzH6jl/B+5N/5/bDMMNExkY1SxFwP5TQiLQhzs0HLlO5TEHMtSa/+ghAcEgMLvnzYfZ8W7o7EBSsvvZsY2PJ9GldOXLUn3UbThmt02rNmPTZO4SFx/LjT7tzPO6cZGttwdyPO3HgXACrd5w1LK9expsGVUuw6buBbPpuIAC/T+lNqcJupgr1pTZeuUot7xcfg/6tpnch+rxVlQODB7K2RzfyWVlxYPBA8tvY5GCkWRMSHE1+l3yYaZ/1W3d3B4KDjUe9g4Oi8HjucpeHpyPBQWllKlcpTMdO1Vm1Zig//NgbWzsrVq0ZiqOjbe5UgjxwieF5Tk5OlCpVips3b6a73srKCisrq1yO6pkHYVHUK1eMZXvPkKrTU79CMW4Gps1K9XK2JyI2gcSUVDQaaF6lFH6P1LOQTcne0gobC3OC49JOBs2K+xCRmEhkYqJRuSKOTjyMiSZVr8fCzIwWJUpyPTStLpZP5iREJyXhbG3DB9VrMvv4kVyvy4tERMdz43YwLXzLsfXAFRq9XYrgsBjV3QmPgiKpXbU4q/8+RapOT91qJbh1L609v/zReHLU0T8+ps/Hy/LUjPCI6ARu3AmmZb1ybDl4hUY1SxIcHmN0eQHA0kKLlaU5MXFJONrb0Kd9TX5Zl9Zmj4IjaduwPDZWFiQkpVCvagnuPgrPM5PcIiPj8b8ZRLOmFdix8xL1fUsTEhKjurxgbW3BjGldOXXqNitXHTVaZ2amYdJnHYiJSWDW7O25GH32s7FKSw6OXbrD4r9PGK37fOE2o8enlo2mx2cr8lSftbeywsb8uWOQTwkiEhNUx6AX6b7m2QfIgg4ObO7zHg1+XZTtsb6OyMh4/P0e06xZBXZsv0T9BmXS+u1D48mGhw7c4Pt5vVm29BAR4XG061CVfXvT5hR99OEKQzkPT0d++W0gvbrn7t0aeSpBiI2NJSAggN69e+f6a3/erQm+5YvhYm/Hgg86EpeUQruvlzC5R1P2X7rFgcu3WHPoAsU88rNu3Huk6vSExcTz9do9AJQq6MbwNnWAtEmK1x4EM339/lyvx4s4WFnyU6t2WJmboygKYQkJDPw77frft02as/tWALtvB1CnkDd9q7yFXq9Ha2bG0fv3mHvyeNo2LK34/d2u6BUFM42GJefPsuf2LVNWK10zftnJZ8Na0afj28QlJDN1ftqJ4dMhzTl8OoDDZwLYsOM8RQu5sHxmX1J1esIj45jx6y4TR5413/62i8//15K+79QkLiGZKT/vAGDC4OYcOhvAoTMB5LO1Yv7nz9ps7fazHD6b1mb7T92kbAlPlkx9j5TUVBKSUpk0b4spq6Qye852xo1tQ6+etYmPS2L6d2nJ28ejW3H0mD9Hj93k3U7VKVPGC2trC3zrlQJg/8HrrFp9jMYNy1LftzQBAcH8+nN/AC5fecAPP+atth7fryl1KxfDxdGOuR93Ij4xhU5jFzNxQDMOnQvg4LlbdG9elfLFPLGxtKBRNR8Adp/yY8k/eXOy3r/ZW1kyr107rM3N0SsK4QnxDN74FwDfNG/GnoAA9gTcwtrcnN0D+2Op1WJvZcXhIYP56+o1vjtkmrvcXsWcWdsYN74tPd+rQ1xcMjOnbwZgzCetOXrEn2NH/QkMjGTZkkPMndcHgAvn77L573OmDNuIRlFMN8vj448/pl27dhQpUoRHjx4xefJkzp8/z9WrV3Fze/mwWHR0NI6OjpQf8g1ay8wPUf0XRfvkjU90OcnrcN6ccJTd9M8NO77JbELy9kS57BJb0NLUIeS48IqmjiB3FP0n8yMZ/1WpqYkcPPo1UVFROLxkgrlJRxAePHhAjx49CAsLw83NjXr16nH8+PFMJQdCCCGEyDkmTRDWrFljypcXQgghRAbyxlRlIYQQQuQpkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEEJFEgQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVMxNHUB2sIhV0Foqpg4jR7me0Zg6hByX6Pjm1xEg3DfZ1CHkCk24halDyBUON9/8flt4e5KpQ8gVN/u8EafEF9InmMPRzJWVEQQhhBBCqEiCIIQQQggVSRCEEEIIoSIJghBCCCFUJEEQQgghhIokCEIIIYRQkQRBCCGEECqSIAghhBBCRRIEIYQQQqhIgiCEEEIIFUkQhBBCCKEiCYIQQgghVCRBEEIIIYSKJAhCCCGEUJEEQQghhBAqkiAIIYQQQkUSBCGEEEKoSIIghBBCCBVJEIQQQgihIgmCEEIIIVQkQRBCCCGEiiQIQgghhFCRBEEIIYQQKpIgCCGEEELF3NQBPHz4kHHjxrFt2zbi4+Px8fFhyZIlVK9ePVfj+LhnI+pXKU4BV0d6Tl6B3/0QVZmKJbz4tHcTAMy1Zlzwf8TM1ftISdVRrXQhfhjVibuPww3lB0xdQ1JKaq7VITPG9GpE/arFKeDmSK/PV+B3T13P6mW9Gd7VFxsrC0Dh8PnbzFt3CEWBAq4OfPthO7QaDVqtGbcfhfPNkl3ExCflfmUy8En3RjSonNaW3b9Kvy2ft3BMZ8oUdqfByPmGZX2aV6dtnXKYaTTcCYrgiyU7iE3IO3V8XpcSFZlZtw3v79vAzvv+qvWNC5ZgYvXGmGk03IgI4eOjW4hNSQaggJ0DX9VsTjEHZ/SKwkq/cyy7fia3q/BSyzu+i5utHXoU4pKT+WL/Pq6GBBuVqerlxZTGTQEwNzPj9KOHfLl/H8k6HQBdy1fgf9VrYqbRcOz+PT7ft4dUvT7X65KRcZ0b0qBicQq6ONJ12kpuPFT32w61ytGzYVXDYw8ne87efMDo3zYblfvqveZ0qFWeep/MJyaP9duCBZ35dGwbHB1tiYtLYvqMLdy5G2pUpmqVIgwe1AAbG0tQFI6fCOCX3/ajKODp6ciXkzpiZpZ2DLp7L4xZc7YRG5u36rmiZRfcbOzQKwpxKcl8cXwPV8KM+6wG+OztRjQoWIxURU9kYiLjDm/nbkwkAAXs7Pm6TjOKOeRHr+hZef08S6+ezbU6mDRBiIiIoG7dujRq1Iht27bh5uaGv78/zs7OuR7LntN+LN92it/Gd8uwjN/9EPp8vRqdTo9GAzOGtadLo8qs3pXWYHcfh9Pri5W5FfIr2XvajxVbT/HrxIzrGR2XyMT5W3gYEoWlhZafxnamTd1ybD58lZDIOAZPWWtIfMb0asj7HWsza9X+XKrBy+0548eyHadYPDbjOj7Vq9lb3A+Ookxhd8Oyt8sWpn3d8vT5ZjXxSSkMbPM2wzvW5dvVe3My7FdSyM6R7iUrczbkYbrrbc0tmF6nNd13rCIgOpwvazbjw0p1mXZmHwALG3ZiweVjbL17AwBXa9tciz0rhm3dTExS2gmgeQkfvmvegtarVhiVuRYSQoffV5Gq16MBFrRtz3uVKrP43FkKOTgwunZd2q5eQWh8PL+2e4ceFSqx4uL53K9MBnad82fJ7tMsHdU1wzKbjl9l0/GrhscbJvRmy+nrRmWaVPYhVZd3Ep9/G/1RSzZvucCOnZeo71uacWPb8MGwZUZlYmIT+XrqJgIDo7Cw0DJrZneaN6vIjp2XCAuL5cOPVpKcnHYMGj60Kf36+DJv/m5TVCdDw/b+TXRyWp9tUaQk3/m2ptVfS43KNCviQzX3grT8cympip4Pq9RmbPX6DNv3NwC/NO3I/Asn2HrHNPunSS8xTJ8+HW9vb5YsWULNmjUpVqwYzZs3p0SJErkeyzm/hwRHxL6wTFJyKronO56FVouVhTkKSm6El23O3Xh5Pf3uhfAwJAqA5BQdfvdC8HJ1BCAlVWdIDsw0GqytLFDy2Ftw1v/ldQQoXsCFRlV8WLr9pNHyUt5unL/5kPikFACOXLpN61plcyTW16EBvq3Tiskndxk+Jf9bw4LFuRoeREB02sjWihtnaV80rS51vYqQrEs1JAcAoYnxOR73q3iaHADYW1mlu9clpqYaRgQstFqszc0N5VqXLMXuWwGExqfVb9WlC7QrXSaHo86aswEPCY58eb99qmIRT/Lb23Lg4i3Dsvz2tgxsUZPvNh7IiRBfm5OTLaVLebJr92UADh66gbubPQUKOBmVu3kziMDAtGNQSoqOmzeD8fR0NDx+mhyYmWmwtrZAyWsHITAkBwD2llbpniv+r737Do+iXPs4/t2UTS8QSEgPkNCCCaEKAaR3pBlQUYO0o6KIoHKQVwMWYkMRDyAIBA6IeKQJHDpCAAGpoQQMhAAJBBJaet+d94/oas6EpslOwPtzXXtdzMyzs78nu7N77zzPsIoCektLbKxKv6s7Wuu5mpcNQLiXP0UGg6k4APMfn5qeQVi7di3du3cnIiKC2NhYvL29eemllxg1alS57QsLCyn8wxtFVlaWuaKaeLo5M31sP3xqurDn+Hm+//GYaZuPuytLo4ZiMCqs2xPPih3H7rCnB4Obiz2dmwfx2udrTOusLC1YHPU0tWo4k5hynfEz1tz2/lWVlaUFbz/blXcXb8FgLHvgnr6YTkSHUNyc7bmRlUfPVg1wtLPB2d6WrLwCjRKrjWzUksPplzh5M+22bbwcXLicm2lavpSTibudI5Y6HUEuNbhZmM+X7R6njosbl3Iyef/QdlJyMm+7Py1N79aDR339ABi+ZlW5bbydnfm6bz/8XFzZceE8S4/FAeDl5Mzl7N/fLy5lZeLl5FTpmStT/zaNWX/gdJlhkqinuzBjzW5TcVvVuNd04sbNHIx/OObS0rPwcHchNTWj3PtUq+bAY+3rM+n/VpjWWVlZMOdfkXh4uJCUlM7kd1ZWdvQ/5bP2vWjtWfqaHbZlhWr7tuREWnv6ceipl8gpLiItL4fB//0WgCBXN24U5PFlx77UdalOSnYm7x/YQUq2+Y5PTc8gJCUlMWfOHIKCgti8eTMvvvgiY8eOZfHixeW2j46OxsXFxXTz9fU1c2K4ciOLp6OW0P21ueitLOnULAiAXy6m02vCPJ6Z+g1v/GstgzqE0KVFPbPnq0gOtno+G9eff284yOkLv38IlRiMDH1nKd1f+YoLV24ysGOIhin/nNF9H+XHo2c5/4c5I785lJDCki2H+eKV/iye9BS3svMBMFSh8ep6rjXo6V+fL4/v/dP7sNRZ0LqWHzOP76X3+hh2pSYxq33/igtZwSZs2UT4gnlM37uHiW3bldvmclYWvb5ZQsuvv0JvaUn3wCAzpzQPO70VPZrWY/W+k6Z1A1o35urNbA6cSdEwWcWyt9cz7f0nWP7dz5w5c9W0vqTEyKgXYhgYMZPklBv07d1Eu5B3MH7XBlp/9xWfHt7NpBaPqbaH1KhF/Wo1aLV8Di2/nc1PqReZFt4NKJ1H08bTn5lH99JrzWJ2Xb7A7I79zJpf0wLBaDTStGlTpk2bRlhYGKNHj2bUqFF89dVX5bafNGkSmZmZpltKinYHQn5hMVsOJNDj0dLTlLkFReTml078Sr+Vw+affyEsyFuzfH+Vva01M18fSOzRcyzbXP6kmBKDkXW74+nVppGZ0/11zer58GSnMNZHj2DhxCE42NqwPnoEro52AHy/8xjPfLCMyOhvOXzmEldvZpNbUKRx6t+1dPfFx8GFnQP+wZ6BLxJW04tpj/bgmXphZdql5mbi7eBiWvZxdCE9PweDopCam0X8zXTOZpZOEFuVFE9jt1pY6ar2xU2rTp+ita8vrra2t22TV1zMuoRf6N+gdDglNTsLbydn03YfZxdSs7MrPWtl6RpWj3NXb5D0hwK3RT0fOoTUZcPU4WyYOhyA7yc9QwOfmlrFVEm/lo1bdUcsLHSmdR7uzqSlq78V29np+Sh6MD/tPcv3Kw+Wu7+SEiMbN5+ga9fgSstcEVYmxtPa0w9Xm7Kv2UFBjdl7JZmsokIUYMXZk6YzDqk5WcTfTONsxg0AViXG07iGh1mPT03fCTw9PWnUqOyHS8OGDUlOTi63vY2NDc7OzmVu5uTj7oqlZemfzMrSgg5NA0m8VPrm6ubigO7X17y9rTXtQuuQkJx+u11VaXY2pcXBvhMXWLj25zLbark5YaMvHZnS6aBLiyAS73KVQFU04uP/0Puf8+kzaQHDP/qO3IJC+kxaQEZO6dmCGi4OANjqrXjh8dYs3lz+G5RWlp45SssV/6Ltqjm0XTWHo9dSeWv/JpaeOVqmXezl8wRX96Cuc3UAnq3flHUXTgOwMzUJT3snPOwcAejoXYfEzOuUKFXnTAmUzjlwd3AwLXetG8it/AIyCsoO9/i7uGJlUXp8WltY0D0wiF+ul742N549S5c6dalhXzrJa+gjoaw/U3Zy34NkQOvGrN4bX2bdW4s30f3t+fSKWkivqIUAREQv5ZdLVef4zMjI42xiGl27NAagfbv6XLuWrRpesLW15uPowRw8eJ6l35Q9S+bh7oyNze/vQR3aNyApqer0EcBZb4O7vaNpuZt/ILcKC8goLPuaTc7OoI2nH9a/vm47+9Ul4VbpZ8qOS+dLj89f99PRpw6JGTfMenxqOgchPDychISEMuvOnDmDv7+/2bO89VwXwkNq4+biwJfjB5JXUMyASQv5v2Fd2RV3jl1xSbRo6MuTncMwKAqWFjoOnk5h/tr9AHRuFsSgjiEYjKXbth86y9o98Xd5VPObNKwL4aGl/Zz5emk/B765kMnDu7L76Dl2HU3iyW5hBNeuhZ3emo7NAgHYdvAMMesOEORbkxefCAdKJyn+cjGdT5fu0LJLKpOf6ULbkNq4OTswa1xpH/tNXsjbz3Ul9tg5dh1Luus+Zo0bhIVF6WS3/+4/zXc/xlV+8AryWmg70vOz+eZMHLklRfxz30bmdRyEpc6CMxnXmPDTfwHILylm8v5NxHSOQIeOrOJCXtm1VuP0as56PbN698XGygpFUbiRn8+ItasB+LBLN7YlnWNb0jna+PoS2aQpRsWIpYUFe5OTmflz6fGZkpXJjP17WTH4KQD2X0ph2YnjmvWpPG8/2Zl2waWv2zljBpBbWEzfqTFEPd2FnSeSiD1R+rr1d69GfZ+abJ6TcJc9Vk2ffb6JiW/2ZujTrcnLLeSjTzcA8Pr4nuzdd5a9+xIZNLA5DRp4YmtrTbu2pUO1O3f9wjfL9lGnjjsjhrcHSt+Dziam8eWsrZr1pzxOehtmd+qHraUVRkXhZkEew7eUzpP4qG0PtiYnsi05kX+fOkqgqxubBjxPsdHAtfxc3vppC1B6fL61dwsx3QahQ0d2cSEv7zDv8alTNJz+efDgQdq0acPUqVMZPHgwBw4cYNSoUcybN4+hQ4fe9f5ZWVm4uLjQZOgHWOpvf7rxYaArf5L6Q8Wg1zqBedxsV3WGKiqT7qa11hHMwjmxag/JVITqv1St/2OgsiQ99fA/l8b8Ai69EkVmZuZdz8Jr+tdo0aIFq1ev5ttvv6Vx48a89957zJgx456KAyGEEEJUHs3/J8U+ffrQp08frWMIIYQQ4g8e/vMpQgghhLhvUiAIIYQQQkUKBCGEEEKoSIEghBBCCBUpEIQQQgihIgWCEEIIIVSkQBBCCCGEihQIQgghhFCRAkEIIYQQKlIgCCGEEEJFCgQhhBBCqEiBIIQQQggVKRCEEEIIoSIFghBCCCFUpEAQQgghhIoUCEIIIYRQkQJBCCGEECpSIAghhBBCRQoEIYQQQqhIgSCEEEIIFSkQhBBCCKEiBYIQQgghVKy0DlARihx1WNrotI5RqazyFK0jVDq7W0atI5iF/qKN1hHMwuNgidYRzEI39qrWESpd0jl3rSOYhd3Fh+Ij8Y4MhYZ7bitnEIQQQgihIgWCEEIIIVSkQBBCCCGEihQIQgghhFCRAkEIIYQQKlIgCCGEEEJFCgQhhBBCqEiBIIQQQggVKRCEEEIIoSIFghBCCCFUpEAQQgghhIoUCEIIIYRQkQJBCCGEECpSIAghhBBCRQoEIYQQQqhIgSCEEEIIFSkQhBBCCKEiBYIQQgghVKRAEEIIIYSKFAhCCCGEUJECQQghhBAqUiAIIYQQQkUKBCGEEEKoWGkdoKqYOLADHRrXwdvNhYiPl5Jw+ZqqjU4HE/q1J7xhAAajkYzcAqYu30rK9UwAnu/cnMdbNqK4xEBRiYEPV+7gZHKaubtyR2882ZHHQuvgVcOFJ99dwpkUdT//aO6EJ2jg585jr842rYvs0YK+rRtRbDBQVGzg4293EH/hamVHvy8+tVz5v1d64uJkR25eER/8ayPnL90o06ZXx8YM7tXUtFzTzZFjpy/x1idrAXj68Rb07BCMTqcjOfUm02ZtIiev0Kz9uBO9pSUzBvWibg03CktKuJGbR9SG7STfyizTrm0df97o3Na0XN3Bnus5uQyYvwyAmU/0IczHEw8nR5p9PJvswqrTRwAfT1feGtcLV2c7cvIKmTZjIxdSyj6XTRr78mnUIJIv3zKte+HNbygqKkGngxeHdaBV0wAsLS04cfoy0+dspaTEaO6u3NGBZ+ZhYW2Jhb70bdn3qVbU7NCgTJuMo8mcX7ALQ34ROp2O6q3qEDCiPToLHQVXMjj93joUgxHFaMTe143A17ph7WSrRXdua0mvCGraO2BUFHKLi5jy03bib6Sr2tWvXoOp4Z2paecAwCcHd7Pp/Nkybb7tM5jgGh6ELPrSLNnvld7Kks8iehFY042CX4/Nqeu2k3yz7LHp7erMlnHPcybtumnd2OXrSfmfYzh6QDcGhAXTYtpssgvMd3xqWiAEBARw8eJF1fqXXnqJWbNmmTXLtmNnWbT9EIteHXzbNh0a1yWsthcRHy2lxGhkVLeWjO3TljcW/Zf63jUZ0jaUAdH/Jr+omN7NGzDpiU4M/exbM/bi7rYfPsPizQdZ+OaQu7Yd2rUpKemZNPBzN62r51uTwR1CeSJqMfmFxfRq1ZCJT3fiuWnLKjP2fXvzH91Yu/U4G3bG0+HRekx+uScj/7m0TJsNO06yYcdJ0/KSz4axZddpAFqE+NOrY2NGT1pKXkExkYMeZfTTbfls/naz9uNuvjtygtjECwA80zyUD/p05dklK8q02ZN0kT1Jvx9nc4f04+eLKabl5YePM2XDdvZPeMEsme/X62O6sW7zMTb+GE+HNvV4a1xPRk9YqmqXfPkWw8ctVq3v0zWEenXdGfHavykpMfLmmG5E9G3Gt6sPmiP+fWkwuS+Oge633W7lZEODyX2w83TFWFTCiTe/J31rPB7dG6N3cyTk8yextLEG4NysH0n+917qjulkrvj3ZMy2tWQVlX7IdQ8I4tOOPem5ouzzZmtlxfzuA3htxwYOXb2MhU6Hq03ZQmfkI825mJVBcA0Ps2W/H/85dIJdZy8AMLRlKO/368pzMStU7XILixgw55vb7qdrw0CKDdoUs5oOMRw8eJArV66Yblu3bgUgIiLC7FkOn7tMWmbOHdsoioK1lSV6a0sAHG1tSMvINm2zsrTA7teD08nu921VyZGzl0m/ded+AtTxcqNjk0AWbTpQZr2pn/pf+2lvQ/qtqtVPV2d7GtT1YPOuUwDs3H8GdzcnvGu53vY+jYJqUc3Fnt2HzgEQGFCT479cIq+gGIB9R5Lo0b5RpWe/H0UGg6k4AIi7fBVvV+c73sfd0YHWtf1Yc/y0ad3e88nczMuvrJh/iauLPQ0Ca7Fl56/P5d4zuNdwxtvT9Z73Ubd2TQ4fu2g6Y7D/yHm6dwyujLiVzjHQA7tf+26ht8Khbk0K0jJNy78VB4rBiKGgGHRaJb2934oDACe9HkVRt+kf2JCjaakcunoZAKOicLPg99doUDU3utUOZHbcAfWdq4CiEoOpOAA4dunux2Z53Bzs+Uf7lny0KbYC0907Tc8g1KxZs8zyhx9+SN26dXnsscc0SnRnsfFJtAzyZcd7/yC3sIj0zByen/k9AGdSr7N05xE2vjOcrLwCikoMPD/zPxon/nOsLC14+9muvLt4CwZj2aP37KXrfLP1COuiR5T2s9jAyE+qVj89ajhx41Zumexp17PwqOHE5asZ5d6nT6dH2BR7CsOvlXrCuTQGdG9CdVd7bmbk0a1dIxzsbXBytCU7p8Ac3bhvkS3D2J6QdMc2A0MbEZt4vsoWBP/LvYYTN27+z3N5LQuPms5cvpJRpq13LVcWfP4cBqORDdtOsmZjHABnEtN4vEcoK9cfpbCohE7h9anlfv9v1uaQ8PEGUMCpQS0CRrRH72p/27ZFN3O5vvsMwe8NNK0zFhuIe3kphelZ2NeuSfB7A8wR+7591rEXrb18ARi2caVqe1C1GhQaDSzsMZBaDk78cvMa7+/bwc2CfKwsLPiofXfejN2E0Vi1holu59lHw9j+S/nHpp3emu//8RQWOh3bfznHV7EHMP5aNb3XrwufbNlNblGxOeOaVJk5CEVFRSxdupTx48ej05Vf9hYWFlL4h/HRrKwsc8UDINjXg0DPGnSJ+pqcgkLG9W3H20M689aSTXhXd6ZzSCB93ovhWlYuT7YL5eNhvRn2RdX68LwXo/s+yo9Hz3L+6k083cq+kXrVcKZT00D6TV7I9cxchnRswoejezPi4+80SvvX2dpY0yW8AaPf+n2Y5Eh8Ct+uPcQnkwZiMCrs+rl07NOg0am+u3khvAV+1V14e8m2O7Yb1KQx72/eYaZU5nPmXBoDh88hN6+Imm6OfBL1BJlZ+ez4KYEN20/i4e7Ml9FPUlhYwuFjF2lRBZ/HkM+exNbdGWOJgYsxezjz8UYaTxtUbtuS3ELi316Fz+CWONWvZVpvYW1J07mRGIsNnJu1nSvrj+E7pKW5unDPxu/YAMCgesFMavWYqkiw1Olo6+1P/9XfkJaXw5st2/FBu668uHUt45q1YdP5syRm3MTHsWoWen/0j/Yt8HdzYdgi9bGZnp3LY59+zc3cfFzsbPhscG+eD2/Ggj2HeKJpY65kZvPz+ZRy9moeVeYqhjVr1pCRkcGwYcNu2yY6OhoXFxfTzdfX13wBgb4tGnHgbDLZ+YUoCqw9cIoWgaUZuoQGcfbKDa5l5QLww8/xNK3jjZVllfkT37Nm9Xx4slMY66NHsHDiEBxsbVgfPQJXRzs6Nw0i8fJ1rmf+2s+fThIWVLX6mXY9G7dqDlha/F5oetRwJu16+UMhnVrX43zKDS78zyTG1ZvjGDFxKaMnfcPR+BTSrmeRl19Uqdn/jOGPNqNrgyBGLltDQUnJbdu19PfBxsqS3efU836qqvTr2bhV/5/nsqYzadfKfjnIyy8iN6/0ubl2I4dtu04TGuxj2h7z7V5GjPs3L01cxoWUG5xPLvtcVwW2v57VsLCyxHtgM7JOXiq3XUleESffWolbm0B8nmhebhsLa0s8ujUmfdupSstbEVaeiae1l69qfkFqTjb7UlNIyysdDl199hRh7l4AtPL0JbJxGHueHs2Kfk/jpLdhz9OjqW5rZ/b8dzM8vBldGwYxaskaCorVx2axwcDN3NKzeZn5haw6Ek9zf28AWtX2oVODumx/bTjbXxsOwA8vPUPDWjVV+6ksVeYMwoIFC+jZsydeXl63bTNp0iTGjx9vWs7KyjJrkXDpRibtGtVm0Y+HKTEYaR9cm8Qr103b+rUKxk5vTX5RMe2D63Ah7SYlVfCbyt2M+Pj3sx6ebs4sf+cZ+kxaAMDla5k8Hh6MnY01+YXFtA+pw4WrVaufGVl5JJxPp3v7RqZJitduZt9+eKHzI6z/8YRqvZurAzcycrHRWzHyyXCW/VD1JrU936opfRrXZ9jSlXe9+iCiSWNWHTtlOn35IMjIzOPMuTS6dWhkmqR47Xq2anjBrZoDNzNyURSws7OmTYu6rN96HAC9tSV6vRU5uYW4ONkxdFAr5n+zR4Pe3J4hvwjFYMTKsfSDMn3HLzgEqiffGfKLiJ+0gmrNA/Ab2rrMtoK0TKxd7LG0tUYxKlzflYBDHfN9mNwLZ70NtlZWpOeVfsHoFhDIrYICMgrLDtutT/qFIQ0ewdFaT05xEZ386nD61ysdItb+PvHbx9GZDU9E0nbZPPN14h4Na9OU3o/U5/lFK2975UF1Bzuy8gspMRqxtrSka8NATl0p7ecbKzeVafvLu6/Rb/bSv89VDL+5ePEi27ZtY9WqVXdsZ2Njg42NTaVkeHtwZ9oH18bNyYGvXhxAbkExfd6PYcqTXdh5MomdJ5NYvvsYdTyqs+LNZygxGrmelcd7/ymd1b79eCLBfh4sf/1pikoM5BcVM/HfGysl618x+ZkutA2pjZuzA7PGDSSvoJh+kxfy9nNdiT12jl3H7jyG/ePRRBoF1GLp5KEUlxjILyxm8tcbzJT+3n0ydwuTx/Tk2YGtyMsv4oNZpQfbP1/oxp5D59jz62REP69qBAW48/pP6nHQz99+AgsLHVZWlmyOPcWKjUfN2oe78XByZFK3x0i+mcGSZ58ASicuRixcztjHWpOencvyI6Ufko42ero2CKTv3CWq/cx7sh8NPEo/SDa88BwXbt5SXQmhpU9mb+GtV3vxbMSj5OYVET2z9Lia+HJ39hxI5KcD53isTT3692yCwWDE0tKidGhhW+kVKg4ONsz84EkURUGn07Fi3WH2HjynZZdUijLyOD11LYrRCArYerpQ/82eAJyZvhm31nVxaxPI5VVHyE64iqGgmBt7Soe9arSvj9/QR8lNus6FmN2lO1QUHAM9qtwVDE56G2Z3fRxbSyuMKNzMz2f4ptL3/Y/ad2frxUS2XTxHak42/zq6n1X9n8aoQFpuNv/ctUXj9PfOw9mRf/YoPTYXP//7sTlk3nJe6dSa9Kxcvjt0nGZ+3rzSqTVGRcHSwoKfk5L5KrbqTLzUKYr2XyemTJnC3LlzSUlJwcrq3muWrKwsXFxcaPSPaVjaVK1rfSuaVZ7mT1Ols7tVdc5CVKZroZZaRzALj4O3H+54mOjGqq/hf9hcPHf7Sy8fJnaXq8R35kplKCwg8eO3yMzMxNn5znM4NB84NhqNxMTEEBkZeV/FgRBCCCEqj+YFwrZt20hOTmb48OFaRxFCCCHErzT/yt6tWzeqwCiHEEIIIf5A8zMIQgghhKh6pEAQQgghhIoUCEIIIYRQkQJBCCGEECpSIAghhBBCRQoEIYQQQqhIgSCEEEIIFSkQhBBCCKEiBYIQQgghVKRAEEIIIYSKFAhCCCGEUJECQQghhBAqUiAIIYQQQkUKBCGEEEKoSIEghBBCCBUpEIQQQgihIgWCEEIIIVSkQBBCCCGEihQIQgghhFCRAkEIIYQQKlIgCCGEEELFSusAf4WiKAAYigo0TlL5dEWK1hEqXUmxUesIZmEosNQ6glmUFJdoHcEsdLmFWkeodMb8h/89FsBQ+EB/JN4TY2Hpc/nb5+ed6JR7aVVFXbp0CV9fX61jCCGEEA+UlJQUfHx87tjmgS4QjEYjqampODk5odPpzPKYWVlZ+Pr6kpKSgrOzs1keUwvSz4fH36GPIP182Pwd+qlFHxVFITs7Gy8vLyws7jzL4IE+n2JhYXHXCqiyODs7P7Qv2j+Sfj48/g59BOnnw+bv0E9z99HFxeWe2skkRSGEEEKoSIEghBBCCBUpEO6TjY0NUVFR2NjYaB2lUkk/Hx5/hz6C9PNh83foZ1Xv4wM9SVEIIYQQlUPOIAghhBBCRQoEIYQQQqhIgSCEEEIIFSkQhBBCCKEiBcJ9mjVrFgEBAdja2tKqVSsOHDigdaQKtWvXLvr27YuXlxc6nY41a9ZoHanCRUdH06JFC5ycnHB3d6d///4kJCRoHavCzZkzh5CQENN/wtK6dWs2btyodaxK9eGHH6LT6Rg3bpzWUSrUlClT0Ol0ZW4NGjTQOlaluHz5Ms888wxubm7Y2dnxyCOPcOjQIa1jVaiAgADV86nT6RgzZozW0cqQAuE+fPfdd4wfP56oqCiOHDlCaGgo3bt3Jz09XetoFSY3N5fQ0FBmzZqldZRKExsby5gxY9i/fz9bt26luLiYbt26kZubq3W0CuXj48OHH37I4cOHOXToEJ06daJfv37Ex8drHa1SHDx4kLlz5xISEqJ1lEoRHBzMlStXTLc9e/ZoHanC3bp1i/DwcKytrdm4cSOnTp1i+vTpVKtWTetoFergwYNlnsutW7cCEBERoXGy/6GIe9ayZUtlzJgxpmWDwaB4eXkp0dHRGqaqPICyevVqrWNUuvT0dAVQYmNjtY5S6apVq6bMnz9f6xgVLjs7WwkKClK2bt2qPPbYY8qrr76qdaQKFRUVpYSGhmodo9JNnDhRadu2rdYxzO7VV19V6tatqxiNRq2jlCFnEO5RUVERhw8fpkuXLqZ1FhYWdOnShX379mmYTPxVmZmZAFSvXl3jJJXHYDCwfPlycnNzad26tdZxKtyYMWPo3bt3mePzYXP27Fm8vLyoU6cOQ4cOJTk5WetIFW7t2rU0b96ciIgI3N3dCQsL4+uvv9Y6VqUqKipi6dKlDB8+3Gw/OnivpEC4R9evX8dgMODh4VFmvYeHB1evXtUolfirjEYj48aNIzw8nMaNG2sdp8KdOHECR0dHbGxseOGFF1i9ejWNGjXSOlaFWr58OUeOHCE6OlrrKJWmVatWLFq0iE2bNjFnzhzOnz9Pu3btyM7O1jpahUpKSmLOnDkEBQWxefNmXnzxRcaOHcvixYu1jlZp1qxZQ0ZGBsOGDdM6isoD/WuOQvxVY8aM4eTJkw/leC5A/fr1iYuLIzMzkxUrVhAZGUlsbOxDUySkpKTw6quvsnXrVmxtbbWOU2l69uxp+ndISAitWrXC39+f//znP4wYMULDZBXLaDTSvHlzpk2bBkBYWBgnT57kq6++IjIyUuN0lWPBggX07NkTLy8vraOoyBmEe1SjRg0sLS1JS0srsz4tLY1atWpplEr8FS+//DLr169nx44dmv1seGXT6/UEBgbSrFkzoqOjCQ0N5YsvvtA6VoU5fPgw6enpNG3aFCsrK6ysrIiNjWXmzJlYWVlhMBi0jlgpXF1dqVevHomJiVpHqVCenp6q4rVhw4YP5XAKwMWLF9m2bRsjR47UOkq5pEC4R3q9nmbNmrF9+3bTOqPRyPbt2x/KMd2HmaIovPzyy6xevZoff/yR2rVrax3JbIxGI4WFhVrHqDCdO3fmxIkTxMXFmW7Nmzdn6NChxMXFYWlpqXXESpGTk8O5c+fw9PTUOkqFCg8PV11yfObMGfz9/TVKVLliYmJwd3end+/eWkcplwwx3Ifx48cTGRlJ8+bNadmyJTNmzCA3N5fnn39e62gVJicnp8y3kvPnzxMXF0f16tXx8/PTMFnFGTNmDMuWLeOHH37AycnJNIfExcUFOzs7jdNVnEmTJtGzZ0/8/PzIzs5m2bJl7Ny5k82bN2sdrcI4OTmp5o44ODjg5ub2UM0pef311+nbty/+/v6kpqYSFRWFpaUlTz31lNbRKtRrr71GmzZtmDZtGoMHD+bAgQPMmzePefPmaR2twhmNRmJiYoiMjMTKqop+FGt9GcWD5ssvv1T8/PwUvV6vtGzZUtm/f7/WkSrUjh07FEB1i4yM1DpahSmvf4ASExOjdbQKNXz4cMXf31/R6/VKzZo1lc6dOytbtmzROlalexgvcxwyZIji6emp6PV6xdvbWxkyZIiSmJiodaxKsW7dOqVx48aKjY2N0qBBA2XevHlaR6oUmzdvVgAlISFB6yi3JT/3LIQQQggVmYMghBBCCBUpEIQQQgihIgWCEEIIIVSkQBBCCCGEihQIQgghhFCRAkEIIYQQKlIgCCGEEEJFCgQhhBBCqEiBIITQTEBAADNmzLhjmylTptCkSROz5BFC/E4KBCEeEMOGDaN///5l1q1YsQJbW1umT59eKY+5c+dOdDqd6ebh4cGgQYNISkqqkP0fPHiQ0aNHm5Z1Oh1r1qwp0+b1118v8yNpQgjzkAJBiAfU/PnzGTp0KHPmzGHChAmV+lgJCQmkpqby/fffEx8fT9++fSvkp5Rr1qyJvb39Hds4Ojri5ub2lx9LCHF/pEAQ4gH08ccf88orr7B8+fIyvyb6ww8/0LRpU2xtbalTpw5Tp06lpKQEgOHDh9OnT58y+ykuLsbd3Z0FCxbc8fHc3d3x9PSkffv2vPPOO5w6dcr0q59z5syhbt266PV66tevz5IlS0z3UxSFKVOm4Ofnh42NDV5eXowdO9a0/Y9DDAEBAQAMGDAAnU5nWv7fIQaj0ci7776Lj48PNjY2NGnShE2bNpm2X7hwAZ1Ox6pVq+jYsSP29vaEhoayb98+U5uLFy/St29fqlWrhoODA8HBwWzYsOEuf3Uh/l6q6G9MCiFuZ+LEicyePZv169fTuXNn0/rdu3fz3HPPMXPmTNq1a8e5c+dMp++joqIYOXIk7du358qVK3h6egKwfv168vLyGDJkyD0//m8/iV1UVMTq1at59dVXmTFjBl26dGH9+vU8//zz+Pj40LFjR1auXMnnn3/O8uXLCQ4O5urVqxw7dqzc/R48eBB3d3diYmLo0aMHlpaW5bb74osvmD59OnPnziUsLIyFCxfy+OOPEx8fT1BQkKnd5MmT+fTTTwkKCmLy5Mk89dRTJCYmYmVlxZgxYygqKmLXrl04ODhw6tQpHB0d7/lvIMTfgsa/JimEuEeRkZGKXq9XAGX79u2q7Z07d1amTZtWZt2SJUsUT09P03KjRo2Ujz76yLTct29fZdiwYbd9zN9+/vvWrVuKoihKamqq0qZNG8Xb21spLCxU2rRpo4waNarMfSIiIpRevXopiqIo06dPV+rVq6cUFRWVu39/f3/l888/Ny0DyurVq8u0iYqKUkJDQ03LXl5eygcffFCmTYsWLZSXXnpJURRFOX/+vAIo8+fPN22Pj49XAOX06dOKoijKI488okyZMuW2/RZCKIoMMQjxAAkJCSEgIICoqChycnLKbDt27Bjvvvsujo6OptuoUaO4cuUKeXl5AIwcOZKYmBgA0tLS2LhxI8OHD7/r4/r4+ODg4ICXlxe5ubmsXLkSvV7P6dOnCQ8PL9M2PDyc06dPAxAREUF+fj516tRh1KhRrF692jTk8WdkZWWRmpp6x8f8TUhIiOnfv50xSU9PB2Ds2LG8//77hIeHExUVxfHjx/90JiEeVlIgCPEA8fb2ZufOnVy+fJkePXqQnZ1t2paTk8PUqVOJi4sz3U6cOMHZs2extbUF4LnnniMpKYl9+/axdOlSateuTbt27e76uLt37+b48eNkZWURFxdHq1at7imvr68vCQkJzJ49Gzs7O1566SXat29PcXHxn/sD3Adra2vTv3U6HVA6fwFKC6WkpCSeffZZTpw4QfPmzfnyyy8rPZMQDxIpEIR4wPj7+xMbG8vVq1fLFAlNmzYlISGBwMBA1c3CovRQd3Nzo3///sTExLBo0aIyExzvpHbt2tStWxcnJ6cy6xs2bMhPP/1UZt1PP/1Eo0aNTMt2dnb07duXmTNnsnPnTvbt28eJEyfKfRxra+s7Xh3h7OyMl5fXXR/zXvj6+vLCCy+watUqJkyYwNdff31f9xfiYSeTFIV4APn6+rJz5046duxI9+7d2bRpE++88w59+vTBz8+PJ554AgsLC44dO8bJkyd5//33TfcdOXIkffr0wWAwEBkZ+ZdyvPHGGwwePJiwsDC6dOnCunXrWLVqFdu2bQNg0aJFGAwGWrVqhb29PUuXLsXOzg5/f/9y9xcQEMD27dsJDw/HxsaGatWqlfuYUVFR1K1blyZNmhATE0NcXBzffPPNPeceN24cPXv2pF69ety6dYsdO3bQsGHDP/dHEOIhJWcQhHhA+fj4sHPnTq5fv0737t1p3bo169evZ8uWLbRo0YJHH32Uzz//XPVh3KVLFzw9PenevTteXl5/KUP//v354osv+PTTTwkODmbu3LnExMTQoUMHAFxdXfn6668JDw8nJCSEbdu2sW7dutv+vwbTp09n69at+Pr6EhYWVm6bsWPHMn78eCZMmMAjjzzCpk2bWLt2bZkrGO7GYDAwZswYGjZsSI8ePahXrx6zZ8++7/4L8TDTKYqiaB1CCGE+OTk5eHt7ExMTw8CBA7WOI4SoomSIQYi/CaPRyPXr15k+fTqurq48/vjjWkcSQlRhUiAI8TeRnJxM7dq18fHxYdGiRVhZyeEvhLg9GWIQQgghhIpMUhRCCCGEihQIQgghhFCRAkEIIYQQKlIgCCGEEEJFCgQhhBBCqEiBIIQQQggVKRCEEEIIoSIFghBCCCFU/h/YtGdLX5V8IQAAAABJRU5ErkJggg==", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "L'implementazione è compatibile? True\n" ] } ], "source": [ "# Test GPTuxAttention\n", "sample_input = torch.randn(2, 8, 768) # batch size 2, sequence length 8, embedding size 768\n", "position_ids = torch.arange(8).unsqueeze(0).repeat(2, 1)\n", "\n", "attn = GPTuxAttention(hf_model.gpt_neox.config,\n", " Wqkv=hf_model.gpt_neox.layers[0].attention.query_key_value.weight,\n", " bqkv=hf_model.gpt_neox.layers[0].attention.query_key_value.bias,\n", " Wo=hf_model.gpt_neox.layers[0].attention.dense.weight,\n", " bo=hf_model.gpt_neox.layers[0].attention.dense.bias\n", " )\n", "out = attn(sample_input, position_ids, show=True)\n", "\n", "# Creiamo la causal mask per il modello hf\n", "batch_size, seq_len = sample_input.shape[:2]\n", "causal_mask = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool)).view(1, 1, seq_len, seq_len)\n", "attention_mask = torch.zeros((batch_size, 1, seq_len, seq_len), dtype=sample_input.dtype)\n", "attention_mask = attention_mask.masked_fill(causal_mask == 0, -1e9)\n", "\n", "\n", "hf_attn_layer = hf_model.gpt_neox.layers[0].attention\n", "# La classe GPTNeoXAttention necessita di calcolare prima i rotary embedding\n", "position_embeddings = hf_model.gpt_neox.rotary_emb(sample_input, position_ids=position_ids)\n", "hf_out, _ = hf_attn_layer(hidden_states=sample_input, position_embeddings=position_embeddings, attention_mask=attention_mask)\n", "\n", "# Check\n", "print(f\"L'implementazione è compatibile? {torch.allclose(out, hf_out, atol=1e-5)}\")" ] }, { "cell_type": "markdown", "id": "a3183ec9", "metadata": {}, "source": [ "## Modello Completo\n", "\n", "Il modello completo combina tutti i componenti discussi in precedenza in modo sequenziale.\n", "Prima di tutto si trova l'Embedding layer che mappa i token in vettori di dimensione fissa. \n", "Successivamente, questi vettori passano attraverso una serie di blocchi transformer, ognuno composto da LayerNorm, Multi-Head Attention e MLP. \n", "Infine, l'output del transformer viene passato attraverso un'altra LayerNorm e un Language Modeling Head per prevedere il token successivo.\n" ] }, { "cell_type": "code", "execution_count": 35, "id": "3f0740c2-eeee-47fc-b573-63cf62393972", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GPTNeoXForCausalLM(\n", " (gpt_neox): GPTNeoXModel(\n", " (embed_in): Embedding(50304, 768)\n", " (emb_dropout): Dropout(p=0.0, inplace=False)\n", " (layers): ModuleList(\n", " (0-11): 12 x GPTNeoXLayer(\n", " (input_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (post_attention_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (post_attention_dropout): Dropout(p=0.0, inplace=False)\n", " (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n", " (attention): GPTNeoXAttention(\n", " (query_key_value): Linear(in_features=768, out_features=2304, bias=True)\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " )\n", " (mlp): GPTNeoXMLP(\n", " (dense_h_to_4h): Linear(in_features=768, out_features=3072, bias=True)\n", " (dense_4h_to_h): Linear(in_features=3072, out_features=768, bias=True)\n", " (act): GELUActivation()\n", " )\n", " )\n", " )\n", " (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (rotary_emb): GPTNeoXRotaryEmbedding()\n", " )\n", " (embed_out): Linear(in_features=768, out_features=50304, bias=False)\n", ")" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hf_model" ] }, { "cell_type": "code", "execution_count": 24, "id": "84935414", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gli output sono compatibili? True\n" ] } ], "source": [ "class GPTuxLayer(nn.Module):\n", " def __init__(self, config, \n", " Wqkv=None, bqkv=None, Wo=None, bo=None,\n", " W1=None, b1=None, W2=None, b2=None,\n", " ln1_gamma=None, ln1_beta=None,\n", " ln2_gamma=None, ln2_beta=None):\n", " super().__init__()\n", " self.ln1 = GPTuxLayerNorm(config.hidden_size, gamma=ln1_gamma, beta=ln1_beta)\n", " self.attn = GPTuxAttention(config, Wqkv=Wqkv, bqkv=bqkv, Wo=Wo, bo=bo)\n", " self.ln2 = GPTuxLayerNorm(config.hidden_size, gamma=ln2_gamma, beta=ln2_beta)\n", " self.mlp = GPTuxMLP(config, W1=W1, b1=b1, W2=W2, b2=b2)\n", "\n", " def forward(self, x, position_ids):\n", " x = x + self.attn(self.ln1(x), position_ids) + self.mlp(self.ln2(x)) # <--- Nota: Pythia è inusuale, tipicamente attn e mlp sono applicati sequenzialmente! \n", " return x\n", "\n", "# Test GPTuxLayer\n", "sample_input = torch.randn(2, 8, 768) # batch size 2, sequence length 8, embedding size 768\n", "position_ids = torch.arange(8).unsqueeze(0).repeat(2, 1)\n", "layer = GPTuxLayer(hf_model.config,\n", " Wqkv=hf_model.gpt_neox.layers[0].attention.query_key_value.weight,\n", " bqkv=hf_model.gpt_neox.layers[0].attention.query_key_value.bias,\n", " Wo=hf_model.gpt_neox.layers[0].attention.dense.weight,\n", " bo=hf_model.gpt_neox.layers[0].attention.dense.bias,\n", " W1=hf_model.gpt_neox.layers[0].mlp.dense_h_to_4h.weight,\n", " b1=hf_model.gpt_neox.layers[0].mlp.dense_h_to_4h.bias,\n", " W2=hf_model.gpt_neox.layers[0].mlp.dense_4h_to_h.weight,\n", " b2=hf_model.gpt_neox.layers[0].mlp.dense_4h_to_h.bias,\n", " ln1_gamma=hf_model.gpt_neox.layers[0].input_layernorm.weight,\n", " ln1_beta=hf_model.gpt_neox.layers[0].input_layernorm.bias,\n", " ln2_gamma=hf_model.gpt_neox.layers[0].post_attention_layernorm.weight,\n", " ln2_beta=hf_model.gpt_neox.layers[0].post_attention_layernorm.bias\n", " )\n", "output = layer(sample_input, position_ids)\n", "hf_layer = hf_model.gpt_neox.layers[0]\n", "# The hf_layer expects position_embeddings to be passed, not position_ids.\n", "# We need to calculate them first using the model's rotary embedding layer.\n", "position_embeddings = hf_model.gpt_neox.rotary_emb(sample_input, position_ids=position_ids)\n", "hf_output = hf_layer(sample_input, position_embeddings=position_embeddings)[0]\n", "\n", "# Check\n", "print(\"Gli output sono compatibili?\", torch.allclose(output, hf_output, atol=1e-5))" ] }, { "cell_type": "code", "execution_count": 25, "id": "44759bdc", "metadata": {}, "outputs": [], "source": [ "class GPTux(nn.Module):\n", " def __init__(self, config, \n", " Wqkv=None, bqkv=None, Wo=None, bo=None,\n", " W1=None, b1=None, W2=None, b2=None,\n", " ln1_gamma=None, ln1_beta=None,\n", " ln2_gamma=None, ln2_beta=None,\n", " emb_weights=None, lmh_weights=None, \n", " lmh_bias=None):\n", " super().__init__()\n", " self.config = config\n", " \n", " self.transformer = nn.ModuleDict(dict(\n", " embedding = GPTuxEmbedding(config.vocab_size, config.hidden_size, weights=emb_weights),\n", " layers = nn.ModuleList([GPTuxLayer(config, \n", " Wqkv=Wqkv[i], bqkv=bqkv[i], Wo=Wo[i], bo=bo[i],\n", " W1=W1[i], b1=b1[i], W2=W2[i], b2=b2[i],\n", " ln1_gamma=ln1_gamma[i], ln1_beta=ln1_beta[i],\n", " ln2_gamma=ln2_gamma[i], ln2_beta=ln2_beta[i]) for i in range(config.num_hidden_layers)]),\n", " final_ln = GPTuxLayerNorm(config.hidden_size, gamma=hf_model.gpt_neox.final_layer_norm.weight, beta=hf_model.gpt_neox.final_layer_norm.bias),\n", " lm_head = GPTuxLMH(config.vocab_size, config.hidden_size, weights=lmh_weights, bias=lmh_bias)\n", " ))\n", "\n", " def forward(self, token_ids, position_ids):\n", " bs, T = token_ids.size()\n", " assert T <= self.config.max_position_embeddings, f\"Cannot forward sequence of length {T}, block size is only {self.config.max_position_embeddings}\"\n", "\n", " # Forward pass\n", " x = self.transformer.embedding(token_ids) # token embeddings of shape ( bs, seq, d_model)\n", " for block in self.transformer.layers:\n", " x = block(x, position_ids)\n", " \n", " x = self.transformer.final_ln(x)\n", " logits = self.transformer.lm_head(x)\n", "\n", " return logits" ] }, { "cell_type": "markdown", "id": "43111c68", "metadata": {}, "source": [ "### Carichiamo i pesi nel nostro modello\n", "\n", "Questo pugno in un occhio di codice permette di inizializzare il nostro modello prendendo i pesi dal modello di huggingface nel nostro modello.\n", "Se tutto va bene i modelli saranno equivalenti in termini di input-output" ] }, { "cell_type": "code", "execution_count": 26, "id": "02d0cea4", "metadata": {}, "outputs": [], "source": [ "our_model = GPTux(hf_model.gpt_neox.config, \n", " Wqkv=[hf_model.gpt_neox.layers[i].attention.query_key_value.weight for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " bqkv=[hf_model.gpt_neox.layers[i].attention.query_key_value.bias for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " Wo=[hf_model.gpt_neox.layers[i].attention.dense.weight for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " bo=[hf_model.gpt_neox.layers[i].attention.dense.bias for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " W1=[hf_model.gpt_neox.layers[i].mlp.dense_h_to_4h.weight for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " b1=[hf_model.gpt_neox.layers[i].mlp.dense_h_to_4h.bias for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " W2=[hf_model.gpt_neox.layers[i].mlp.dense_4h_to_h.weight for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " b2=[hf_model.gpt_neox.layers[i].mlp.dense_4h_to_h.bias for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln1_gamma=[hf_model.gpt_neox.layers[i].input_layernorm.weight for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln1_beta=[hf_model.gpt_neox.layers[i].input_layernorm.bias for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln2_gamma=[hf_model.gpt_neox.layers[i].post_attention_layernorm.weight for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln2_beta=[hf_model.gpt_neox.layers[i].post_attention_layernorm.bias for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " emb_weights=hf_model.gpt_neox.embed_in.weight,\n", " lmh_weights=hf_model.embed_out.weight,\n", " lmh_bias=hf_model.embed_out.bias\n", ")" ] }, { "cell_type": "markdown", "id": "d4a188e6", "metadata": {}, "source": [ "### Risultati e Decoding" ] }, { "cell_type": "code", "execution_count": 27, "id": "22f47913", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tokenized_input: ['<|endoftext|>', 'My', 'Ġname', 'Ġis', 'ĠBond', ',', 'ĠJames']\n", "Tokenized input: ['<|endoftext|>', 'My', 'Ġname', 'Ġis', 'ĠBond', ',', 'ĠJames']\n", "The initial special token is: <|endoftext|> with ID: 0\n" ] } ], "source": [ "input_ids = tokenizer(\"<|endoftext|>My name is Bond, James\", return_tensors=\"pt\", add_special_tokens=True).input_ids\n", "str_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])\n", "print(\"tokenized_input:\", str_tokens)\n", "print(\"Tokenized input:\", str_tokens)\n", "print(\"The initial special token is:\", tokenizer.special_tokens_map['bos_token'], \"with ID:\", tokenizer.convert_tokens_to_ids(tokenizer.special_tokens_map['bos_token']))" ] }, { "cell_type": "code", "execution_count": 28, "id": "5941cd2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La nostra implementazione di GPTux produce (quasi) gli stessi logits del modello HuggingFace GPT-NeoX!\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logits_out = our_model.forward(input_ids, position_ids=torch.tensor([[0,1,2,3,4,5]]))\n", "# Per HuggingFace GPT-NeoX serve che position_ids abbia la stessa shape di input_ids\n", "logits_out_hf = hf_model.forward(input_ids).logits\n", "\n", "# Testiamo che siano \"quasi\" identici\n", "assert torch.allclose(logits_out, logits_out_hf, atol=1e-5)\n", "\n", "print(\"La nostra implementazione di GPTux produce (quasi) gli stessi logits del modello HuggingFace GPT-NeoX!\")\n", "\n", "plot_topk_logits(logits_out[0, -1], tokenizer, topk=10, title=\"Top-10 Token Probability Distribution from GPTux\")\n", "plot_topk_logits(logits_out_hf[0, -1], tokenizer, topk=10, title=\"Top-10 Token Probability Distribution from HuggingFace GPT-NeoX\")" ] }, { "cell_type": "markdown", "id": "165be863", "metadata": {}, "source": [ "## Bonus: Training su un singolo esempio\n", "\n", "Per ovvi motivi non possiamo fare un training completo di un modello in quanto richiede centinaia di ore di calcolo su GPU e dataset di almeno centinaia di Gb per ottenere risultati accettabili.\n", "\n", "Tuttavia nulla ci vieta di fare una breve demo per vedere come un training possa funzionare.\n", "\n", "In particolare " ] }, { "cell_type": "code", "execution_count": 29, "id": "3dba6d27", "metadata": {}, "outputs": [], "source": [ "prompt = \"I use Arch\"\n", "target_completion = \" btw\"\n", "input_ids = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True).input_ids\n", "baseline_out = hf_model(input_ids, position_ids=torch.arange(input_ids.shape[1]).unsqueeze(0)).logits" ] }, { "cell_type": "code", "execution_count": 30, "id": "ede8c4c5", "metadata": {}, "outputs": [], "source": [ "def train_model(learning_rate, num_iterations, tokenizer, model, prompt=\"I use Arch btw\"):\n", " \"\"\"\n", " Main function to set up and run the training loop.\n", " \"\"\"\n", " # Setup\n", " torch.manual_seed(42)\n", " device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", " \n", " # Initialize tokenizer with our specific prompt\n", " \n", " # Create training data\n", " # The goal is to predict the next character in the sequence.\n", " # For each subsequence, the model must predict the character that follows.\n", " all_tokens = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True).input_ids[0]\n", " \n", " \n", " \n", " # Optimizer and Loss\n", " optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n", " loss_fn = nn.CrossEntropyLoss()\n", " \n", " print(\"--- Starting Training ---\")\n", " print(f\"Prompt: '{prompt}'\")\n", " print(f\"Device: {device}, LR: {learning_rate}, Iterations: {num_iterations}\\n\")\n", " \n", " # The Training Loop\n", " model.train()\n", " for i in range(num_iterations):\n", " total_loss = 0\n", " \n", " # Prepare input and target\n", " input_tensor = all_tokens[:-1].unsqueeze(0)\n", " target_tensor = all_tokens[-1].unsqueeze(0)\n", " \n", " # Core Training Steps \n", " # 1. Forward pass\n", " logits = model(input_tensor, position_ids=torch.arange(input_tensor.shape[1]).unsqueeze(0).to(device))\n", " \n", " # 2. We only care about the prediction for the very last token\n", " # in the input sequence.\n", " logits_last_token = logits[:, -1, :] # Shape: (batch_size, vocab_size)\n", " \n", " # 3. Calculate loss\n", " loss = loss_fn(logits_last_token, target_tensor)\n", " total_loss += loss.item()\n", " \n", " # 4. Backward pass and optimization\n", " optimizer.zero_grad(set_to_none=True)\n", " loss.backward()\n", " optimizer.step()\n", " \n", " print(f\"Iteration {i}/{num_iterations-1} | Average Loss: {loss.item():.4f}\")\n", "\n", " print(\"\\n--- Training Complete ---\\n\")\n", " \n", " # 6. Generation / Inference\n", " # Let's see what the model has learned.\n", " print(\"--- Generating Text ---\")\n", " model.eval()\n", " with torch.no_grad():\n", " input_tensor = all_tokens[:-1].unsqueeze(0)\n", " target_completion = all_tokens[-1].unsqueeze(0)\n", " print(\"Input Prompt: \", tokenizer.decode(input_tensor[0]).replace(\"Ġ\", \"_\"))\n", " print(\"Expected Completion: \", tokenizer.decode(target_completion).replace(\"Ġ\", \"_\"))\n", " print(\"Decoding: \")\n", " logits = model(input_tensor, position_ids=torch.arange(input_tensor.shape[1]).unsqueeze(0).to(device))\n", " logits_last_token = logits[:, -1, :]\n", " plot_topk_logits(logits_last_token[0], tokenizer, topk=10, title=\"Top-10 Token Probability Distribution after Training\")" ] }, { "cell_type": "code", "execution_count": 31, "id": "9dbb94c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tokenized input for training: ['<|endoftext|>', 'The', 'Ġbest', 'ĠOS', 'Ġis', 'ĠLinux']\n", "Tokens: tensor([[ 0, 510, 1682, 9485, 310, 13492]])\n" ] } ], "source": [ "tokenized_input = tokenizer(\"<|endoftext|>The best OS is Linux\", return_tensors=\"pt\", add_special_tokens=True).input_ids\n", "str_tokens = tokenizer.convert_ids_to_tokens(tokenized_input[0])\n", "print(\"Tokenized input for training:\", str_tokens)\n", "print(\"Tokens:\", tokenized_input)" ] }, { "cell_type": "code", "execution_count": 32, "id": "3b2be6bc", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "--- Starting Training ---\n", "Prompt: '<|endoftext|>The best OS is Linux'\n", "Device: cpu, LR: 1e-06, Iterations: 5\n", "\n", "Iteration 0/4 | Average Loss: 5.3946\n", "Iteration 1/4 | Average Loss: 3.9140\n", "Iteration 2/4 | Average Loss: 2.6881\n", "Iteration 3/4 | Average Loss: 1.8794\n", "Iteration 4/4 | Average Loss: 1.4169\n", "\n", "--- Training Complete ---\n", "\n", "--- Generating Text ---\n", "Input Prompt: <|endoftext|>The best OS is\n", "Expected Completion: Linux\n", "Decoding: \n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "LEARNING_RATE = 1e-6\n", "NUM_ITERATIONS = 5\n", "\n", "gc.collect()\n", "torch.cuda.empty_cache()\n", "our_model = GPTux(hf_model.gpt_neox.config, \n", " Wqkv=[hf_model.gpt_neox.layers[i].attention.query_key_value.weight.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " bqkv=[hf_model.gpt_neox.layers[i].attention.query_key_value.bias.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " Wo=[hf_model.gpt_neox.layers[i].attention.dense.weight.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " bo=[hf_model.gpt_neox.layers[i].attention.dense.bias.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " W1=[hf_model.gpt_neox.layers[i].mlp.dense_h_to_4h.weight.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " b1=[hf_model.gpt_neox.layers[i].mlp.dense_h_to_4h.bias.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " W2=[hf_model.gpt_neox.layers[i].mlp.dense_4h_to_h.weight.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " b2=[hf_model.gpt_neox.layers[i].mlp.dense_4h_to_h.bias.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln1_gamma=[hf_model.gpt_neox.layers[i].input_layernorm.weight.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln1_beta=[hf_model.gpt_neox.layers[i].input_layernorm.bias.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln2_gamma=[hf_model.gpt_neox.layers[i].post_attention_layernorm.weight.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " ln2_beta=[hf_model.gpt_neox.layers[i].post_attention_layernorm.bias.detach().clone() for i in range(hf_model.gpt_neox.config.num_hidden_layers)],\n", " emb_weights=hf_model.gpt_neox.embed_in.weight.detach().clone(),\n", " lmh_weights=hf_model.embed_out.weight.detach().clone(),\n", " lmh_bias=hf_model.embed_out.bias\n", ")\n", "input_ids = tokenizer(\"<|endoftext|>The best OS is\", return_tensors=\"pt\", add_special_tokens=True).input_ids\n", "logits = our_model.forward(input_ids, position_ids=torch.arange(input_ids.shape[1]).unsqueeze(0))\n", "plot_topk_logits(logits[0,-1], tokenizer, topk=10, title=\"Top-10 Token Probability Distribution Before Training\")\n", "train_model(learning_rate=LEARNING_RATE, num_iterations=NUM_ITERATIONS, tokenizer=tokenizer, model=our_model, prompt=\"<|endoftext|>The best OS is Linux\")" ] }, { "cell_type": "markdown", "id": "9b8edeb8", "metadata": {}, "source": [ "### Nota: Non Fine Tunate così a casa!!!\n", "\n", "Quando si fa fine tuning è importante stare attenti per non sovrascrivere le conoscenze già apprese dal modello pre-addestrato.\n", "\n", "In generale possono succedere molte cose inaspettate, come il modello che \"dimentica\" tutto quello che aveva imparato prima (catastrophic forgetting) o che si adatta troppo strettamente al nuovo esempio (overfitting), perdendo la capacità di generalizzare.\n", "\n", "Nel nostro caso il modello va ad overfittare sul singolo esempio, predicendo troppo spesso Linux anche quando è **CHIARAMENTE SBAGLIATO**." ] }, { "cell_type": "code", "execution_count": 33, "id": "6f4b6a31", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "input_ids = tokenizer(\"<|endoftext|>The worst OS is\", return_tensors=\"pt\", add_special_tokens=True).input_ids\n", "logits = our_model.forward(input_ids, position_ids=torch.arange(input_ids.shape[1]).unsqueeze(0))\n", "plot_topk_logits(logits[0,-1], tokenizer, topk=10, title=\"Top-10 Token Probability Distribution Before Training\")\n", "\n", "logits_hf = hf_model.forward(input_ids).logits\n", "plot_topk_logits(logits_hf[0,-1], tokenizer, topk=10, title=\"Top-10 Token Probability Distribution from HuggingFace GPT-NeoX\")\n" ] } ], "metadata": { "kernelspec": { "display_name": "ml", "language": "python", "name": "ml" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }