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alexcpn/llm_probability1.ipynb

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"import torch\n",
"from transformers import (\n",
" AutoModelForCausalLM, # with decoder only - GPTs , LLAMAs\n",
" AutoTokenizer,\n",
" AutoModelForSeq2SeqLM, # with Encoder and Decoder (t5)\n",
")\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n"
]
},
{
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"source": [
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],
"metadata": {
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"colab": {
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}
},
"execution_count": 3,
"outputs": [
{
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"name": "stdout",
"text": [
"\n",
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
"\n",
" To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
"Enter your token (input will not be visible): \n",
"Add token as git credential? (Y/n) n\n",
"Token is valid (permission: read).\n",
"The token `test` has been saved to /root/.cache/huggingface/stored_tokens\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful.\n",
"The current active token is: `test`\n"
]
}
]
},
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"# Load model and tokenizer\n",
"model_name = 'gpt2-medium'\n",
"model_name = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
"model_name = \"meta-llama/Llama-3.2-1B\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" torch_dtype=\"float16\",\n",
" device_map=\"auto\"\n",
")\n",
"model.eval()\n"
],
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},
"id": "IeleLpDVW4OY",
"outputId": "6bb9819a-8af0-4264-a21d-a14d200b5995"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/50.5k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "562ccaf9e74a4f969fe280a8dbf81281"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer.json: 0%| | 0.00/9.09M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d800213d82564b5ebc63a70990ec95c0"
}
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/301 [00:00<?, ?B/s]"
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{
"output_type": "display_data",
"data": {
"text/plain": [
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{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/2.47G [00:00<?, ?B/s]"
],
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"data": {
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],
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}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LlamaForCausalLM(\n",
" (model): LlamaModel(\n",
" (embed_tokens): Embedding(128256, 2048)\n",
" (layers): ModuleList(\n",
" (0-15): 16 x LlamaDecoderLayer(\n",
" (self_attn): LlamaSdpaAttention(\n",
" (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (k_proj): Linear(in_features=2048, out_features=512, bias=False)\n",
" (v_proj): Linear(in_features=2048, out_features=512, bias=False)\n",
" (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (mlp): LlamaMLP(\n",
" (gate_proj): Linear(in_features=2048, out_features=8192, bias=False)\n",
" (up_proj): Linear(in_features=2048, out_features=8192, bias=False)\n",
" (down_proj): Linear(in_features=8192, out_features=2048, bias=False)\n",
" (act_fn): SiLU()\n",
" )\n",
" (input_layernorm): LlamaRMSNorm((2048,), eps=1e-05)\n",
" (post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-05)\n",
" )\n",
" )\n",
" (norm): LlamaRMSNorm((2048,), eps=1e-05)\n",
" (rotary_emb): LlamaRotaryEmbedding()\n",
" )\n",
" (lm_head): Linear(in_features=2048, out_features=128256, bias=False)\n",
")"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"def get_probabilities(input_ids):\n",
"# Create an attention mask (1 for actual tokens, 0 for padding)\n",
"\n",
" with torch.no_grad():\n",
" # Pass input_ids and attention_mask to the model\n",
" attention_mask = torch.ones(input_ids.shape)\n",
" outputs = model(input_ids.to(model.device), attention_mask=attention_mask.to(model.device),output_attentions=True)\n",
" attentions = outputs.attentions\n",
" logits = outputs.logits\n",
" probabilities = torch.softmax(logits[0, -1], dim=0).cpu().numpy()\n",
"\n",
" return probabilities,attentions\n",
"\n",
"def plot_probability_distribution(current, probabilities, tokenizer, top_n=10):\n",
" # Get top N tokens and their probabilities\n",
" top_indices = np.argsort(probabilities)[-top_n:][::-1]\n",
" top_probs = probabilities[top_indices]\n",
" top_tokens = [tokenizer.decode([i]) for i in top_indices]\n",
"\n",
" # Find the next token (highest probability token)\n",
" max_token = tokenizer.decode([top_indices[0]])\n",
"\n",
" # Plot\n",
" plt.figure(figsize=(12, 7))\n",
" bars = plt.bar(top_tokens, top_probs, color=\"blue\")\n",
" bars[0].set_color(\"red\") # Highlight the next token\n",
"\n",
" # Add the current context inside the graph\n",
" plt.text(\n",
" 0.5,\n",
" 0.9,\n",
" f\"Context: {current}\\nNext Token: {max_token}\",\n",
" ha=\"center\",\n",
" va=\"center\",\n",
" transform=plt.gca().transAxes,\n",
" fontsize=12,\n",
" bbox=dict(facecolor=\"white\", alpha=0.8, edgecolor=\"black\"),\n",
" )\n",
"\n",
" plt.xlabel(\"Tokens\")\n",
" plt.ylabel(\"Probabilities\")\n",
" plt.xticks(rotation=45)\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"original_text = \"I went to the bank.I wanted to get some\"\n",
"print(f\"Original Text: {original_text}\")\n",
"input_id_list = list(tokenizer.encode(original_text))\n",
"\n",
"\n",
"current_input = torch.tensor([input_id_list], dtype=torch.long)\n",
"probabilities,attentions = get_probabilities(current_input)\n",
"print(\"Vocalbulary Size=probabilities.shape\",probabilities.shape) #vocabulary size\n",
"print(\"Attention Heads attentions.shape\", attentions[-1].shape)\n",
"probabilities /= probabilities.sum() # Normalize\n",
"\n",
"# Find the token with the maximum probability\n",
"max_prob = np.max(probabilities)\n",
"max_token_id = np.argmax(probabilities)\n",
"max_token = tokenizer.decode([max_token_id])\n",
"current = tokenizer.decode(input_id_list)\n",
"print(f\"Current Tokens: {list(input_id_list)}\")\n",
"print(f\"Decoded Context: {current}\")\n",
"print(f\"Max Probability Token: '{max_token}' (ID: {max_token_id}) with Probability: {max_prob:.6f}\")\n",
"\n",
"# Plot the probability distribution\n",
"plot_probability_distribution(current,probabilities, tokenizer, top_n=10)"
],
"metadata": {
"id": "QO7MXz0FSkjb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 856
},
"outputId": "6cd6d168-540e-4899-e9e1-b6f55ee9f037"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Original Text: I went to the bank.I wanted to get some\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Vocalbulary Size=probabilities.shape (128256,)\n",
"Attention Heads attentions.shape torch.Size([1, 32, 11, 11])\n",
"Current Tokens: [128000, 40, 4024, 311, 279, 6201, 2506, 4934, 311, 636, 1063]\n",
"Decoded Context: <|begin_of_text|>I went to the bank.I wanted to get some\n",
"Max Probability Token: ' money' (ID: 3300) with Probability: 0.458496\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
],
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"original_text = \"I swam to the bank.I wanted to get some\"\n",
"print(f\"Original Text: {original_text}\")\n",
"input_id_list = list(tokenizer.encode(original_text))\n",
"\n",
"\n",
"current_input = torch.tensor([input_id_list], dtype=torch.long)\n",
"probabilities,attentions = get_probabilities(current_input)\n",
"print(\"Vocalbulary Size=probabilities.shape\",probabilities.shape) #vocabulary size\n",
"print(\"Attention Heads attentions.shape\", attentions[-1].shape)\n",
"probabilities /= probabilities.sum() # Normalize\n",
"\n",
"# Find the token with the maximum probability\n",
"max_prob = np.max(probabilities)\n",
"max_token_id = np.argmax(probabilities)\n",
"max_token = tokenizer.decode([max_token_id])\n",
"current = tokenizer.decode(input_id_list)\n",
"print(f\"Current Tokens: {list(input_id_list)}\")\n",
"print(f\"Decoded Context: {current}\")\n",
"print(f\"Max Probability Token: '{max_token}' (ID: {max_token_id}) with Probability: {max_prob:.6f}\")\n",
"\n",
"# Plot the probability distribution\n",
"plot_probability_distribution(current,probabilities, tokenizer, top_n=10)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 818
},
"id": "xSrXdO0vSnJp",
"outputId": "964cbc24-3529-435c-ce45-e4a8972837c5"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Original Text: I swam to the bank.I wanted to get some\n",
"Vocalbulary Size=probabilities.shape (128256,)\n",
"Attention Heads attentions.shape torch.Size([1, 32, 12, 12])\n",
"Current Tokens: [128000, 40, 2064, 309, 311, 279, 6201, 2506, 4934, 311, 636, 1063]\n",
"Decoded Context: <|begin_of_text|>I swam to the bank.I wanted to get some\n",
"Max Probability Token: ' money' (ID: 3300) with Probability: 0.435791\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"original_text = \"I swam to the river bank.I wanted to get some\"\n",
"print(f\"Original Text: {original_text}\")\n",
"input_id_list = list(tokenizer.encode(original_text))\n",
"\n",
"\n",
"current_input = torch.tensor([input_id_list], dtype=torch.long)\n",
"probabilities,attentions = get_probabilities(current_input)\n",
"print(\"Vocalbulary Size=probabilities.shape\",probabilities.shape) #vocabulary size\n",
"print(\"Attention Heads attentions.shape\", attentions[-1].shape)\n",
"probabilities /= probabilities.sum() # Normalize\n",
"\n",
"# Find the token with the maximum probability\n",
"max_prob = np.max(probabilities)\n",
"max_token_id = np.argmax(probabilities)\n",
"max_token = tokenizer.decode([max_token_id])\n",
"current = tokenizer.decode(input_id_list)\n",
"print(f\"Current Tokens: {list(input_id_list)}\")\n",
"print(f\"Decoded Context: {current}\")\n",
"print(f\"Max Probability Token: '{max_token}' (ID: {max_token_id}) with Probability: {max_prob:.6f}\")\n",
"\n",
"# Plot the probability distribution\n",
"plot_probability_distribution(current,probabilities, tokenizer, top_n=10)"
],
"metadata": {
"id": "3GwYBGjxqjer",
"outputId": "86cf73e9-37bc-4fd1-e343-d9234f36b8c5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 818
}
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Original Text: I swam to the river bank.I wanted to get some\n",
"Vocalbulary Size=probabilities.shape (128256,)\n",
"Attention Heads attentions.shape torch.Size([1, 32, 13, 13])\n",
"Current Tokens: [128000, 40, 2064, 309, 311, 279, 15140, 6201, 2506, 4934, 311, 636, 1063]\n",
"Decoded Context: <|begin_of_text|>I swam to the river bank.I wanted to get some\n",
"Max Probability Token: ' water' (ID: 3090) with Probability: 0.342041\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
}
]
}
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