From f8237b9f8a1d410c6619440434eb2d465dcf4df4 Mon Sep 17 00:00:00 2001
From: Armin Bacher <armin.bacher@student.uni-halle.de>
Date: Mon, 31 Mar 2025 21:51:31 +0000
Subject: [PATCH] Delete GPT-2-Small-1k.py
---
Testing/GPT-2-Small-1k.py | 86 ---------------------------------------
1 file changed, 86 deletions(-)
delete mode 100644 Testing/GPT-2-Small-1k.py
diff --git a/Testing/GPT-2-Small-1k.py b/Testing/GPT-2-Small-1k.py
deleted file mode 100644
index a9dfa35..0000000
--- a/Testing/GPT-2-Small-1k.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import time
-import torch
-import torch.nn as nn
-from transformers import GPT2LMHeadModel, GPT2Tokenizer, DataCollatorForLanguageModeling, TrainingArguments, Trainer
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-
-# --- Einstellungen ---
-BATCH_SIZE = 16
-SEQ_LEN = 1024
-NUM_STEPS = 100
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-MIXED_PRECISION = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
-
-# GPT-2 Small Model Parameter
-NUM_LAYERS = 12
-HIDDEN_SIZE = 768
-NUM_HEADS = 12
-HEAD_DIM = HIDDEN_SIZE // NUM_HEADS
-
-dataset = load_dataset("wikitext", "wikitext-103-v1", streaming=False)
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-tokenizer.pad_token = tokenizer.eos_token
-
-def tokenize_function(examples):
- tokens = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=SEQ_LEN)
- tokens["labels"] = tokens["input_ids"].copy() # Labels = Shifted input_ids
- return tokens
-
-tokenized_datasets = dataset.map(tokenize_function, batched=True)
-tokenized_datasets.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
-
-data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-dataloader = DataLoader(tokenized_datasets["train"], batch_size=BATCH_SIZE, collate_fn=data_collator)
-
-def compute_flops(batch_size, seq_len, num_layers, hidden_size):
- """ Berechnet die FLOPs für das Training """
- return 6 * num_layers * (hidden_size ** 2) * seq_len * batch_size
-
-def benchmark_training(model, dataloader, num_steps=NUM_STEPS):
- model.to(DEVICE)
- model.train()
- optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4)
-
- torch.cuda.synchronize()
- start_time = time.time()
-
- for step, batch in enumerate(dataloader):
- if step >= num_steps:
- break
- batch = {k: v.to(DEVICE, dtype=torch.long) if k == "input_ids" or k == "labels" else v.to(DEVICE, dtype=MIXED_PRECISION) for k, v in batch.items()}
- loss = model(**batch).loss
- loss.backward()
- optimizer.step()
- optimizer.zero_grad()
-
- torch.cuda.synchronize()
- total_time = time.time() - start_time
- tokens_per_second = (num_steps * BATCH_SIZE * SEQ_LEN) / total_time
- flops_per_step = compute_flops(BATCH_SIZE, SEQ_LEN, NUM_LAYERS, HIDDEN_SIZE)
- tflops_per_sec = (flops_per_step * (tokens_per_second / (BATCH_SIZE * SEQ_LEN))) / 1e12
- return tokens_per_second, tflops_per_sec
-
-results = {}
-
-for attn_type in ["standard", "flash2"]:
- print(f"Teste GPT-2 Small mit {attn_type} Attention...")
- if attn_type == "standard":
- model = GPT2LMHeadModel.from_pretrained("gpt2", torch_dtype=MIXED_PRECISION, device_map="auto")
- elif attn_type == "flash2":
- model = GPT2LMHeadModel.from_pretrained("gpt2", attn_implementation="flash_attention_2", torch_dtype=MIXED_PRECISION, device_map="auto")
-
- tokens_per_sec, tflops_per_sec = benchmark_training(model, dataloader)
- results[attn_type] = (tokens_per_sec, tflops_per_sec)
- print(f"{attn_type} Attention: {tokens_per_sec:.2f} Tokens pro Sekunde, {tflops_per_sec:.2f} TFLOPS/s")
-
-print("Endergebnisse:")
-for attn_type, (speed, tflops) in results.items():
- print(f"{attn_type.capitalize()} Attention: {speed:.2f} Tokens pro Sekunde, {tflops:.2f} TFLOPS/s")
-
-"""
-Ergebnisse Job 17932
-Endergebnisse:
-Standard Attention: 66471.08 Tokens pro Sekunde, 2.82 TFLOPS/s
-Flash2 Attention: 92981.89 Tokens pro Sekunde, 3.95 TFLOPS/s
-"""
\ No newline at end of file
--
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