Pinned to CPU cores 0–7 for both Python and Rust runs. OMP_NUM_THREADS=1, 8 rayon/tiktoken threads. Results are the best of (warmup + iters) per combination.
| from transformers.models.sam.modeling_sam import SamVisionAttention, SamModel, SamVisionLayer | |
| from transformers import SamProcessor | |
| import torch.nn as nn | |
| import torch | |
| from transformers.models.sam import modeling_sam | |
| from PIL import Image | |
| import requests | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from transformers import pipeline |
| from datasets import load_dataset | |
| from transformers import WhisperForConditionalGeneration, AutoProcessor, StaticCache | |
| import torch | |
| import torch._dynamo.config | |
| import torch._inductor.config | |
| import time | |
| from tqdm import tqdm | |
| import logging | |
| torch._inductor.config.coordinate_descent_tuning = True |
| from datasets import load_dataset | |
| from trl import SFTTrainer | |
| from peft import LoraConfig | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments | |
| tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") | |
| model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") | |
| dataset = load_dataset("Abirate/english_quotes", split="train") | |
| training_args = TrainingArguments( | |
| output_dir="./results", | |
| num_train_epochs=3, |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache | |
| import torch | |
| from typing import Optional | |
| import time | |
| import os | |
| os.environ["TOKENIZERS_PARALLELISM"] = "1" | |
| device = "cuda:1" | |
| torch.set_float32_matmul_precision('high') | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache | |
| import torch | |
| from typing import Optional | |
| device = "cuda" | |
| # Copied from the gpt-fast repo | |
| def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization | |
| q = torch.empty_like(probs_sort).exponential_(1) | |
| return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) |
| FRANCE_ARTICLE = """<s>Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a phone at the wreckage site. The two publications described the supposed video, but did not post it on their websites. The publications said that they watched the video, which was found by a source close to the investigation. \"One can hear cries of 'My God' in several languages,\" Par |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache, set_seed | |
| torch.set_printoptions(linewidth=400) | |
| attn_implementation = "sdpa" | |
| tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf", padding_side="left", pad_token="<s>") | |
| model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf",torch_dtype=torch.bfloat16,attn_implementation=attn_implementation).to("cuda:1") | |
| inputs = tokenizer( | |
| ["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt" |
| import torch | |
| import os | |
| import argparse | |
| import matplotlib.pyplot as plt | |
| from tqdm import tqdm | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import seaborn as sns | |
| def get_parser(): |