shreyansh26

ncu --list-sets  # The configuration for sets. A set defines a set of sections.
ncu --list-sections  # The configuration for sections. A section defines a set of metrics.
ncu --query-metrics   # All individual metrics.
ncu --query-metrics-mode suffix --metrics <metrics list> # Check various suffixes for a base metric name.

ncu_cli

shreyansh26

from dataclasses import dataclass


@dataclass
class Args:
    vocab_size: int = 129280
    dim: int = 7168
    inter_dim: int = 18432
    moe_inter_dim: int = 2048
    n_layers: int = 61

shreyansh26

import sklearn
import torch
from sentence_transformers import SentenceTransformer
import torch.nn.functional as F

def get_score_diff(vectors):
	scores = torch.matmul(vectors, vectors.T)
	scores = scores[torch.triu(torch.ones_like(scores), diagonal=1).bool()]
	score_diff = scores.reshape((1, -1)) - scores.reshape((-1, 1))
	score_diff = score_diff[torch.triu(torch.ones_like(score_diff), diagonal=1).bool()]

shreyansh26

import torch
import torch._inductor.config
import time

torch._inductor.config.triton.cudagraphs = False
torch.set_float32_matmul_precision('high')

def bench(f, name=None, iters=100, warmup=5, display=True, profile=False):
    for _ in range(warmup):
        f()

shreyansh26

import json

import sentencepiece as spm
import sentencepiece.sentencepiece_model_pb2 as sp_pb2
from google.protobuf.json_format import MessageToDict

PATH = "tokenizer.model"

s = spm.SentencePieceProcessor(model_file=PATH)

shreyansh26

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")

inputs = tok(["Hello how"], return_tensors="pt")
len_inp = len(inputs.input_ids[0])
print(len_inp)

generated = model.generate(**inputs, do_sample=False, max_new_tokens=10)

shreyansh26

import math
import torch
import torch.nn as nn
from torch.nn import functional as F


class RelativePositionBias(nn.Module):
    def __init__(self, bidirectional=True, num_buckets=32, max_distance=128, n_heads=2):
        super(RelativePositionBias, self).__init__()
        self.bidirectional = bidirectional

shreyansh26

import numpy as np  # import numpy library
from util.paramInitializer import initialize_parameters  # import function to initialize weights and biases


class LinearLayer:
    """
        This Class implements all functions to be executed by a linear layer
        in a computational graph

        Args:

shreyansh26

#!/usr/bin/python
 
from z3 import *
 
s = [BitVec("s[%d]" % i,32)for i in range(0,8)]
 
# shouldve
z3_solver = Solver()
flag = ""
for i in range(0,len(s)):

shreyansh26

if(strlen(input) != 23) {
    print_wrong;
    return 0;
}
if((input[4] ^ 0x6c) != 0) {
    print_wrong;
    return 0;
}
 
if(input[3] + 1 != input[6]) {