Matt Walsh mattwalsh

## maker.py
#!/usr/bin/python3

import sys
from string import Template

# fun defaults
M = 4
N = 3
K = 2

## matmul.py
#!/usr/bin/python3
import sys
import os
import time
import re
import tempfile
import subprocess
import argparse
import numpy as np
from string import Template

## intrinsic_fun.cpp
include <cstdlib>
#include <iostream>

#define FORCE_INLINE __attribute__((always_inline))
#define MASK_TRUE 0xffffffff

// our machine's vector length.  This #defins is a bit naive in that the
//  vector length would be a function of the datatype (e.g. on AVX512,
//  you'd have 16 floats but only 8 double's
#define VLEN 16

## attention_toy.py
#!bin/python

# main YouTube I learned from: https://www.youtube.com/watch?v=g2BRIuln4uc
# down the rabbit hole from: https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/

import os
import sys

import numpy as np
import pandas as pd
	#!/usr/bin/python3

	import sys
	from string import Template

	# fun defaults
	M = 4
	N = 3
	K = 2
	#!/usr/bin/python3
	import sys
	import os
	import time
	import re
	import tempfile
	import subprocess
	import argparse
	import numpy as np
	from string import Template
	include <cstdlib>
	#include <iostream>

	#define FORCE_INLINE __attribute__((always_inline))
	#define MASK_TRUE 0xffffffff

	// our machine's vector length. This #defins is a bit naive in that the
	// vector length would be a function of the datatype (e.g. on AVX512,
	// you'd have 16 floats but only 8 double's
	#define VLEN 16
	#!bin/python

	# main YouTube I learned from: https://www.youtube.com/watch?v=g2BRIuln4uc
	# down the rabbit hole from: https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/

	import os
	import sys

	import numpy as np
	import pandas as pd