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eteq/C quick inner-looping with numpy.ipynb

Created November 10, 2017 02:37
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C quick inner-looping with numpy.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Easy(ish) notebook-based inner-loop optimization with C"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Python "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def py_addmul(inarr, tomul, toadd):\n",
" out = [val*tomul + toadd for val in inarr]\n",
" return np.array(out)\n",
"\n",
"def np_addmul(inarr, tomul, toadd):\n",
" return inarr*tomul + toadd "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.2, 3.2, 5.2, 7.2, 9.2, 11.2, 13.2, 15.2, 17.2, 19.2])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"py_addmul(np.arange(10, dtype=float), 2, 1.2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.2, 3.2, 5.2, 7.2, 9.2, 11.2, 13.2, 15.2, 17.2, 19.2])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np_addmul(np.arange(10, dtype=float), 2, 1.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cython "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import Cython\n",
"%load_ext Cython"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%cython\n",
"\n",
"import numpy as np\n",
"\n",
"def fast_addmul(inarr, tomul, toadd):\n",
" outarr = np.empty_like(inarr)\n",
" cdef double[:] inarr_memview = inarr\n",
" \n",
" c_fast_addmul(inarr_memview, outarr, len(inarr), toadd, tomul)\n",
" \n",
" return outarr\n",
"\n",
"cdef c_fast_addmul(double[:] inarr, double[:] outarr, int arrlen, double toadd, double tomul):\n",
" cdef int i\n",
" for i in range(arrlen):\n",
" outarr[i] = inarr[i]*tomul + toadd"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.2, 3.2, 5.2, 7.2, 9.2, 11.2, 13.2, 15.2, 17.2, 19.2])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fast_addmul(np.arange(10, dtype=float), 2, 1.2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"41 µs ± 1.69 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"2.53 µs ± 94.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
"2.08 µs ± 45.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
]
}
],
"source": [
"n = 100\n",
"\n",
"arr = np.arange(n, dtype=float)\n",
"\n",
"\n",
"%timeit py_addmul(arr, 1.2, 2)\n",
"%timeit np_addmul(arr, 1.2, 2)\n",
"%timeit fast_addmul(arr, 1.2, 2)\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"423 ms ± 29.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"3.15 ms ± 80.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"2.48 ms ± 40.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"n = 1000000\n",
"\n",
"arr = np.arange(n, dtype=float)\n",
"\n",
"%timeit py_addmul(arr, 1.2, 2)\n",
"%timeit np_addmul(arr, 1.2, 2)\n",
"%timeit fast_addmul(arr, 1.2, 2)\n",
"None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Optimize "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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"%%cython -a\n",
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"import numpy as np\n",
"\n",
"def fast_addmul(inarr, tomul, toadd):\n",
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" cdef double[:] inarr_memview = inarr\n",
" \n",
" c_fast_addmul(inarr_memview, outarr, len(inarr), toadd, tomul)\n",
" \n",
" return outarr\n",
"\n",
"cdef c_fast_addmul(double[:] inarr, double[:] outarr, int arrlen, double toadd, double tomul):\n",
" cdef int i\n",
" for i in range(arrlen):\n",
" outarr[i] = inarr[i]*tomul + toadd"
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"<pre class=\"cython line score-28\" onclick='toggleDiv(this)'>+<span class=\"\">09</span>: <span class=\"n\">c_fast_addmul_opt</span><span class=\"p\">(</span><span class=\"n\">inarr_memview</span><span class=\"p\">,</span> <span class=\"n\">outarr</span><span class=\"p\">,</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">inarr</span><span class=\"p\">),</span> <span class=\"n\">toadd</span><span class=\"p\">,</span> <span class=\"n\">tomul</span><span class=\"p\">)</span></pre>\n",
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"<pre class=\"cython line score-1\" onclick='toggleDiv(this)'>+<span class=\"\">14</span>: <span class=\"k\">cdef</span> <span class=\"nf\">c_fast_addmul_opt</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">inarr</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">outarr</span><span class=\"p\">,</span> <span class=\"nb\">int</span> <span class=\"n\">arrlen</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">toadd</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">tomul</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-1 '>static PyObject *__pyx_f_46_cython_magic_eb169e70499a2d4a8f6fd3a12b169ce5_c_fast_addmul_opt(__Pyx_memviewslice __pyx_v_inarr, __Pyx_memviewslice __pyx_v_outarr, int __pyx_v_arrlen, double __pyx_v_toadd, double __pyx_v_tomul) {\n",
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"<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">16</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">arrlen</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_1 = __pyx_v_arrlen;\n",
" for (__pyx_t_2 = 0; __pyx_t_2 &lt; __pyx_t_1; __pyx_t_2+=1) {\n",
" __pyx_v_i = __pyx_t_2;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">17</span>: <span class=\"n\">outarr</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">inarr</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">tomul</span> <span class=\"o\">+</span> <span class=\"n\">toadd</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_3 = __pyx_v_i;\n",
" if (__pyx_t_3 &lt; 0) __pyx_t_3 += __pyx_v_inarr.shape[0];\n",
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" if (__pyx_t_4 &lt; 0) __pyx_t_4 += __pyx_v_outarr.shape[0];\n",
" *((double *) ( /* dim=0 */ (__pyx_v_outarr.data + __pyx_t_4 * __pyx_v_outarr.strides[0]) )) = (((*((double *) ( /* dim=0 */ (__pyx_v_inarr.data + __pyx_t_3 * __pyx_v_inarr.strides[0]) ))) * __pyx_v_tomul) + __pyx_v_toadd);\n",
" }\n",
"</pre></div></body></html>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%cython -a\n",
"\n",
"import cython\n",
"import numpy as np\n",
"\n",
"def fast_addmul_opt(inarr, tomul, toadd):\n",
" outarr = np.empty_like(inarr)\n",
" cdef double[:] inarr_memview = inarr\n",
" \n",
" c_fast_addmul_opt(inarr_memview, outarr, len(inarr), toadd, tomul)\n",
" \n",
" return outarr\n",
"\n",
"@cython.boundscheck(False)\n",
"cdef c_fast_addmul_opt(double[:] inarr, double[:] outarr, int arrlen, double toadd, double tomul):\n",
" cdef int i\n",
" for i in range(arrlen):\n",
" outarr[i] = inarr[i]*tomul + toadd"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"474 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"365 µs ± 39.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"n = 500000\n",
"\n",
"arr = np.arange(n, dtype=float)\n",
"\n",
"%timeit fast_addmul(arr, 1.2, 2)\n",
"%timeit fast_addmul_opt(arr, 1.2, 2)\n",
"None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# cffi in \"inline\" mode "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from cffi import FFI"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"generating ./_cffi_addmul_mod.c\n",
"(already up-to-date)\n",
"running build_ext\n",
"building '_cffi_addmul_mod' extension\n",
"clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/erik/miniconda3/include -arch x86_64 -I/Users/erik/miniconda3/include/python3.5m -c _cffi_addmul_mod.c -o ./_cffi_addmul_mod.o\n",
"clang -bundle -undefined dynamic_lookup -L/Users/erik/miniconda3/lib -arch x86_64 ./_cffi_addmul_mod.o -L/Users/erik/miniconda3/lib -o ./_cffi_addmul_mod.cpython-35m-darwin.so\n"
]
},
{
"data": {
"text/plain": [
"'/Users/erik/tmp/_cffi_addmul_mod.cpython-35m-darwin.so'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ffibuilder = FFI()\n",
"\n",
"ffibuilder.cdef(\"void cffi_addmul(double *, double *, double add, double mul, int arrlen);\")\n",
"\n",
"ffibuilder.set_source(\"_cffi_addmul_mod\",\n",
"r\"\"\"\n",
"void cffi_addmul(double * a, double * b, double add, double mul, int arrlen) {\n",
" for (int i=0;i<arrlen;i++){\n",
" b[i] = a[i]*mul+add;\n",
" }\n",
"}\n",
"\"\"\")\n",
"\n",
"ffibuilder.compile(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from _cffi_addmul_mod import ffi, lib"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cffi_addmul(inarr, toadd, tomul):\n",
" outarr = np.empty_like(inarr)\n",
" \n",
" inptr = ffi.cast(\"double *\", ffi.from_buffer(inarr))\n",
" outptr = ffi.cast(\"double *\", ffi.from_buffer(outarr))\n",
" \n",
" lib.cffi_addmul(inptr, outptr, toadd, tomul, len(inarr))\n",
" \n",
" return outarr"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.2, 3.2, 5.2, 7.2, 9.2, 11.2, 13.2, 15.2, 17.2, 19.2])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cffi_addmul(np.arange(10, dtype=float), 1.2, 2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"468 µs ± 7.51 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"400 µs ± 26.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"338 µs ± 25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"n = 500000\n",
"\n",
"arr = np.arange(n, dtype=float)\n",
"\n",
"%timeit fast_addmul(arr, 1.2, 2)\n",
"%timeit fast_addmul_opt(arr, 1.2, 2)\n",
"%timeit cffi_addmul(arr, 1.2, 2)\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"202 ms ± 9.11 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"692 µs ± 5.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"470 µs ± 7.27 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"347 µs ± 7.75 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"314 µs ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"n = 500000\n",
"\n",
"arr = np.arange(n, dtype=float)\n",
"\n",
"%timeit py_addmul(arr, 1.2, 2)\n",
"%timeit np_addmul(arr, 1.2, 2)\n",
"%timeit fast_addmul(arr, 1.2, 2)\n",
"%timeit fast_addmul_opt(arr, 1.2, 2)\n",
"%timeit cffi_addmul(arr, 1.2, 2)\n",
"None"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 643.31210191, 2.20382166, 1.49681529, 1.10509554, 1. ]),\n",
" array([ 291.90751445, 1. , 0.67919075, 0.50144509,\n",
" 0.45375723]),\n",
" array([ 1. , 0.00342574, 0.00232673, 0.00171782, 0.00155446]))"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"us = [202000,\n",
" 692,\n",
" 470,\n",
" 347,\n",
" 314]\n",
"us = np.array(us)\n",
"us/us[-1], us/us[1], us/us[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DOOM! (maybe?)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 4.17232506e-08, 1.90195310e+00, 3.15544367e-31,\n",
" 3.25468755e+00, 2.83953730e+31, 5.50014639e+00,\n",
" 3.85365778e+31, 7.50000906e+00, -3.19744223e-15,\n",
" 1.00000019e+01], dtype=float32)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cffi_addmul(np.arange(10, dtype='float32'), 1.2, 2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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