From 46febe05c530580cb42e8ed77c04c6a5133178a9 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Fri, 10 Jan 2025 22:38:23 +0100 Subject: [PATCH] burnin for demean --- benchmarks/gpu_benchmarks.ipynb | 425 +++++++++++++++++++++----------- benchmarks/gpu_results.csv | 48 ++-- 2 files changed, 308 insertions(+), 165 deletions(-) diff --git a/benchmarks/gpu_benchmarks.ipynb b/benchmarks/gpu_benchmarks.ipynb index 30b1a056..eeda48ed 100644 --- a/benchmarks/gpu_benchmarks.ipynb +++ b/benchmarks/gpu_benchmarks.ipynb @@ -29,7 +29,7 @@ "data": { "text/html": [ "\n", - "
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\n", " \n", @@ -69,7 +69,7 @@ "data": { "text/html": [ "\n", - "
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\n", " \n", @@ -124,7 +124,7 @@ "from pyfixest.estimation.demean_ import demean\n", "from pyfixest.estimation.demean_jax_ import demean_jax\n", "\n", - "rng = np.random.default_rng(98768)" + "rng = np.random.default_rng(239291)" ] }, { @@ -136,7 +136,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: 2025-01-10T22:08:06.641925+01:00\n", + "Last updated: 2025-01-10T22:33:11.321606+01:00\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.8\n", @@ -167,9 +167,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:2025-01-10 22:08:06,684:jax._src.xla_bridge:927: Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", + "INFO:2025-01-10 22:33:11,368:jax._src.xla_bridge:927: Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", "INFO:jax._src.xla_bridge:Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'\n", - "INFO:2025-01-10 22:08:06,717:jax._src.xla_bridge:927: Unable to initialize backend 'tpu': UNIMPLEMENTED: LoadPjrtPlugin is not implemented on windows yet.\n", + "INFO:2025-01-10 22:33:11,374:jax._src.xla_bridge:927: Unable to initialize backend 'tpu': UNIMPLEMENTED: LoadPjrtPlugin is not implemented on windows yet.\n", "INFO:jax._src.xla_bridge:Unable to initialize backend 'tpu': UNIMPLEMENTED: LoadPjrtPlugin is not implemented on windows yet.\n" ] }, @@ -378,52 +378,52 @@ { "data": { "text/html": [ - "
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\n", " \n", " \n", @@ -754,8 +760,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -766,8 +772,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -778,7 +784,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -790,8 +796,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -802,8 +808,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -814,8 +820,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
X10.369205***
(0.041655)		0.369205***
(0.041655)		0.257668***
(0.029134)		0.257668***
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fe
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1130.1933730.1316260.0
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81190.1347680.00.1586010.001009
911100.134080.00.1561010.000999
\n", @@ -823,28 +829,28 @@ ], "text/plain": [ " method solver demeaner_backend n_obs k G rep full_feols_timing \\\n", - "0 feols np.linalg.lstsq numba 1000 1 1 1 0.127649 \n", - "1 feols np.linalg.lstsq numba 1000 1 1 2 0.207509 \n", - "2 feols np.linalg.lstsq numba 1000 1 1 3 0.193373 \n", - "3 feols np.linalg.lstsq numba 1000 1 1 4 0.141442 \n", - "4 feols np.linalg.lstsq numba 1000 1 1 5 0.135638 \n", - "5 feols np.linalg.lstsq numba 1000 1 1 6 0.133958 \n", - "6 feols np.linalg.lstsq numba 1000 1 1 7 0.134492 \n", - "7 feols np.linalg.lstsq numba 1000 1 1 8 0.134543 \n", - "8 feols np.linalg.lstsq numba 1000 1 1 9 0.134768 \n", - "9 feols np.linalg.lstsq numba 1000 1 1 10 0.13408 \n", + "0 feols np.linalg.lstsq numba 1000 1 1 1 0.160429 \n", + "1 feols np.linalg.lstsq numba 1000 1 1 2 0.131225 \n", + "2 feols np.linalg.lstsq numba 1000 1 1 3 0.131626 \n", + "3 feols np.linalg.lstsq numba 1000 1 1 4 0.135497 \n", + "4 feols np.linalg.lstsq numba 1000 1 1 5 0.130112 \n", + "5 feols np.linalg.lstsq numba 1000 1 1 6 0.133429 \n", + "6 feols np.linalg.lstsq numba 1000 1 1 7 0.147166 \n", + "7 feols np.linalg.lstsq numba 1000 1 1 8 0.140105 \n", + "8 feols np.linalg.lstsq numba 1000 1 1 9 0.158601 \n", + "9 feols np.linalg.lstsq numba 1000 1 1 10 0.156101 \n", "\n", " demean_timing \n", - "0 3.50542 \n", + "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", - "3 0.0 \n", - "4 0.001001 \n", - "5 0.000996 \n", + "3 0.001003 \n", + "4 0.001006 \n", + "5 0.0 \n", "6 0.0 \n", - "7 0.0 \n", - "8 0.0 \n", - "9 0.0 " + "7 0.000984 \n", + "8 0.001009 \n", + "9 0.000999 " ] }, "execution_count": 10, @@ -919,8 +925,8 @@ " 1\n", " 1\n", " 1\n", - " 0.136045\n", - " 0.13469\n", + " 0.146162\n", + " 0.196979\n", " \n", " \n", " 1\n", @@ -931,8 +937,8 @@ " 1\n", " 1\n", " 2\n", - " 0.128686\n", - " 0.011849\n", + " 0.146127\n", + " 0.011776\n", " \n", " \n", " 2\n", @@ -943,8 +949,8 @@ " 1\n", " 1\n", " 3\n", - " 0.136943\n", - " 0.010932\n", + " 0.153895\n", + " 0.012497\n", " \n", " \n", " 3\n", @@ -955,8 +961,8 @@ " 1\n", " 1\n", " 4\n", - " 0.135532\n", - " 0.010759\n", + " 0.164696\n", + " 0.012709\n", " \n", " \n", " 4\n", @@ -967,8 +973,8 @@ " 1\n", " 1\n", " 5\n", - " 0.132929\n", - " 0.010949\n", + " 0.147778\n", + " 0.012649\n", " \n", " \n", " 5\n", @@ -979,8 +985,8 @@ " 1\n", " 1\n", " 6\n", - " 0.137025\n", - " 0.010561\n", + " 0.156842\n", + " 0.012004\n", " \n", " \n", " 6\n", @@ -991,8 +997,8 @@ " 1\n", " 1\n", " 7\n", - " 0.134386\n", - " 0.012075\n", + " 0.144683\n", + " 0.012104\n", " \n", " \n", " 7\n", @@ -1003,8 +1009,8 @@ " 1\n", " 1\n", " 8\n", - " 0.133583\n", - " 0.013626\n", + " 0.144666\n", + " 0.011525\n", " \n", " \n", " 8\n", @@ -1015,8 +1021,8 @@ " 1\n", " 1\n", " 9\n", - " 0.128547\n", - " 0.0109\n", + " 0.138009\n", + " 0.009692\n", " \n", " \n", " 9\n", @@ -1027,8 +1033,8 @@ " 1\n", " 1\n", " 10\n", - " 0.135759\n", - " 0.01371\n", + " 0.13397\n", + " 0.01277\n", " \n", " \n", "\n", @@ -1036,28 +1042,28 @@ ], "text/plain": [ " method solver demeaner_backend n_obs k G rep full_feols_timing \\\n", - "0 feols np.linalg.lstsq jax 1000 1 1 1 0.136045 \n", - "1 feols np.linalg.lstsq jax 1000 1 1 2 0.128686 \n", - "2 feols np.linalg.lstsq jax 1000 1 1 3 0.136943 \n", - "3 feols np.linalg.lstsq jax 1000 1 1 4 0.135532 \n", - "4 feols np.linalg.lstsq jax 1000 1 1 5 0.132929 \n", - "5 feols np.linalg.lstsq jax 1000 1 1 6 0.137025 \n", - "6 feols np.linalg.lstsq jax 1000 1 1 7 0.134386 \n", - "7 feols np.linalg.lstsq jax 1000 1 1 8 0.133583 \n", - "8 feols np.linalg.lstsq jax 1000 1 1 9 0.128547 \n", - "9 feols np.linalg.lstsq jax 1000 1 1 10 0.135759 \n", + "0 feols np.linalg.lstsq jax 1000 1 1 1 0.146162 \n", + "1 feols np.linalg.lstsq jax 1000 1 1 2 0.146127 \n", + "2 feols np.linalg.lstsq jax 1000 1 1 3 0.153895 \n", + "3 feols np.linalg.lstsq jax 1000 1 1 4 0.164696 \n", + "4 feols np.linalg.lstsq jax 1000 1 1 5 0.147778 \n", + "5 feols np.linalg.lstsq jax 1000 1 1 6 0.156842 \n", + "6 feols np.linalg.lstsq jax 1000 1 1 7 0.144683 \n", + "7 feols np.linalg.lstsq jax 1000 1 1 8 0.144666 \n", + "8 feols np.linalg.lstsq jax 1000 1 1 9 0.138009 \n", + "9 feols np.linalg.lstsq jax 1000 1 1 10 0.13397 \n", "\n", " demean_timing \n", - "0 0.13469 \n", - "1 0.011849 \n", - "2 0.010932 \n", - "3 0.010759 \n", - "4 0.010949 \n", - "5 0.010561 \n", - "6 0.012075 \n", - "7 0.013626 \n", - "8 0.0109 \n", - "9 0.01371 " + "0 0.196979 \n", + "1 0.011776 \n", + "2 0.012497 \n", + "3 0.012709 \n", + "4 0.012649 \n", + "5 0.012004 \n", + "6 0.012104 \n", + "7 0.011525 \n", + "8 0.009692 \n", + "9 0.01277 " ] }, "execution_count": 11, @@ -1156,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "colab": { "background_save": true, @@ -1177,7 +1183,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running Benchmarks: 11%|█▏ | 11/96 [03:56<1:19:58, 56.45s/it]" + "Running Benchmarks: 100%|██████████| 24/24 [02:38<00:00, 6.59s/it]\n" ] } ], @@ -1192,7 +1198,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "colab": { "background_save": true, @@ -1208,7 +1214,106 @@ "id": "7zEIHj5nXXvq", "outputId": "238dedce-a9f5-4a1b-b9a2-b84d4a89c091" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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methoddemeaner_backendkGn_obsfull_feols_timingdemean_timing
0feolsjax11100000.228840.082133
1feolsjax111000000.2783820.659936
2feolsjax12100000.2594130.070082
3feolsjax121000000.5564650.676255
4feolsjax13100000.294520.080892
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" + ], + "text/plain": [ + " method demeaner_backend k G n_obs full_feols_timing demean_timing\n", + "0 feols jax 1 1 10000 0.22884 0.082133\n", + "1 feols jax 1 1 100000 0.278382 0.659936\n", + "2 feols jax 1 2 10000 0.259413 0.070082\n", + "3 feols jax 1 2 100000 0.556465 0.676255\n", + "4 feols jax 1 3 10000 0.29452 0.080892" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = (\n", " res_all.drop([\"rep\", \"solver\"], axis=1)\n", @@ -1221,7 +1326,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1242,7 +1347,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1300,18 +1405,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize_results(df, \"full_feols_timing\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize_results(df, \"demean_timing\")" ] diff --git a/benchmarks/gpu_results.csv b/benchmarks/gpu_results.csv index 2cac72fc..1ab18b30 100644 --- a/benchmarks/gpu_results.csv +++ b/benchmarks/gpu_results.csv @@ -1,25 +1,25 @@ ,method,demeaner_backend,k,G,n_obs,full_feols_timing,demean_timing -0,feols,jax,1,1,10000,0.27587409019470216,0.07204418182373047 -1,feols,jax,1,1,100000,0.3294011354446411,0.6732793092727661 -2,feols,jax,1,2,10000,0.3239504098892212,0.08255081176757813 -3,feols,jax,1,2,100000,0.8856719970703125,1.101165223121643 -4,feols,jax,1,3,10000,0.4663480281829834,0.11542174816131592 -5,feols,jax,1,3,100000,0.9265661239624023,0.9134762048721313 -6,feols,jax,10,1,10000,0.286394739151001,0.0806546688079834 -7,feols,jax,10,1,100000,0.4652519702911377,1.1236234903335571 -8,feols,jax,10,2,10000,0.33887062072753904,0.10658795833587646 -9,feols,jax,10,2,100000,0.9290453195571899,1.1521157503128052 -10,feols,jax,10,3,10000,0.35740249156951903,0.0934922456741333 -11,feols,jax,10,3,100000,1.0702104330062867,1.0646081447601319 -12,feols,numba,1,1,10000,0.28046157360076907,0.002096128463745117 -13,feols,numba,1,1,100000,0.3228717088699341,0.021485161781311036 -14,feols,numba,1,2,10000,0.2938842296600342,0.0024126768112182617 -15,feols,numba,1,2,100000,0.33892054557800294,0.02569558620452881 -16,feols,numba,1,3,10000,0.2918412685394287,0.002186441421508789 -17,feols,numba,1,3,100000,0.3111621141433716,0.01918654441833496 -18,feols,numba,10,1,10000,0.288060736656189,0.00507059097290039 -19,feols,numba,10,1,100000,0.3898226976394653,0.07133381366729737 -20,feols,numba,10,2,10000,0.3217403173446655,0.004642939567565918 -21,feols,numba,10,2,100000,0.3861077308654785,0.05822854042053223 -22,feols,numba,10,3,10000,0.29346532821655275,0.005108952522277832 -23,feols,numba,10,3,100000,0.4223452091217041,0.06810879707336426 +0,feols,jax,1,1,10000,0.22883999347686768,0.08213257789611816 +1,feols,jax,1,1,100000,0.27838222980499266,0.6599355697631836 +2,feols,jax,1,2,10000,0.2594131946563721,0.07008242607116699 +3,feols,jax,1,2,100000,0.5564646482467651,0.6762551069259644 +4,feols,jax,1,3,10000,0.29452004432678225,0.08089225292205811 +5,feols,jax,1,3,100000,0.7971727609634399,0.7361236095428467 +6,feols,jax,10,1,10000,0.23609719276428223,0.08923766613006592 +7,feols,jax,10,1,100000,0.35655946731567384,0.8534042596817016 +8,feols,jax,10,2,10000,0.2677977800369263,0.07926526069641113 +9,feols,jax,10,2,100000,0.7200102090835572,0.880342435836792 +10,feols,jax,10,3,10000,0.30958850383758546,0.0926206350326538 +11,feols,jax,10,3,100000,0.9418511629104614,0.8934823036193847 +12,feols,numba,1,1,10000,0.2558188199996948,0.0025653839111328125 +13,feols,numba,1,1,100000,0.26205954551696775,0.025234317779541014 +14,feols,numba,1,2,10000,0.23201637268066405,0.0021322011947631837 +15,feols,numba,1,2,100000,0.33342432975769043,0.03563899993896484 +16,feols,numba,1,3,10000,0.23092274665832518,0.0023543596267700194 +17,feols,numba,1,3,100000,0.25670347213745115,0.021407365798950195 +18,feols,numba,10,1,10000,0.4105666160583496,0.0046176910400390625 +19,feols,numba,10,1,100000,0.32753868103027345,0.08355832099914551 +20,feols,numba,10,2,10000,0.23588848114013672,0.005055141448974609 +21,feols,numba,10,2,100000,0.3476848602294922,0.0862342357635498 +22,feols,numba,10,3,10000,0.25297954082489016,0.005847930908203125 +23,feols,numba,10,3,100000,0.3176179170608521,0.056423068046569824