diff --git a/dashboard.py b/dashboard.py index 01a2ba7..f7231fd 100644 --- a/dashboard.py +++ b/dashboard.py @@ -1,145 +1,164 @@ -import os -from kernel_tuner import read_cache # actually kernel_tuner.util read_cache - -import panel as pn -import panel.widgets as pnw -import pandas as pd -from bokeh.models import HoverTool, LinearColorMapper -from bokeh.plotting import ColumnDataSource, figure -pn.extension() - - -class KTdashboard: - """ Main object to instantiate to hold everything related to a running dashboard""" - - def __init__(self, cache_file, demo=False): - self.demo = demo - self.cache_file = cache_file - # read in the cachefile - self.cache_last_modified = os.stat(cache_file).st_mtime - cached_data = read_cache(cache_file, open_cache=False) - self.kernel_name = cached_data["kernel_name"] - self.device_name = cached_data["device_name"] - - # get the performance data - data = list(cached_data["cache"].values()) - data = [d for d in data if d["time"] != 1e20] - - # use all data or just the first 1000 in demo mode - self.index = len(data) - if self.demo: - self.index = 1000 - - # figure out which keys are interesting - single_value_tune_param_keys = [key for key in cached_data["tune_params_keys"] if len(cached_data["tune_params"][key]) == 1] - tune_param_keys = [key for key in cached_data["tune_params_keys"] if key not in single_value_tune_param_keys] - single_value_keys = [key for key in data[0].keys() if not isinstance(data[0][key],list) and key not in single_value_tune_param_keys] - output_keys = [key for key in single_value_keys if key not in tune_param_keys] - float_keys = [key for key in output_keys if isinstance(data[0][key], float)] - - self.single_value_tune_param_keys = single_value_tune_param_keys - self.tune_param_keys = tune_param_keys - self.single_value_keys = single_value_keys - self.output_keys = output_keys - self.float_keys = float_keys - - self.data_df = pd.DataFrame(data[:self.index])[single_value_keys] - self.source = ColumnDataSource(data=self.data_df) - self.data = data - - plot_options=dict(plot_height=600, plot_width=900) - plot_options['tools'] = [HoverTool(tooltips=[(k, "@{"+k+"}" + ("{0.00}" if k in float_keys else "")) for k in single_value_keys]), "box_select,box_zoom,save,reset"] - - self.plot_options = plot_options - - # setup widgets - self.yvariable = pnw.Select(name='Y', value='GFLOP/s', options=single_value_keys) - self.xvariable = pnw.Select(name='X', value='index', options=['index']+single_value_keys) - self.colorvariable = pnw.Select(name='Color By', value='GFLOP/s', options=single_value_keys) - - self.scatter = pn.bind(self.make_scatter, xvariable=self.xvariable, yvariable=self.yvariable, color_by=self.colorvariable) - - self.dashboard = pn.template.BootstrapTemplate(title='Kernel Tuner Dashboard') - self.dashboard.sidebar.append(pn.Column(self.yvariable, self.xvariable, self.colorvariable)) - self.dashboard.main.append(self.scatter) - self.dashboard.servable() - - def update_colors(self, color_by): - color_mapper = LinearColorMapper(palette='Viridis256', low=min(self.data_df[color_by]), - high=max(self.data_df[color_by])) - color = {'field': color_by, 'transform': color_mapper} - return color - - def make_scatter(self, xvariable, yvariable, color_by): - - color = self.update_colors(color_by) - - x = xvariable - y = yvariable - - f = figure(**self.plot_options) - f.circle(x, y, size=5, color=color, alpha=0.5, source=self.source) - f.xaxis.axis_label = x - f.yaxis.axis_label = y - - pane = pn.Column(pn.pane.Markdown(f"## Auto-tuning {self.kernel_name} on {self.device_name}"), pn.pane.Bokeh(f)) - - return pane - - def update_plot(self, i): - stream_dict = {k:[v] for k,v in dict(self.data[i], index=i).items() if k in ['index']+self.single_value_keys} - self.source.stream(stream_dict) - - def update_data(self): - if os.stat(self.cache_file).st_mtime > self.cache_last_modified: - cached_data = read_cache(self.cache_file, open_cache=False) - - data = list(cached_data["cache"].values()) - data = [d for d in data if d["time"] != 1e20] - - # stream the data that has not been streamed yet - self.source.stream(pd.DataFrame([data[self.index:]])) # this may not work, we might need to convert it into a dict like in update_plot - self.index = len(self.data) - if self.demo: - if self.index < (len(self.data)-1): - self.update_plot(self.index) - self.index += 1 - - - -def print_usage(): - print("Usage: ./dashboard.py [-demo] filename") - print(" -demo option to enable demo mode that mimicks a running Kernel Tuner session") - print(" filename name of the cachefile") - exit(0) - - -if __name__ == "__main__": - - if len(sys.argv) < 2: - print_usage() - - filename = "" - demo = False - if len(sys.argv) == 2: - if os.path.isfile(sys.argv[1]): - filename = sys.argv[1] - else: - print("Cachefile not found") - exit(1) - elif len(sys.argv) == 3: - if sys.argv[1] == "-demo": - demo = True - else: - print_usage() - if os.path.isfile(sys.argv[2]): - filename = sys.argv[2] - - - db = KTdashboard(filename, demo=demo) - - def dashboard_f(): - pn.state.add_periodic_callback(db.update_data, 1000) - return db.dashboard - - server = pn.serve(dashboard_f) +#!/usr/bin/env python +import json +import sys +import os + +import panel as pn +import panel.widgets as pnw +import pandas as pd +from bokeh.models import HoverTool, LinearColorMapper +from bokeh.plotting import ColumnDataSource, figure + + +class KTdashboard: + """ Main object to instantiate to hold everything related to a running dashboard""" + + def __init__(self, cache_file, demo=False): + self.demo = demo + self.cache_file = cache_file + + # read in the cachefile + self.cache_file_handle = open(cache_file, "r") + filestr = self.cache_file_handle.read().strip() + # if file was not properly closed, pretend it was properly closed + if not filestr[-3:] == "}\n}": + # remove the trailing comma if any, and append closing brackets + if filestr[-1] == ",": + filestr = filestr[:-1] + filestr = filestr + "}\n}" + + cached_data = json.loads(filestr) + self.kernel_name = cached_data["kernel_name"] + self.device_name = cached_data["device_name"] + + # get the performance data + data = list(cached_data["cache"].values()) + data = [d for d in data if d["time"] != 1e20] + + # use all data or just the first 1000 in demo mode + self.index = len(data) + if self.demo: + self.index = 1000 + + # figure out which keys are interesting + single_value_tune_param_keys = [key for key in cached_data["tune_params_keys"] if len(cached_data["tune_params"][key]) == 1] + tune_param_keys = [key for key in cached_data["tune_params_keys"] if key not in single_value_tune_param_keys] + single_value_keys = [key for key in data[0].keys() if not isinstance(data[0][key],list) and key not in single_value_tune_param_keys] + output_keys = [key for key in single_value_keys if key not in tune_param_keys] + float_keys = [key for key in output_keys if isinstance(data[0][key], float)] + + self.single_value_tune_param_keys = single_value_tune_param_keys + self.tune_param_keys = tune_param_keys + self.single_value_keys = single_value_keys + self.output_keys = output_keys + self.float_keys = float_keys + + self.data_df = pd.DataFrame(data[:self.index])[single_value_keys] + self.source = ColumnDataSource(data=self.data_df) + self.data = data + + plot_options=dict(plot_height=600, plot_width=900) + plot_options['tools'] = [HoverTool(tooltips=[(k, "@{"+k+"}" + ("{0.00}" if k in float_keys else "")) for k in single_value_keys]), "box_select,box_zoom,save,reset"] + + self.plot_options = plot_options + + # setup widgets + self.yvariable = pnw.Select(name='Y', value='GFLOP/s', options=single_value_keys) + self.xvariable = pnw.Select(name='X', value='index', options=['index']+single_value_keys) + self.colorvariable = pnw.Select(name='Color By', value='GFLOP/s', options=single_value_keys) + + # connect widgets with the function that draws the scatter plot + self.scatter = pn.bind(self.make_scatter, xvariable=self.xvariable, yvariable=self.yvariable, color_by=self.colorvariable) + + # actually build up the dashboard + self.dashboard = pn.template.BootstrapTemplate(title='Kernel Tuner Dashboard') + self.dashboard.sidebar.append(pn.Column(self.yvariable, self.xvariable, self.colorvariable)) + self.dashboard.main.append(self.scatter) + self.dashboard.servable() + + def __del__(self): + self.cache_file_handle.close() + + def update_colors(self, color_by): + color_mapper = LinearColorMapper(palette='Viridis256', low=min(self.data_df[color_by]), + high=max(self.data_df[color_by])) + color = {'field': color_by, 'transform': color_mapper} + return color + + def make_scatter(self, xvariable, yvariable, color_by): + color = self.update_colors(color_by) + + x = xvariable + y = yvariable + + f = figure(**self.plot_options) + f.circle(x, y, size=5, color=color, alpha=0.5, source=self.source) + f.xaxis.axis_label = x + f.yaxis.axis_label = y + + pane = pn.Column(pn.pane.Markdown(f"## Auto-tuning {self.kernel_name} on {self.device_name}"), pn.pane.Bokeh(f)) + + return pane + + def update_plot(self, i): + stream_dict = {k:[v] for k,v in dict(self.data[i], index=i).items() if k in ['index']+self.single_value_keys} + self.source.stream(stream_dict) + + def update_data(self): + if not self.demo: + new_contents = self.cache_file_handle.read().strip() + if new_contents: + + # process new contents (parse as JSON, make into dict that goes into source.stream) + new_contents_json = "{" + new_contents[:-1] + "}" + new_data = list(json.loads(new_contents_json).values()) + + for i,element in enumerate(new_data): + + stream_dict = {k:[v] for k,v in dict(element, index=self.index+i).items() if k in ['index']+self.single_value_keys} + self.source.stream(stream_dict) + + self.index += len(new_data) + + if self.demo: + if self.index < (len(self.data)-1): + self.update_plot(self.index) + self.index += 1 + + + +def print_usage(): + print("Usage: ./dashboard.py [-demo] filename") + print(" -demo option to enable demo mode that mimicks a running Kernel Tuner session") + print(" filename name of the cachefile") + exit(0) + + +if __name__ == "__main__": + + if len(sys.argv) < 2: + print_usage() + + filename = "" + demo = False + if len(sys.argv) == 2: + if os.path.isfile(sys.argv[1]): + filename = sys.argv[1] + else: + print("Cachefile not found") + exit(1) + elif len(sys.argv) == 3: + if sys.argv[1] == "-demo": + demo = True + else: + print_usage() + if os.path.isfile(sys.argv[2]): + filename = sys.argv[2] + + db = KTdashboard(filename, demo=demo) + + def dashboard_f(): + """ wrapper function to add the callback, doesn't work without this construct """ + pn.state.add_periodic_callback(db.update_data, 1000) + return db.dashboard + server = pn.serve(dashboard_f) diff --git a/tests/simulator.py b/tests/simulator.py new file mode 100644 index 0000000..0d17b10 --- /dev/null +++ b/tests/simulator.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python +import shutil +import time +import json + +from kernel_tuner.util import read_cache + + + + +if __name__ == "__main__": + + target_file = "test_cache.json" + + # reset target_file for appending + shutil.copy("test_cache_template.json", target_file) + + source_cache = read_cache("test_cache_1000.json", open_cache=False)["cache"] + source = list(source_cache.values()) + source_keys = list(source_cache.keys()) + + index = 0 + + with open(target_file, 'a') as fh: + + while index < len(source): + + time.sleep(5) # seconds + + fh.write("\n" + json.dumps({source_keys[index]: source[index]})[1:-1] + ",") + + index += 1 + + fh.flush() diff --git a/tests/test_cache.json b/tests/test_cache.json new file mode 100644 index 0000000..f68bbfa --- /dev/null +++ b/tests/test_cache.json @@ -0,0 +1,867 @@ +{ +"device_name": "NVIDIA A100-PCIE-40GB", +"kernel_name": "Xgemm", +"tune_params_keys": [ +"nvml_pwr_limit", +"nvml_gr_clock", +"MWG", +"NWG", +"KWG", +"MDIMC", +"NDIMC", +"MDIMA", +"NDIMB", +"KWI", +"VWM", +"VWN", +"STRM", +"STRN", +"SA", +"SB", +"PRECISION" +], +"tune_params": { +"nvml_pwr_limit": [ +250, +225, +200, +175, +150 +], +"nvml_gr_clock": [ +1410 +], +"MWG": [ +16, +32, +64, +128 +], +"NWG": [ +16, +32, +64, +128 +], +"KWG": [ +32 +], +"MDIMC": [ +8, +16, +32 +], +"NDIMC": [ +8, +16, +32 +], +"MDIMA": [ +8, +16, +32 +], +"NDIMB": [ +8, +16, +32 +], +"KWI": [ +2 +], +"VWM": [ +1, +2, +4, +8 +], +"VWN": [ +1, +2, +4, +8 +], +"STRM": [ +0 +], +"STRN": [ +0 +], +"SA": [ +0, +1 +], +"SB": [ +0, +1 +], +"PRECISION": [ +32 +] +}, +"cache": { +"250,1410,16,16,32,8,8,8,8,2,1,1,0,0,0,0,32": {"nvml_pwr_limit": 250, "nvml_gr_clock": 1410, "MWG": 16, "NWG": 16, "KWG": 32, "MDIMC": 8, "NDIMC": 8, "MDIMA": 8, "NDIMB": 8, "KWI": 2, "VWM": 1, "VWN": 1, "STRM": 0, "STRN": 0, "SA": 0, "SB": 0, "PRECISION": 32, "time": 34.469408, "nvml_energys": [5.413259627938269, 8.178231586933137, 8.050311354875566, 7.725465894699097, 8.024782383441924, 9.124794993638991, 10.261546146869659, 10.930669015645982, 6.106207755208016, 5.729768569469451, 7.8791889013051986, 9.518414793491363, 10.039209164857866, 7.1156847213506715, 7.05137178325653, 8.741471053123474, 9.874504126548768, 10.340653751850128, 7.189362099289894, 7.113114807128906, 8.79900212609768, 9.768010798931122, 10.227754051208496, 7.263797357082367, 7.181563110351562, 8.539362243413926, 10.268449327707291, 11.242832755804061, 6.084804659128189, 5.642959995269776, 7.580417582273483, 9.990105897903444], "nvml_energy": 8.343658513627947, "temperatures": [34, 36, 36, 36, 37, 36, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 37, 38, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38], "temperature": 37.28125, "core_freqs": [1410, 1350, 1335, 1335, 1335, 1335, 1335, 1335, 1350, 1335, 1335, 1350, 1335, 1350, 1335, 1335, 1350, 1335, 1350, 1335, 1335, 1350, 1335, 1350, 1335, 1335, 1335, 1335, 1350, 1335, 1335, 1335], "core_freq": 1341.5625, "mem_freqs": [1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215], "mem_freq": 1215.0, "times": [33.811456, 33.910784, 34.425855999999996, 34.514944, 34.503679999999996, 34.54464, 34.536448, 34.516992, 34.535424, 34.448384, 34.64704, 34.576384, 34.467839999999995, 34.500608, 34.40128, 34.547712, 34.535424, 34.42176, 34.543616, 34.468863999999996, 34.614272, 34.535424, 34.431999999999995, 34.47296, 34.410496, 34.546687999999996, 34.543616, 34.497536, 34.456576, 34.417663999999995, 34.658304, 34.576384], "GFLOP/s": 3988.24685077272, "GFLOPS/W": 16.47628647306958}, + +"250,1410,16,16,32,8,8,8,8,2,1,1,0,0,0,0,32": {"nvml_pwr_limit": 250, "nvml_gr_clock": 1410, "MWG": 16, "NWG": 16, "KWG": 32, "MDIMC": 8, "NDIMC": 8, "MDIMA": 8, "NDIMB": 8, "KWI": 2, "VWM": 1, "VWN": 1, "STRM": 0, "STRN": 0, "SA": 0, "SB": 0, "PRECISION": 32, "time": 34.469408, "nvml_energys": [5.413259627938269, 8.178231586933137, 8.050311354875566, 7.725465894699097, 8.024782383441924, 9.124794993638991, 10.261546146869659, 10.930669015645982, 6.106207755208016, 5.729768569469451, 7.8791889013051986, 9.518414793491363, 10.039209164857866, 7.1156847213506715, 7.05137178325653, 8.741471053123474, 9.874504126548768, 10.340653751850128, 7.189362099289894, 7.113114807128906, 8.79900212609768, 9.768010798931122, 10.227754051208496, 7.263797357082367, 7.181563110351562, 8.539362243413926, 10.268449327707291, 11.242832755804061, 6.084804659128189, 5.642959995269776, 7.580417582273483, 9.990105897903444], "nvml_energy": 8.343658513627947, "temperatures": [34, 36, 36, 36, 37, 36, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 37, 38, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38], "temperature": 37.28125, "core_freqs": [1410, 1350, 1335, 1335, 1335, 1335, 1335, 1335, 1350, 1335, 1335, 1350, 1335, 1350, 1335, 1335, 1350, 1335, 1350, 1335, 1335, 1350, 1335, 1350, 1335, 1335, 1335, 1335, 1350, 1335, 1335, 1335], "core_freq": 1341.5625, "mem_freqs": [1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215, 1215], "mem_freq": 1215.0, "times": [33.811456, 33.910784, 34.425855999999996, 34.514944, 34.503679999999996, 34.54464, 34.536448, 34.516992, 34.535424, 34.448384, 34.64704, 34.576384, 34.467839999999995, 34.500608, 34.40128, 34.547712, 34.535424, 34.42176, 34.543616, 34.468863999999996, 34.614272, 34.535424, 34.431999999999995, 34.47296, 34.410496, 34.546687999999996, 34.543616, 34.497536, 34.456576, 34.417663999999995, 34.658304, 34.576384], "GFLOP/s": 3988.24685077272, "GFLOPS/W": 16.47628647306958}, +"250,1410,16,16,32,8,8,8,8,2,1,1,0,0,0,1,32": {"nvml_pwr_limit": 250, "nvml_gr_clock": 1410, "MWG": 16, "NWG": 16, "KWG": 32, "MDIMC": 8, "NDIMC": 8, "MDIMA": 8, "NDIMB": 8, "KWI": 2, "VWM": 1, "VWN": 1, "STRM": 0, "STRN": 0, "SA": 0, "SB": 1, "PRECISION": 32, "time": 29.01648, "nvml_energys": [4.606566130757332, 8.028576171875, 8.040655273437501, 9.843196075439453, 8.44529780960083, 8.081400890350343, 9.603330582618714, 9.637184114456177, 8.322972230911255, 8.67029494690895, 8.382404590129852, 8.158714755535126, 9.5080076546669, 8.511955288887023, 8.25285839033127, 8.3342281165123, 7.741019731044771, 9.300376113176346, 8.043710414409638, 7.1191735525131215, 7.250252138614654, 7.382073865413666, 7.150414837479591, 5.987829673290253, 6.411851961612702, 7.355748955488205, 7.842795926332473, 6.977309796094895, 8.084054971694947, 9.054225688934327, 8.249393894910813, 7.988554468393326], "nvml_energy": 8.011450906619428, "temperatures": [35, 37, 38, 38, 38, 38, 39, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 39, 39, 40, 39, 40, 39, 40, 40, 39, 39], "temperature": 38.875, "core_freqs": [1335, 1365, 1305, 1275, 1320, 1320, 1275, 1320, 1320, 1290, 1320, 1320, 1275, 1320, 1305, 1320, 1305, 1275, 1320, 1290, 1305, 1305, 1290, 1335, 1305, 1290, 1305, 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