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cephi_sui 2024-12-16 00:10:30 -05:00
parent cf56df3114
commit 1ba4a701cf
2 changed files with 127 additions and 49 deletions
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48
analysis/data_stat.py
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@ -3,37 +3,37 @@ import re
from enum import Enum
class Stat(Enum):
CPU = 'cpu'
CPU = 'CPU'
CORES = 'cores'
ITERATIONS = 'iterations'
BASELINE_TIME_S = 'baseline time (sec)'
BASELINE_DELAY_S = 'baseline delay (sec)'
CORES = 'Cores'
ITERATIONS = 'Iterations'
BASELINE_TIME_S = 'Baseline Time (sec)'
BASELINE_DELAY_S = 'Baseline Delay (sec)'
SOLVER = 'solver'
SOLVER = 'Solver'
MATRIX_TYPE = 'matrix type'
MATRIX_FILE = 'matrix file'
MATRIX_FORMAT = 'matrix format'
MATRIX_SHAPE = 'matrix shape'
MATRIX_ROWS = 'matrix rows'
MATRIX_SIZE = 'matrix size'
MATRIX_NNZ = 'matrix nnz'
MATRIX_DENSITY = 'matrix density'
MATRIX_TYPE = 'Matrix Type'
MATRIX_FILE = 'Matrix File'
MATRIX_FORMAT = 'Format'
MATRIX_SHAPE = 'Matrix Shape'
MATRIX_ROWS = 'Rows'
MATRIX_SIZE = 'Matrix Size'
MATRIX_NNZ = 'Number of Non-Zeros'
MATRIX_DENSITY = 'Density'
#POWER_BEFORE = 'power before'
#POWER = 'power'
#POWER_AFTER = 'power after'
TIME_S = 'time (sec)'
TIME_S_1KI = 'time (sec) per 1k iterations'
J = 'joules'
J_1KI = 'joules per 1k iterations'
J_D = 'Δ joules'
J_D_1KI = 'Δ joules per 1k iterations'
W = 'watts'
W_1KI = 'watts per 1k iterations'
W_D = 'Δ watts'
W_D_1KI = 'Δ watts per 1k iterations'
TIME_S = 'Time (s)'
TIME_S_1KI = 'Time (s) per 1k Iterations'
J = 'Joules'
J_1KI = 'Joules per 1k iterations'
J_D = 'Δ Joules'
J_D_1KI = 'Δ Joules per 1k iterations'
W = 'Watts'
W_1KI = 'Watts per 1k iterations'
W_D = 'Δ Watts'
W_D_1KI = 'Δ Watts per 1k iterations'
#DELTA_WATT = 'Δ watt'
TASK_CLK = 'task clock (msec)'

128
pytorch/analyze.py
View File

@ -19,15 +19,32 @@ class Plot(Enum):
BOX = 'box'
LINE = 'line'
def accumulate(data_list: list[dict[str, str | int | float]], category: Stat, value: Stat):
#print(category.name)
#print(value.name)
category_list = np.array([stats[category.name] for stats in data_list if value.name in stats])
def accumulate(
data_list: list[dict[str, str | int | float]],
category: Stat,
category2: Stat,
value: Stat
) -> dict[np.ndarray]:
result: dict[np.ndarray] = dict()
value_list = np.array([stats[value.name] for stats in data_list if value.name in stats])
result: dict[np.ndarray] = dict()
for category in np.sort(np.unique(category_list)):
result[category] = value_list[category_list == category]
if category2 is None:
category_list = np.array([(stats[category.name])
for stats in data_list if value.name in stats])
for category in np.unique(category_list):
result[category] = value_list[category_list == category]
else:
category_list = np.array([(stats[category.name],
stats[category2.name])
for stats in data_list if value.name in stats], dtype='object')
for category in category_list:
mask = np.logical_and(category_list[:, 0] == category[0], category_list[:, 1] == category[1])
assert (tuple(category) not in result.keys()
or np.array_equal(result[tuple(category)], value_list[mask]))
result[tuple(category)] = value_list[mask]
return result
@ -41,25 +58,58 @@ def box_plot(ax, data_list: list[dict[str, str | int | float]], x: Stat, y: Stat
def line_plot(
ax, data_list: list[dict[str, str | int | float]],
x: Stat, y: Stat, color: Stat,
font_size: int
x: Stat, y: Stat, color: Stat, linestyle: Stat
):
print(x)
print(y)
print(color)
x_data: dict[str, np.ndarray] = accumulate(data_list, color, x)
y_data: dict[str, np.ndarray] = accumulate(data_list, color, y)
x_data: dict[str, np.ndarray] = accumulate(data_list, color, linestyle, x)
y_data: dict[str, np.ndarray] = accumulate(data_list, color, linestyle, y)
for category in x_data.keys():
linestyle_tuple = [
('solid', (0, ())),
#('loosely dotted', (0, (1, 10))),
('dotted', (0, (1, 1))),
#('densely dotted', (0, (1, 1))),
('long dash with offset', (5, (10, 3))),
('loosely dashed', (0, (5, 10))),
('dashed', (0, (5, 5))),
('densely dashed', (0, (5, 1))),
('loosely dashdotted', (0, (3, 10, 1, 10))),
('dashdotted', (0, (3, 5, 1, 5))),
('densely dashdotted', (0, (3, 1, 1, 1))),
('dashdotdotted', (0, (3, 5, 1, 5, 1, 5))),
('loosely dashdotdotted', (0, (3, 10, 1, 10, 1, 10))),
('densely dashdotdotted', (0, (3, 1, 1, 1, 1, 1)))]
if linestyle is None:
color_mapping = dict(zip(
sorted({color for color in x_data.keys()}),
matplotlib.colors.BASE_COLORS.values()))
else:
color_mapping = dict(zip(
sorted({color for (color, _) in x_data.keys()}),
matplotlib.colors.BASE_COLORS.values()))
linestyle_mapping = dict(zip(
sorted({linestyle for (_, linestyle) in x_data.keys()}),
[linestyle for (_, linestyle) in linestyle_tuple]))
for category in sorted(x_data.keys()):
sorted_indices = np.argsort(x_data[category])
x_data[category] = x_data[category][sorted_indices]
y_data[category] = y_data[category][sorted_indices]
ax.plot(x_data[category], y_data[category], label=category)
if linestyle is None:
ax.plot(x_data[category], y_data[category], label=str(category), marker='o')
else:
ax.plot(x_data[category], y_data[category], label=str(category[0]) + ", " + str(category[1]),
color=color_mapping[category[0]],
linestyle=linestyle_mapping[category[1]],
marker='o')
#print("Plotted x data: " + str(x_data[category]))
#print("Plotted y data: " + str(y_data[category]))
#for category, (_, linestyle) in zip(sorted(x_data.keys()), linestyle_tuple):
#ax.plot(x_data[category], y_data[category], label=category, marker='o', linestyle=linestyle)
ax.set_ylabel(y.value, fontsize=font_size)
ax.grid(True)
#ax.set_yscale('log')
#ax.set_xscale('log')
def visualize(
data_list: list[dict[str, str | int | float]],
@ -70,6 +120,9 @@ def visualize(
x: Stat,
ys: list[Stat],
color: Stat,
linestyle: Stat,
x_log: bool,
y_log: bool,
filter_list: list[str],
title: str
):
@ -98,22 +151,32 @@ def visualize(
print(int(math.ceil(len(ys) / rows)))
fig, axes = plt.subplots(rows, int(math.ceil(len(ys) / rows)),
figsize = (16 * size_multiplier, 9 * size_multiplier))
print(len(axes))
match plot:
case Plot.BOX:
for i, y in enumerate(ys):
box_plot(axes[i % rows][int(i / rows)], data_list, x, y)
ax = axes[i % rows][int(i / rows)]
box_plot(ax, data_list, x, y)
case Plot.LINE:
for i, y in enumerate(ys):
ax = axes[i % rows][int(i / rows)]
line_plot(ax, data_list, x, y, color, font_size)
ax = axes[i % rows]
if len(ys) > len(axes):
ax = ax[int(i / rows)]
line_plot(ax, data_list, x, y, color, linestyle)
ax.set_ylabel(y.value, fontsize=font_size)
ax.grid(True)
if x_log:
ax.set_xscale('log')
if y_log:
ax.set_yscale('log')
if type(ax.get_xaxis().get_major_formatter()) is matplotlib.ticker.ScalarFormatter:
ax.get_xaxis().get_major_formatter().set_scientific(False)
if type(ax.get_yaxis().get_major_formatter()) is matplotlib.ticker.ScalarFormatter:
ax.get_yaxis().get_major_formatter().set_scientific(False)
#ax.ticklabel_format(axis='both', style='plain')
handles, labels = axes[i % rows][int(i / rows)].get_legend_handles_labels()
handles, labels = ax.get_legend_handles_labels()
# else:
# fig, ax = plt.subplots()
#
@ -135,8 +198,12 @@ def visualize(
# #title = f"{plot.value}_plot_of_{y.replace(' ', '_')}_vs_{x.replace(' ', '_')}_by_{color.replace(' ', '_')}_excluding_{filter_list}"
# title = "altra_spmv"
#title = f'{plot.value} plot of {[y.value for y in ys]} vs {x.value} by {color.value} excluding {filter_list}'
#fig.suptitle(title, fontsize = font_size)
fig.legend(handles, labels, fontsize = font_size)
fig.suptitle(title, fontsize = font_size)
fig.legend(handles, labels,
title=color.value if linestyle is None
else color.value + ", " + linestyle.value,
title_fontsize=font_size,
fontsize = font_size)
fig.supxlabel(x.value, fontsize = font_size)
#title = f'{plot.value} plot of {[y.name for y in ys]} vs {x.name} by {color.name} excluding {filter_list}'
@ -175,6 +242,13 @@ def main():
parser.add_argument('-c', '--color',
choices=[c.name.lower() for c in Stat],
help = 'the name of the color')
parser.add_argument('-l', '--linestyle',
choices=[l.name.lower() for l in Stat],
help = 'the name of the marker')
parser.add_argument('--x_log', action='store_true',
help = 'set x axis scale to log')
parser.add_argument('--y_log', action='store_true',
help = 'set y axis scale to log')
parser.add_argument('-f', '--filter', nargs = '+',
help = 'a comma-separated string of names and values to filter out.',
default = [])
@ -184,6 +258,7 @@ def main():
args.ys = ([Stat[y.upper()] for y in args.ys]
if args.ys is not None else None)
args.color = Stat[args.color.upper()] if args.color is not None else None
args.linestyle = Stat[args.linestyle.upper()] if args.linestyle is not None else None
data_list: list[dict] = list()
@ -202,6 +277,9 @@ def main():
args.x,
args.ys,
args.color,
args.linestyle,
args.x_log,
args.y_log,
args.filter,
args.title
)