def test_Image_kwargs() -> None:
glyph = Image(x=0, y=0, dw=10, dh=10)
assert glyph.image == field("image")
assert glyph.x == 0
assert glyph.y == 0
assert glyph.dw == 10
assert glyph.dh == 10
assert glyph.dilate is False
def test_Image():
glyph = Image()
assert glyph.image is None
assert glyph.x is None
assert glyph.y is None
assert glyph.dw is None
assert glyph.dh is None
assert glyph.dilate == False
yield (check_properties_existence, glyph, [
"image",
"x",
"y",
"dw",
"dw_units",
"dh",
"dh_units",
"dilate",
"color_mapper",
], GLYPH)
def test_Image() -> None:
glyph = Image()
assert glyph.image is None
assert glyph.x == field("x")
assert glyph.y == field("y")
assert glyph.dw is None
assert glyph.dh is None
assert glyph.dilate is False
check_properties_existence(glyph, [
"image",
"x",
"y",
"dw",
"dw_units",
"dh",
"dh_units",
"global_alpha",
"dilate",
"color_mapper",
], GLYPH)
def create_plot(self):
# define list of metric names (needed for tooltip)
tmp = []
metric_names = []
yaxis = {}
# number of datapoints
max_width = 0
for key in self.system_metrics.keys():
if key.startswith("CPUUtilization"):
width = self.system_metrics[key]["total"].shape[0]
if width >= max_width:
max_width = width
self.width = max_width
for dimension in self.filtered_dimensions:
for event in self.filtered_events:
if event in self.system_metrics[dimension]:
values = self.system_metrics[dimension][event][:self.width,
1]
tmp.append(values)
metric_names.append(dimension + "_" + event),
timestamps = self.system_metrics[dimension][event][:self.
width,
0]
yaxis[len(tmp)] = dimension + "_" + event
ymax = len(tmp) + 1
yaxis[ymax] = ""
# define figure
start = 0
if self.width > 1000:
start = self.width - 1000
self.plot = figure(
plot_height=self.plot_height,
x_range=(start, self.width),
y_range=(0, ymax),
plot_width=1000,
tools="crosshair,reset,xwheel_zoom, box_edit",
)
self.plot.xaxis.axis_label = "Indices"
# tooltip
hover = HoverTool(tooltips=[("usage",
"@image"), ("metric",
"@metric"), ("index",
"$x{10}")])
color_mapper = bokeh.models.LinearColorMapper(
bokeh.palettes.viridis(100))
color_mapper.high = 100
color_mapper.low = 0
tmp = np.array(tmp)
self.source = ColumnDataSource(data=dict(
image=[np.array(tmp[i]).reshape(1, -1) for i in range(len(tmp))],
x=[0] * ymax,
y=[i for i in range(ymax)],
dw=[self.width] * (ymax),
dh=[1.3] * (ymax),
metric=[i for i in metric_names],
))
images = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=color_mapper)
# plot
self.plot.add_glyph(self.source, images)
self.plot.add_tools(hover)
self.plot.xgrid.visible = False
self.plot.ygrid.visible = False
self.plot.yaxis.ticker = FixedTicker(ticks=np.arange(0, ymax).tolist())
self.plot.yaxis.major_label_text_font_size = "5pt"
self.plot.yaxis.major_label_overrides = yaxis
self.target = show(self.plot, notebook_handle=True)
def create_plot(self):
# define list of metric names (needed for tooltip)
tmp = []
metric_names = []
yaxis = {}
for node in self.system_metrics:
for dimension in self.system_metrics[node]:
if dimension in self.filtered_dimensions:
for event in self.system_metrics[node][dimension]:
if event in self.filtered_events:
values = self.system_metrics[node][dimension][
event][:self.width]
tmp.append(values)
metric_names.append(dimension + "_" + event + "_" +
node)
yaxis[len(
tmp)] = dimension + "_" + event + "_" + node
ymax = len(tmp)
yaxis[ymax] = ""
# define figure
start = 0
if self.width > 1000:
start = self.width - 1000
self.plot = figure(
plot_height=self.plot_height,
x_range=(start, self.width),
y_range=(0, ymax),
plot_width=1000,
tools="crosshair,reset,xwheel_zoom, box_edit",
)
self.plot.xaxis.axis_label = "Indices"
# tooltip
hover = HoverTool(tooltips=[("usage",
"@image"), ("metric",
"@metric"), ("index",
"$x{10}")])
# map colors to values between 0 and 100
color_mapper = bokeh.models.LinearColorMapper(
bokeh.palettes.viridis(100))
color_mapper.high = 100
color_mapper.low = 0
tmp = np.array(tmp)
# create column data source
self.source = ColumnDataSource(data=dict(
image=[np.array(tmp[i]).reshape(1, -1) for i in range(len(tmp))],
x=[0] * ymax,
y=[i for i in range(ymax)],
dw=[self.width] * (ymax),
dh=[1.3] * (ymax),
metric=[i for i in metric_names],
))
# heatmap placeholder
images = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=color_mapper)
# plot
self.plot.add_glyph(self.source, images)
self.plot.add_tools(hover)
self.plot.xgrid.visible = False
self.plot.ygrid.visible = False
self.plot.yaxis.ticker = FixedTicker(ticks=np.arange(0, ymax).tolist())
self.plot.yaxis.major_label_text_font_size = "7pt"
self.plot.yaxis.major_label_overrides = yaxis
self.plot.xaxis.major_label_text_font_size = "0pt"
self.target = show(self.plot, notebook_handle=True)
def create(self):
# set size of figure
self.p = figure(width=900,
plot_height=35 * self.n_tokens,
x_range=Range1d(0, self.n_tokens + 100),
y_range=Range1d(-1, self.n_tokens))
self.p.xgrid.visible = False
self.p.ygrid.visible = False
self.p.axis.visible = False
x = np.zeros(self.n_tokens) + 2
y = np.flip(np.arange(0, self.n_tokens), axis=0) + 0.25
# set input tokens in plot
for token, x_i, y_i in zip(self.input_tokens, x, y):
text1 = Label(x=x_i - 1, y=y_i, text=token, text_font_size='10pt')
text2 = Label(x=x_i + 105,
y=y_i,
text=token,
text_font_size='10pt')
self.p.add_layout(text2)
self.p.add_layout(text1)
# set plot labels
text = Label(x=17, y=-1, text="query", text_font_size='15pt')
self.p.add_layout(text)
text = Label(x=50, y=-1, text="key", text_font_size='15pt')
self.p.add_layout(text)
text = Label(x=80, y=-1, text="q x k", text_font_size='15pt')
self.p.add_layout(text)
text = Label(x=98, y=-1, text="q * k", text_font_size='15pt')
self.p.add_layout(text)
color_mapper = LinearColorMapper(palette="Blues8", nan_color='white')
#get key matrix and query vector
key = self.keys[self.key_name][self.step][0, self.head, :, :]
query = self.queries[self.query_name][self.step][
0, self.head, :self.n_tokens, :]
#plot key matrix
self.source_key = ColumnDataSource(
data=dict(image=[key[:self.n_tokens, :]],
x=[40],
y=[0],
dw=[25],
dh=[self.n_tokens]))
img = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=color_mapper)
self.p.add_glyph(self.source_key, img)
#create an empty query matrix where only one vector is set
query_input = np.zeros((self.n_tokens, query.shape[-1]))
query_input[:, :] = np.nan
query_input[self.query, :] = query[self.n_tokens - self.query - 1, :]
self.source_query = ColumnDataSource(data=dict(
image=[query_input], x=[10], y=[0], dw=[25], dh=[self.n_tokens]))
img = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=color_mapper)
self.p.add_glyph(self.source_query, img)
#compute elementwise product between a query vector and key matrix
product = np.multiply(query[self.n_tokens - self.query - 1, :], key)
self.product = ColumnDataSource(
data=dict(image=[product[:self.n_tokens, :]],
x=[70],
y=[0],
dw=[25],
dh=[self.n_tokens]))
img = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=color_mapper)
self.p.add_glyph(self.product, img)
#compute dot product between query vector and key matrix
dot_product = np.dot(key, query[self.n_tokens - self.query - 1, :])
dot_product = dot_product.reshape((dot_product.shape[0], 1))
self.dot_product = ColumnDataSource(
data=dict(image=[dot_product[:self.n_tokens, :]],
x=[100],
y=[0],
dw=[2],
dh=[self.n_tokens]))
img = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh",
color_mapper=color_mapper)
self.p.add_glyph(self.dot_product, img)
show(self.p, notebook_handle=True)
